Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology

Page 1


Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology Editors

Phing-How Lou Cardiovascular Anaesthesia Research Laboratory, University of Alberta Edmonton, AB T6G 2S2, Canada

Natalia Petersen Hubrecht Institute, Uppsalalaan 8, 3584CT Utrecht, The Netherlands

Research Signpost, T.C. 37/661 (2), Fort P.O., Trivandrum-695 023 Kerala, India


Published by Research Signpost 2012; Rights Reserved Research Signpost T.C. 37/661(2), Fort P.O., Trivandrum-695 023, Kerala, India E-mail IDs: admin@rsflash.com; ggcom@vsnl.com signpost99@gmail.com; rsignpost@gmail.com Websites: http://www.ressign.com/home.aspx http://www.trnres.com http://www.journals.academicpursuits.us http://www.signpostebooks.com Editors Phing-How Lou Natalia Petersen Managing Editor S.G. Pandalai Publication Manager A. Gayathri Research Signpost and the Editors assume no responsibility for the opinions and statements advanced by contributors ISBN: 978-81-308-0487-3


Preface Like the engine in an automobile, the mitochondrion is akin to the machinery of life, where most of ATP is generated via the mitochondrial oxidative phosphorylation. In this process, electrons from reducing substrates are fluxed through a series of respiratory complexes (I-IV) which establishes a proton gradient across the inner mitochondrial membrane. This proton gradient is then used by ATP synthase (complex V) to drive ATP production. Such a simple description of a biochemical process may seem mundane to many students but a lot of effort has been spent into elucidating the mechanics of the mitochondrion since its first description by Albert von Kolliker in 1857. For this, we want to particularly distinguish: (i) David Keilin for his work on cytochrome c; (ii) Peter Mitchell for his chemiosmotic theory which explains how the proton gradient is related to ATP production and respiration in eukaryotic cells; and (iii) Paul Boyer and John Walker for their research on ATP synthase, the enzyme that is solely responsible for the biosynthesis of adenosine triphosphate (ATP). Since the 1990s, it is becoming evident that the mitochondrion is far from just a simple organelle supplying ATP. Novel mechanisms involving the mitochondria have been discovered and implicated in various diseases such as diabetes, cancer and neurodegenerative disorders. This realization has fueled a surge in mitochondrial research simply because it is becoming increasingly important to understand the significance of the multi-faceted mitochondria in the normal functioning of cells as well as in the pathogenesis of various diseases. We begin this book with a review on the recent development in the field of mitochondrial DNA (Chapter 1). This chapter is divided into three distinct parts: genomics, turnover and cellular responses to mitochondrial dysfunction. The intricacies of each aspect should be appreciated since errors in any part of their orchestration will ultimately result in mitochondrial dysfunction. Various signaling pathways triggered by mitochondrial dysfunction that eventually lead to modifications in gene expression, are also described here. Such a complexity in the network of pathways and mechanisms may account for the varied clinical scenarios in human diseases. Chapters 2-5 review the current status of our understanding on the association of mitochondrial dysfunction with some of the world’s main diseases – obesity, diabetes, cancer, and neurodegeneration. Since dysfunctional mitochondria will result in deficient energy supply, free radical generation, disrupted calcium buffering and loss of control over mitochondrial turnover, therapies that ameliorate mitochondrial function or activate mitochondrial biogenesis/apoptosis are potentially effective strategies in the prevention or treatment of these diseases.


In Chapter 6, we present a review on how mitochondrial status affects stem cell viability, proliferative and differential potential. This, undoubtedly, will have profound implications for the development of induced or differentiated pluripotent cells, which could be of particular interest for future regenerative therapies. Although much remains to be learned about mitochondria, elucidating the interplay between the various aspects of this fascinating organelle will definitely expand our understanding of human diseases. Finally, the editors wish to dedicate this book to Britton Chance as a tribute to his omniscience and, who like the multi-faceted mitochondrion, was always full of surprises! Phing-How Lou Natalia Petersen


Contents

Chapter 1 Mammalian mitochondrial genetics, genomics and turnover Patricia Renard, Sébastien Michel, Guillaume Rommelaere and Thierry Arnould Chapter 2 Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice] Per Lindström Chapter 3 Defects in the biogenesis and respiratory function of mitochondria in insulin insensitivity and type 2 diabetes Chih-Hao Wang, Hsin-Chang Huang and Yau-Huei Wei Chapter 4 Mitochondria and cancer Jean-François Dumas, Damien Roussel and Stéphane Servais Chapter 5 Modified mitochondrial dynamics, turnover and function in neurodegeneration: A focus on Huntington’s disease Tatiana R. Rosenstock and A. Cristina Rego Chapter 6 Mitochondrial involvement in stemness and stem cell differentiation Anaïs Wanet, Thierry Arnould and Patricia Renard

1

85

99

115

149

195


Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology, 2012: 1-83 ISBN: 978-81-308-0487-3 Editors: Phing-How Lou and Natalia Petersen

1. Mammalian mitochondrial genetics, genomics and turnover Patricia Renard, SĂŠbastien Michel, Guillaume Rommelaere and Thierry Arnould Laboratory of Biochemistry and Cell Biology (URBC), NARILIS (Namur Research Institute for Life Sciences) University of Namur (FUNDP), Belgium

Abstract. Mitochondria are eukaryotic organelle of endosymbiotic origin, specialized in the energy production, among many other various functions. One of the most remarkable features of mitochondrial proteins is that they are encoded by two genomes, located in different cellular compartments: the large majority of mitochondrial proteins being encoded by the nuclear genome, while the mitochondrial genome only encodes 13 polypeptides of the electron transport chain and mitochondrial tRNAs and rRNAs. This chapter first outlines the modern view of the main characteristics of the mitochondrial genome of mammalian cells, sharing number of similarities with bacterial genomes (circular structure, replication, repair systems, organization in nucleoids) and the consequences on mitochondrial genetics (maternal inheritance, polyploidy, hetero/homoplasmy). The second part of the chapter depicts the processes involved in the turnover of this organelle: the biogenesis of the mitochondrial DNA, proteins and lipids, as well as the degradation of the organelle, either by mitophagy or by the protease-dependent mitochondrial quality control. A particular Correspondence/Reprint request: Dr. Patricia Renard, Laboratory of Biochemistry and Cell Biology (URBC) NARILIS (Namur Research Institute for Life Sciences), University of Namur (FUNDP), Belgium E-mail: patsy.renard@fundp.ac.be


2

Patricia Renard et al.

attention is paid to the coordination, between the nuclear and the mitochondrial compartments, that maintains the mitochondrial homeostasis, either in the basal state or in response to mitochondrial dysfunction. This is illustrated by the retrograde responses triggeredto enhance mitochondrial biogenesis and/or to increase the mitochondrial quality control in case of mitochondrial unfolded protein responses (mitoUPR).

Introduction The term “mitochondria”, from the greek “mitos” (thread) and “chondros” (grain) initially described a thread-like or granular intracellular organelle, generally represented by a bean-shaped organelle in textbooks. However, it is now clear that mitochondria is a highly dynamic organelle, continuously undergoing fusion and fission events. The dynamic aspect of mitochondria is important because it is directly linked with its function, as mitochondria dynamics influences its function, and vice-versa [1]. Briefly, fusion of the mitochondrial outer membranes (MOM) requires mitofusin 1 and 2, two large GTPases forming complexes between apposing mitochondria and between mitochondria and the endoplasmic reticulum (ER), while fusion of the inner membrane depends on OPA1, a dynamin-like GTPase protein. Mitochondrial fission depends on the recruitment of Drp1, a dynamin family member, from the cytosol to the MOM by the integral membrane Fis1 (fission protein 1 homologue) located in the MOM. The consecutive oligomerization of Drp1, further regulated by post-transcriptional modifications, finally leads to fission (for extended reviews on mitochondrial dynamics, see [2, 3]. Mitochondria are the well-known powerhouse of cells, providing the oxidative ATP production via the tricarboxylic cycle (TCA) and oxidative phosphorylation (OXPHOS), but they are also responsible for other functions, such as formation of Fe-S clusters, free fatty acid β-oxidation, heme synthesis, steroidogenesis (in some cell types), urea cycle, and calcium homeostasis. Moreover, mitochondria are an integration platform for cell survival/cell death signals leading to apoptosis, necrosis, or autophagy [4]. To ensure all these functions, mitochondria are organized in 4 suborganelle compartments: 2 lipid membranes characterized by different composition and permeability properties, an intermembrane space and the central mitochondrial matrix housing the mitochondrial genome. Altogether, it is estimated that about 1500 proteins are distributed in these 4 suborganelle compartments [5]. One of the most remarkable features of mitochondrial proteins is that they are encoded by two genomes, located in different cellular compartments: the large majority of mitochondrial proteins are encoded by the nuclear genome, while 13 mitochondrial proteins are still


Mammalian mitochondrial genetics, genomics and turnover

3

encoded by the human mitochondrial genome (mtDNA). This situation is attributed to the endosymbiotic origin of mitochondria. The endosymbiotic theory of eukaryotes, proposed in 1970 by the microbiologist Lynn Margulis [6], hypothesizes that mitochondria originate from an alpha-proteobacteria (gender: Rickettsia) that would have been engulfed by a heterotrophic host cell, probably an archeobacterium, 1.5 billion years ago. Several features of mitochondria account for their bacterial origin such as the transcriptional machinery that is reminiscent from T7 bacteriophage, or the mtDNA genome structure (circular, intron-free) and packaging. Evolutive mutual adaptations would then have occurred, during which the alpha-proteobacteria would have gradually transferred the majority of their genetic material into the host genome (pseudogenes) or lost some of their genes. The current mtDNA would be the remnant of this ancestral genome retained in the organelle (reviewed in [7]). Another consequence of 1.5 billion years of symbiotic evolution is that mitochondria substantially differ between different organisms. Among the most striking differences between human and S. cerevisiae mitochondria, let us mention the absence of the respiratory complex I in yeast, the presence of 11 protein-coding genes in the yeast mtDNA (instead of 13 in Metazoans), the presence of 5’untranslated regions (5’UTR) in yeast mitochondrial transcripts (while they are absent in humans) and variations in the genetic code. This chapter is devoted to mammalian mitochondria, although most processes are better understood in simpler model organisms like yeast. In this chapter, we first discuss mitochondria genetics, characterized by several features like maternal inheritance, polyploidy (one cell contains thousands of mtDNA molecules) or the absence of recombination. The recent data questioning these widely accepted principles of mitochondrial genetics are discussed. The characteristics of the mitochondrial genome organization are also emphasized, including its packaging in nucleoids, different from the nucleosomes packing the nuclear genome, as well as the different mtDNA repair systems. Indeed, mtDNA is particularly prone to encounter chemical damages, notably due to its proximity with the electron-transport chain (ETC), an important source of reactive oxygen species (ROS). On the contrary to what has long been thought, several mtDNA repair pathways exist in mitochondria, such as the base excision repair, the mismatch repair, the double strand break repair pathways, or the degradation of damaged mtDNA molecules. The second part of this chapter concentrates on the controlled regulation of mitochondrial mass, a crucial step for proper cell/organism functions. The production (biogenesis) and degradation of the organelle are overviewed, as well as the main pathways involved in the mitochondrial retrograde responses. This term encompass the specific responses triggered by mitochondrial


4

Patricia Renard et al.

dysfunction and inducing modifications of nuclear gene expression in order to resolve the mitochondrial stress.

1. Mitochondrial genetics and genomics 1.1. Mitochondrial genetics More than 20 years ago, myopathies have been linked with mtDNA defects [8, 9], thereby boosting the research field of mitochondria genetics. In most textbooks, readers will learn that mtDNA 1) 2) 3) 4)

is exclusively maternally inherited in metazoan, escaping the Mendelian transmission rules; is present in multiple copies (polyploidy) in each cell; is generally present under one haplotype (homoplasmy); does not undergo recombination.

However, in light of recent advances in genomics, a number of exceptions to several of these rules have been reported, suggesting the need to re-address mammalian mtDNA genetics. This has been reviewed by [10]; the most important considerations are summarized below, underlining that mitochondrial genetics is a rapid and constantly evolving field.

1.1.1. Maternal inheritance The first evidence for maternal transmission of mtDNA in animals was published in 1972 in Xenopus [11] and two years later in mammals [12]. This has been largely confirmed later on and mechanistically documented by the group of Sutovsky with the description of active processes of sperm mitochondria elimination by ubiquitination of outer membrane proteins, followed by proteolytic degradation of paternal mitochondria by the oocyte [13, 14]. More recently, two groups working independently on C. elegans and M. musculus fertilized oocytes provided evidence for an active degradation of paternal mitochondria by the maternal autophagosomes targeting paternal mitochondria (and thus mtDNA of paternal origin) to autophagic degradation [15, 16]. However, examples of “paternal leakage� - this term referring to the entry of paternal mtDNA into the oocyte at fertilization - have been described more recently in various organisms/species such as birds, reptiles, molluscs, nematodes and arthropodes (reviewed by [10]). These examples generally report very low levels of mtDNA of paternal origin, which is consistent with the fact that this phenomenon was not initially detected with low sensitive


Mammalian mitochondrial genetics, genomics and turnover

5

sequencing methods. Nevertheless, there has been a single remarkable report of a high level of paternally inherited mtDNA in a patient with a mitochondrial myopathy due a 2-bp deletion in the ND2 mtDNA-encoded gene [17]. The authors showed that the mutation was of paternal origin and accounted for 90% of the patient's muscle mtDNA [17]. However, this report is apparently an isolated phenomenon, which has neither been confirmed by other laboratories nor found in other cases of sporadic myopathies. Even through deep exploration of medical genetics literature of case reports containing mtDNA, and using phylogenetic criteria, other reliable examples of clearly demonstrated paternal mtDNA transmission could not be highlighted [18]. The group of Salas proposed that anomalous mtDNA are best explained by accidental contaminations rather than potential paternal leakage. They have demonstrated that when using technically challenging single cell analysis of mtDNA, the risk of external contamination reaches a rate of 0.6%, resulting in anomalous results [19]. In conclusion, although progresses in sensitive sequencing techniques have generated some data challenging the dogma of maternal mitochondrial inheritance in animals, these are highly controversial since they might be artefactual and most likely accidental and abnormal phenomenon in which monitoring and degradation machineries might not have worked properly.

1.1.2. Polyploidy While nuclear DNA is diploid in somatic cells and haploid in gametes, each cell possesses several copies of mtDNA, a phenomenon referred as polyploidy. The multicopy state of mtDNA in both human and mice cells was first revealed in 1974 by Bogenhagen and Clayton [20]. The number of mtDNA copies per cell is highly variable, depending on the species, the cell type, the differentiation status (see this book chapter devoted to mitochondria and stemness/differentiation). In mammals, the oocyte is the cell containing the largest number of mtDNA copies, estimated at 200,000 copies in mice [21] while the mtDNA content in human fibroblasts is estimated at only 2-5000 copies [22]. It is generally estimated that the copy number in human cells vary from 103 to 104, according to the oxidative capacity of the cells [23].

1.1.3. Homoplasmy/heteroplasmy It is generally believed that, in normal individuals, mtDNA molecules are substantially identical [24]. However, analyses of restriction patterns of mtDNA extracted from human platelets provided early evidence for nucleotide sequences polymorphism in individuals [25]. This was confirmed


6

Patricia Renard et al.

by the analyses of mtDNA from patients suffering from maternally-inherited myopathies, demonstrating the existence of (at least) two populations of muscle mtDNA in these patients [8, 9]. Incidentally, mitochondrial DNA sequence polymorphism, coupled to maternal inheritance, can be used to confirm people identity, as shown in a study establishing the authenticity of the remains of Tsar Nicholas II [26]. Later on, the use of single cell mtDNA sequencing has clearly evidenced that different mtDNA sequences can coexist, even within a single cell. The presence of two or more mitochondrial genomes in the same cell is referred as heteroplasmy, on the contrary to homoplasmy, corresponding to a situation where all mtDNA molecules within a cell are identical. Heteroplasmy can either result from initial heterogeneous mtDNA molecules transmitted to the zygote, or from mutations arising during mtDNA replication accompanying further cell divisions. Indeed, it is known that the mitochondrial genome has a very high mutation rate, about 10- to 17fold higher to what is observed for nuclear DNA [27]. This high mutation rate can be attributed to the proximity of the mtDNA with the respiratory chain (which was proposed to be a major site of ROS production as early as 1972 [28]), the absence of protective histones, and the poor efficiency of the DNA repair machinery in the mitochondria (see section 1.2.3.). Although mtDNA is highly coding, mtDNA mutations are not necessarily deleterious, because of the existence of silent mutations, or because potentially deleterious mtDNA sequences can be compensated by wild-type mtDNA molecules co-existing in the same cells. However, although most mtDNA mutations are neutral polymorphism, at least 250 point mutations or rearrangements listed in the MITOMAP database (www.mitomap.org), have been demonstrated or suspected to be pathogenic. For these pathogenic mutations/rearrangements, the consequences will be phenotypically seen only if the level of mutated/deleted copies is so high that the expression of the wild-type mtDNA copies is not sufficient to functionally compensate the defect due to the mutated/deleted mtDNA copies. This phenomenon is referred as the threshold effect [27] and is critical in numerous mitochondrial myopathies. Considering the high number of mtDNA molecules present in a mature oocyte, and the high mutation rate of mtDNA, homoplasmy might actually appear very unlikely. However, a particular genetic mechanism characterizes mtDNA transmission to the offspring.

1.1.4. MtDNA transmission to the offspring Pedigree analysis in cows first showed that the mtDNA genotype transmitted to the progeny can completely shift in a few generations [29, 30],


Mammalian mitochondrial genetics, genomics and turnover

7

suggesting that only a few molecules of mtDNA are transmitted to the next generation. Similar observations have been made later on in humans [31, 32]. The study of trans-mitochondrial mice, i.e. mice carrying two variant mtDNA genotypes from different mouse strains, confirmed that the proportions of mutant mtDNA vary greatly between the offspring, the mtDNA sequence variants segregating rapidly between generations. The segregation pattern corresponds to a “random genetic drift” [33], a notion referring to the change in one allele frequency in a population due to random sampling. A consequence of genetic drift is that large differences can exist in the progeny in terms of proportions of variant mtDNAs, and that gene variants might disappear completely, thereby reducing genetic diversity [34]. Poulton and co-workers proposed that the genetic drift of mtDNA would be due to a restriction in the number of mtDNA molecules early during embryogenesis, followed by amplification, a process termed “genetic bottleneck” [24]. The exact mechanism contributing to the developmental bottleneck for the transmission of mtDNA is currently debated (reviewed in [35]). The most cited mechanism accounts for a drastic reduction of mtDNA copy number in primary germ cells. The mouse mature oocyte is estimated to contain about 200,000 mtDNA molecules. Once fertilized, the egg undergoes several rounds of cell cleavage without replication of mtDNA, at least until the blastocyst stage [36, 37]. The 200,000 mtDNA copies of the zygote are distributed to the inner cell mass cells – the future embryo- and to the trophoblast cells. At this stage, embryonic stem cells and in particularly primary germ cells (PGC) are estimated to contain a low number of mtDNA copies, around 200 copies [33], a number compatible with the observed genetic drift. In addition to a relatively small number of mtDNA molecules in PGC, it has been suggested that the mtDNA molecules could partition into (homoplasmic?) segregating units [38] in which the nucleoid structure (see section 1.2.4.) would play a role. However, more recent studies evaluated the number of mtDNA molecules in PGC at approximately 2000 [36], a number too high to explain the genetic drift observed throughout generations. These authors suggest that the bottleneck would not depend on a reduction of germ line mtDNA copy number, but rather on the preferential replication of some mtDNA molecules during oogenesis [36]. Finally, by measuring mtDNA heteroplasmy and copy number in mouse single germ cells, the group of Shoubridge showed that the genetic bottleneck occurs during postnatal folliculogenesis and not during embryogenesis, through replication of only a subgroup of mtDNA molecules [39]. As a prospect, it has to be mentioned that a specific oocyte ultrastructure called the “mitochondrial cloud”, or the Balbiani body, is found in different animal species, including mouse [40]. Although the exact role of the Balbiani


8

Patricia Renard et al.

body is currently unknown, the fact that it is specifically found in developing oocytes and is associated with a concentration of organelles including mitochondria, endoplasmic reticulum and Golgi elements, suggests that it might take part in a possible unequal segregation of mitochondria during oogenesis.

1.1.5. Recombination It has long been thought that mtDNA does not undergo recombination events as it was first assumed that mtDNA is homoplasmic. However, with the growing evidence that individuals often do not contain only one mtDNA haplotype (see above point 1.1.3.), and with the demonstration of the presence of a recombination enzymatic machinery in mitochondria [41, 42], indications for mtDNA recombination were more deeply explored (reviewed in [43]). In addition, the occurrence of recombination events in numerous plant and fungi species has been largely documented (reviewed in [44]. Evidence for (partially) duplicated mtDNA in cybrid cell lines [45] as well as in somatic tissues of aged individuals [46] suggested that intra-molecular recombination might exist. Trying to demonstrate inter-molecular recombination, the group of Khrapko took advantage of the above cited Danish individual with 90% of paternal mtDNA and 10% of maternal mtDNA [17]. The authors demonstrated a frequency of ~0.7% of total mtDNA of recombination between maternal and paternal mtDNA in muscle tissue of this individual. However, this data must be considered with caution as paternal mtDNA might have been strongly selected in this exceptional patient’s muscle. Later on, muscle biopsies of 10 individuals with multiple mtDNA heteroplasmy (a heteroplasmic D-loop sequence and a point mutation in a tRNA coding gene or a large deletion) were analysed using single cell PCR. The four allelic combinations were observed, suggesting that recombination occurred [47]. The presence of four-way junctions in human heart mitochondrial DNA [48] further confirmed the occurrence of recombination, although recombination events between different mtDNA haplotypes are believed to be very rare [49, 50].

1.2. Mitochondrial genomics Supporting this hypothetic ancestral origin of mitochondria, Barrel and co-workers showed in 1979 that the genetic code of human mitochondria is different from that of nuclear DNA: the universal codons AUA (isoleucine) and UGA (stop) code for methionine and tryptophan, respectively, in human mitochondria [51]. In addition, the “universal� AGA and AGG arginine codons


Mammalian mitochondrial genetics, genomics and turnover

9

were recoded to stop codons in human mitochondria, although results recently obtained contradict this interpretation of the human mitochondrial genome sequence [52], as will be discussed in the section 2.1.1.3. related to mitochondrial translation. Other deviations to the “universal� genetic code were pointed out in other species [53]. Although the large majority of mitochondrial proteins are encoded by the nuclear genome while only 13 polypeptides are encoded by the mitochondrial genome, this section will be dedicated to the genomics of mitochondrial DNA (mtDNA) as alteration in only one mitochondrial gene might fully hamper the mitochondrial activity.

1.2.1. MtDNA genome organization Human mtDNA is a double-stranded, closed circular 16,569-bp long molecule. In addition to the monomeric circular mtDNA molecule described in textbooks, there are many evidence for the existence of more complex structures, called catenanes, resulting from the inter-connection of several mtDNA circular molecules. Although their biological significance is currently unknown, the abundance of catenanes seems to be tissue-specific, with the highest level in heart, and to increase with age (for a recent review, see [54]). The densities of the two strands vary in alkaline CsCl gradients due to their nucleotide composition: the heavy (H) strand is rich in guanine and the light (L) strand is cytosine-rich [27]. The complete sequences of mouse [55] and human [56] mtDNA have been released in 1981, the latter being revised in 1999 [57]. They revealed a highly coding genome, with no intron and very little intergenic regions. The genes are mainly disposed end to end and code for 22 tRNAs, 2 rRNAs and 13 polypeptides corresponding to subunits of the respiratory chain. Beside these genes, a majority of which are being coded by the H strand, mtDNA contains a 1.1 kb non coding sequence, called the displacement loop (D-loop). This non-coding region spans between tRNAPhe gene and the tRNAPro gene (Figure 1) and contains important sequences for mtDNA replication and transcription: the replication origin for the heavy strand (OH), the promoter regions of the heavy (HSP) and light strands (LSP) and a termination-associated sequence (TAS) [54, 58]. Some mtDNA molecules contain, within the D-loop, a triple-stranded region formed by the association with a third 650bp-long single stranded DNA molecule, called the 7S DNA. The exact function of 7S DNA is not clear, but it has been proposed to have a role in transcription and/or in replication of mtDNA [59]. The 7S DNA could be an aborted product of the heavy strand replication, and/or might constitute a primer for the


10

Patricia Renard et al.

Figure 1. Organization of the human mtDNA. Both strands (light and heavy) are coding for 2 rRNAs (yellow), 22tRNAs (red), 7 complex I subunits (green), 1 complex III subunit (grey), 3 complex IV subunits (purple) and 2 complex V subunits (blue). The non-coding D-loop region is often hybridized to a 650 bp strand DNA (7S DNA). The D-loop region contains the replication origin for the heavy strand (OH), and the promoter regions for transcription of the light strand (LSP) and the heavy strand (HSP). The 2 transcription start sites of the heavy strand transcription are noted H1 and H2. TERM: termination sequence ; OL : replication origin of the light strand; dashed blue lines = transcripts. (modified from mitomap.org).

heavy strand replication [60]. The synthesis of 7S DNA has been shown to be positively influenced by the activity of the mitochondrial topoisomerase [61] and by high nucleotide concentrations, on the contrary to the circular mtDNA molecules, a finding suggesting that mtDNA and 7S DNA synthesis could proceed independently [62].


Mammalian mitochondrial genetics, genomics and turnover

11

1.2.2. MtDNA replication MtDNA is continuously turned over, even in post-mitotic tissues [63]. There are currently several models describing the molecular mechanism of human mtDNA replication that have been reviewed recently [54, 64, 65]. The main two models, which could coexist, are summarized below and illustrated in Figure 2. 1.2.2.1. MtDNA replication models The bidirectional strand displacement mechanism was proposed 40 years ago [66]. It is also called the strand-asynchronous replication model because of the delay in the replication of the lagging strand and the leading strand. Replication starts at the heavy strand origin (OH), displacing the light strand. Replication of the heavy strand proceeds until the polymerase reaches 2/3 of the molecule, displacing the lagging strand, until the light strand origin (OL) is exposed. The displaced single strand DNA would be protected by the binding of multiple copies of mitochondrial single strand binding protein (mtSSB), as explained below. At this point, the light strand replication starts, on the opposite direction. The primers required for DNA replication would come from transcripts formed near the OH, leading to the formation of RNA/DNA hybrids [67] or alternatively from the 7S DNA molecule [60]. More recently, a second putative replication origin for the heavy strand (Orib), was located more downstream in the D-loop [68]. In the strand displacement model, DNA synthesis is continuous on both strands. However, indications of multiple duplex replication intermediates prompted the Holt’s group to propose a more classical coupled leading- and lagging-strand synthesis for mtDNA [69]. While the coupled leading- and lagging-strand replication mechanism of nuclear DNA involves the synthesis of ± 200 nt-long Okazaki fragments, in the replication of mtDNA these fragments are ribonucleotides, as supported by numerous RNA/DNA hybrid regions found in replicating DNA molecules. These RNA fragments Incorporated ThroughOut the Lagging Strand, called RITOLS [70], would then serve as primers for the synthesis of the DNA strand (reviewed in [64]). Of importance, this mechanism requires further resolution of RNA/DNA hybrids by RNaseH, which is consistent with the fact that RNaseH1 knockout mice have an embryonic lethal phenotype linked to mtDNA loss [71]. These two models could both operate in human cells. Indeed, it is currently believed that the preferential use of one of them could depend on the physiological context. The leading- and lagging-strand synthesis of mtDNA would be preferred in a “maintenance mode” of mtDNA, while the


12

Patricia Renard et al.

Figure 2. MtDNA replication models: strand-displacement model (left) and RITOLSreplication model (right) (see text).

strand-displacement model would mainly occur in response to environmental stresses/conditions requiring rapid mtDNA synthesis [27]. 1.2.2.2. The mtDNA replication machinery The minimal machinery required for mtDNA replication (the mtDNA “replisome”) is composed of the gamma polymerase holoenzyme (Polγ), the helicase Twinkle and mtSSB (mitochondrial single stranded DNA binding protein) [72]. The mitochondrial DNA is exclusively replicated by the Polγ holoenzyme [73], a hetero-trimeric enzyme consisting of one 140 kDa catalytic subunit encoded by the POLG1 gene and two 54 kDa accessory subunits encoded by the POLG2 gene [74]. The catalytic subunit has DNA polymerase and proof-reading activities. It shares homology with the T7 bacteriophage DNA polymerase [75], suggesting a viral origin. The two accessory subunits improve the processivity of the catalytic subunit. Modifications of the POLG2 abundance modify the number and size of nucleoids, but do not alter mtDNA copy number [76]. MtSSB was originally identified in X. laevis oocytes [77] and then in rat [78] as a protein presenting a high preference for single-stranded DNA, without any sequence specificity required for binding. MtSSB, like nuclear SSBs, plays a role in mtDNA replication, by protecting the single-stranded DNA intermediates used as templates by the DNA polymerase (Figure 2). Mitochondrial SSB is a highly abundant protein active as a 56kDa tetramer. It has been shown in vitro to increase the replicative activity of Polγ [72] and


Mammalian mitochondrial genetics, genomics and turnover

13

the unwinding activity of Twinkle [79]. Studies of mtSSB knock-down in HeLa cells revealed the role of mtSSB on mtDNA maintenance and replication: mtSSB knock-down was accompanied by a moderate decrease in mtDNA copy number, but had a dramatic impact on the abundance of 7S DNA [59]. Twinkle is a mitochondrial 5’-3’ DNA helicase, with structural similarity to phage T7 primase/helicase and other hexameric ring helicases [80]. Twinkle is stimulated by mtSSB to increase Polγ processivity and fidelity, both proteins working together to stabilize the DNA helix [79]. The Twinkle DNA helicase also participates in recombination processes, as abundant 4-way junctions in mtDNA can be observed when Twinkle (or TFAM, transcription factor A of mitochondria) is overexpressed [48]. Besides the minimal replisome machinery, other proteins also participate in the mtDNA replication. Encoded by the nuclear genome, they can be either mitochondria-specific proteins or proteins directed to both mitochondrial and nuclear compartments. Topoisomerases 1 and 3, resolving the tension generated in the circular mitochondrial genome during replication and transcription, have been located in mitochondria. Topoisomerase 1 is specifically encoded by TOP1mt gene, a highly homologous gene to TOP1 gene encoding nuclear topoisomerase 1 [81]. On the contrary, human DNA topoisomerase IIIα [82] and RNase H1 [83] are proteins targeted to both nuclear and mitochondria compartments by alternative translation initiation.

1.2.3. MtDNA repair Mainly due to its proximity with the mitochondrial inner membrane (MIM), mtDNA is largely exposed to reactive oxygen species generated by electrons leaking from the respiratory chain. It has been calculated that mtDNA accumulates mutations at a 10 to 50-fold higher rate than nuclear DNA [43]. One can categorize the deleterious mutations into three clinically different classes: i) recently emerged germline mutations resulting in mitochondrial diseases, ii) ancient regional variants - referred as mitochondrial haplotypes - and constituting a potential substrate for evolution, and iii) somatic mutations that accumulate with age [84]. While DNA repair has long been thought to be limited to nuclear DNA, it is now clearly established that mtDNA lesions can be repaired. DNA repair pathways have been classified in different categories according to the type of DNA damage and repair mechanism [85]. Mitochondria possess several repair pathways, all relying on enzymes encoded by the nuclear genome. These enzymes may be either shared with the nucleus repair machinery, or


14

Patricia Renard et al.

exclusively found in mitochondria. Interestingly, bacterial or viral origins have been proposed for some of these mitochondrial repair systems [64]). Because mtDNA repair pathways have been reviewed recently [43, 64, 85], we will here summarize the most important information related to these machineries. Base excision repair (BER) was the first DNA repair pathway discovered in mitochondria [86] and is currently the best understood mtDNA repair pathway. Indeed, BER is considered to be the prominent way to repair “small” DNA lesions provoked by spontaneous hydrolysis, alkylation, or oxidation, as exampled by ROS-induced deamination of cytosine, forming uracil. Considering mtDNA susceptibility to oxidative damage, it is not surprising that mitochondria is relatively well-equipped in BER machinery [43]. Mitochondrial BER pathway is divided into two branches: the shortpatch BER (spBER, insertion of a single base) and the long-patch BER (lpBER, insertion of a 2- to 6-nucleotide sequence). Mitochondrial spBER involves the sequential activity of several enzymes 1) a DNA glycosylase removes damaged bases and generates apurinic/apyrimidic (A/P) sites; 2) an AP endonuclease 1 (Ape1) cleaves the 5’ site of the A/P site, leaving a 3’hydroxyl residue; 3) the DNA polymerase γ inserts a nucleotide at this 3’hydroxyl end and 4) the ligation is performed by a DNA ligase III [43]. LpBER additionally requires the structure-specific flap endonuclease FEN1 [87] and the nuclease/helicase DNA2 at step 3 [88]. In addition to these well-described enzymes, the presence of tyrosylDNA phosphodiesterase (TDP1) in mitochondria and its role in mtDNA repair have been recently suggested [89]. TDP1 acts as a 3’-end processing repair enzyme and could be involved in the BER pathway at the processing step classically attributed to Ape1 activity, or in the removal of Top1 peptide adducts (the mitochondrial topoisomerase, Top1mt, has been shown to form Top1-DNA adducts in mitochondria [61]). Considering the association of nucleoids with the MIM (see below), it is not surprising that BER activities have been associated with MIM [90, 91]. Taking advantage of the natural ability of mammalian mitochondria to import DNA, at low levels [92], the group of Lightlowers developed an in organello DNA import and repair assay showing that the mitochondrial BER functions to remove uracil from exogenous DNA fragments associated with the MIM [91]. The Cockayne syndrome group B (CSB) protein, which plays a role in mtDNA genomic maintenance, would participate in the MIM anchoring of the mitochondrial BER machinery [93]. mtDNA mismatch repair (MMR) was first reported in 2003 [94]. A


Mammalian mitochondrial genetics, genomics and turnover

15

recently published report indicates that the mtDNA MMR enzymes would be distinctive from the ones involved in the nuclear MMR pathway, as the Y box binding protein YB-1 would be involved in mismatch recognition and binding [95]. Double strand breaks (DSB) can also be repaired in mammalian mitochondria, either by homologous recombination or by non-homologous end joining [85]. Although the machinery responsible for mitochondrial DSB repair is not totally identified, it could be shared with the nuclear compartment. Indeed, Rad51 and the related homologous recombination proteins Rad51c and Xrcc3 have been located in the mitochondria. Rad51 is considered as a major catalyst of nuclear DSB repair and interacts with mtDNA [96]. In addition, the DNA-end binding activity required for nonhomologous end joining would be mediated by a mitochondrial truncated isoform of Ku80, while the same activity in the nucleus is performed by the full-length Ku80 enzyme [97]. The mitochondrial location of distinctive isoforms of nuclear DNA repair proteins is apparently not an isolated case, as shown also for DSB repair aprataxin [98]. Nucleotide excision repair (NER) involves the removal and de novo synthesis of a short fragment on the damaged strand. Currently, no evidence exist for such a DNA repair mechanism in mammalian mitochondria although a NER-like pathway has been identified in yeast mitochondria [85]. MtDNA degradation The idea that altered mtDNA is selectively degraded in response to UVinduced damage is old [99] and was later confirmed towards chemical and oxidative insults (reviewed in [43]). This concept is compatible with the polyploid nature of mtDNA implying that unaltered mtDNA molecules can still transmit the genetic information to daughter mitochondria/cells. In addition, the oxidative stress-induced degradation of mtDNA is enhanced when Ape1 activity (and thus the BER pathway) is inhibited [100], suggesting that damaged mtDNA could be degraded when it cannot be repaired. The signal for mtDNA degradation could come from stalled DNA or RNA polymerases on DSB, but the exact mechanisms and the identity of the nuclease(s) involved are still unknown [43]. Elimination of oxidized dNTP MtDNA molecules are not the only targets of reactive oxygen species (ROS): the free pool of dNTP is also exposed to oxidative damage. This is the case for the oxidized nucleotide 8-oxo-2’-deoxyguanosine triphosphate


16

Patricia Renard et al.

(8-oxo-dGTP) that can be incorporated into a nascent DNA strand opposite a template A. The resulting 8-oxo-dG:dA base pair is refractory to the proofreading activity of Polγ, leading to mtDNA transversions (interchange of purine for pyrimidine base, in this case an adenine is mutated into a cytosine). This process is estimated to seriously threaten mtDNA integrity and highlights the importance of MTH1 (MutT homolog-1) that hydrolyses 8-oxo-dGTP to 8-oxo-dGMP, a nucleotide that cannot be incorporated into DNA (reviewed by [43]). mtDNA damage prevention Interestingly, mitochondria do not only possess several pathways devoted to repair the lesions encountered by mtDNA, but is also equipped to prevent mtDNA damage with the presence of “fidelity proteins”. Oxidative stress cannot only damage mtDNA, but also mitochondrial proteins. The catalytic activity of Polγ is sensitive to oxidative stress [101], which might result in additional mtDNA mutations. Antioxidant enzymes, Manganese Superoxide Dismutase (MnSOD/SOD2) and glutathione peroxidase, have been shown to be associated with Polγ in nucleoid fractions [102]. Recently, Bakthavatchalu and co-workers have highlighted a two-step strategy involving p53 and MnSOD/SOD2 to counteract UVB-induced decrease in Polγ activity. In the first step, UVB stress induces the recruitment of p53 in mitochondria, where the tumour suppressor protein is thought to assist Polγ in mtDNA repair. In a second step, the antioxidant enzyme MnSOD/SOD2, located in nucleoids, would form a complex with p53 and Polγ, thereby protecting Polγ function [103].

1.2.4. MtDNA molecules are packaged into nucleoids Because mtDNA is not organized as chromatin, like nuclear DNA, it has long been thought that the mtDNA was naked. However, it is now clearly erroneous to say that mtDNA is naked, as accumulating evidence clearly shows that multiple mtDNA copies are packaged with non-histone proteins to form spheroid bodies, termed nucleoids (reviewed in [104, 105]). The presence of proteins assembled into a complex with mtDNA was first shown in Xenopus laevis oocytes mitochondria, from electron microscopic studies coupled with nuclease digestion experiments. These experiments revealed that mtDNA molecules were packaged into regular beaded structures, and associated with specific proteins and membrane structures [106].


Mammalian mitochondrial genetics, genomics and turnover

17

The number of mtDNA molecules contained in a nucleoid varies depending on several parameters such as the organism, cell type, differentiation state, and growth conditions. Based on analyses of various human cell lines, it has been estimated that each nucleoid typically contains between 2 to 10 mtDNA molecules [107, 108]. This number might have been overestimated, as more recent studies based on super-resolution microscopy indicate an average number of 3 mtDNA molecules per nucleoid in 3T3 fibroblasts [109] and an average of 1.4 mtDNA molecules per nucleoid in a panel of mammalian tissue culture cells [110]. In addition, while Kukat and co-workers suggested that nucleoids have a uniform mean size of ~100 nm in mammals [110], Brown and coworkers found that nucleoids vary largely in size and shape [109]. The exact composition of mammalian nucleoids is currently not totally defined, which is probably partly due to the dynamic feature of this structure [104], and to the fact that there is no gold standard method for identifying nucleoid-associated proteins. The main technical approaches used to identify nucleoid protein composition can be listed under different categories: -

-

-

Microscopy-based co-localization of candidate proteins with wellknown components of nucleoids (like TFAM, Twinkle or mtSSB) or with mtDNA using either anti-DNA antibodies, or intercalating agents like bromodeoxyuridine, 4',6-diamidino-2-phenylindole (DAPI), ethidium bromide, or PicoGreen staining. Of note, PicoGreen staining depends on the arrangement of DNA: supercoiled DNA produces a lower signal per unit mass of DNA than relaxed circular DNA [111]. Co-immunoprecipitation methods of candidate proteins with wellestablished nucleoid components like mtSSB or TFAM [112]. mtDNA immunoprecipitation (MIP) of a candidate protein, eventually after formaldehyde cross-linking, followed by PCR detection of mtDNA sequence. This method is the equivalent of the well-known chromatin immuno precipitation used to study interactions between transcriptional regulators and nuclear DNA sequences [103]. Biochemical methods to purify nucleoids. These methods require mitochondria purification and lysis followed by purification of nucleoid complexes by differential centrifugation and/or by centrifugation on iodixanol gradients [76]. The fractions enriched in mtDNA can then be analysed by mass spectrometry for the identification of new nucleoid protein candidates, by Southern blotting for the presence of mtDNA or by immunoblotting to confirm the presence of candidate proteins [76,


18

-

Patricia Renard et al.

111]. These preparations can also be treated with formaldehyde to reveal proteins cross-linked with mtDNA [113]. DNA affinity capture of proteins either bound to endogenous mtDNA (using the HU protein to capture mtDNA [114]) or bound to a synthetic mtDNA sequence, typically the D-loop sequence [115]. The identification of captured proteins is then performed using mass spectrometry-based methods.

However, as false-positive candidates might come out of each of these methods, we have considered that consolidated nucleoid protein should have been identified as such by at least two complementary techniques or confirmed by functional analysis. This criterion was chosen to constitute the updated nucleoid protein composition listed in Table 1. Besides the expected proteins such as TFAM, mtSSB, Polγ or Twinkle, other proteins involved in mtDNA replication (helicases, topoisomerases), or in mtDNA transcription (mtRNA polymerase, transcription actors, transcription termination factor) are listed. If the presence of these proteins in association with mtDNA is expected, other categories of proteins have also been found, including proteases (Lon, Clpx), chaperones (HSP40, HSP60, HSP70, prohibitin 1 and 2, LRP130) metabolic enzymes (Hydroxyacyl dehydrogenases A and B, Serine hydroxymethyltransferase, carnitine palmitoyltransferase 1A, ATP synthase β) and adenine nucleotide translocase 2 (ANT2). Clearly, the presence and function of these proteins in nucleoids needs to be further substantiated. In an attempt to define the proteins tightly associated with mtDNA, thereby constituting the “core nucleoid”, the group of Bogenhagen has used formaldehyde cross-linking to determine which proteins are in close contact with mtDNA [113]. Alternatively, the isolation of nucleoprotein complexes in the presence of high salt concentration (900 mM) also gives an indication on high-affinity bound proteins [111]. Finally, protease protection assays, consisting of treating (or not) mitochondrial lysates with proteases before analysis of the nucleoproteins could help define the core nucleoid protein composition, as the non-degraded proteins are interpreted to be proteins embedded inside nucleoids [111]. The concept of layered nucleoids has recently emerged from these studies suggesting that a subset of proteins, directly involved in mtDNA replication and/or transcription (including Polγ1 and 2, Twinkle, mtSSB, POLRMT, TFAM, TFBMs, mTERFs) would constitute the core protein content of nucleoids, directly associated with mtDNA [113]. The limited number of different proteins in the core nucleoid complex could be due to


Mammalian mitochondrial genetics, genomics and turnover

19

Table 1. Proteins associated with nucleoids in mammalian mitochondria. Protein Polγ1 and Polγ2 (catalytic and accessory DNA polymerase subunits) mtSSB (mt single stranded DNA binding protein)

TFAM (transcription factor A of mitochondria)

Twinkle (mt DNA helicase) Top1 (mt topoisomerase 1) MnSOD/SOD2 (Manganese superoxide dismutase) M19 ATAD3 (ATPase family AAA domaincontaining 3A) Dna2

beta-actin

Techniques used (detailed in the text) Biochemically purified with nucleoids [116] Co-IP (TFAM-Polγ1) [112] Cross-linked with mtDNA [113] Microscopy [116]. DNA affinity (D-loop sequence) [115] Co-IP (TFAM) [112] Biochemically purified with nucleoids [112, 117] Cross-linked with mtDNA [113] Microscopy [108, 116, 119] DNA affinity (D-loop sequence) [115] Co-IP (mtSSB) [112] Biochemically purified with nucleoids [111, 114] Cross-linked with mtDNA [113] Biochemically purified with nucleoids [116] Microscopy [80] Cross-linked with mtDNA [113] Biochemically purified with nucleoids [61] Cross-linked with mtDNA [113] Co-IP (Polγ [102])

Identified role(s) related to mtDNA mtDNA replication, transcription, repair (see text)

Biochemically purified with nucleoids [122]

Correlation between M19 and mtDNA abundance [122]

Co-IP (TFAM and mtSSB) 112] Biochemically purified with nucleoids [111, 112, 114] Microscopy [114] Gel-shift (D-loop sequence [114]) Biochemically purified with nucleoids [123] Microscopy [123]

mtDNA maintenance; organization of mtDNA copies and segregation [114]

Biochemically purified with nucleoids [111]

- mtDNA maintenance [116] and replication [118] - Control of 7S DNA synthesis [59]

- mtDNA packaging [118, 119]. - mtDNA replication [120] - mtDNA transcription [121]

mtDNA maintenance and integrity [80] mtDNA replication [61]

mtDNA repair (fidelity protein associated with Polγ [103]

mtDNA repair [123] mtDNA replication (stimulates Polγ activity) [88, 123] Organization and structural maintenance of mtDNA [111]


20

Patricia Renard et al.

Table 1. Continued Non-muscular myosin heavy chain IIA POLRMT (mt RNA polymerase) TFB2M TFB1M (transcription actors B) mTERF1 and 2 (mitochondrial transcription termination actors) LRP130 (leucine-rich protein 130) Suv3-like helicase DEAD box 28 EF-Tu

Prohibitin 1 and 2 Lon protease

ClpX protease RNA helicase DHX30 Hydroxyacyl dehydrogenase A and B Serine hydroxymethyl transferase ATP synthase β

Biochemically purified with nucleoids [111]

Organization and structural maintenance of mtDNA [111]

Co-IP (TFAM and with mtSSB) [112] Cross-linked with mtDNA [113] Gel shift assays [125, 126] Immunoprecipitated with mtSSB [112] Cross-linked with mtDNA [113] Gel shift assays [127] Cross-linked with mtDNA [113, 128]

mtDNA transcription [124]

Co-IP (mtSSB) [112] Cross-linked with mtDNA [113] Co-IP (TFAM) [112] Cross-linked with mtDNA [113] Cross-linked with mtDNA [113] Co-IP (TFAM and mtSSB) [112] DNA-affinity (HU-precipitated mtDNA) [114] Cross-linked with mtDNA [113] Co-IP (mtSSB and TFAM) [112] Co-IP (mtSSB and TFAM) [135] Cross-linked with mtDNA [113, 136] Co-IP (TFAM) [112] Cross-linked with mtDNA [113] Co-IP (TFAM and mtSSB) [112] Cross-linked with mtDNA [113] Co-IP (TFAM and mtSSB) [112] DNA-affinity (HU-precipitated mtDNA: HADHA, [114]) Cross-linked with mtDNA [113] Co-IP (TFAM and mtSSB) [112] Cross-linked with mtDNA [112, 113] Co-IP (TFAM) [112] Cross-linked with mtDNA [113]

mtDNA transcription [131]

mtDNA transcription [125]

mtDNA transcription [129, 130]

Belong to the RNA degradosome [132] RNA helicase [133] Translation elongation [114]

Nucleoid organization and maintenance of mtDNA [134] mtDNA maintenance [136]


Mammalian mitochondrial genetics, genomics and turnover

21

Table 1. Continued Acyl-CoA dehydrogenase HSP70 HSP40, DNAJ 3 HSP60 VDAC 1 and 2 Carbamoylphosphate synthase alpha-keto acid dehydrogenase complex (E2 subunit) PDIP38 (polymerase delta-interacting protein 2) ANT2 (adenine nucleotide transferase 2)

Immunoprecipitated with TFAM [112] Cross-linked with mtDNA [113] Co-IP (mtSSB) and (TFAM (for HSP70)) [112] Cross-linked with mtDNA [113] Co-IP (mtSSB and TFAM) [112, 135] Co-IP (mtSSB and TFAM) [112] Cross-linked with mtDNA [113] Co-IP (mtSSB and TFAM) [112] Cross-linked with mtDNA [113] Co-IP (mtSSB) [112] Cross-linked with mtDNA [113] Co-IP (mtSSB and TFAM) [135] Cross-linked with mtDNA [113] Co-purification with mtDNA and with TFAM [112, 117]. Co-purified with X. Laevis oocyte mtDNA [117]

the high density of packaged mtDNA. Proteins located in the periphery might have restricted access to mtDNA and interact with the core nucleoid in a dynamic way [137]. Such transient interaction could possibly depend on the remodelling of mtDNA, as shown in yeast. Indeed, the group of Butow has shown that nucleoids are dynamic structures with an ability to adapt in response to metabolic cues. For instance, culture conditions favouring respiration provoke a decrease in the Abf2 (the TFAM yeast homolog)/mtDNA ratio, leading to a more “open” structure for nucleoids, while glucose-induced repression of respiration triggers the specific recruitment of Hsp60 to the nucleoid structure [138]. TFAM is the main constituent of nucleoids TFAM was first identified as the transcription factor responsible for the transcription of mtDNA (see section 2.1.1.2), but this protein is also important for mtDNA packaging and replication. Belonging to the High Mobility Group box proteins, TFAM functions as a homodimer and binds to DNA with a high affinity (in the nanomolar range), inducing DNA bending. Due to its high affinity for DNA, it is thought that only a few molecules of


22

Patricia Renard et al.

TFAM can exist in an unbound form in mitochondria [119]. TFAM binding to DNA would be essentially not sequence-specific, although it could have a higher affinity for a 500 bp region containing the D-loop sequence and the two promoter regions [139, 140]. In addition to its participation in mtDNA transcription, TFAM also has a clear structural role in mtDNA packaging into nucleoids [110]. Although several groups estimated that the abundance of TFAM is too low to totally cover the mtDNA sequence [141, 142], other studies suggest that the number of TFAM molecules could reach hundreds of copies of TFAM per mtDNA molecule [118, 119], this would be sufficient to wrap mtDNA with dimeric TFAM bound every 34-40 bp. In addition, TFAM alone is capable of structuring and compacting several mtDNA molecules into spheroid structures, as shown by atomic force microscopy [140]. For these reasons, it has been suggested that TFAM plays a structural role for mtDNA, similar to histones in nucleosomal DNA, which explains the emerging term of “mitochondrial chromatin� [104]. Nucleoids are in close contact with the MIM There is a general agreement to consider that nucleoids are anchored to the MIM, as recently confirmed by high resolution microscopy [109]. However, the proteins responsible for nucleoid anchorage to MIM are still under investigation. The group of Ian Holt used a combination of differential centrifugation and high-salt purification proteins interacting strongly with mtDNA to identify several proteins of the mitochondrial nucleocomplexes. The presence of beta-actin and non-muscular myosin heavy chain IIA (encoded by MYH9) in nucleocomplexes could have been interpreted as a contamination, as the majority of these molecules reside in the cytoplasm, but they demonstrated that beta-actin is imported into the mitochondria through a membrane potential-dependent process [111]. In addition, the silencing of beta-actin, non-muscular myosin heavy chain IIA and/or IIB affects the mtDNA copy number [111]. Their results suggest the existence of a mitoskeleton, an intra-mitochondrial association of actomyosin filaments to support mitochondrial nucleoids. This view is reinforced by the presence of ATAD3 (ATPase family AAA domain-containing 3A) in nucleoids, a protein anchored to the mitochondrial IM at contact sites with the OM [143]. Interestingly, the abundance of ATAD3 is directly correlated with the size of nucleoids, and inversely correlated with the number of nucleoids, suggesting a role for ATAD3 in the regulation of nucleoid division [105]. Recently, Elachouri and co-workers elegantly demonstrated the involvement of a 10-kDa peptide of the mitochondrial fusion protein OPA1


Mammalian mitochondrial genetics, genomics and turnover

23

in the MIM-anchoring of nucleoids. This peptide is generated out of a particular isoform of OPA1, containing the exon4b, by the activity of the inner mitochondrial membrane protease YME1L. This 10-kDa peptide contains 2 transmembrane domains surrounding an intermediate loop oriented towards the mitochondrial matrix, which would interact with the nucleoid as suggested by co-immunoprecipitation experiments. Interestingly, OPA1 exon4b silencing affects mtDNA replication and nucleoid distribution in the mitochondrial network [144]. What is the biological significance of nucleoids? One can interrogate on the biological significance of such sub-structures. The first hypothesis that pops up in mind is that nucleoids could provide a protective micro-environment against oxidative insults, among others. In addition, as packaged mtDNA is highly dense and difficult to be accessed by matrix proteins, the selective capture of the proteins needed for DNA replication, transcription, and repair, would allow organized temporal and spatial regulation of the different processes linked to mtDNA. For instance, nucleoids are considered as the fundamental units of mtDNA segregation [140] and the sites for mtDNA replication [107, 108, 116]. It is clear that not all nucleoids participate in mtDNA replication at a defined moment. In yeast, the homologue of PolÎł was located in nucleoids in which mtDNA was actively replicated, labelled by BrdU incorporation, while nucleoids stained with DAPI where associated with the yeast TFAM homologue [145]. Fractionation experiments of mitochondrial nucleoids from HeLa cells highlighted two subpopulations of nucleoids differing by their sedimentation velocity, their mtDNA replication activity and their association to cytoskeletal proteins [112]. Although more studies are required to understand the functional diversity of nucleoids, these sub-organelle structures are consensually considered as the autonomous mtDNA replication units. One of the main functions of nucleoids is probably to organise the mtDNA segregation in the dynamic mitochondrial network. In a recent review, Spelbrink has proposed the existence of a membrane scaffold structure, that would coordinate mtDNA maintenance with mitochondrial translation, import and assembly, in tight contact with endoplasmic reticulum membrane and cytoskeleton [104]. This hypothetical structure should also integrate members of the mitochondrial fusion/fission machinery, such as mitofusins, Drp1, Fis1 and OPA1. The down-regulation of mitofusin1 and 2 [146] and Drp1 [147] showed that these proteins are not only involved in mitochondrial dynamics but also in mtDNA maintenance, as their deletion results in a strong decrease in the mitochondrial DNA. In this context, the recent identification of a nucleoid


24

Patricia Renard et al.

interaction with a fragment of an OPA1 isoform [144] could explain, at least partly, how nucleoids may organise mtDNA segregation in a dynamic mitochondria network.

2. Mitochondrial turnover Mitochondria are a highly active network, continuously undergoing fusion and fission events, which ensure proper distribution and activity of mitochondria. In terms of abundance, the global mitochondrial mass is relatively constant under steady state conditions, due to a continuous synthesis and degradation of mitochondria, with a variable half-life depending on the tissue considered (about 10 days in a hepatocyte) [148]. The fact that mitochondria are continuously synthesized, even in post-mitotic tissues, suggests that mtDNA replication is independent of the cell cycle. This concept is generally accepted in mammalian cells, although in yeast mtDNA replication has been shown to be tightly coordinated with the cell cycle. However, a recent report suggests that mitochondrial mass increases from early G1 to the mitotic phase in HeLa cells. A concomitant increase in the abundance of the nuclear respiratory factor 1 (NRF-1) (but not TFAM and PGC-1-related coactivator (PRC)) was also observed [149, 150]. While mitochondrial abundance is generally stable under steady state conditions, an increase in mitochondrial mass can be observed in response to several environmental cues characterized by an increased energy demand, like cold exposure, physical exercise, or stem cell differentiation. Although in these cases, mitochondrial biogenesis is a beneficial adaptive response, there are pathological examples where an excessive increase in mitochondrial mass might be deleterious, like in myopathies with ragged red fibers [151] and in several cancers (see this book chapter on mitochondria in cancer). On the contrary, the mitochondrial content is often down-regulated in ageing, type II diabetes and obesity [152]. It is now widely accepted that a tight control of mitochondrial mass is essential for the proper function of the organelle, the cell and the tissue. Controlling mitochondrial mass means balancing the production of the organelle (biogenesis), composed of DNA, RNA, proteins, and lipids, with the degradation of (parts of) old or damaged organelles (quality control). This will be the topic of this section, divided in three parts: mitochondria biogenesis, mitochondria degradation, and retrograde cell responses to mitochondrial dysfunction.

2.1. Mitochondrial biogenesis It is widely accepted that mitochondria biogenesis arise from growth and division of pre-existing organelles, although one cannot exclude that other


Mammalian mitochondrial genetics, genomics and turnover

25

processes might exist, such as de novo synthesis of mitochondria from cytoplasmic precursors or formation of mitochondria from other membranous cellular structures [153]. Mitochondria biogenesis is a fascinating process requiring the coordinated expression of two different genomes (see section 1.2.) localized in two cellular compartments, the targeting and import of about 1500 different nuclear-encoded proteins [5] in 4 different organelle sub-compartments, together with their assembly with 13 locally-encoded proteins. Finally, these events have to be coordinated with the biogenesis of two mitochondrial lipid membranes differing in their composition and properties. We have divided this section into 1) mtDNA maintenance and expression; 2) the expression of nuclear-encoded mitochondrial proteins; 3) the assembly of complexes composed of proteins of these two origins; 4) the coordination between the expression of the both genomes and 5) the biogenesis of mitochondrial membranes.

2.1.1. MtDNA maintenance and expression 2.1.1.1. MtDNA copy number If the mammalian mtDNA replication mechanism and machinery have been described in the previous section (1.2.2.), the regulation of mtDNA copy number, although still largely unclear, has not yet been described. First, the abundance and/or activity of proteins constituting the minimal mtDNA replication machinery affect the mtDNA copy number. Mutations in the human POLG gene have been associated with number of mitochondrial diseases (reviewed in [154]), and shown to provoke mtDNA depletion [155]. Similarly, POLG2 knockdown reduces mtDNA copy number, although the reverse is not true: the overexpression of the PolÎł accessory subunit does not affect mtDNA copy number but modifies the size and mtDNA content of nucleoids [76]. The abundance of TFAM [156] and Twinkle [157] is directly correlated with mtDNA copy number. Notably, the control of mtDNA copy number by TFAM could be independent of the effect of TFAM on mtDNA transcription [205]. Besides the core mtDNA replication machinery, proteins participating in the organization and maintenance of mtDNA may also affect the mtDNA copy number, although the underlying mechanisms are not precisely known. This is the case for ATAD3, which is anchored in the MIM at contact points with the MOM [143] and plays a role in the regulation of nucleoid division: ATAD3 silencing provokes a modest decrease in mtDNA copy number [114]. Prohibitin 1 (PHB1), another multifunctional protein found in nucleoids, is not only necessary to organize mtDNA and anchor it to the


26

Patricia Renard et al.

MIM, but also to regulate maintenance of mtDNA, partly through TFAM stabilization [134]. Let’s also mention TFB2M, a second mitochondrial transcription factor (see section 2.1.1.2.), whose overexpression induces an increase in mtDNA copy number in Drosophila melanogaster, although this has not been shown in mammals, to our knowledge [158]. Although the current knowledge on mammalian mtDNA replication regulation is far from being complete, these data collectively suggest that the main control of mtDNA copy number depends on the mitochondrial abundance of the proteins directly involved in mtDNA replication, i.e. the mitochondrial DNA polymerase (Polγ and Polγ2) and helicase (Twinkle), and the main mitochondrial transcription factor TFAM. Variations in the mtDNA copy number provoked by modified abundance of the other cited actors (ATAD3, PHB1, TFB2M) are of lower amplitudes, and might be a secondary effect of altered organization of mtDNA. Notably, if the pathological effect of mtDNA depletion can be easily understood, it is interesting to note that an excessive amount of mtDNA is also deleterious, as shown in bitransgenic mice overexpressing TFAM and Twinkle. The detrimental effect of this double overexpression is attributed to an enlargement of nucleoid structure and respiratory defects [159]. 2.1.1.2. MtDNA transcription MtDNA is transcribed into 3 polycistronic transcripts MtDNA transcription is bidirectional and starts from the heavy (HSP) and light (LSP) strand promoters located in the D-loop (Figure 1) generating polycistronic transcripts. The genes coding for proteins and rRNAs are interspersed with tRNA genes, following a “tRNA punctuation model”: the polycistronic transcripts do not undergo splicing, and are processed by an RNase that excises tRNAs to release the mRNA and rRNA [160]. The light strand promoter region contains one identified initiation site, while the heavy strand promoter region contains two transcription start sites, noted H1 and H2. Transcription of the light strand produces a transcript that is processed into 1 mRNA and 8 of the 22 tRNAs. Interestingly, transcription of the light strand would be directly linked to mtDNA replication, as prematurely truncated transcripts serve as primers for the initiation of the heavy strand replication (see point 1.2.2.). This RNA primer results from the activity of a mitochondrial RNA processing (MRP) endonuclease that forms a ribonucleocomplex with a nucleus-encoded RNA essential for catalysis [23].


Mammalian mitochondrial genetics, genomics and turnover

27

The H2-derived transcript spans almost the entire mtDNA genome and generates 2 rRNAs, 14 tRNAs and 12 mRNAs. The H1-derived transcript, produced at higher rate than H2 and LSP-derived transcripts, is dedicated to the production of ribosomal RNAs. It initiates within the tRNAPhe gene, contains the both rRNAs and terminates within the tRNAleu at a 28 bp terminator site called TERM. Unexpectedly, this terminator sequence is bidirectional and is used for the arrest of both the H1-derived transcript and the LSP-initiated transcript (see Figure 1) [161]. The mitochondrial transcription machinery On the contrary to the nuclear transcription machinery that is composed of a large complex containing numerous proteins, the basal transcription machinery of mitochondria is only composed of a few members: the RNA polymerase (POLRMT), 2 transcription actors (TFAM and TF2BM), and a termination factor (mTERF1) [162]. POLRMT is a 140-kDa single subunit enzyme presenting a strong homology with the T7 bacteriophage polymerase [124]. Like other T7-related polymerases, it binds to promoters with sequence specificity and is capable of RNA elongation [163]. However, as POLRMT cannot unwind the DNA duplex at the promoter site, vertebrate mitochondrial transcription is strictly dependent on additional transcription actors: TFAM and TF2BM. TFAM (also called mtTF-1 or mTFA) is a transcription factor that binds DNA in a sequence-unspecific manner, although it preferentially binds to the regulatory region of the D-loop containing the two promoter sequences [139, 140]. In addition to enhancing transcription thanks to a carboxy-terminal activation domain, TFAM can also induce DNA bending and unwinding, two properties facilitating mtDNA transcription. It has been proposed that the interaction of TFAM and POLRMT with specific distal and proximal elements is required to initiate transcription of the LSP element, TFAM probably inducing a structural change of the promoter required for POLRMT promoter recognition [163]. The seminal experiments to reconstitute the human mtDNA transcription machinery in vitro involved POLRMT, TFAM, TFB1M and TFB2M. Mitochondrial transcription actors B1 (TFB1M) and 2 (TFB2M) are the results of a gene duplication [164]. These proteins interact with POLRMT stoichiometrically, but also display a rRNA adenine methyltransferase activity [125]. It appeared that TF2BM is much more active than TFB1M in basal transcription [125], while TF1BM has greater rRNA methyltransferase activity [164]. In addition to facilitating promoter melting, TF2BM directly interacts with the +1 DNA base template and with the first ribonucleotide,


28

Patricia Renard et al.

thereby being a transient component of the catalytic transcription machinery [165]. Later on, experiments conducted with human untagged recombinant proteins suggested that the minimal mitochondrial transcription machinery could be composed of only two proteins: POLRMT and TFB2M. Shutt and co-workers demonstrated that these two proteins could initiate transcription from the LSP and HSP1 promoters, although the addition of recombinant TFAM strongly enhances transcription. Interestingly, these experiments also confirmed that the transcription initiation from the heavy and light strand promoters have specific requirements, as suggested before [166, 167]. In addition, both promoters respond differently to the TFAM concentration: a low TFAM concentration strongly stimulates LSP transcription, while larger amounts of TFAM are required for HSP1 transcription enhancement. As mentioned previously, LSP-dependent transcripts are necessary for mtDNA replication, while HSP1-initiated transcripts, encoding both mitochondrial rRNAs, determine the translation capacity of the organelle. Therefore, the TFAM content of a nucleoid could be decisive for its function, which could be “replicative” or “transcriptionally/translationally active” [168]. This model is consistent with the heterogeneity in the TFAM labelling in nucleoids observed by microscopy in mouse germ cells [39]. mTERF1 (mitochondrial transcription termination factor) binds with a high affinity to the 28-bp terminator sequence (TERM) (Figure 1) [129]. Using a DNA methyltransferase assay to map the accessibility of human mtDNA to different proteins, Rebelo and colleagues showed that TERM was strongly protected from methylation, suggesting that mTERF1 binds frequently and with a high affinity to TERM [137]. MTERF1 binds simultaneously to TERM and to the HSP1 promoter, inducing the formation of loop containing the rRNA coding genes. As mTERF1 has been shown to stimulate transcription, this simultaneous interaction of mTERF1 with the promoter and terminator sequences is thought to enhance the re-initiation rate of HSP-1 transcript, resulting in a higher rate of rRNA (40-50 folds) as compared to mRNA transcription levels. In mammals, mTERF1 belongs to a family of 4 related proteins named mTERF1-4 (for a review on mTERFs, see [169]). MTERF2, a nucleoid protein interacting with mtDNA [128], has been shown to interact with the mtDNA promoter region and to stimulate mtDNA transcription [130]. On the contrary, Park et al recently showed that mTERF3 is a negative regulator of mtDNA transcription. MTERF3 binds to mtDNA promoter region and represses the transcription of both the heavy and light strands. As mTERF3 knockout mice are embryonic lethal, this suggests that mTERF3 is an essential gene, probably because an excess of transcription initiation induces


Mammalian mitochondrial genetics, genomics and turnover

29

a decrease in the expression of the tRNA genes located at long distances from the promoters [170]. Beside the basal transcription machinery described above, additional actors interacting with POLRMT and participating to mtDNA transcription have been described. The recently identified transcription elongation factor of mitochondria (TEFM) interacts with the catalytic region of POLRMT. TEFM invalidation decreases transcription of both H- and L-strands, leading to respiratory defects [171]. The mitochondrial ribosomal protein 130, LRP130, involved in the Leigh syndrome, has recently attracted attention. Liu and co-workers showed that LRP130 interacts with POLRMT to activate transcription, but does not interact directly with mtDNA, which classifies LRP130 in the category of transcriptional coactivators. LRP130 increases OXPHOS activity by promoting the assembly of respiratory supercomplexes due to increased expression of mitochondria-encoded subunits, provoking the remodelling of mitochondria with denser cristae (see section 2.1.2.5.). However, these events were not accompanied by an increase in mtDNA, indicating that oxidative metabolism can be enhanced independently of mitochondria biogenesis [131]. MtDNA transcription level can also be indirectly affected by proteins that regulate the availability of nucleotides. It is the case for the mitochondrial pyrimidine nucleotide carrier 1 (PNC1) which is essential for the transcription and replication of mtDNA as shown in PNC1-deficient cells, supposedly because reduced availability of UTP could impair mtDNA transcription, as well as mtDNA replication as UTP is a cofactor for Twinkle, the mtDNA helicase [172]. Although mitochondrial transcription and translation are clearly linked in yeast, with proteins Nam1p and Sls1P binding to the N-terminal extension of RNA polymerase to couple transcription and translation [173], things are less clear in mammals, as the N-terminal extension of POLRMT diverges between mammals and yeast. It has recently been shown that one of the 80 mitochondrial ribosomal proteins (MRPs), MRPL7/L12, not only belongs to mitochondrial ribosomes, but also exists as a “free� pool able to associate with POLRMT to enhance transcription. The transcriptional function of MRPL7/L12 could be to facilitate the transition between transcription and translation [174, 175]. 2.1.1.3. MtDNA translation The mitochondrial translation machinery includes mtDNA-encoded tRNAs and rRNAS, but also a large number (about 150) of nuclear-encoded


30

Patricia Renard et al.

proteins including ribosomal proteins, ribosomal assembly proteins, amino-acyl synthetases, tRNA-modifying enzymes, translation initiation, elongation and termination actors, and a ribosome recycling factor. Several features of mammalian mitochondrial translation are distinctive from cytosolic translation, including a deviated genetic code, a reverse ribosomal RNA/protein ratio, and the absence of 3’ and 5’UTRs. These characteristics are summarized below, but interested readers are invited to refer to recent extensive reviews on mitochondrial translation [176, 177].

Mt mRNA Polyadenylated mitochondrial transcripts Polyadenylation of mammalian mitochondrial RNA was discovered a long time ago [178]. In humans, the poly(A) tail length is controlled by the relative activities of mitochondrial poly(A) polymerase (mtPAP) that polyadenylates the transcript, and of the deadenylating activity of polynucleotide phosphorylase (PNPase) [179]. What are the functions of mitochondrial transcripts polyadenylation? First, for some mitochondrial transcripts terminating by an A or a UA, polyadenylation is a means to generate an appropriate stop codon postranscriptionally [180]. Second, mitochondrial transcripts polyadenylation plays a role in RNA decay and/or in RNA stability. While most of the nucleus-encoded transcripts have a 3’ polyadenylation tail necessary for mRNA stability and translation initiation, in bacteria, the poly(A)-tail is a mark for degradation [180]. In agreement with its bacterial ancestral origin, mitochondria (as well chloroplast) transcripts that are to be degraded are first processed by an endoribonucleolytic cleavage before being polyadenylated. This process, sometimes referred as transient internal polyadenylation, targets the molecule for 3’-5’ exonucleotidic degradation (see below) [181]. Intriguingly, this polyadenylation-dependent RNA decay process apparently coexists with stable polyadenylated transcripts, more comparable to nuclear polyadenylated transcripts [181]. Non-coding regions of mtDNA transcripts are degraded by the mitochondrial degradosome RNA decay is important for determining the half-life of RNAs and degrading the unnecessary or aberrant RNAs [180]. This is particularly important for mitochondria, as the polycistronic transcript produced from the light strand, although lacking introns, contains large uncoding regions which have to be excised and degraded (see Figure 1) [162]. The RNA-degrading


Mammalian mitochondrial genetics, genomics and turnover

31

complexes, called degradosomes (“mtEXO”), contain at least an ATPdependent RNA helicase to unwind secondary structures and a 3’-5’ exoribonuclease to trim the RNA to degrade [182]. These two central members of the human mitochondrial “degradosome (“mtEXO”) have recently been identified as hSuv3p (human suppressor of Var1 3p), harbouring an ATP-dependent helicase activity for dsRNA, dsDNA, RNADNA hybrids [183], and polynucleotide phosphorylase (PNPase) activity, a phosphorolytic ambivalent enzyme catalysing both RNA degradation and RNA polyadenylation [184]. A dimer of Suv3p interacts with a trimer of PNPase to form a heteropentameric complex able to degrade double-stranded RNA [182]. The expression of a missense hSuv3p mutant induces the accumulation of intergenic non-coding regions of both heavy and light strands and polyadenylation modifications [185]. Mt ribosomes Consistently with coordinated transcription and translation in mitochondria, mitochondrial ribosomes are anchored to the MIM through interactions with the long C-terminal tail of Oxa1, a protein participating in the assembly of OXPHOS subunits from the matrix to the MIM [186]. Due to the bacterial ancestral origin of mitochondria, one would expect mitoribosomes to be similar to bacterial ribosomes. However, mitoribosomes differ from bacterial ribosomes, as well as from cytosolic ribosomes. They contain about 80 nuclear-encoded mitochondrial ribosomal proteins (MRPs) that associate with the 2 mitochondria-encoded rRNAs to ensure the translation of the 13 mitochondrial mRNAs. Half of the MRPs are considered as mitochondria-specific, with no bacterial orthologs. The mitochondrial 12S and 16S rRNA are 40% and 50% shorter than their bacterial counterparts, respectively. There is no small rRNA equivalent to E. Coli 5S rRNA encoded by mtDNA (reviewed in [187]), although it has been suggested that a nuclearencoded 5S rRNA could be imported into mitochondria [188]. Consequently, the bacterial ratio of 70% RNA and 30% proteins in ribosomes is reversed in mitochondria, affecting the sedimentation properties. Mitoribosomes, composed of a large 39S and a small 28S subunits, have a 55S sedimentation value, to be compared with the bacterial 70S and the cytosolic 80S ribosomes [189]. Although the sedimentation value is different, the mass of bacterial and mitochondrial ribosomes is similar, implicating that mitochondrial ribosome is more porous. Interestingly, the large mammalian mitoribosome subunit would lack an E-site, which could explain its property to retain tRNAs in the P-site. This feature could facilitate frame-shifting in mitoribosomes [189], which could be important to terminate the translation of some transcripts (see below).


32

Patricia Renard et al.

It has to be mentioned that, besides their obvious role in mitochondrial protein synthesis, mitochondrial 12S and 16S rRNAs can also participate to protein folding. Indeed, 12S and 16S rRNAs have been shown to exert an ATP-independent chaperone activity, as they can fold chemically denatured proteins and reactivate in vitro heat-induced aggregated proteins [190]. An intrinsic protein folding activity of the ribosome (designed as PFAR) has been largely documented for both prokaryotic and eukaryotic ribosomes (reviewed in [191]). Mt tRNAs In humans, the 22 tRNAs necessary for the mitochondrial translation are encoded by the mitochondrial genome, on the contrary to many other organisms which import some nuclearly-encoded tRNAs into the mitochondria. However, to be functional, tRNAs need to be matured and aminoacylated by tRNA modifying enzymes and aminoacyl-tRNA synthetases imported from the cytosol. Although some of the modifications of tRNAs have been identified, the enzymatic pathways involved in these modifications are far from being fully identified in mammals. However, the existence of several pathologies linked with defects in these nucleoside modifications highlight their importance for proper physiological functions of tRNAs [176]. The most studied examples of these pathologies are probably the mitochondrial encephalomyopathies MELAS (myopathy, encephalopathy, lactic acidosis, and stroke-like episodes) or MERRF (myoclonic epilepsy with ragged red fibres), in which a point mutation in the tRNAleu and tRNAlys, respectively, hamper the proper post-transcriptional modification of the Wobble position in the anticodon (for a review on mitochondrial tRNAs features, see [192]). Translation initiation MtDNA-encoded proteins are translated from 9 monocistronic and 2 bicistronic mRNAs (encoding ATP synthase subunits 6 and 8, and NADH dehydrogenase subunits 4 and 4L, see Figure 1). On the contrary to their yeast counterpart, mammalian mitochondrial transcripts lack 5’ and 3’ untranslated regions. They contain few, if any, noncoding nucleotides before the AUG start codon. The 5’ end of the mRNA is highly unstructured, which is compatible with a model in which the “porous” mitochondrial ribosome would allow the passage of unstructured 5’ mRNA sequences to initiate translation [193]. The first event in translation is the initiation step, which requires the formation of an initiation complex, composed of the small ribosomal subunit,


Mammalian mitochondrial genetics, genomics and turnover

33

mRNA, and the initiator fMet-tRNA. In bacteria, this is accomplished in the presence of 3 initiation actors (IF1, IF2, IF3). Two translation initiation actors have been identified in mammalian mitochondria: initiation actors 2 and 3 (IF2mt and IF3mt). The binding of fMet-tRNA to the small ribosome subunit is stimulated by IF2mt while IF3mt prevents the binding of fMet-tRNA to the ribosome in the absence of mRNA and premature docking of the large subunit [194]. No equivalent of IF1 has been found in mitochondria, but the stabilization function of initiation complex attributed to IF1 in bacterial ribosomes would be ensured by a short segment of IF2mt [195]. In the yeast S. cerevisiae, the translation of mitochondrial mRNAs is mediated by specific translational activators that bind to the 5’UTRs of mRNAs. The regulation of the cytochrome c oxidase subunit 1 (COX1) translation, which is tightly coupled with complex IV assembly, has been particularly studied and has recently been reviewed in [196]. As mammalian mitochondria transcripts lack significant 5’UTR, other regulatory mechanisms are expected, although not yet identified. The only mitochondrial translation regulation factor described to date in mammals, to our knowledge, is the specific mitochondrial translational activator of COX1 (TACO1), that shares some homology with bacterial translational regulators. TACO1 was identified as mutated in patients presenting a late onset Leigh syndrome with a specific COX1 deficiency [197]. Translation elongation Protein synthesis elongation proceeds with the assistance of several elongation actors: EF-Tumt, which delivers the aminoacyl-tRNA to the ribosome; EF-Tsmt, replacing EF-Tumt-GDP by an EF-Tumt-GTP [198]; and EF-G1mt and EF-G2mt, playing a role in translation elongation and recycling of the ribosome [199]. Like for cytosolic tRNAs, numerous post-transcriptional modifications decorate mitochondrial tRNAs. As human mitochondria translation is totally ensured by 22 mtDNA-encoded tRNAs, this implies that mitochondrial tRNA anticodon must have an enlarged ability to pair with several codons, as compared with cytosolic tRNAs. This is mediated by particular posttranscriptional modifications characterizing the nucleosides located at the Wobble position of several mitochondrial tRNAs, thereby extending for mitochondria the “Wobble rule” applied to the universal genetic code. For instance, the AUA codon pairs with the cytosolic tRNAIle, and with the tRNAMet in mitochondria. The formylation of the C at the wobble position of tRNAMet allows this tRNA to pair with both AUG and AUA codons (reviewed in [53, 192]). Interestingly, several human myopathies like


34

Patricia Renard et al.

MELAS or MERRF are due to point mutations in mitochondrial tRNAcoding genes. These mutations affect the tri-dimensional structure of tRNAs, and the enzymatic post-transcriptional modifications of anti-codon nucleosides, thereby profoundly disturbing the codon-anticodon pairing and thus the whole mitochondrial protein synthesis [200]. Translation termination Following the sequencing of the human mitochondria genome, it appeared that the mitochondrial genetic code diverge from the “universal genetic code”. Actually, the mitochondrial genetic code can even diverge between different species [53]. The AUA triplet coding for isoleucine, according to the universal genetic code, actually codes for methionine in mammalian mitochondria. Similarly, the UGA triplet, corresponding to the third universal stop codon (opal), specifies for tryptophan in mammals mitochondria. A corollary of this recoding of the opal codon is that the mitochondria translation apparatus would use only two stop codons. However, the examination of the human mitochondria genome sequence also revealed that none of the 22 mtDNA-encoded tRNAs has an anti-codon to decode the AGA and AGG triplets (conventionally specifying for arginine). As the human mitochondria is not able to import tRNAs from the cytosol, it was therefore assumed that mitochondria was using an extended repertoire of 4 different stop codons: the regular UAG (amber) and UAA (ochre) stop codons, terminating 11 out of 13 polypeptides-encoding transcripts, and the AGA and AGG codons to terminate the translation of MTCOI and MTND6, respectively [189]. However, recent advances regarding mitochondrial translation termination have brought new elements that contradict this view. A transcript translation terminates when a stop codon positioned in the A-site of the ribosome provokes the ribosome arrest, as there is no tRNA recognizing these triplets. Instead, the stop codons are recognized by proteins called “release actors” (RF) that promote the release of the newly synthesized protein from the ribosome [189]. There is a general agreement to consider mtRF1a as the human mitochondrial release factor [201], although ICT1 (immature colon carcinoma transcript-1), a second member of the mitochondrial release factor family, has recently been shown to be an integral component of the mitoribosome [202]. ICT-1, an essential mitochondrial protein, would be necessary for the hydrolysis of prematurely terminated peptide-tRNA in stalled ribosomes, rather than a key player in the regular translation termination at stop codons [202]. Interestingly, mtRF1a is active on UAA and UAG codons, but has no specificity for the non-cognate AGA and AGG codons involved in the translation termination of MTCOI and MTND6 [201].


Mammalian mitochondrial genetics, genomics and turnover

35

An elegant molecular dissection of the interactions between the ribosome, mtRF1a and these two transcripts led the group of Lightowlers to identify a 1 programmed ribosome frameshift induced by the ribosome stalling on AGA and AGG codons [52]. As both the MTCOI terminal AGA codon and the MTND6 terminal AGG codon are preceded by a U, a -1 frameshift can position a UAG codon in the A-site of the ribosome, allowing the recruitment of mtRF1a to terminate translation. According to these recent data, human mitochondria would use only 2 cognate stop codons instead of 4, as written in textbooks (reviewed in [189]). After termination of the translation, the polypeptide is released by the action of mtRF1a, leaving a complex containing the mRNA and the deacylated tRNA in the P site of the ribosome. This complex has to be disassembled to allow ribosome recycling, a field poorly described in mammalian mitochondria. The mitochondrial recycling factor, mtRRF, has been shown to associate with mitoribosomes and with several components of the nucleoid, which is consistent with the idea that mitochondria transcription and translation are linked. The downregulation of mtRRF inhibits mitoribosomal disassembly, partially affects mitochondrial translation and impairs ETC complexes [203]. Interestingly, the translation of mitochondria-encoded RNAs is coupled with the assembly of respiratory complexes. The mechanisms and regulation of mitochondrial translation and assembly of respiratory complexes have been well dissected for the yeast cytochrome c oxidase, and recently reviewed in [196]. Although they are still poorly described for mammals, this topic will be discussed in the section devoted to protein assembly (section 2.1.2.5.).

2.1.2. Expression of nuclear-encoded mitochondrial proteins As previously explained, the large majority (about 1500) of mitochondria-located proteins are encoded by the nuclear genome. The expression of nuclear-encoded mitochondrial proteins can be regulated at different levels: epigenetic, transcriptional, translational, import, folding and functional assembly. The transcriptional control of nuclear genes encoding mitochondrial proteins is decisive, and certainly the best described. Only the major aspects of this topic will be summarized here below as they have been extensively reviewed before ([204-208]. 2.1.2.1. Transcriptional regulation Most nuclear genes encoding mitochondrial proteins are transcriptionally regulated by one or several of the transcription actors listed in Table 2,


36

Patricia Renard et al.

including nuclear respiratory actors 1 and 2 (NRF1 and NRF2), cAMP response-element binding protein (CREB), activating transcription factor-2 (ATF2), estrogen-related receptors (ERRs), peroxisome-proliferator activated receptors (PPARs), forkhead box protein O1 (FoxO1), hepatocyte nuclear factor 4 (HNF4), myocyte enhancer factor-2 (MEF-2), specificity protein 1 (SP1) or yin-yang 1 (YY1). As mechanisms and signals regulating the expression and activity of these actors have been extensively reviewed in [204-208], we have chosen to briefly describe the first nuclear transcriptional regulators discovered to control mitochondria biogenesis: the nuclear respiratory actors 1 and 2 (NRF1 and NRF2). Our purpose is to highlight the principles underlying their integrated regulation and function as, basically, these principles can be extended to the other transcription actors listed in Table 2. NRF1 and NRF2 have been identified in 1993 by Scarpulla’s group as transcriptional regulators of the cytochrome c and COXIV promoters, respectively [232, 233]. From then on, they have been shown to control the expression of numerous OXPHOS components, mitochondrial transporters and mitochondrial ribosomal proteins. NRF2 controls the expression of the 10 nuclear-encoded COX genes [234]. This is exemplary of other transcription actors listed in Table 2, which generally control a group of genes involved in a specific mitochondrial process. In addition, they indirectly control the expression of the mitochondrial genome, as TFAM, TFB1M and TFB2M are under the transcriptional control of both NRF1 and NRF2 [208], and NRF2 also controls mTERF, POLRMT, POLG2, TWINKLE, and mtSSB [209]. The different transcription actors involved in the biogenesis of mitochondria form a regulatory network to cross-control their own expression. For example, the NRF2 promoter contains binding sites for ERRι and NRF2, providing a positive autoregulatory loop sensitive to the overall regulation by the PGC-1 family of coactivators (see below). Similarly, NRF1 induces the expression of the MEF2A gene, also listed in Table 2, thereby indirectly controlling the expression of other categories of mitochondrial proteins [204-208]. The abundance and/or activity of these transcription actors is posttranscriptionally regulated in response to several signal transduction pathways. For instance, NRF1 activity can be regulated by phosphorylation, affecting its translocation to the nucleus, its DNA binding and/or its transcriptional activity via interaction with the PGC-1 family of coactivators (see below). Its abundance and/or activity is essentially controlled by 2 major signals: increase in intracellular calcium and activation of AMP-dependent kinase (AMPK), which can be triggered by a panel of extracellular cues


Mammalian mitochondrial genetics, genomics and turnover

37

Table 2. Main transcription actors involved in mitochondrial biogenesis (modified from [152]). Abreviations: ANT2: Adenine Nucleotide Translocator 2; ATF2: Activating Transcription Factor-2; COX: Cytochrome c oxidase; CREB: cAMP Response-Element Binding protein; ERRα: Estrogen-related receptor alpha; FoxO1: Forkhead box protein O1; HNF4: Hepatocyte Nuclear Factor 4; MEF-2: Myocyte enhancer factor-2; NFAT: Nuclear factor of activated T-cells; NRF1/2: Nuclear respiratory factor 1/2; PGC-1α: Peroxisome-proliferator activated receptor Gamma Co-activator-1 alpha; POLG: DNA Polymerase gamma ; POLG2: DNA Polymerase gamma 2 (accessory subunit); PPAR: Peroxisome proliferator-activated receptor; SP1: Specificity Protein 1; TFAM: mitochondrial transcription factor A ; TFB1/2M: mitochondrial transcription actors B (1 or 2); TOP1MT : mitochondrial topoisomerase; TR: thyroid hormone receptor; UCP1: uncoupling protein 1; YY1: yin-yang 1. Transcription factor NRF-1

Regulated genes

References

-

Respiratory subunits Mitochondrial Heme synthesis Mitochondrial importation system and assembly Mitochondrial translation (ribosomal proteins, tRNA synthetases) MtDNA replication, transcription and translation Respiratory subunits, MtDNA transcription (TFAM, TFB1M, TFB2M, mTERF) MtDNA replication (TFAM, Twinkle, POLG2, mtSSB) NRF2 mtDNA transcription Respiratory subunits Mitochondrial import Fatty acid oxidation TCA cycle Mitochondrial dynamicsassociated genes NRF2

PPARα*

-

Oxidative metabolism

[212]

PPARγ* TR

-

Uncoupling proteins

[213]

-

NRF1

[214, 215]

CREB*

-

Respiratory subunits (cyt c, COX genes) Transcriptional network (PGC1α, NRF1) UCP1

[205, 216-218]

-

NRF-2

-

TFAM ERRα*

-

-

[205]

[205, 209]

[121] [210, 211]


38

Patricia Renard et al.

Table 2. Continued YY1

-

COX genes

[219, 220]

c-MYC*

-

mtDNA replication (POLG, POLG2, TOPIMT) Transcriptional network (NRF-1, PGC-1ß) PGC-1α Muscle-specific COX genes MtDNA transcription (TFAM, TFB1M, TFB2M) MtDNA replication (POLG2) ANT2 PGC-1α, UCP1 PGC-1α

[221-224]

MEF-2* SP1

-

ATF2* NFAT*

-

[225-227] [228, 229]

[230] [231]

* Regulate PGC-1 expression

(physical exercise in muscle cells, electrical stimulation of cardiomyocytes) or intracellular stimuli (respiratory uncoupling, mtDNA depletion) [204-208]. Although NRF1 is absolutely necessary for mitochondrial biogenesis, as shown by the study of embryonic lethal NRF1-/- mice [235], its expression is not sufficient to control mitochondria biogenesis, as transgenic overexpression of NRF1 in muscle does not increase the respiration capacity in response to physical exercise, indicating that other transcriptional regulators are also required for a coordinated mitochondria biogenesis [236]. Among the transcription actors listed in Table 2, several of them are coactivated by the master transcriptional coactivators of the PGC-1 (Peroxisome-proliferator activated receptor Gamma Co-activator) family, including nuclear respiratory factor (NRF1), peroxisome proliferatoractivated receptors (PPARα and γ), estrogen receptor-related α (ERRα), hepatocyte nuclear factor 4 (HNF4) and FoxO1 [204]. PGC-1α, the founding member of the PGC-1 family, was initially discovered as a coactivator of the nuclear receptor PPARγ in brown adipose tissue [213]. A PGC1-α homologue was later identified as PGC-1β [237]. Like PGC-1α, PGC-1β interacts with several transcription actors to activate mitochondrial biogenesis. However, the mechanisms of PGC-1β activation remain unclear. On the contrary to initially reported, the functions of the two related co-activators are not redundant, both of them playing specific roles in mitochondrial biogenesis [204-206]. For instance, PGC-1β, on the contrary to PGC1α, does not transactivate HNF4α and FoxO1 [238] and is not induced in brown fat following cold exposure [239]. The third member of the family,


Mammalian mitochondrial genetics, genomics and turnover

39

PRC (PGC-1 related co-activator), is ubiquitously expressed and would mainly play a role in mitochondrial biogenesis during cell division [206]. PGC-1α is the most studied member of the PGC-1 family. This master transcriptional coordinator is itself tightly regulated transcriptionally and post-transcriptionally by different signalling pathways including cAMP/PKA, NO/cGMP, Ca2+/CaMKIV, or AMPK. Consequently, PGC1-α abundance and/or activity can be finely tuned by metabolic sensors in response to several environmental cues, like hormones (glucagon/insulin), physical exercise, energy deprivation, caloric restriction, cold exposure and cytokines [206]. Regulatory binding sites for several transcription actors have been functionally identified in the promoter of PGC-1α, like binding sites for PPARs, MEF2, C/EBP, FoxO1, ATF2 and CREB, ERRs and MyoD (reviewed in [208]). The abundance and/or activity of these transcription actors can be increased, through different signalling pathways, in response to environmental changes requiring mitochondrial biogenesis. For instance, exercise induces an increase in the intracellular calcium concentration in muscle cells, which activates both the calmodulin kinase IV (CaMKIV)CREB pathway and the calcineurin-MEF2 pathway. Exercise is also a strong activator of p38MAPK, which phosphorylates and activates MEF2 and ATF2. Finally, physical exercise rapidly induces a decrease in the ATP/ADP ratio, thereby activating the AMP-dependent kinase (AMPK), which can activate PGC-1α by phosphorylation. Indeed, PGC1α activity is also regulated by several post-translational modifications, including phosphorylation (activating or inactivating) by mitogen activated protein kinase 38 kDa (p38MAPK), cAMP-dependent protein kinase (PKA), glycogen synthase kinase 3β (GSK3β) and Akt, methylation by the protein arginine N-methyltransferase 1 (PRMT1), ubiquitination, acetylation and N-acetylglucosamination. These post-translational modifications can have different effects on PGC-1α: they can modify the stability of the protein, which is of importance considering the short half-life of PGC-1α (2-3 hours); they can disrupt the inactivating interaction between PGC-1α and the co-repressor p160MBP [240], or they can modulate the ability of PGC-1α to interact specifically with some transcription actors (reviewed in [204])). In addition to regulation by transcriptional and post-translational modification, PGC-1α activity can also be regulated by alternative splicing, as recently highlighted by the discovery of NT-PGC-1α, a splicing variant of PGC-1α. As it is more stable then PGC-1α, NT-PGC1-α activity would be essentially regulated by subcellular location, upon phosphorylation by PKA [241].


40

Patricia Renard et al.

Importantly, the activity of the PGC-1 family co-activators can be counteracted by the receptor interacting protein 140 (RIP140). This nuclear receptor co-repressor interacts with several nuclear receptors controlling mitochondrial biogenesis, including PPARs and ERRs. Once docked on these nuclear receptors, RIP140 recruits additional transcriptional co-repressors like histone deacetylases (HDACs). Silencing of RIP140 leads to increased expression of many genes coding for mitochondrial proteins, including the TCA cycle genes and OXPHOS genes (reviewed in [208]). SIRT1 is another coordinator of transcriptional regulators involved in mitochondrial biogenesis. SIRT1 stands for sirtuin (silent mating type information regulation 2 homolog) 1 and is a class III histone deacetylase (HDACIII). SIRT1 enzymatic activity depends on NAD+, providing a direct link between its enzymatic activity and the energetic status of the cell. Like other histone deacetylases, SIRT1 modifies the chromatin status towards a gene silencing, but SIRT1 also deacetylates a number of transcriptional regulators (listed in [242]), including FoxO and PPARγ. Deacetylation of transcription actors can modify their expression level, subcellular localization, DNA-binding activity or transactivation capacity (reviewed in [242]). Interestingly, PGC1-α is also a target of SIRT1: SIRT1 deacetylates PGC1α at multiple lysine residues, enhancing PGC-1α-dependent transcription to response to energy demand, such as fasting or physical exercises (reviewed in [243]). 2.1.2.2. Epigenetic regulation Modification of gene transcription does not only depend on transcription actors, co-activators and co-repressors, but also on epigenetic modifications that modulate the chromatin accessibility to transcriptional regulators and to the transcription machinery. Epigenetic modifications include DNA (de)methylation, mainly in CpG islands, as well as histone reversible modifications by a large panel of kinases and phosphatases, acetylases and deacetylases, ubiquitin ligases and deubiquitinases, methylases and demethylases, and sumoylases (for a review, see [244]). Although still largely unexplored, indications derived from pathologies examination suggest that epigenetic modifications can affect mitochondrial structure and/or function, morphology, protein content, or antioxidant capacity. The gene targeted by epigenetic modifications can indirectly affect mitochondria function, like in the case of loss of imprinting (LOI) and hypomethylation found in many cancer types increasing insulin growth factor-II (Igf2) gene expression [245, 246]. Enhanced Igf2 signalling, due to an increase in the Akt/PKB pathway [247], can in turn modify mitochondrial


Mammalian mitochondrial genetics, genomics and turnover

41

activity via OXPHOS suppression and provoke a glycolytic metabolic switch through an IGF2-PI3K-Akt-FOXO-PGC1α signalling pathway. Alternatively, nuclear genes encoding for mitochondrial proteins can be directly affected by epigenetic modifications. This is the case for the promoter of NDUFB6 gene, encoding a peptide of the complex I in the respiratory chain, which was found hyper-methylated and thus downregulated in skeletal muscle cells from a type 2 diabetic patient [248]. Interestingly, the expression of the major transcriptional regulator of mitochondria biogenesis, PGC1α, has also been shown to be modulated in type 2 diabetic patients due to cytosine hypermethylation, mainly in non CpG nucleotides. This hypermethylation of the PGC1α promoter, probably mediated by DNA methyltransferase 3B (DNMT3B), is linked with decreased levels of PGC-1α and with a reduced mitochondrial content in these patients [249]. Finally, it has also been shown that Bmi1, a member of the Polycomb family (repressors that mediate gene silencing by regulating chromatin structure) is critical for mitochondrial activity and function as Bmi1-deficient mice have severe inhibition of mitochondrial electron transport chain, reduced oxygen consumption, reduced ATP production and higher ROS content [250]. If these examples indicate that epigenetic events can affect mitochondrial gene expression, either directly or indirectly, it is worth mentioning that the reverse is also true: the mitochondrial status can alter the epigenetic status. Several groups analysed epigenetic modifications (CpG island methylation) of cancer cell lines that display mtDNA depletion, and found significant changes in methylation pattern (mainly hypomethylation) of a number of genes that could be specifically reversed by the restoration of mtDNA in cells otherwise lacking the entire mitochondrial genome [251, 252]. More generally, most epigenetic events are indirectly under the influence of mitochondrial energetics as chromatin structure and DNA methylation are mainly modified by key energy intermediates, playing a crucial role in metabolism and energetics (phosphorylation by ATP, acetylation by acetylCoA, deacetylation dependent on NAD+-dependent deacetylases and even methylation by S-adenosyl-methionine as a donor). Since mitochondrial metabolism/bioenergetics controls the substrates for epigenomic regulations, modifications in the mitochondrial activity might have, potentially, a strong impact on gene expression through epigenetic or chromatin structure/function alterations. 2.1.2.3. Post-transcriptional regulation Several control levels exist to regulate the abundance of mitochondrial proteins encoded by the nuclear genome: the transcriptional control


42

Patricia Renard et al.

mentioned above is probably the best described, but other mechanisms, including translational, post-translational and even import controls, also exist. For instance, an increased stability of the beta subunit of F1-ATP synthase mRNA has been observed in rat liver during early neonatal life, together with a rapid postnatal activation of translation rates affecting mitochondrial proteins [253]. Alternative splicing has been described to regulate the intracellular distribution of enzymes exerting functions both in the nuclear compartment and in mitochondria. This is the case for several enzymes involved in DNA repair, like the human 8-oxoG DNA glycosylase (hOGG1), 2-OH-A/adenine DNA glycosylase (hMYH) and hMTH1, an oxidized purine nucleoside triphosphatase. Of note, the alternative splicing of hMTH1 is conditioned by the presence of a single nucleotide polymorphism (SNP) in the human genome [254]. The use of alternative translation start AUG codons has also been described for some proteins with the dual subcellular location, like RNaseH1 [83] and DNA topoisomerase IIIa [82], two enzymes involved in mtDNA replication. The differential translation initiation at each AUG codon determines the relative abundance of these enzymes in the mitochondrial and nuclear compartments, although the regulation of this differential translation initiation is still obscure. 2.1.2.4. Mitochondrial import Most mammalian mitochondrial proteins encoded by the nuclear genome are translated by cytosolic ribosomes before being actively imported in to the mitochondria, although some proteins are cotranslationally translocated in to mitochondria, a phenomenon mainly studied in yeast. Indeed, one can distinguish two categories of nuclear-encoded mitochondrial proteins according to the localization their mRNA translation: class I mRNAs are translated on free cytosolic polysomes, while class II mRNAs are translated on mitochondriabound polysomes. Interestingly, class I genes are mostly of eukaryotic origin while class II genes have a prokaryotic origin. The 3’UTR sequence of the classII mRNAs is necessary for the proper adressing of the mRNA to the close vicinity of the mitochondria [255]. The precursor proteins synthesized by cytosolic ribosomes are kept unfolded by cytosolic chaperones like Hsp70 and Hsp90, and guided to the translocase receptors located in the outer membrane thanks to their mitochondrial targeting elements or “zip codes”. Protein precursors are then directed to the translocase of the outer membrane of 40 kDa (TOM40), the β-barrel pore of TOM complex, which is the entry gate of the mitochondria.


Mammalian mitochondrial genetics, genomics and turnover

43

From this point, multiple mitochondrial import pathways exist, depending on the final location of the protein, which is dictated by mitochondrial targeting elements. The classical mitochondria-targeted sequence, called presequence, is typically an N-terminal positively charged sequence. The presequence typically directs proteins to the mitochondrial matrix, or in fewer cases to the MIM or the inter membrane space. However, most proteins residing in the MIM, in the intermembrane space or in the MOM lack this presequence but present internal cryptic signal sequences on the precursor protein. Four import pathways depicted in details in recent reviews [177, 256, 257] are summarized below. The translocase of inner membrane (TIM)23 complex, embedded in the inner membrane, is composed of the channel Tim23 subunit, Tim17, and Tim50 exposing a large subunit in the intermembrane space. Notably, in the absence of presequence-containing polypeptide, Tim50 closes the Tim23 channel to avoid ions leakage over the MIM. The TIM23 complex takes over the presequence-containing precursors. If the preproteins contain hydrophobic residues downstream the presequence, their transit through the TIM23 complex is stopped and they are released in the MIM, by a mechanism termed TIM23SORT. In the absence of such hydrophobic sorting signal, the preproteins reach the matrix. Their complete translocation requires a driving force provided by Δψm and the ATP-dependent interaction with the mitochondrial heat shock protein 70kDa (mtHsp70). Once in the matrix, the presequence is cleaved by the mitochondrial processing peptidase (MPP). Once released by the TOM complex, large hydrophobic precursors targeted to the MIM and deprived of a presequence are protected from aggregation in the intermembrane space by the dedicated chaperones Tim9/Tim10. Precursors are handed over the carrier translocase, or TIM22 complex, to undergo insertion into the MIM in a ∆ψ-dependent manner. Proteins of the mitochondrial intermembrane space can derive from an arrested TIM23 translocation mechanism, or from the Mitochondrial Intermembrane space Assembly (MIA) pathway. The MIA pathway takes over the small intermembrane space proteins containing specific cysteine motifs out of the TOM complex to deliver and fold them in the intermembrane space. This pathway, much less understood than the other ones, involves dithiol-disulfide exchange reactions and is independent of ∆ψm, confirming that the internal membrane is bypassed [258]. Beta-barrel proteins of the outer membrane are first translocated in the intermembrane space through the TOM complex. They are then taken in charge by chaperones delivering these proteins to the sorting and assembly machinery (SAM) complex for outer membrane integration (for a review on the import of proteins in the MOM, see [259]).


44

Patricia Renard et al.

2.1.2.5. Assembly of nuclear-encoded and mitochondria-encoded proteins Biogenesis of respiratory complexes is fascinating, since it requires the synthesis of proteins encoded by 2 different genomes, their assembly, together with number of metal ions and prosthetic groups insertion, and their anchoring in the MIM lipid bilayer. Although numerous questions remain open, the current knowledge on respiratory complexes assembly has recently been extensively reviewed in [196, 260] and will be summarized hereafter. Respiratory complexes are composed of about 90 different proteins, the large majority of which are encoded by the nuclear genome while the 13 mitochondria-encoded proteins are distributed in complex I (7), complex III (1), complex IV (3) and FoF1-ATP synthase (2). Most nuclear-encoded OXPHOS subunits are hydrophilic and form subcomplexes in the mitochondrial matrix. On the opposite, mitochondria-encoded hydrophobic proteins are co-translationally incorporated into the MIM, where they serve as structural scaffold for the assembly of nuclear-encoded proteins [261]. As an important proportion of mitochondrial proteins are translated on mitochondria-bound polysomes (see section 2.1.2.4.), a current proposed model depicts a co-localization of cytosolic and mitochondrial translation apparatus on either side of the mitochondrial membranes, thereby favouring the assembly of respiratory complexes [177]. In addition to the proteins contained in the respiratory complexes per se, the assembly of the electron transport chain requires assembly actors, as suggested by the presence of proteins bound to intermediate subcomplexes but not associated with the functional active complexes. In yeast, the sequence, subcomplexes intermediates and assembly actors of the respiratory complexes are pretty well described, especially for the cytochrome c oxidase complex [196], but the mammalian assembly control is far less understood. Several assembly actors have been identified for complexes I and IV, among which complex-specific chaperones, the apoptosis inducing factor (AIF), Oxa1L and a subunit homologous to a mitochondrial membrane translocase (reviewed in [260]). Although still hypothetical, the mechanism of action of assembly actors might involve the insertion and/or stability of hydrophobic subunits in the membrane, and/or the insertion of FeS clusters in the different protein subunits. Assembly actors are probably of crucial importance, as suggested by the pathological homozygous missense mutation in the assembly factor-coding gene C6ORF66, associated with a strong complex I deficiency and responsible for infantile mitochondrial encephalomyopathy [262]. A functional respiratory chain not only requires the adequate abundance of the different subunits of each respiratory complex, but also the proper


Mammalian mitochondrial genetics, genomics and turnover

45

stoichiometry of the different respiratory complexes to ensure optimal electron transfer. A decade ago, the analysis of mitochondria preparation by blue native electrophoresis suggested that the respiratory complexes are not distributed randomly in the MIM, but associate into supercomplexes, also called “respirasomes” [263]. In bovine heart mitochondria, at least two major respirasomes have been identified, formed by the association of different stoichiometry of complexes I, III and IV (respirasomes composed of I1III2IV1 and I1III2IV2 containing 54% and 9% of total complex I, respectively) [264]. The structure of the I1III2IV1 ”respirasome” has been recently resolved by cryoelectron microscopy [265]. Although most complexes I belong to supercomplexes, a majority of complexes III and IV would be kept in free forms, due to stoichiometric excess [264]. The biological significance of supercomplexes is still unclear, but several hypothesis have been proposed: i) a more efficient electron transfer due to shorter diffusion distances between complexes [265]; ii) a limited ROS production due to reduced electron leakage; iii) an increased stability and/or assembly of respiratory complexes. Experimental data in favour of this third hypothesis come from the analysis of complex III deficiencies caused by genetic alterations, and leading to the “secondary” loss of complex I activity. However, complex III stability is not influenced by complex I defects, suggesting that the formation of “respirasomes” could essentially be dedicated to stabilizing complex I [266]. Although the mitochondria-encoded respiratory subunits are minority among the 90 or so ETC subunits, they are absolutely essential to the electron transport chain, both functionally and structurally, as they serve as structural scaffold for the assembly of nuclear-encoded proteins [261]. Interestingly, mtDNA-encoded subunits might often represent the limiting actors for the assembly of ETC complexes, as an increase in mtDNA expression without modification of the level of nuclear-encoded OXPHOS subunits, through the forced expression of LRP130, allows the formation of more supercomplexes, denser cristae, and increased oxygen consumption [131]. This principle of one limiting subunit preventing the assembly of a whole respiratory complex (and supercomplex) may explain some tissue-specific features of OXPHOS. This is the case for the low abundant FoF1-ATP synthase complex in brown adipose tissues, in which all the other respiratory complexes are highly abundant. The relative low abundance of complex V in this tissue is compatible with the physiological role of brown adipose tissue in dissipating energy from oxidative processes as heat. Most of the subunits of the ATP synthase are expressed at high levels in brown adipose tissue, except the c-Fo subunit P1 isoform for which the mRNA level is remarkably low. The group of Nedergaard has shown that overexpression of this mRNA is sufficient to drastically increase the amount of functional FoF1-ATP synthase complexes in this tissue [267].


46

Patricia Renard et al.

The case of the c-Fo subunit of the ATP synthase clearly highlights the requirement of a balanced expression of the two genomes encoding mitochondrial proteins for proper OXPHOS function. The concerted regulation of the expression of nuclear-encoded mitochondrial genes and of mtDNA will be discussed in the next section.

2.1.3. Coordinated expression of the two genomes A large-scale genomic analysis of 60 cell lines has demonstrated the overall co-expression of the 978 mitochondrial nuclear-encoded genes listed in the MitoCarta. Moreover, cluster analysis coupled with gene ontology classification revealed that clusters of genes expressed at comparable intensities are enriched in genes with linked biological functions. For instance, highly expressed genes are related to OXPHOS, DNA replication and translation, and DNA maintenance [224]. These data confirm a highly coordinated expression of mitochondrial nuclear-encoded transcripts. The coordinated control of the mitochondrial genome and nuclearencoded mitochondrial genes is mainly exerted at the transcriptional level, although not exclusively. This control can either be direct, as some nuclear transcriptional regulators are able to translocate to the mitochondria, or indirect, as the nuclear transcriptional regulators control the expression of nuclear genes involved in mitochondrial function and biogenesis.

MtDNA expression is indirectly controlled by nuclear transcriptional regulators This is clearly the most known mechanism coordinating the expression of the two genomes. As mentioned in section 2.1.2.1, a network of transcription actors, among which NRF1 and NRF2, coordinated by the PGC-1 coactivator family, controls both the expression of a large number of mitochondrial genes, including genes coding for respiratory subunits, and the expression of the genes encoding the key regulators of mtDNA expression (i.e. POLRMT, TFAM, TFB2M, TWINKLE), as well as mtDNA replication (i.e. POLG, POLG2, TFAM, mtSSB) (see Table 2). This intricate cross-talk between mitochondria and nucleus to coordinate mitochondrial biogenesis has been reviewed recently by several authors [204-208]. Dually-localized transcriptional regulators: present in the nucleus AND in the mitochondria Several nuclear transcription actors have been found in mitochondria, including CREB, thyroid hormone receptor (TR), glucocorticoid receptor


Mammalian mitochondrial genetics, genomics and turnover

47

(GR), PPARγ2, estrogen receptor (ER) α and β, AP-1, nuclear factor kappa B (NF-κB), STAT3, and p53. Although not all of them are thought to exert a transcriptional activity inside the mitochondria, specific consensus binding sites have been identified in the D-loop sequence for several of those transcription actors (like CREB, TR, GR, PPARγ2, ERs, AP-1) (reviewed in [268, 269]). CREB is probably the mitochondria-localized nuclear transcription factor for which the involvement in the transcriptional control of mtDNA is best understood. Disruption of CREB activity in the mitochondria provokes a decreased expression of some mitochondrial genes and a reduced mitochondrial respiration [270]. In the nucleus, full CREB transcriptional activity requires phosphorylation on ser133 residue, and this is also the case in mitochondria, where CREB is phosphorylated by mitochondrial matrixlocated PKA. Mitochondrial PKA not only phosphorylates the transcription factor CREB, but also several OXPHOS proteins, including COX subunits, thereby stimulating mitochondrial respiration. Interestingly, cAMP that activates mitochondrial PKA is generated within mitochondria by the carbon dioxide/bicarbonate-regulated soluble adenylyl cyclase in response to carbon dioxide generated by the TCA cycle [271]. These findings link a metabolic coordination between the TCA cycle and OXPHOS activity, through the activation of a protein kinase that also activates a transcriptional regulator of mtDNA. Thyroid hormones have a well-known stimulatory effect on mitochondrial metabolism, explained by a strong increase in the mRNA levels of TFAM, TF2BM, Twinkle, in mtDNA copy number, and in mtDNA transcripts [272]. These effects are, at least partly, mediated by the induction of NRF1 gene expression [214]. Although a transcriptional regulatory role of TR in the mitochondria is not clear, the expression of a truncated form of TR is associated with mitochondrial localization and stimulation of mitochondrial activity [272]. Remarkably, if the coordinated expression of both genomes can be influenced by nuclear transcriptional regulators migrating in the mitochondria, the inverse is also true, as the bona fide mitochondrial transcription factor, TFAM, has recently been shown to be active in the nuclear compartment of cancer cells. As TFAM was shown to bind the BCL2L1 promoter and activate the transcription of this antiapoptotic gene, its expression was associated with poor outcome prognosis of these cancer patients [273]. Interestingly, the dual location of some transcriptional regulators is not limited to transcription actors, but has recently been extended to coactivators.


48

Patricia Renard et al.

More precisely, the major co-activator of mitochondrial biogenesis, PGC-1α, and the SIRT1 deacetylase have been shown to be associated with nucleoids in which they interact with TFAM, in association with TFAM consensus binding site, suggesting that these two coactivators might exert a comparable function of transcriptional regulators in both mitochondria and in the nucleus [274]. Clearly, these results add a supplementary layer of control on mitochondria biogenesis to the powerful PGC-1α master coordinator. Although the control on the coordinated expression of both genomes has been mostly described at the transcriptional level, one cannot exclude posttranscriptional coordinated control of the proteins characterized by a dual localization to mitochondria and nuclei. In this case, the coordinated expression between the two compartments, in terms of relative abundance for instance, is not a matter of transcription anymore, but relies on posttranscriptional processes like differential translation initiation. This is the case for the RNase H1 [83] and for the human DNA topoisomerase IIIα (TOP3α) [82]. Each of these transcripts undergoes a differential translation initiation at 2 in-frame AUG start codons of the same mRNA. Regarding the RNaseH1, the preferential use of the first or the second AUG is determined by the presence of a short ORF upstream, and influences the translation level [83]. As we have seen, mitochondrial biogenesis is a very complex process that requires mtDNA abundance control, coordinated protein expression and lipid synthesis to allow mitochondrial population expansion. While an increasingly detailed structural and mechanistic view has been proposed to explain biogenesis, sorting/segregation, and targeting of mitochondrial proteins to the various compartments, mechanisms regulating the supply of phospholipids are, comparatively, less understood and will be the topic of the next section.

2.1.4. Mitochondrial lipid synthesis Mitochondrial phospholipid composition is known to be pretty stable among different cell types, suggesting that major modifications are not allowed. This is probably to put into perspective with the crucial role of mitochondrial phospholipids in mitochondrial structure/morphology and dynamics (regulating fusion-fission events), mitochondrial protein import, integration of signals for cell/survival or apoptosis control and even mtDNA stability and segregation (for a review [275]). The phospholipid compositions of mitochondrial membranes in yeast and mammalian cells are comparable (but slightly different in relative proportions between lipid moieties) and contain roughly 40% phosphatidylcholine


Mammalian mitochondrial genetics, genomics and turnover

49

(PC), 30% phosphatidylethanolamine (PE: half-unsaturated) of total mitochondrial phospholipids. In addition to these abundant phospholipids, cardiolipin (CL), a mitochondria-specific dimeric glycerophospholipid for which a significant difference in the relative abundance is observed between MOM and MIM, and phosphatidylinositol (PI) account for 10-15% while phosphatidic acid (PA) and phosphatidylserine (PS) only represent 5% of the total phospholipids [276, 277]. In addition to these phospholipids, precursors essential for their synthesis such as CDP-DAG, phosphatidylglycerol (PG) and phosphatidylglycerol phosphate (PGP) are present but do not accumulate in the organelle. In non-steroidogenic cells, other membrane lipids such as sphingolipids and sterols (important for plasma membrane, lysosomal membrane and Golgi apparatus) are only found in trace amounts [278]. The diversity of lipids in membranes does also result from variation in carbon chain length, the degree of unsaturation of fatty acids present in the various classes of phospholipids, and the lateral lipid diffusion/distribution as a structural role is also supported by different phospholipids. Indeed, PC, PI and PS belong to the « bilayer forming lipids » while PE, CL and PA composed the « non-bilayer forming lipids », and are enriched in membrane curvature of cristae. In addition, lipid composition in the mitochondria membrane contact sites also controls lipid mobility in "inner membrane contact sites": a higher molecular mobility is related to a lower cholesterol to phospholipid ratio, as well as to a lower saturation of the fatty acyl chains when compared with "outer membrane contact sites." Indeed, phospholipids like cardiolipin (CL) play a crucial role in the fluidity of mitochondrial membrane, controlling enzymatic activities, metabolite carriers, respiratory chain [279] and protein import complex assembly/stability [280]. It is important to note that maintenance of a defined lipid composition and mitochondrial lipid synthesis depends on both the capacity of mitochondria to synthesize phospholipids such as CL, PE, PG and PA as well as import of PI, PC, and PS (primarily synthesized in the ER) used as final products or precursors for other lipids. While relative contribution of both organelles is still unknown, the de novo synthesis of PA occurs both in the ER and within mitochondria in which phospholipases such as MitoPLD is active [281]. The biochemical synthesis of PA, as a major phospholipid precursor, starts with the acylation of the sn-1 position of glycerol-3-phosphate (G3P) or dihydroxyacetone phosphate by acyltransferases (G3P acyltransferases: GPATs) that produces lyso-PA (for a detailed review please refer to [275]). In yeast, GPATs are associated with ER membranes while mammalian GPATs are localized in membranes of multiple organelles including mitochondria [282]. Lyso-PA is then converted into PA by different lyso-PA


50

Patricia Renard et al.

acyltransferases and used into two major metabolic pathways for phospholipids synthesis. In the so-called Kennedy pathway, PA is converted into DAG (a reaction catalysed by the phosphatase Pah1 [283] that eventually produces PE and PC [284]). The other pathway leads to the formation of CDP-DAG catalysed by Cds1 [285] and produces the acidic phospholipids PS, PI, PG, and CL. However, if newly synthesized CL molecules are formed on the matrixexposed leaflet of the inner membrane [286], acyl chain remodelling, a process mediated by phospholipase A and transacetylase (Taz1) [287, 288] seems to appear on the MOM [289] even if the mechanisms involved in the redistribution of the molecule are still poorly understood but most likely involve physical contact sites between MIM and MOM to allow intramitochondrial phospholipid distribution. Many and major enzymes involved in the final stages of lipid biosynthesis are thus integral membrane proteins of the endoplasmic reticulum (ER) such as cholinephosphotransferase, ethanolaminephosphotransferase, phosphatidylethanolamine-N-methyltransferase, diacylglycerol acyltransferase, glycerol-3-phosphate acyltransferase, acyl-CoA: cholesterol acyltransferase, 3-hydroxy-3-methylglutaryl-CoA reductase and phosphatidylserine synthase [290, 291]. More than 20 years ago, the specific ER region that interacts with mitochondria has been identified and named mitochondria-associated membranes (MAMs) by Jean Vance, who first identified their important function for the intimate relationship between ER and mitochondria in the exchange of glycerophospholipids [290]. Indeed, the fact that lipids are poorly soluble entities and thus not supposed to move in hydrophilic environment, strongly suggested that direct exchanges between membranes of different organelles should exist. It was first thought that phospholipid traffic occurred by protein-dependent phospholipid exchange or by transfer vesicles, but the most accepted view is the transfer by direct membrane contacts [292-294]. Supporting this hypothesis, is the finding that phosphatidylserine synthase-1 and-2 are also localized and enriched to MAMs [295], structures that participate to the tethering regions between both organelles (for a recent review on intimate interaction between ER and mitochondria, [296]). MAMs not only allow ER tubules to encircle and constrict mitochondria, marking sites for a future mitochondrial division event as recently and elegantly demonstrated by Voeltz and collaborators [297], but also concentrate several enzymes involved in lipid synthesis, especially glycerophospholipid synthesis [290], and are associated with contact sites between MIM and MOM [298]. MAMs might have several functions, but are certainly involved in the import of phosphatidylserine (PS) into mitochondria [299] mediated by a


Mammalian mitochondrial genetics, genomics and turnover

51

membrane collision-based mechanism. Mitochondrial PE is also largely produced from decarboxylation of the imported PS (the PS decarboxylase 1/PSD1 being located at the external leaflet of MIM) [300, 301]. The existence of a second pathway for PE synthesis is necessary as amount produced by the Kennedy pathway is insufficient and poorly assimilated by the mitochondria. PE could also get back to MAMs in which it would be transformed in PC into the ER in a reaction catalysed by PE methyltransferases [302]. In summary, the biosynthesis of PE and CL occurs, at least in part, within mitochondria and relies on an intricate exchange of precursors. Numerous experimental evidence suggest that exchange and mitochondrial import of glycerophospholipids occur by close physical interactions between ER and MOM [290, 295] and more generally that ER and mitochondria collaborate to produce lipids, including sphingolipids and ceramides [303], cholesterol and derived metabolites [291] that could be exchanged by MAMs contact site structures (reviewed in [296]). While independent of ATP consumption but sensitive to proteases and ERMES (ER Mitochondria Encounter Structures) complexes (suggesting the importance of ER-mitochondria tethering in phospholipid import) [304], PS import from MAMs might be dependent on Met30. This ubiquitin ligase inactivates the transcription factor Met4, leading to an increased transport of PS from MAMs into mitochondria. These molecular effectors could be modulated during biogenesis of the organelle even if Met4 target genes are still poorly characterized [294]. Eventually, mediated by add that in addition to synthesizing its own phospholipids during mitochondrial biogenesis, the organelle has the capacity to synthesize the entire pool of PE required for cell growth, and added to the flux of PS into mitochondria and subsequent decarboxylation and export as PE, can account for the large majority of glycerophospholipids that compose all cellular membranes, a phenomenon largely underappreciated that requires mitochondrial phospholipid trafficking [278, 305]. For recent advances and still unresolved questions regarding ERmitochondria inter-organelle communication and intra-mitochondrial trafficking of phospholipids, refer to excellent review [275]. Mitochondrial abundance, in terms of proteins, nucleic acids and lipids not only relies on the biogenesis of the organelle, as discussed above, but also on its degradation, which will be the topic of the next section.

2.2. Mitochondrial degradation Mitochondria abundance relies both on the genesis of the organelle and its degradation. Indeed, mitochondrial components are constantly injured,


52

Patricia Renard et al.

partly due to ROS exposure, requiring a quality control process to clear out misfolded and aggregated proteins. Depending on mitochondrial impairment severity, the cell response to restore protein homeostasis is adapted, ranging from basal protease and chaperone activities to mitophagy, the specific degradation of entire fractions of the organelle by autophagy.

2.2.1. Quality control by proteases Mitochondria contain proteases in each of their compartment to degrade misfolded proteins. This process will be described in the section 2.3.2. devoted to mitochondrial unfolded protein responses (mtUPRs). In addition, the maintenance of mitochondria protein homeostasis also relies on the cytosolic degradation apparatus of the proteasome, as suggested by the presence of ubiquitin-conjugated proteins in purified mitochondria and by the accumulation of ubiquitinated mitochondrial proteins in the presence of proteasome inhibitors. Several proteins of the MOM have been shown to be polyubiquitinated and degraded by the proteasome (like mitofusin1 and 2 and Mcl-1) or most likely targeted by proteasomal degradation as they accumulate in the presence of proteasome inhibitors (VDAC, Tom20, Tom70). More surprisingly, turnover of mitochondrial proteins of other compartments, such as EndoG in the intermembrane space, UCPs 2 and 3 in the MIM, or oligomycin-sensitivity conferral protein (OSCP) in the matrix, has also been shown to depend on the proteasomal degradation. Although the precise mechanisms are still unclear, it has been proposed that mitochondrial ubiquitinated substrates would translocate to the cytoplasm to be delivered to the proteasome, through a process involving Cdc48/p97 (reviewed in [306]).

2.2.2. Mitophagy Autophagy can be defined as a catabolic pathway by which various cellular targets, from part of cytoplasm to organelles, are engulfed into double membranes vesicles to be delivered to lysosomes for hydrolytic digestion [307]. Already more than 40 years ago, De Duve proposed the term “autophagy� when he described double membrane structures related to lysosomes [308]. Nowadays, this process has been extensively studied in various models, including yeast, where at least 33 autophagy-related-genes (Atg) have been identified, most of which have mammalian counterpart. Three kinds of autophagy have been described: micro-, macro- and chaperone-mediated autophagy, but we will only focus on macroautophagy, which will be hereafter referred as autophagy. Briefly, autophagy is initiated


Mammalian mitochondrial genetics, genomics and turnover

53

by the formation of an isolation membrane (phagophore), isolating part of cytoplasm or organelles, which extends to form a closed double membrane vesicle (autophagosome) and finally fuses with lysosome (autophagolysosome) in which engulfed material and internal membrane are degraded and recycled. Initiation of phagophore formation depends on a macromolecular complex consisting among others of Vps34 (a class III PI3 kinase) and Beclin1, whereas elongation is regulated by two ubiquitin-like systems. During autophagosome formation and elongation, Atg12 is activated by Atg7, an E1 ubiquitin activating like enzyme, and transferred to Atg10, an E2 ubiquitin conjugating like enzyme, to be conjugated to Atg5 and form the Atg12-Atg5 complex. A same conjugation system is required for LC-3 (MAP1LC3A-microtubule-associated protein 1 light chain 3) lipidation during elongation and maturation of the autophagosome. LC3-I is activated by Atg7, transferred to Atg3 and conjugated to phosphatidylethanolamine (PE) to form LC-3II at autophagosome membrane. LC-3II at autophagolysosome surface is recycled by Atg4b to form LC3-I (for interested readers on autophagy initiation regulation and conjugation complexes see [309]). Two types of macroautophagy have been described: starvation-induced non-selective autophagy and cargo-specific selective autophagy such as degradation of mitochondria (mitophagy) but also peroxisome (pexophagy), ribosome (ribophagy), intracellular bacteria (xenophagy) and endoplasmic reticulum (reticulophagy). Non-selective autophagy sequesters parts of cytoplasm under starvation condition to recycle biomolecules and refuel the cells with metabolites. If the general process of autophagy remains the same between non-specific and specific autophagy, the latter nevertheless requires a recognition step to selectively sequester its cargo. So far in mammals, three main key actors have been highlighted to be associated with mitophagy: the E3-ubiquitin ligase Parkin, the PTEN-induced putative kinase 1 (PINK1) and the MOM protein Nix (NIP3-like protein X). These actors have been described in two different models of mitophagy: Parkin and PINK1 have been implicated in dysfunctional organelle recognition and autophagic machinery recruitment [310] whereas Nix has been shown to be required for total elimination of the mitochondrial pool during erythrocyte maturation [311]. 2.2.2.1. PINK1 and Parkin: Regulators of mitochondrial quality control Implication of Parkin and PINK1 has been discovered in the context of Parkinson disease research. As another chapter of this book is devoted to neurodegenerative diseases, we will keep focussing on the mechanistic


54

Patricia Renard et al.

aspects of these two proteins regarding mitophagy, without describing the impact at the pathological level. In mammals, mutations in genes coding for these two proteins are associated with recessive forms of Parkinsonism as well as mitochondrial dysfunction. Indeed, primary cells derived from Parkinson patients with Parkin or PINK1 mutation show altered mitochondrial function and/or morphology as well as in mice deficient for these proteins [312-315]. Further evidence of Parkin implication in mitochondrial quality control has been shown by its accumulation at damaged organelle in cells treated with the chemical uncoupler carbonylcyanide m-chlorophenylhydrazone CCCP [316]. The use of various uncoupling agents and oxidative phosphorylation inhibitors has progressively shown that mitochondrial membrane potential collapse is required for its re-localization (reviewed in [310]). However, how Parkin specifically accumulates at damaged mitochondria was unknown. Kim and colleagues were the first to demonstrate that PINK1 overexpression is sufficient to induce mitochondrial translocation of Parkin, even in the absence of mitochondrial uncoupler [317]. Under basal condition, PINK1 is kept at low level, as once imported in mitochondria the kinase is turned-over by proteolytic cleavage and proteasome degradation. When mitochondrial function is impaired, PINK1 degradation is prevented, thereby allowing accumulation of the kinase at the MOM where it recruits Parkin [318]. However, if PINK1 kinase activity is required for Parkin recruitment and activation [319], the direct or indirect nature of the interaction between the two proteins is not resolved yet. Searching for mitochondrial targets of Parkin recruited at OMM, Geisler and colleagues identified VDAC1 as substrate for Lys 27 poly-ubiquitylation upon cell treatment with CCCP [319]. They have also shown that p62 is recruited to mitochondria through its ubiquitin-associated domain (UBA) and acts as link between damaged mitochondria and the autophagy machinery. Indeed, p62 is an adaptor protein linking ubiquitylated proteins of the MOM and LC3 through its LC3-interacting region (LIR), allowing machinery recruitment for autophagosome formation. Even if a similar model has been proposed for pexophagy [320], the implication of p62 and VDAC1 during mitophagy is controversial, as it has been shown that Parkin-mediated mitophagy is still initiated in MEF lacking VDAC1 or p62 [321]. Redundant functions, with still unidentified Parkin target(s), might most likely explain this result, in addition to other Parkin-dependent mechanisms that are also possibly engaged. Recently, Van Humbeeck and colleagues identified Ambra1, a Beclin-1 interacting protein that promotes phagophore nucleation by activating class III PI3K, as a Parkin partner after prolonged mitochondrial depolarization [322]. If Ambra1 ubiquitylation by Parkin has not been reported, the interaction of the two proteins allows recruitment of


Mammalian mitochondrial genetics, genomics and turnover

55

Ambra1 to clusters of depolarized mitochondria and thereby may help in mitophagy initiation [322]. Two other mitochondrial proteins have been shown to be ubiquitylated after Parkin recruitment at OMM: the fusion proteins Mfn1 and Mfn2 [323]. However, at the opposite to VDAC1, Mfn1/2 are not bound by p62 but tagged for proteasome degradation (see above) and may prevent damaged mitochondria to fuse with healthy organelle. Indeed, while constantly cycling between fusion and fission under basal condition, it has been shown that, under oxidative stress, fission generates unequal daughter organelles, isolating mitochondria with low membrane potential to be degraded by autophagy [324]. Moreover, isolated mitochondria with low membrane potential have a reduced probability of fusion event [324], which may be explained by the degradation of key players of the fusion process such as described for Mfn1/2. In addition to regulating mitochondrial dynamics, Parkin/PINK1 can also affect organelle trafficking. Wang and colleagues have shown that Miro, a rho-like GTPase, implicated in mitochondrial movement within de cells, is targeted by PINK1 and Parkin [325]. Indeed, PINK1 phosphorylates Miro which triggers its proteasomal degradation in a Parkin-depend manner. Miro degradation would lead to the release of kinesin from mitochondrial surface, thus preventing the movement of damaged mitochondria within the cell. In addition to isolating depolarized mitochondria by fission, the immobilization of a defective organelle may facilitate its degradation. They also proposed that Miro, as a common substrate, might help Parkin recruitment after PINK1 stabilization at MOM as the precise mechanism and the nature of the interactions between the two proteins are not fully understood [325, 326]. Finally, peri-nuclear clustering of depolarized mitochondria is mediated by retrograde transport. As an ubiquitin binding protein, the HDAC6 (histone deacetylase 6) has been implicated in transport of depolarized mitochondria around nucleus periphery after organelle ubiquitylation by Parkin [327], and as it has been previously explained that Miro prevents mitochondrial movement, one can imagine that depolarized mitochondria are first transported to form peri-nuclear clusters and then kinesin is released, preventing anterograde transport of damaged mitochondria until they get degraded. Further studies are required to decipher the precise timing of events during the quality control of damage mitochondria regarding depolarization, transport and degradation by mitophagy. 2.2.2.2. Nix: A crucial receptor of mitochondrial elimination Nix (also known as Bnip3L) is a BH3-only protein that has been associated with the total elimination of the mitochondrial pool during


56

Patricia Renard et al.

reticulocytes maturation [328, 329]. In this model, mitophagy does not act as a quality control, targeting dysfunctional mitochondria, but as an elimination process of the entire mitochondrial population. However, Parkin/PINK1- and Nix-induced mitophagy share similarities. Indeed, Nix triggers mitophagy partly by a similar mechanism than p62 does, by interacting with LC3 through its LIR domain. However, if mitochondrial clearance has been shown to be Nix-dependent in reticulocytes [328, 329], mutation of the Nix LIR domain prevents its interaction with LC3 and only partially blocks mitochondrial clearance [311]. Thus, Nix may have other functions in mitochondria elimination than just the recruitment of phagophore formation machinery. As a MOM resident protein, the question of how Nix is regulated to trigger at some point mitochondrial elimination has not been fully addressed yet. However, even if no Atg32 mammalian homologue has been described so far, it shares functional similarities with Nix such as the MOM targeting and LIR domains as well as the stress-induced capacity of Atg32 to recruit Atg8 (LC3 yeast homolog) and induce mitophagy [311]. Interestingly, as it has been shown that Atg32 serine 114 phosphorylation regulates mitophagy initiation in yeast [330], similar post-translational regulation might also be required for fine-tuning of Nix-mediated mitophagy. Contradicting data also raised the question of mitochondrial membrane depolarization as a cause or a consequence of mitophagy. Indeed, if it is generally considered that mitochondrial membrane potential collapse is a cause of mitophagy, others have suggested the opposite [331]. It has been hypothesized that Nix and/or Bnip3 may be responsible for mitochondrial membrane depolarization [332, 333]. Even if Nix and Bnip3 have been described to form dimers, direct evidence that the dimeric form affects mitochondrial membrane potential during mitophagy initiation has not been reported. However, very recently, Rikka and colleagues have described how Bnip3 induces membrane depolarization by targeting ETC proteins and triggers mitophagy [334]. Briefly, they have shown that Bnip3 induces mitochondrial depolarization without membrane permeabilization in Bax/Bak deficient MEFs. Actually, Bnip3 triggers mitochondrial respiratory chain proteins degradation by increasing protease activity and reduction of both nuclear- and mitochondrial-encoded OXPHOS subunits lead to organelle depolarization and induction of mitophagy [334]. Finally, although Parkin/PINK1 and Nix models of mitophagy are usually described separately from each other, Ding and colleagues reconciled both models and proposed that they act at different steps of a common process [335]. On one hand, the Parkin/ubiquitin/p62 axis prepares


Mammalian mitochondrial genetics, genomics and turnover

57

mitochondrial recognition and clearance by the autophagic machinery (denominated as “mitochondrial priming”) but does not directly impact on autophagy induction. On the other hand, Nix helps in Parkin recruitment to the organelle but also regulates autophagy induction in cooperation with ROS, coordinating both steps of mitophagy initiation [335]. One way Nix/Bnip3 might regulate autophagy initiation is by releasing Beclin1. Indeed, Bnip3 and Nix are able to interact with Bcl2 family members and as it has been shown that Bcl2 and Bcl-XL sequester Beclin1 [336]. Autophagy could thus be induced by Nix interaction with Bcl-2 members, triggering autophagosome formation by free Beclin1. As similar mechanism has been shown with Parkin recruiting Ambra1, thus affecting autophagy initiation, further investigations are required to understand the nature of crosstalk between both models at the different sequential steps of mitophagy. In conclusion, mitophagy is a cellular process that helps in mitochondrial abundance regulation. On one hand, damaged organelles are turned-over to prevent consequence of mitochondrial dysfunction, such as excessive ROS production. On the other hand, mitophagy is also crucial to some maturation and specific developmental steps requiring total elimination of the mitochondrial pool such as during reticulocyte terminal differentiation in mammals.

2.3. Cellular responses to mitochondrial dysfunction: The retrograde responses As seen in the previous section, the intercommunication between the nucleus and the mitochondria needs to be tightly regulated as the mitochondrial proteins are encoded by both the mitochondrial and the nuclear genomes. By controlling the expression of several mitochondriarelated genes, including mitochondrial transcription actors, the nucleus regulates mitochondrial biogenesis (see point 2.1.2.1.), a phenomenon that can be designated as “anterograde control” (nucleus to mitochondria communication). However, in response to a mitochondrial stress, mitochondria are able to modulate the expression of nuclear-encoded genes in an attempt to attenuate that stress. In contrast to the “anterograde control” exerted by the nucleus on mitochondria, this adaptive response has been referred as the “retrograde response” (mitochondria to nucleus communication, [337]). The mitochondrial retrograde response was initially described in contexts requiring enhanced biogenesis of the whole organelle, particularly in response to insufficient mitochondrial respiration (due to mtDNA depletion,


58

Patricia Renard et al.

impaired mitochondrial translation, OXPHOS inhibition). However, more focused stresses have been described more recently, generated by the formation of mitochondrial protein aggregates, a stress termed “mitochondrial unfolded protein response” (mtUPR). Since these stresses lead to the activation of nuclear genes coding for mitochondrial proteins devoted to resolve the stress, we consider these as another type of retrograde response. Therefore, in this section we will first summarize the different pathways involved in the “classical” retrograde response leading to mitochondria biogenesis, and secondly we will relate the current knowledge on mtUPR.

2.3.1. The retrograde response to enhance mitochondria biogenesis The retrograde response has been initially studied in the budding yeast model Saccharomyces cerevisiae. It has first been shown that, in ρ0 yeast cells devoid of mtDNA (obtained by treatment with ethidium bromide) or in yeast treated with mitochondrial inhibitors, the expression of nuclear-encoded genes could vary when compared to yeast cells containing mtDNA (ρ+) or left untreated, respectively [338-340]. This communication between mitochondria and the nucleus involves proteins of the Rtg (retrograde) family: the mitochondrial sensor Rtg2p signals to the Rtg1p/Rtg3p complex, inducing the nuclear translocation of this heterodimeric transcription factor (for more information concerning the yeast retrograde pathway, see the reviews [337, 341]). Although the Rtg pathway is apparently not conserved in mammals, various mitochondrial stresses can induce a retrograde response or “mitochondrial stress response”. This is the case for the accumulation of unfolded proteins in mitochondria (mitochondrial Unfolded Protein Response, mtUPR, [342]), the inhibition of oxidative phosphorylation [343], the altered expression of mtDNA, due to mtDNA mutations or depletion [344]. Except for the particular case of mtUPR discussed below, these mitochondrial stresses activate the expression of nucleus-encoded genes following modifications in the concentration of mitochondria-related metabolites. Indeed, the mammalian retrograde response is induced by modifications in the concentration of intracellular calcium, of reactive oxygen species (ROS) concentration, and/or in ATP/ADP and NAD+/NADH ratios. All these primary signals can lead to the activation of different transcription regulators such as CREB, NF-κB or the co-activator PGC-1α [345], leading to modifications in the expression of mitochondrial [346] and non-mitochondrial proteins [347] (see Figure 3).


Mammalian mitochondrial genetics, genomics and turnover

59

Figure 3. Main signal transduction pathways induced by mitochondrial dysfunction in the retrograde response. Mitochondrial dysfunction can activate different signal transduction pathways involving primary signals (intracellular calcium or ROS increase, modifications in the ATP/ADP or NAD+/NADH ratios) that activate intermediate effectors (kinases, phosphatases, deacetylase). These effectors activate a panel of transcriptional regulators which in turn induce modifications in gene expression to resolve the mitochondrial dysfunction.

2.3.1.1. Calcium-mediated retrograde response A cellular stress disturbing mitochondria can affect several mitochondrial functions such as the ATP production by oxidative phosphorylation, the mitochondrial ROS generation, the maintenance of mitochondrial membrane potential and the calcium buffering capacity of mitochondria. Calcium homeostasis disruption has been observed in case of altered expression of the mitochondrial genome, due to mtDNA depletion [344, 348] or to critical mtDNA mutations, either in patients’ cells [348, 349] or in cybrid cells [350]. The calcium influx into the mitochondrial matrix as well as the calcium efflux from the cells would be reduced in case of mitochondrial dysfunction due to a lower mitochondrial membrane potential and to the decreased activity of ATP-dependent calcium channels in the plasma membrane, respectively [343]. The consequent cytosolic calcium concentration increase is


60

Patricia Renard et al.

able to induce the activation of several transcription actors, leading to the expression of nuclear-encoded genes. The involvement of calcium in the retrograde response has been largely studied in the C2C12 mouse myoblastic cell line. In these cells it was first shown that mitochondrial stress induced either by mtDNA depletion, or by the use of metabolic inhibitors such as Antimycin A or mchlorophenylhydrazone (CCCP) causes an increase in cytosolic calcium concentration and consequent expression of two nuclear genes: the Ryanodine Receptor 1 (RyR1) calcium channel, involved in calcium homeostasis regulation, and the subunit Vb of the cytochrome oxidase. Furthermore, in these conditions, the transcription actors NFAT (Nuclear factor of activated T-cells) and ATF2 (Activating Transcription Factor 2) are activated through the calcium-dependent phosphatase calcineurin (Cn) and the c-Jun N-terminal Kinase (JNK), respectively [343]. In the same cell type, it was later shown that mtDNA-depletion provokes calcium-mediated NF-κB activation. Indeed, calcineurin mediates the inactivation of IκΒβ, allowing the p50/cRel heterodimer to translocate to the nucleus and activate gene expression [351]. This pathway is not restricted to C2C12 cells since in the human pulmonary adenocarcinoma cell line A549, the expression of NFAT and ATF2 are also induced in a calcium-dependent fashion following mtDNA depletion or CCCP treatment [352]. Moreover in these cells, the mitochondrial stress induces the overexpression of anti-apoptotic proteins Bcl-2 and Bcl-XL as well as the overexpression of the tumor specific markers cathepsin L and TGFβ1 (Transforming Growth Factor β1). The authors postulate that the retrograde response induced by mtDNA depletion in A549 cells favors an invasive behavior [352]. CREB, a transcription factor that controls the expression of genes involved in mitochondrial biogenesis, is also activated in response to mitochondrial dysfunction through a calcium-dependent pathway [344, 353, 354]. Impaired mitochondrial functions caused either by mtDNA depletion in L929 murine fibrosarcoma cells and in 143B human osteosarcoma cells or by the A8344G mtDNA mutation responsible for the MERRF syndrome (Myoclonic Epilepsy with Ragged Red Fibers) induce the activation of CREB by the phosphorylation of the ser133 residue. This phosphorylation is achieved by the calcium-dependent Calmoduline Kinase IV (CaMKIV) activated in response to increased cytosolic calcium concentrations. Moreover, activated CREB interacts with the transcription factor p53, inhibiting the cell cycle in mtDNA-depleted cells [344]. Slowing the cell cycle in mtDNA-depleted cells is not be the sole adaptive function of this transcription factor: first, CREB also controls the expression of the


Mammalian mitochondrial genetics, genomics and turnover

61

mitochondrial chloride intracellular channel (mtCLIC), a protein that allows these oxidative phosphorylation-deficient cells to maintain a mitochondrial membrane potential necessary for mitochondrial protein import [353]. Second, CREB also induces mitochondrial biogenesis by controlling nuclear encoded mitochondrial genes such as cytochrome c, TIM44 or β-ATPase [354]. In cells with mitochondrial dysfunction, calcium-dependent CREB activation thus represents a pathway to maintain mitochondrial function as effective as possible. According to an integrated model proposed by the group of Avadhani, CREB is only one of the components of the calcium-dependent response to mitochondrial dysfunction, as shown in mtDNA-depleted C2C12 [355-357]. Increased cytosolic calcium concentrations induce the activation of Akt1 that in turn activates the heterogeneous ribonucleoprotein A2 (hnRNPA2), a coactivator of C/EBP and of the calcium-dependent transcription actors NF-κB, NFAT and CREB. This mechanism leads to the expression of several genes such as GLUT4, RyR1 and cathepsin L, and probably to the expression of several other genes described to be upregulated in response to mitochondrial dysfunction. 2.3.1.2. ROS-mediated retrograde response In mammalian cells, mitochondria are an important site of ROS production. ROS regulates several pathways and the activity of many transcription actors is sensitive to the oxidative status of the cells (for reviews on ROS signaling, see [358, 359]. Under mitochondrial stress, an increased mitochondrial ROS production can lead to the modulation of nuclear gene expression contributing to resolve the mitochondrial stress, in an adaptive response. In 1998, it was first shown that cytochrome c1 and b expression was increased in antimycin A-treated human fibroblasts. The overexpression of these mitochondrial proteins was accompanied by H2O2 production and was inhibited by the use of antioxidant molecules [360]. Following these observations, several ROS-dependent mechanisms leading to gene expression have been described, involving the activation of several transcription actors. In MCF-7 breast cancer cells, suppression of the mitochondrial pyrimidine nucleotide carrier 1 (PNC1) expression induces mitochondrial dysfunction through reduced OXPHOS accompanied by ROS leakage and ROSdependent AMPK activation. Once activated, AMPK induces mitochondrial biogenesis mediated by the co-activator PGC-1α [172]. In Human umbilical vein cells (HUVECs) treated with homocystein, ROS-mediated activation of NF-κB is accompanied by a compensatory mitochondrial biogenesis [361] while in HeLa cells, NF-κB increases the expression of the mitochondrial


62

Patricia Renard et al.

Manganese Superoxide Dismutase in order to attenuate the oxidative stress [362]. Interestingly, it has also been shown that mitochondrial ROS are able to activate the transcription actors NRF-1 (Nuclear Respiratory Factor 1) and CREB [363, 364], a factor also involved in calcium-mediated retrograde response and mitochondrial biogenesis [344, 354]. Altogether, these independent studies show that mitochondria-produced ROS are able to regulate gene expression in response to mitochondrial dysfunction by regulating the activity of transcription actors such as NRF-1, NF-κB and CREB or of the co-activator PGC-1α. It is interesting to note that these actors, whether activated by ROS or by elevated calcium concentrations linked to mitochondrial stress, generally allow the cells to adapt to the stress by increasing mitochondrial biogenesis or by inducing the expression of antioxidant enzymes. 2.3.1.3. Retrograde response mediated by ATP depletion and modifications of the NAD+/NADH ratio In addition to the well-described calcium and ROS-mediated pathways, other messengers can activate the retrograde response following mitochondrial dysfunction. The ATP/ADP ratio can decrease following a mitochondrial stress such as mtDNA depletion, leading to the activation of the protein kinase AMPK considered as a cellular energy sensor [365, 366]. Once activated, AMPK is able to stimulate the mitochondrial biogenesis by phosphorylating and activating the co-activator PGC-1α [367] and the transcription factor NRF-1 [368]. Activated AMPK also has a protective effect in cells containing impaired mitochondria by inhibiting TOR (Target of Rapamycin) signaling [369]. In mammalian cells, there are two mTOR kinases complexes: mTORC1, regulating transcription, protein synthesis, autophagy, metabolism, ribosomal biogenesis and mTORC2, regulating survival, cell proliferation, protein synthesis, metabolism and motility [369]. The inhibition of mTOR by the AMPK has thus a protective effect by inhibiting energy consuming processes like cell proliferation and protein synthesis. A fourth primary signal able to modulate nuclear genes expression under energy depletion would be NAD+, through the activation of the NAD+dependent deacetylase Sirt1 (Silent Information Regulator T1). Indeed, following muscle exercise, NAD+ accumulates in myocytes, inducing Sirt1 activation and subsequent PGC-1α activation by deacetylation [370]. However, if SIRT1 is activated by NAD+ under energy depletion, mitochondrial respiratory chain inhibition is associated with an increase in NADH concentration [207]. Moreover, cytosolic NADH has been shown to induce the translocation to the nucleus of the transcriptional repressor


Mammalian mitochondrial genetics, genomics and turnover

63

carboxyl-terminal binding protein (CtBP) [371, 372], to modulate the expression of PGC-1α and Sirt1 [373, 374]. It should be noted that if NAD+/NADH can modulate the activation of genes following energy depletion, Sirt1 and CtBP involvement in the retrograde response per se (under mitochondrial dysfunction) has not been demonstrated yet. Interestingly, the primary signals described to initiate the retrograde response (calcium, ATP, NAD+/NADH, ROS) can lead to the activation of the same transcription actors as PGC-1α activity/expression can be regulated by ROS, ATP and NAD+ [172, 367, 370]. In addition, these four signals can be interconnected. Calcium homeostasis perturbation caused by the inhibition of the electron transfer chain can lead to the overproduction of ROS, activating NF-κB and leading to the expression of genes involved in inflammation [375] while ROS produced by a mitochondrial dysfunction are able to modulate the ATP-dependent AMPK activity [172]. Although these four primary signals depict an interconnected retrograde response to mitochondrial dysfunction in terms of mitochondrial proteins abundance, this picture needs to be completed with the lipid component of mitochondria biogenesis. An isolated paper has reported the involvement of MIDAS (mitochondrial DNA absence sensitive factor) in the mitochondrial lipid abundance in response to the absence of mtDNA. MIDAS expression induced by mtDNA depletion provokes an increase in the total mass of mitochondria. Interestingly, mitochondrial DNA, RNA and protein levels are not modified in response to MIDAS expression, on the contrary to cardiolipin, the mitochondriaspecific lipid [376]. In summary, as described by numerous studies, the mammalian retrograde response can be mediated by calcium, ROS, ATP and NAD/NADH (Figure 3). All of these four signals can modulate the expression of nuclear genes by regulating the activity of several transcription actors/regulators (PGC-1α, NRF1, CREB, ATF2, etc) through pathways involving intermediary proteins like CaMKIV, Cn and AMPK for example. This leads to the expression of nuclear-encoded genes allowing the cells to adapt to the stress (compensatory mitochondrial biogenesis, antioxidant enzymes synthesis, resistance to apoptosis, invasiveness).

2.3.2. The mitochondrial unfolded protein response (mtUPR) As seen so far, mitochondrion is able to communicate with nucleus in response to organelle dysfunction and bioenergetic impairment. Like extensively described for the endoplasmic reticulum [377], a specific mitochondrial unfolded protein response (mtUPR) could be also initiated in response to unfolded and


64

Patricia Renard et al.

aggregated proteins accumulation within mitochondrial compartments. Indeed, mitochondrial protein homeostasis is preserved by retrograde communication to coordinate transcriptional activation of nuclear-encoded mitochondrial chaperones and proteases. In mammals, two different models of mtUPR have been described. On one hand, Hoogenraad and colleagues described a CHOP-10dependent mtUPR induced by the overexpression of a truncated form of a mitochondrial enzyme [378]. On the other hand, Germain and colleagues reported a CHOP-10-independent mtUPR model where protein aggregates accumulate within mitochondrial intermembrane space when a mutant form of EndoG (N174A) is overexpressed in breast adenocarcinoma MCF-7 cells [379]. Pioneering work by Hoogenraad and collaborators has first shown the selective induction of mitochondrial chaperones such as HSP60 and HSP10 in response to mitochondrial genome total depletion (rho0) of rat hepatoma

Figure 4. Proposed models for mitochondrial matrix UPR (left) and mitochondrial intermembrane space UPR (right). Protein aggregates in the matrix, experimentally induced by the overexpression of OTC∆, trigger the activation of JNK by an unknown mechanism (dashed line). JNK phosphorylates c-Jun, which activates the expression of the CHOP-10 gene. CHOP-10 transactivates genes coding for proteases and chaperones that will resolve the mitochondrial stress. In the mitochondrial intermembrane space UPR, the expression of a mutant form of EndoG (EndoG (N174A)) induces the formation of protein aggregates that induce the activation of Akt, probably through a ROS-dependent mechanism. Akt phosphorylates and activates ErÎą, inducing the expression of NRF1 and the protease HtrA2/OMI.


Mammalian mitochondrial genetics, genomics and turnover

65

cells (H4) [380]. Later on, they set up an mtUPR model based on overexpression of a truncated form (in the carbamyl-phosphate binding domain) of the mitochondrial enzyme Ornithine Transcarbamylase (OTC∆). OTC∆ correctly localises to the organelle matrix but forms aggregates associated with the insoluble fraction after detergent extraction [381]. First clues of mtUPR have been described in COS-7 as aggregated proteins elimination correlates with the upregulation of HSP60 and ClpP, a protease. Indeed, OTC∆ abundance decreases as chaperones and proteases are upregulated whereas wtOTC abundance does not change with time. In addition, co-immunoprecipitation experiments have also shown that HSP60 and ClpP were bound to OTC∆ but not to its wild type counterpart [381]. So far, several mitochondrial chaperones (HSP60, HSP10, MtDnaJ, MPPβ, Trx2), proteases (ClpP, YME1L1) and other proteins (TIMM17A, NDUFB2, CARD12, Endonuclease G, Cytochrome C reductase) have been described to be upregulated in response to OTC∆ overexpression [382]. On the contrary, no change has been detected in the abundance of ER (BiP, Grp94) or cytosolic (Hsp73, Hsp72) chaperones and the induction of mitochondrial proteins is lost when cells are transfected with OTC∆ lacking the mitochondrial signal peptide [378, 381]. Thus, mtUPR can be defined as a mitochondrial specific stress response induced to prevent accumulation of toxic aggregates and to restore protein homeostasis by induction of nuclear genes encoding for mitochondrial chaperones and proteases. Detailed mechanisms as well as several actors of the mtUPR are still missing in mammals. However, analysis of HSP60/HSP10 bidirectional promoter revealed the existence of a CHOP-10/c-EBPβ binding element crucial for gene upregulation and the abundance of these two transcription actors is increased when OTC∆ is overexpressed [381, 382]. Current model of mtUPR-induced genes expression is a two-step process, which first requires the expression of transcription actors such as CHOP-10 that, in turn activate mitochondrial chaperones and proteases genes expression. Few details are known about events upstream CHOP-10 activation except that it requires c-Jun phosphorylation by JNK2. Phosphorylated c-Jun binds to an AP-1 element within the CHOP-10 promoter which allows a mtUPR-specific genes expression regulation that does not require the well described ERSEelements of the classic endoplasmic reticulum UPR (erUPR) [382]. However, considering the multiple cell responses involving CHOP-10 and the 3522 promoters of nuclear genes predicted to contain the binding consensus element for this transcription factor, it is likely that additional actors are required to specifically regulate mtUPR gene expression [382]. Sequence alignment of mtUPR-induced genes promoters revealed two conserved elements surrounding the CHOP-10/c-EBPβ binding site, named MURE1/2 (Mitochondrial Unfolded


66

Patricia Renard et al.

Protein Response Element 1/2) but ligands have not been identified yet [382]. In addition, the sensor(s) of aggregated proteins and initiating events leading to JNK2 activation are also still unknown in mammals. Mechanisms are better described in C. elegans where ClpP activity is required to initiate the mtUPR [383]. In addition, a large scale siRNA experiment has shown that mitochondrial peptides efflux through exporter HAF-1 and activation of ZC376.7 transcription factor participate to mtUPR early signalling (for interested readers about mtUPR in C. elegans see [342, 384]). More recently, Germain and Papa described another mtUPR model triggered by mitochondrial intermembrane space (IMS) protein aggregates accumulation [379]. Overexpression of a mutant form of Endonuclease G (EndoG-N174A) in breast cancer MCF-7 cells lead to aggregates formation and increase in HtrA2/OMI and NRF1 expression as well as enhanced proteasome activity. On the contrary to OTC∆-induced mtUPR, this IMS stress response is not dependent on CHOP-10 expression but rather relies on ligand-independent estrogen receptor α (ERα) activation. Indeed, it has been shown that EndoGN174A overexpression induces ERα phosphorylation on serine 167 by Akt. Besides, AKT activation is dependent on ROS overproduction following intermembrane space stress as demonstrated by NAC treatment that abolished ERα activation [379]. In conclusion, the compartmentalized nature of mitochondria may be at the origin of different retrograde responses depending on the location of the initiating stress. Indeed, different quality control actors have been described depending on the organelle compartment such as ClpP and HSP60 for matrix proteins or HtrA2/OMI and the proteasome for IMS proteins. Thus, as initiating stress and actors to recover protein homeostasis are different between matrix and IMS, it may explain the existence of independent signalling such as described with CHOP-10 and OTC∆ on one hand, or ERα and EndoG-N174A on the other hand.

Concluding remarks The endosymbiotic origin of mitochondria is crucial to the understanding of the regulation and function of this organelle. During the 1.5 billion years of evolution, most the genes of the proto-mitochondrion have progressively been transferred to the nucleus, resulting in the current human mitochondria retaining a small multi-copy genome coding for 37 genes. These 37 genes encode 13 subunits of the respiratory ETC, 22tRNAs and 2 rRNAs necessary to the mitochondrial translation apparatus. The synthesis of these 13 mitochondrial polypeptides represents an expensive investment for the cell: it is estimated that about 150 different proteins participate directly to the


Mammalian mitochondrial genetics, genomics and turnover

67

synthesis of these 13 polypeptides (as is the case for the 80 ribosomal proteins, ribosomal assembly proteins, aminoacyl-tRNA synthetases, tRNAmodifying enzymes, etc) [176] and that one third of the mitochondrial proteins participate indirectly to their functional expression (in this category of proteins, one can find the proteins involved in mtDNA replication, maintenance, repair and transcription, the translocases necessary to import these proteins, etc) [196]. From an evolutionary point of view, one can wonder why mitochondria has retained a mitochondrial genome to express these 13 mitochondrial polypeptides inside the organelle. Different hypotheses have been proposed to answer this question some years ago [7]. First, considering that the 13 mitochondria-encoded polypeptides are highly hydrophobic, Von Heijne proposed that if they were encoded by the nuclear genome, these peptides wouldn’t be imported into the organelle [385]. Second, as the genetic code of the human mitochondria differs from the conventional “universal� code, this might preclude the transfer of these 13 genes into the nucleus, as they would encode an inappropriate amino acid sequence [386]. Alternatively, keeping the expression of critical respiratory subunits under a local control might provide an additional control level to the organelle. Although the c-Fo subunit of the ATP synthase is a nuclear-encoded protein, the control on its expression is an interesting concept: the expression of this respiratory subunit is limiting the assembly and function of the complex V in brown adipocytes [267], and thus strongly influences the ratio between coupled and uncoupled respiration. An important consequence of the compartmentalization of mitochondrial proteins-encoding genes is that the expression of both genomes must be simultaneously and tightly coordinated to allow proper function of the organelle. We have overviewed the currently known mechanisms underlying this functional coordination. The transcriptional control exerted by a panel of nuclear transcription actors, coordinated by the PGC-1 coactivator family, is crucial and provides a direct link between the expression of both genomes, through the controlled expression of genes regulating the replication and transcription of the mitochondrial genome (POLG, TFAM, TF2BM, POLRMT, Twinkle). In addition, this network of transcriptional regulators is sensitive to environmental cues as well as internal signals, like mitochondrial dysfunction, via several signalling pathways leading to (de)activating posttranscriptional modifications. An important point to underline is that while this complex PGC1dependent regulatory network tightly controls mitochondrial biogenesis as a whole, a few recent reports indicate that some aspects of mitochondrial biogenesis can be uncoupled from the rest of the organelle. This is the case


68

Patricia Renard et al.

for the protein LRP130 that increases the mitochondrial transcription without affecting mtDNA copy number [131]. Similarly, MIDAS upregulates the mitochondrial mass by increasing cardiolipin synthesis, without modifying mtDNA replication and transcription, and oxidative phosphorylation [376]. TOP1mt, the mitochondrial topoisomerase 1, has a positive influence on the synthesis of 7S DNA without affecting the number of circular mtDNA molecules. Although the biological significance of this finding is unknown, it suggests that mtDNA and 7S DNA synthesis could proceed independently [61]. These examples highlight the importance, in future studies, to document the abundance of multiple mitochondrial components when studying mitochondrial biogenesis. Although they are much less understood, we have to stress that nontranscriptional regulatory mechanisms also exist to coordinate the abundance and function of mitochondrial proteins, like alternative start codon translation, alternative splicing, or controls at the protein import, folding or assembly levels. This complex regulatory network allows the maintenance of the appropriate mitochondria abundance and function, although all the mitochondria secrets haven’t been released yet. For instance, a still open question is the exact function of the nuclear transcription actors (GR, STAT3, PPAR) found in mitochondria. The recently described presence of microRNAs in the mitochondria [387] suggests that a whole regulatory layer of mitochondrial genome expression might be revealed in the near future.

Acknowledgments S. Michel is recipient of the Fonds pour la Recherche dans l’Industrie et l’Agriculture (FRIA, Belgium). This work was supported by the Association Belge contre les Maladies neuro-Musculaires (ABMM, Belgium). The authors also thank Michel Savels for his contribution to the figures layout.

References 1. 2. 3. 4. 5. 6. 7.

Kuznetsov A.V., Hermann M., Saks V., Hengster P., Margreiter R. 2009, Int J Biochem Cell Biol 41: 1928-39. Westermann B. 2010, Nat Rev Mol Cell Biol 11: 872-84. Otera H., Mihara K. 2011, J Biochem 149: 241-51. Duchen M.R. 2004, Mol Aspects Med 25: 365-451. Calvo S.E., Mootha V.K. 2010, Annu Rev Genomics Hum Genet 11: 25-44. Margulis L. 1970. Origin of eukaryotic cells; evidence and research implications for a theory of the origin and evolution of microbial, plant, and animal cells on the Precambrian earth. New Haven,: Yale University Press. xxii, 349 p. pp. Castellana S., Vicario S., Saccone C. 2011, Genome Biol Evol.


Mammalian mitochondrial genetics, genomics and turnover

8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40.

69

Holt I.J., Harding A.E., Morgan-Hughes J.A. 1988, Nature 331: 717-9. Wallace D.C., Singh G., Lott M.T., Hodge J.A., Schurr T.G., Lezza A.M., Elsas L.J., 2nd, Nikoskelainen E.K. 1988, Science 242: 1427-30. White D.J., Wolff J.N., Pierson M., Gemmell N.J. 2008, Mol Ecol 17: 4925-42 Dawid I.B., Blackler A.W. 1972, Dev Biol 29: 152-61. Hutchison C.A., 3rd, Newbold J.E., Potter S.S., Edgell M.H. 1974, Nature 251: 536-8. Thompson W.E., Ramalho-Santos J., Sutovsky P. 2003, Biol Reprod 69: 254-60. Sutovsky P., Moreno R.D., Ramalho-Santos J., Dominko T., Simerly C., Schatten G. 2000, Biol Reprod 63: 582-90. Sato M., Sato K. 2011, Science. Al Rawi S., Louvet-Vallee S., Djeddi A., Sachse M., Culetto E., Hajjar C., Boyd L., Legouis R., Galy V. 2011, Science. Schwartz M., Vissing J. 2002, N Engl J Med 347: 576-80. Bandelt H.J., Kong Q.P., Parson W., Salas A. 2005, J Med Genet 42: 957-60. Yao Y.G., Bandelt H.J., Young N.S. 2007, PLoS One 2: e681. Bogenhagen D., Clayton D.A. 1974, J Biol Chem 249: 7991-5. Piko L., Taylor K.D. 1987, Dev Biol 123: 364-74. Shmookler Reis R.J., Goldstein S. 1983, J Biol Chem 258: 9078-85. Shadel G.S., Clayton D.A. 1997, Annu Rev Biochem 66: 409-35. Poulton J., Macaulay V., Marchington D.R. 1998, Am J Hum Genet 62: 752-7. Giles R.E., Blanc H., Cann H.M., Wallace D.C. 1980, Proc Natl Acad Sci U S A 77: 6715-9. Ivanov P.L., Wadhams M.J., Roby R.K., Holland M.M., Weedn V.W., Parsons T.J. 1996, Nat Genet 12: 417-20. Tuppen H.A., Blakely E.L., Turnbull D.M., Taylor R.W. 2010, Biochim Biophys Acta 1797: 113-28. Harman D. 1972, J Am Geriatr Soc 20: 145-7. Hauswirth W.W., Laipis P.J. 1982, Proc Natl Acad Sci U S A 79: 4686-90. Laipis P.J., Van de Walle M.J., Hauswirth W.W. 1988, Proc Natl Acad Sci U S A 85: 8107-10. Blok R.B., Gook D.A., Thorburn D.R., Dahl H.H. 1997, Am J Hum Genet 60: 1495-501. Larsson N.G., Tulinius M.H., Holme E., Oldfors A., Andersen O., Wahlstrom J., Aasly J. 1992, Am J Hum Genet 51: 1201-12. Jenuth J.P., Peterson A.C., Fu K., Shoubridge E.A. 1996, Nat Genet 14: 146-51. Masel J. 2011, Curr Biol 21: R837-8. Stewart J.B., Freyer C., Elson J.L., Larsson N.G. 2008, Nat Rev Genet 9: 657-62. Cao L., Shitara H., Horii T., Nagao Y., Imai H., Abe K., Hara T., Hayashi J., Yonekawa H. 2007, Nat Genet 39: 386-90. Shoubridge E.A., Wai T. 2007, Curr Top Dev Biol 77: 87-111. Cree L.M., Samuels D.C., de Sousa Lopes S.C., Rajasimha H.K., Wonnapinij P., Mann J.R., Dahl H.H., Chinnery P.F. 2008, Nat Genet 40: 249-54. Wai T., Teoli D., Shoubridge E.A. 2008, Nat Genet 40: 1484-8. Pepling M.E., Wilhelm J.E., O'Hara A.L., Gephardt G.W., Spradling A.C. 2007, Proc Natl Acad Sci U S A 104: 187-92.


70

Patricia Renard et al.

41. Thyagarajan B., Padua R.A., Campbell C. 1996, J Biol Chem 271: 27536-43. 42. Mbantenkhu M., Wang X., Nardozzi J.D., Wilkens S., Hoffman E., Patel A., Cosgrove M.S., Chen X.J. 2011, J Biol Chem. 43. Liu P., Demple B. 2010, Environ Mol Mutagen 51: 417-26. 44. Barr C.M., Neiman M., Taylor D.R. 2005, New Phytol 168: 39-50. 45. Holt I.J., Dunbar D.R., Jacobs H.T. 1997, Hum Mol Genet 6: 1251-60. 46. Bodyak N.D., Nekhaeva E., Wei J.Y., Khrapko K. 2001, Hum Mol Genet 10: 17-24. 47. Zsurka G., Kraytsberg Y., Kudina T., Kornblum C., Elger C.E., Khrapko K., Kunz W.S. 2005, Nat Genet 37: 873-7. 48. Pohjoismaki J.L., Goffart S., Tyynismaa H., Willcox S., Ide T., Kang D., Suomalainen A., Karhunen P.J., Griffith J.D., Holt I.J., Jacobs H.T. 2009, J Biol Chem 284: 21446-57. 49. Sato A., Nakada K., Akimoto M., Ishikawa K., Ono T., Shitara H., Yonekawa H., Hayashi J. 2005, Proc Natl Acad Sci U S A 102: 6057-62. 50. Bacman S.R., Williams S.L., Moraes C.T. 2009, Nucleic Acids Res 37: 4218-26. 51. Barrell B.G., Bankier A.T., Drouin J. 1979, Nature 282: 189-94. 52. Temperley R., Richter R., Dennerlein S., Lightowlers R.N., ChrzanowskaLightowlers Z.M. 2010, Science 327: 301. 53. Watanabe K., Yokobori S. 2011, J Nucleic Acids 2011: 623095. 54. Pohjoismaki J.L., Goffart S. 2011, Bioessays 33: 290-9. 55. Bibb M.J., Van Etten R.A., Wright C.T., Walberg M.W., Clayton D.A. 1981, Cell 26: 167-80. 56. Anderson S., Bankier A.T., Barrell B.G., de Bruijn M.H., Coulson A.R., Drouin J., Eperon I.C., Nierlich D.P., Roe B.A., Sanger F., Schreier P.H., Smith A.J., Staden R., Young I.G. 1981, Nature 290: 457-65. 57. Andrews R.M., Kubacka I., Chinnery P.F., Lightowlers R.N., Turnbull D.M., Howell N. 1999, Nat Genet 23: 147. 58. Leigh-Brown S., Enriquez J.A., Odom D.T. 2010, Genome Biol 11: 215. 59. Ruhanen H., Borrie S., Szabadkai G., Tyynismaa H., Jones A.W., Kang D., Taanman J.W., Yasukawa T. 2010, Biochim Biophys Acta 1803: 931-9. 60. Clayton D.A. 1982, Cell 28: 693-705. 61. Zhang H., Pommier Y. 2008, Biochemistry 47: 11196-203. 62. Antes A., Tappin I., Chung S., Lim R., Lu B., Parrott A.M., Hill H.Z., Suzuki C.K., Lee C.G. 2010, Nucleic Acids Res 38: 6466-76. 63. Kai Y., Takamatsu C., Tokuda K., Okamoto M., Irita K., Takahashi S. 2006, Mitochondrion 6: 299-304. 64. Holt I.J. 2009, Trends Biochem Sci 34: 358-65. 65. Wanrooij S., Falkenberg M. 2010, Biochim Biophys Acta 1797: 1378-88. 66. Robberson D.L., Kasamatsu H., Vinograd J. 1972, Proc Natl Acad Sci U S A 69: 737-41. 67. Shadel G.S. 1999, Am J Hum Genet 65: 1230-7. 68. Yasukawa T., Yang M.Y., Jacobs H.T., Holt I.J. 2005, Mol Cell 18: 651-62. 69. Holt I.J., Lorimer H.E., Jacobs H.T. 2000, Cell 100: 515-24.


Mammalian mitochondrial genetics, genomics and turnover

71

70. Yasukawa T., Reyes A., Cluett T.J., Yang M.Y., Bowmaker M., Jacobs H.T., Holt I.J. 2006, EMBO J 25: 5358-71. 71. Cerritelli S.M., Frolova E.G., Feng C., Grinberg A., Love P.E., Crouch R.J. 2003, Mol Cell 11: 807-15. 72. Korhonen J.A., Pham X.H., Pellegrini M., Falkenberg M. 2004, EMBO J 23: 2423-9. 73. Bolden A., Noy G.P., Weissbach A. 1977, J Biol Chem 252: 3351-6. 74. Gray H., Wong T.W. 1992, J Biol Chem 267: 5835-41. 75. Ito J., Braithwaite D.K. 1990, Nucleic Acids Res 18: 6716. 76. Di Re M., Sembongi H., He J., Reyes A., Yasukawa T., Martinsson P., Bailey L.J., Goffart S., Boyd-Kirkup J.D., Wong T.S., Fersht A.R., Spelbrink J.N., Holt I.J. 2009, Nucleic Acids Res 37: 5701-13. 77. Mignotte B., Barat M., Mounolou J.C. 1985, Nucleic Acids Res 13: 1703-16. 78. Hoke G.D., Pavco P.A., Ledwith B.J., Van Tuyle G.C. 1990, Arch Biochem Biophys 282: 116-24. 79. Korhonen J.A., Gaspari M., Falkenberg M. 2003, J Biol Chem 278: 48627-32. 80. Spelbrink J.N., Li F.Y., Tiranti V., Nikali K., Yuan Q.P., Tariq M., Wanrooij S., Garrido N., Comi G., Morandi L., Santoro L., Toscano A., Fabrizi G.M., Somer H., Croxen R., Beeson D., Poulton J., Suomalainen A., Jacobs H.T., Zeviani M., Larsson C. 2001, Nat Genet 28: 223-31. 81. Zhang H., Barcelo J.M., Lee B., Kohlhagen G., Zimonjic D.B., Popescu N.C., Pommier Y. 2001, Proc Natl Acad Sci U S A 98: 10608-13. 82. Wang Y., Lyu Y.L., Wang J.C. 2002, Proc Natl Acad Sci U S A 99: 12114-9. 83. Suzuki Y., Holmes J.B., Cerritelli S.M., Sakhuja K., Minczuk M., Holt I.J., Crouch R.J. 2010, Mol Cell Biol 30: 5123-34. 84. Wallace D.C. 2010, Environ Mol Mutagen 51: 440-50. 85. Boesch P., Weber-Lotfi F., Ibrahim N., Tarasenko V., Cosset A., Paulus F., Lightowlers R.N., Dietrich A. 2011, Biochim Biophys Acta 1813: 186-200. 86. Stierum R.H., Dianov G.L., Bohr V.A. 1999, Nucleic Acids Res 27: 3712-9. 87. Liu P., Qian L., Sung J.S., de Souza-Pinto N.C., Zheng L., Bogenhagen D.F., Bohr V.A., Wilson D.M., 3rd, Shen B., Demple B. 2008, Mol Cell Biol 28: 4975-87. 88. Zheng L., Zhou M., Guo Z., Lu H., Qian L., Dai H., Qiu J., Yakubovskaya E., Bogenhagen D.F., Demple B., Shen B. 2008, Mol Cell 32: 325-36. 89. Das B.B., Dexheimer T.S., Maddali K., Pommier Y. 2010, Proc Natl Acad Sci USA 107: 19790-5. 90. Stuart J.A., Mayard S., Hashiguchi K., Souza-Pinto N.C., Bohr V.A. 2005, Nucleic Acids Res 33: 3722-32. 91. Boesch P., Ibrahim N., Dietrich A., Lightowlers R.N. 2010, Nucleic Acids Res 38: 1478-88. 92. Koulintchenko M., Temperley R.J., Mason P.A., Dietrich A., Lightowlers R.N. 2006, Hum Mol Genet 15: 143-54. 93. Aamann M.D., Sorensen M.M., Hvitby C., Berquist B.R., Muftuoglu M., Tian J., de Souza-Pinto N.C., Scheibye-Knudsen M., Wilson D.M., 3rd, Stevnsner T., Bohr V.A. 2010, FASEB J 24: 2334-46.


72

Patricia Renard et al.

94. Mason P.A., Matheson E.C., Hall A.G., Lightowlers R.N. 2003, Nucleic Acids Res 31: 1052-8. 95. de Souza-Pinto N.C., Mason P.A., Hashiguchi K., Weissman L., Tian J., Guay D., Lebel M., Stevnsner T.V., Rasmussen L.J., Bohr V.A. 2009, DNA Repair (Amst) 8: 704-19. 96. Sage J.M., Gildemeister O.S., Knight K.L. 2010, J Biol Chem 285: 18984-90. 97. Coffey G., Campbell C. 2000, Nucleic Acids Res 28: 3793-800. 98. Sykora P., Croteau D.L., Bohr V.A., Wilson D.M., 3rd. 2011, Proc Natl Acad Sci U S A 108: 7437-42. 99. Clayton D.A., Doda J.N., Friedberg E.C. 1974, Proc Natl Acad Sci U S A 71: 2777-81. 100. Shokolenko I., Venediktova N., Bochkareva A., Wilson G.L., Alexeyev M.F. 2009, Nucleic Acids Res 37: 2539-48. 101. Graziewicz M.A., Day B.J., Copeland W.C. 2002, Nucleic Acids Res 30: 2817-24. 102. Kienhofer J., Haussler D.J., Ruckelshausen F., Muessig E., Weber K., Pimentel D., Ullrich V., Burkle A., Bachschmid M.M. 2009, FASEB J 23: 2034-44. 103. Bakthavatchalu V., Dey S., Xu Y., Noel T., Jungsuwadee P., Holley A.K., Dhar S.K., Batinic-Haberle I., St Clair D.K. 2011, Oncogene. 104. Spelbrink J.N. 2010, IUBMB Life 62: 19-32. 105. Holt I.J., He J., Mao C.C., Boyd-Kirkup J.D., Martinsson P., Sembongi H., Reyes A., Spelbrink J.N. 2007, Mitochondrion 7: 311-21. 106. Barat M., Rickwood D., Dufresne C., Mounolou J.C. 1985, Exp Cell Res 157: 207-17. 107. Iborra F.J., Kimura H., Cook P.R. 2004, BMC Biol 2: 9. 108. Legros F., Malka F., Frachon P., Lombes A., Rojo M. 2004, J Cell Sci 117: 2653-62. 109. Brown T.A., Tkachuk A.N., Shtengel G., Kopek B.G., Bogenhagen D.F., Hess H.F., Clayton D.A. 2011, Mol Cell Biol. 110. Kukat C., Wurm C.A., Spahr H., Falkenberg M., Larsson N.G., Jakobs S. 2011, Proc Natl Acad Sci U S A 108: 13534-9. 111. Reyes A., He J., Mao C.C., Bailey L.J., Di Re M., Sembongi H., Kazak L., Dzionek K., Holmes J.B., Cluett T.J., Harbour M.E., Fearnley I.M., Crouch R.J., Conti M.A., Adelstein R.S., Walker J.E., Holt I.J. 2011, Nucleic Acids Res 39: 5098-108. 112. Wang Y., Bogenhagen D.F. 2006, J Biol Chem 281: 25791-802. 113. Bogenhagen D.F., Rousseau D., Burke S. 2008, J Biol Chem 283: 3665-75. 114. He J., Mao C.C., Reyes A., Sembongi H., Di Re M., Granycome C., Clippingdale A.B., Fearnley I.M., Harbour M., Robinson A.J., Reichelt S., Spelbrink J.N., Walker J.E., Holt I.J. 2007, J Cell Biol 176: 141-6. 115. Choi Y.S., Ryu B.K., Min H.K., Lee S.W., Pak Y.K. 2005, Ann N Y Acad Sci 1042: 88-100. 116. Garrido N., Griparic L., Jokitalo E., Wartiovaara J., van der Bliek A.M., Spelbrink J.N. 2003, Mol Biol Cell 14: 1583-96. 117. Bogenhagen D.F., Wang Y., Shen E.L., Kobayashi R. 2003, Mol Cell Proteomics 2: 1205-16.


Mammalian mitochondrial genetics, genomics and turnover

73

118. Takamatsu C., Umeda S., Ohsato T., Ohno T., Abe Y., Fukuoh A., Shinagawa H., Hamasaki N., Kang D. 2002, EMBO Rep 3: 451-6. 119. Alam T.I., Kanki T., Muta T., Ukaji K., Abe Y., Nakayama H., Takio K., Hamasaki N., Kang D. 2003, Nucleic Acids Res 31: 1640-5. 120. Poulton J., Morten K., Freeman-Emmerson C., Potter C., Sewry C., Dubowitz V., Kidd H., Stephenson J., Whitehouse W., Hansen F.J., et al. 1994, Hum Mol Genet 3: 1763-9. 121. Parisi M.A., Clayton D.A. 1991, Science 252: 965-9. 122. Sumitani M., Kasashima K., Ohta E., Kang D., Endo H. 2009, J Biochem 146: 725-32. 123. Duxin J.P., Dao B., Martinsson P., Rajala N., Guittat L., Campbell J.L., Spelbrink J.N., Stewart S.A. 2009, Mol Cell Biol 29: 4274-82. 124. Tiranti V., Savoia A., Forti F., D'Apolito M.F., Centra M., Rocchi M., Zeviani M. 1997, Hum Mol Genet 6: 615-25. 125. Falkenberg M., Gaspari M., Rantanen A., Trifunovic A., Larsson N.G., Gustafsson C.M. 2002, Nat Genet 31: 289-94. 126. McCulloch V., Seidel-Rogol B.L., Shadel G.S. 2002, Mol Cell Biol 22: 1116-25. 127. Fernandez-Silva P., Martinez-Azorin F., Micol V., Attardi G. 1997, EMBO J 16: 1066-79. 128. Pellegrini M., Asin-Cayuela J., Erdjument-Bromage H., Tempst P., Larsson N.G., Gustafsson C.M. 2009, Biochim Biophys Acta 1787: 296-302. 129. Kruse B., Narasimhan N., Attardi G. 1989, Cell 58: 391-7. 130. Wenz T., Luca C., Torraco A., Moraes C.T. 2009, Cell Metab 9: 499-511. 131. Liu L., Sanosaka M., Lei S., Bestwick M.L., Frey J.H., Surovtseva Y.V., Shadel G.S., Cooper M.P. 2011, J Biol Chem. 132. Khidr L., Wu G., Davila A., Procaccio V., Wallace D., Lee W.H. 2008, J Biol Chem 283: 27064-73. 133. Valgardsdottir R., Brede G., Eide L.G., Frengen E., Prydz H. 2001, J Biol Chem 276: 32056-63. 134. Kasashima K., Sumitani M., Satoh M., Endo H. 2008, Exp Cell Res 314: 988-96. 135. Cheng X., Kanki T., Fukuoh A., Ohgaki K., Takeya R., Aoki Y., Hamasaki N., Kang D. 2005, J Biochem 138: 673-8. 136. Lu B., Yadav S., Shah P.G., Liu T., Tian B., Pukszta S., Villaluna N., Kutejova E., Newlon C.S., Santos J.H., Suzuki C.K. 2007, J Biol Chem 282: 17363-74. 137. Rebelo A.P., Williams S.L., Moraes C.T. 2009, Nucleic Acids Res 37: 6701-15. 138. Kucej M., Kucejova B., Subramanian R., Chen X.J., Butow R.A. 2008, J Cell Sci 121: 1861-8. 139. Ghivizzani S.C., Madsen C.S., Nelen M.R., Ammini C.V., Hauswirth W.W. 1994, Mol Cell Biol 14: 7717-30. 140. Kaufman B.A., Durisic N., Mativetsky J.M., Costantino S., Hancock M.A., Grutter P., Shoubridge E.A. 2007, Mol Biol Cell 18: 3225-36. 141. Maniura-Weber K., Goffart S., Garstka H.L., Montoya J., Wiesner R.J. 2004, Nucleic Acids Res 32: 6015-27. 142. Cotney J., Wang Z., Shadel G.S. 2007, Nucleic Acids Res 35: 4042-54. 143. Gilquin B., Taillebourg E., Cherradi N., Hubstenberger A., Gay O., Merle N., Assard N., Fauvarque M.O., Tomohiro S., Kuge O., Baudier J. 2010, Mol Cell Biol 30: 1984-96.


74

Patricia Renard et al.

144. Elachouri G., Vidoni S., Zanna C., Pattyn A., Boukhaddaoui H., Gaget K., YuWai-Man P., Gasparre G., Sarzi E., Delettre C., Olichon A., Loiseau D., Reynier P., Chinnery P.F., Rotig A., Carelli V., Hamel C.P., Rugolo M., Lenaers G. 2011, Genome Res 21: 12-20. 145. Meeusen S., Nunnari J. 2003, J Cell Biol 163: 503-10. 146. Chen H., McCaffery J.M., Chan D.C. 2007, Cell 130: 548-62. 147. Parone P.A., Da Cruz S., Tondera D., Mattenberger Y., James D.I., Maechler P., Barja F., Martinou J.C. 2008, PLoS One 3: e3257. 148. Menzies R.A., Gold P.H. 1971, J Biol Chem 246: 2425-9. 149. Lee S., Kim S., Sun X., Lee J.H., Cho H. 2007, Biochem Biophys Res Commun 357: 111-7. 150. Martinez-Diez M., Santamaria G., Ortega A.D., Cuezva J.M. 2006, PLoS One 1: e107. 151. Aure K., Fayet G., Leroy J.P., Lacene E., Romero N.B., Lombes A. 2006, Brain 129: 1249-59. 152. Michel S.W., A. De Pauw, A. Rommelaere, G. Arnould, T. and Renard, P. 2011, J. Cell. Physiol. In submission. 153. Nisoli E., Clementi E., Moncada S., Carruba M.O. 2004, Biochem Pharmacol 67: 1-15. 154. Hudson G., Chinnery P.F. 2006, Hum Mol Genet 15 Spec No 2: R244-52. 155. Lewis W., Day B.J., Kohler J.J., Hosseini S.H., Chan S.S., Green E.C., Haase C.P., Keebaugh E.S., Long R., Ludaway T., Russ R., Steltzer J., Tioleco N., Santoianni R., Copeland W.C. 2007, Lab Invest 87: 326-35. 156. Ekstrand M.I., Falkenberg M., Rantanen A., Park C.B., Gaspari M., Hultenby K., Rustin P., Gustafsson C.M., Larsson N.G. 2004, Hum Mol Genet 13: 935-44. 157. Tyynismaa H., Sembongi H., Bokori-Brown M., Granycome C., Ashley N., Poulton J., Jalanko A., Spelbrink J.N., Holt I.J., Suomalainen A. 2004, Hum Mol Genet 13: 3219-27. 158. Matsushima Y., Garesse R., Kaguni L.S. 2004, J Biol Chem 279: 26900-5. 159. Ylikallio E., Tyynismaa H., Tsutsui H., Ide T., Suomalainen A. 2010, Hum Mol Genet 19: 2695-705. 160. Ojala D., Montoya J., Attardi G. 1981, Nature 290: 470-4. 161. Christianson T.W., Clayton D.A. 1986, Proc Natl Acad Sci U S A 83: 6277-81. 162. Falkenberg M., Larsson N.G., Gustafsson C.M. 2007, Annu Rev Biochem 76: 679-99. 163. Gaspari M., Falkenberg M., Larsson N.G., Gustafsson C.M. 2004, EMBO J 23: 4606-14. 164. Cotney J., Shadel G.S. 2006, J Mol Evol 63: 707-17. 165. Sologub M., Litonin D., Anikin M., Mustaev A., Temiakov D. 2009, Cell 139: 934-44. 166. Ikeda S., Sumiyoshi H., Oda T. 1994, Cell Mol Biol (Noisy-le-grand) 40: 489-93. 167. Fisher R.P., Clayton D.A. 1985, J Biol Chem 260: 11330-8. 168. Shutt T.E., Lodeiro M.F., Cotney J., Cameron C.E., Shadel G.S. 2010, Proc Natl Acad Sci U S A 107: 12133-8. 169. Roberti M., Polosa P.L., Bruni F., Manzari C., Deceglie S., Gadaleta M.N., Cantatore P. 2009, Biochim Biophys Acta 1787: 303-11.


Mammalian mitochondrial genetics, genomics and turnover

75

170. Park C.B., Asin-Cayuela J., Camara Y., Shi Y., Pellegrini M., Gaspari M., Wibom R., Hultenby K., Erdjument-Bromage H., Tempst P., Falkenberg M., Gustafsson C.M., Larsson N.G. 2007, Cell 130: 273-85. 171. Minczuk M., He J., Duch A.M., Ettema T.J., Chlebowski A., Dzionek K., Nijtmans L.G., Huynen M.A., Holt I.J. 2011, Nucleic Acids Res 39: 4284-99. 172. Favre C., Zhdanov A., Leahy M., Papkovsky D., O'Connor R. 2010, Oncogene 29: 3964-76. 173. Rodeheffer M.S., Shadel G.S. 2003, J Biol Chem 278: 18695-701. 174. Surovtseva Y.V., Shutt T.E., Cotney J., Cimen H., Chen S.Y., Koc E.C., Shadel G.S. 2011, Proc Natl Acad Sci U S A. 175. Wang Z., Cotney J., Shadel G.S. 2007, J Biol Chem 282: 12610-8. 176. Rotig A. 2011, Biochim Biophys Acta 1807: 1198-205. 177. Smits P., Smeitink J., van den Heuvel L. 2010, J Biomed Biotechnol 2010: 737385. 178. Ojala D., Attardi G. 1974, Proc Natl Acad Sci U S A 71: 563-7. 179. Nagaike T., Suzuki T., Katoh T., Ueda T. 2005, J Biol Chem 280: 19721-7. 180. Borowski L.S., Szczesny R.J., Brzezniak L.K., Stepien P.P. 2010, Biochim Biophys Acta 1797: 1066-70. 181. Slomovic S., Laufer D., Geiger D., Schuster G. 2005, Mol Cell Biol 25: 6427-35. 182. Wang D.D., Shu Z., Lieser S.A., Chen P.L., Lee W.H. 2009, J Biol Chem 284: 20812-21. 183. Shu Z., Vijayakumar S., Chen C.F., Chen P.L., Lee W.H. 2004, Biochemistry 43: 4781-90. 184. Portnoy V., Palnizky G., Yehudai-Resheff S., Glaser F., Schuster G. 2008, RNA 14: 297-309. 185. Szczesny R.J., Borowski L.S., Brzezniak L.K., Dmochowska A., Gewartowski K., Bartnik E., Stepien P.P. 2010, Nucleic Acids Res 38: 279-98. 186. Haque M.E., Elmore K.B., Tripathy A., Koc H., Koc E.C., Spremulli L.L. 2010, J Biol Chem 285: 28353-62. 187. O'Brien T.W. 2003, IUBMB Life 55: 505-13. 188. Entelis N.S., Kolesnikova O.A., Dogan S., Martin R.P., Tarassov I.A. 2001, J Biol Chem 276: 45642-53. 189. Lightowlers R.N., Chrzanowska-Lightowlers Z.M. 2010, RNA Biol 7: 282-6. 190. Sulijoadikusumo I., Horikoshi N., Usheva A. 2001, Biochemistry 40: 11559-64. 191. Voisset C., Saupe S.J., Blondel M. 2011, Biotechnol J 6: 668-73. 192. Watanabe K. 2010, Proc Jpn Acad Ser B Phys Biol Sci 86: 11-39. 193. Jones C.N., Wilkinson K.A., Hung K.T., Weeks K.M., Spremulli L.L. 2008, RNA 14: 862-71. 194. Christian B.E., Spremulli L.L. 2010, J Biol Chem 285: 28379-86. 195. Yassin A.S., Haque M.E., Datta P.P., Elmore K., Banavali N.K., Spremulli L.L., Agrawal R.K. 2011, Proc Natl Acad Sci U S A 108: 3918-23. 196. Mick D.U., Fox T.D., Rehling P. 2011, Nat Rev Mol Cell Biol 12: 14-20. 197. Weraarpachai W., Antonicka H., Sasarman F., Seeger J., Schrank B., Kolesar J.E., Lochmuller H., Chevrette M., Kaufman B.A., Horvath R., Shoubridge E.A. 2009, Nat Genet 41: 833-7.


76

Patricia Renard et al.

198. Akama K., Christian B.E., Jones C.N., Ueda T., Takeuchi N., Spremulli L.L. 2010, Biochim Biophys Acta 1802: 692-8. 199. Atkinson G.C., Baldauf S.L. 2011, Mol Biol Evol 28: 1281-92. 200. Kirino Y., Suzuki T. 2005, RNA Biol 2: 41-4. 201. Soleimanpour-Lichaei H.R., Kuhl I., Gaisne M., Passos J.F., Wydro M., Rorbach J., Temperley R., Bonnefoy N., Tate W., Lightowlers R., ChrzanowskaLightowlers Z. 2007, Mol Cell 27: 745-57. 202. Richter R., Rorbach J., Pajak A., Smith P.M., Wessels H.J., Huynen M.A., Smeitink J.A., Lightowlers R.N., Chrzanowska-Lightowlers Z.M. 2010, EMBO J 29: 1116-25. 203. Rorbach J., Richter R., Wessels H.J., Wydro M., Pekalski M., Farhoud M., Kuhl I., Gaisne M., Bonnefoy N., Smeitink J.A., Lightowlers R.N., ChrzanowskaLightowlers Z.M. 2008, Nucleic Acids Res 36: 5787-99. 204. Fernandez-Marcos P.J., Auwerx J. 2011, Am J Clin Nutr 93: 884S-90. 205. Scarpulla R.C. 2008, Physiol Rev 88: 611-38. 206. Scarpulla R.C. 2011, Biochim Biophys Acta 1813: 1269-78. 207. Ryan M.T., Hoogenraad N.J. 2007, Annu Rev Biochem 76: 701-22. 208. Hock M.B., Kralli A. 2009, Annu Rev Physiol 71: 177-203. 209. Bruni F., Polosa P.L., Gadaleta M.N., Cantatore P., Roberti M. 2010, J Biol Chem 285: 3939-48. 210. Mootha V.K., Handschin C., Arlow D., Xie X., St Pierre J., Sihag S., Yang W., Altshuler D., Puigserver P., Patterson N., Willy P.J., Schulman I.G., Heyman R.A., Lander E.S., Spiegelman B.M. 2004, Proc Natl Acad Sci U S A 101: 6570-5. 211. Schreiber S.N., Emter R., Hock M.B., Knutti D., Cardenas J., Podvinec M., Oakeley E.J., Kralli A. 2004, Proc Natl Acad Sci U S A 101: 6472-7. 212. Vega R.B., Huss J.M., Kelly D.P. 2000, Mol Cell Biol 20: 1868-76. 213. Puigserver P., Wu Z., Park C.W., Graves R., Wright M., Spiegelman B.M. 1998, Cell 92: 829-39. 214. Patrushev M.V., Patrusheva V.E. 2011, Biochemistry (Mosc) 76: 260-7. 215. Weitzel J.M., Iwen K.A. 2011, Mol Cell Endocrinol 342: 1-7. 216. Suliman H.B., Sweeney T.E., Withers C.M., Piantadosi C.A. 2010, J Cell Sci 123: 2565-75. 217. Vercauteren K., Pasko R.A., Gleyzer N., Marino V.M., Scarpulla R.C. 2006, Mol Cell Biol 26: 7409-19. 218. Franko A., Mayer S., Thiel G., Mercy L., Arnould T., Hornig-Do H.T., Wiesner R.J., Goffart S. 2008, Mol Cell Biol 28: 2446-59. 219. Basu A., Lenka N., Mullick J., Avadhani N.G. 1997, J Biol Chem 272: 5899-908. 220. Cunningham J.T., Rodgers J.T., Arlow D.H., Vazquez F., Mootha V.K., Puigserver P. 2007, Nature 450: 736-40. 221. Kim J., Lee J.H., Iyer V.R. 2008, PLoS One 3: e1798. 222. Li F., Wang Y., Zeller K.I., Potter J.J., Wonsey D.R., O'Donnell K.A., Kim J.W., Yustein J.T., Lee L.A., Dang C.V. 2005, Mol Cell Biol 25: 6225-34. 223. Morrish F., Giedt C., Hockenbery D. 2003, Genes Dev 17: 240-55. 224. Zoppoli G., Douarre C., Dalla Rosa I., Liu H., Reinhold W., Pommier Y. 2011, Nucleic Acids Res 39: 6620-32.


Mammalian mitochondrial genetics, genomics and turnover

77

225. Ramachandran B., Yu G., Gulick T. 2008, J Biol Chem 283: 11935-46. 226. Handschin C., Choi C.S., Chin S., Kim S., Kawamori D., Kurpad A.J., Neubauer N., Hu J., Mootha V.K., Kim Y.B., Kulkarni R.N., Shulman G.I., Spiegelman B.M. 2007, J Clin Invest 117: 3463-74. 227. Wan B., Moreadith R.W. 1995, J Biol Chem 270: 26433-40. 228. Li R., Hodny Z., Luciakova K., Barath P., Nelson B.D. 1996, J Biol Chem 271: 18925-30. 229. Evans M.J., Scarpulla R.C. 1989, J Biol Chem 264: 14361-8. 230. Akimoto T., Pohnert S.C., Li P., Zhang M., Gumbs C., Rosenberg P.B., Williams R.S., Yan Z. 2005, J Biol Chem 280: 19587-93. 231. Olson E.N., Williams R.S. 2000, Cell 101: 689-92. 232. Virbasius C.A., Virbasius J.V., Scarpulla R.C. 1993, Genes Dev 7: 2431-45. 233. Virbasius J.V., Virbasius C.A., Scarpulla R.C. 1993, Genes Dev 7: 380-92. 234. Ongwijitwat S., Liang H.L., Graboyes E.M., Wong-Riley M.T. 2006, Gene 374: 39-49. 235. Huo L., Scarpulla R.C. 2001, Mol Cell Biol 21: 644-54. 236. Baar K., Song Z., Semenkovich C.F., Jones T.E., Han D.H., Nolte L.A., Ojuka E.O., Chen M., Holloszy J.O. 2003, FASEB J 17: 1666-73. 237. Lin J., Puigserver P., Donovan J., Tarr P., Spiegelman B.M. 2002, J Biol Chem 277: 1645-8. 238. Lin J., Tarr P.T., Yang R., Rhee J., Puigserver P., Newgard C.B., Spiegelman B.M. 2003, J Biol Chem 278: 30843-8. 239. Meirhaeghe A., Crowley V., Lenaghan C., Lelliott C., Green K., Stewart A., Hart K., Schinner S., Sethi J.K., Yeo G., Brand M.D., Cortright R.N., O'Rahilly S., Montague C., Vidal-Puig A.J. 2003, Biochem J 373: 155-65. 240. Fan M., Rhee J., St-Pierre J., Handschin C., Puigserver P., Lin J., Jaeger S., Erdjument-Bromage H., Tempst P., Spiegelman B.M. 2004, Genes Dev 18: 278-89. 241. Chang J.S., Huypens P., Zhang Y., Black C., Kralli A., Gettys T.W. 2010, J Biol Chem 285: 18039-50. 242. Dai Y., Faller D.V. 2008, Transl Oncogenomics 3: 53-65. 243. Jeninga E.H., Schoonjans K., Auwerx J. 2010, Oncogene 29: 4617-24. 244. Murr R. 2010, Adv Genet 70: 101-41. 245. Feinberg A.P. 2007, Nature 447: 433-40. 246. Bjornsson H.T., Brown L.J., Fallin M.D., Rongione M.A., Bibikova M., Wickham E., Fan J.B., Feinberg A.P. 2007, J Natl Cancer Inst 99: 1270-3. 247. Kaneda A., Wang C.J., Cheong R., Timp W., Onyango P., Wen B., IacobuzioDonahue C.A., Ohlsson R., Andraos R., Pearson M.A., Sharov A.A., Longo D.L., Ko M.S., Levchenko A., Feinberg A.P. 2007, Proc Natl Acad Sci U S A 104: 20926-31. 248. Ling C., Poulsen P., Simonsson S., Ronn T., Holmkvist J., Almgren P., Hagert P., Nilsson E., Mabey A.G., Nilsson P., Vaag A., Groop L. 2007, J Clin Invest 117: 3427-35. 249. Barres R., Osler M.E., Yan J., Rune A., Fritz T., Caidahl K., Krook A., Zierath J.R. 2009, Cell Metab 10: 189-98.


78

Patricia Renard et al.

250. Liu J., Cao L., Chen J., Song S., Lee I.H., Quijano C., Liu H., Keyvanfar K., Chen H., Cao L.Y., Ahn B.H., Kumar N.G., Rovira, II, Xu X.L., van Lohuizen M., Motoyama N., Deng C.X., Finkel T. 2009, Nature 459: 387-92. 251. Naviaux R.K. 2008, Cancer Biol Ther 7: 1191-3. 252. Smiraglia D.J., Kulawiec M., Bistulfi G.L., Gupta S.G., Singh K.K. 2008, Cancer Biol Ther 7: 1182-90. 253. Izquierdo J.M., Ricart J., Ostronoff L.K., Egea G., Cuezva J.M. 1995, J Biol Chem 270: 10342-50. 254. Nakabeppu Y. 2001, Prog Nucleic Acid Res Mol Biol 68: 75-94. 255. Margeot A., Garcia M., Wang W., Tetaud E., di Rago J.P., Jacq C. 2005, Gene 354: 64-71. 256. Gebert N., Ryan M.T., Pfanner N., Wiedemann N., Stojanovski D. 2011, Biochim Biophys Acta 1808: 1002-11. 257. Schmidt O., Pfanner N., Meisinger C. 2010, Nat Rev Mol Cell Biol 11: 655-67. 258. Koehler C.M., Tienson H.L. 2009, Biochim Biophys Acta 1793: 139-45. 259. Endo T., Yamano K. 2010, Biochim Biophys Acta 1803: 706-14. 260. Lenaz G., Genova M.L. 2010, Antioxid Redox Signal 12: 961-1008. 261. Bourges I., Ramus C., Mousson de Camaret B., Beugnot R., Remacle C., Cardol P., Hofhaus G., Issartel J.P. 2004, Biochem J 383: 491-9. 262. Saada A., Edvardson S., Rapoport M., Shaag A., Amry K., Miller C., Lorberboum-Galski H., Elpeleg O. 2008, Am J Hum Genet 82: 32-8. 263. Schagger H., Pfeiffer K. 2000, EMBO J 19: 1777-83. 264. Schagger H., Pfeiffer K. 2001, J Biol Chem 276: 37861-7. 265. Althoff T., Mills D.J., Popot J.L., Kuhlbrandt W. 2011, EMBO J 30: 4652-64 266. Schagger H., de Coo R., Bauer M.F., Hofmann S., Godinot C., Brandt U. 2004, J Biol Chem 279: 36349-53. 267. Kramarova T.V., Antonicka H., Houstek J., Cannon B., Nedergaard J. 2008, Biochim Biophys Acta 1777: 747-57. 268. Lee J., Sharma S., Kim J., Ferrante R.J., Ryu H. 2008, J Neurosci Res 86: 961-71. 269. Shutt T.E., Shadel G.S. 2010, Environ Mol Mutagen 51: 360-79. 270. Lee J., Kim C.H., Simon D.K., Aminova L.R., Andreyev A.Y., Kushnareva Y.E., Murphy A.N., Lonze B.E., Kim K.S., Ginty D.D., Ferrante R.J., Ryu H., Ratan R.R. 2005, J Biol Chem 280: 40398-401. 271. Acin-Perez R., Salazar E., Kamenetsky M., Buck J., Levin L.R., Manfredi G. 2009, Cell Metab 9: 265-76. 272. Scheller K., Sekeris C.E. 2003, Exp Physiol 88: 129-40. 273. Kurita T., Izumi H., Kagami S., Kawagoe T., Toki N., Matsuura Y., Hachisuga T., Kohno K. 2011, Cancer Sci. 274. Aquilano K., Vigilanza P., Baldelli S., Pagliei B., Rotilio G., Ciriolo M.R. 2010, J Biol Chem 285: 21590-9. 275. Osman C., Voelker D.R., Langer T. 2011, Journal of Cell Biology 192: 7-16. 276. Colbeau A., Nachbaur J., Vignais P.M. 1971, Biochim Biophys Acta 249: 462-92. 277. Zinser E., Daum G. 1995, Yeast 11: 493-536. 278. van Meer G., Voelker D.R., Feigenson G.W. 2008, Nat Rev Mol Cell Biol 9: 112-24.


Mammalian mitochondrial genetics, genomics and turnover

79

279. Houtkooper R.H., Vaz F.M. 2008, Cell Mol Life Sci 65: 2493-506. 280. Kutik S., Rissler M., Guan X.L., Guiard B., Shui G., Gebert N., Heacock P.N., Rehling P., Dowhan W., Wenk M.R., Pfanner N., Wiedemann N. 2008, J Cell Biol 183: 1213-21. 281. Choi S.Y., Huang P., Jenkins G.M., Chan D.C., Schiller J., Frohman M.A. 2006, Nat Cell Biol 8: 1255-62. 282. Wendel A.A., Lewin T.M., Coleman R.A. 2009, Biochim Biophys Acta 1791: 501-6. 283. Han G.S., Wu W.I., Carman G.M. 2006, J Biol Chem 281: 9210-8. 284. Daum G., Lees N.D., Bard M., Dickson R. 1998, Yeast 14: 1471-510. 285. Shen H., Heacock P.N., Clancey C.J., Dowhan W. 1996, J Biol Chem 271: 789-95. 286. Schlame M., Haldar D. 1993, J Biol Chem 268: 74-9. 287. Beranek A., Rechberger G., Knauer H., Wolinski H., Kohlwein S.D., Leber R. 2009, J Biol Chem 284: 11572-8. 288. Xu Y., Malhotra A., Ren M., Schlame M. 2006, J Biol Chem 281: 39217-24. 289. Claypool S.M., McCaffery J.M., Koehler C.M. 2006, J Cell Biol 174: 379-90. 290. Vance J.E. 1990, J Biol Chem 265: 7248-56. 291. Rusinol A.E., Cui Z., Chen M.H., Vance J.E. 1994, J Biol Chem 269: 27494-502. 292. Voelker D.R. 2005, Trends Biochem Sci 30: 396-404. 293. Voelker D.R. 2003, J Lipid Res 44: 441-9. 294. Voelker D.R. 2009, Annu Rev Biochem 78: 827-56. 295. Stone S.J., Vance J.E. 2000, J Biol Chem 275: 34534-40. 296. de Brito O.M., Scorrano L. 2010, EMBO J 29: 2715-23. 297. Friedman J.R., Lackner L.L., West M., DiBenedetto J.R., Nunnari J., Voeltz G.K. 2011, Science 334: 358-62. 298. Ardail D., Gasnier F., Lerme F., Simonot C., Louisot P., Gateau-Roesch O. 1993, J Biol Chem 268: 25985-92. 299. Gaigg B., Simbeni R., Hrastnik C., Paltauf F., Daum G. 1995, Biochim Biophys Acta 1234: 214-20. 300. Clancey C.J., Chang S.C., Dowhan W. 1993, J Biol Chem 268: 24580-90. 301. Trotter P.J., Pedretti J., Voelker D.R. 1993, J Biol Chem 268: 21416-24. 302. Cui Z., Vance J.E., Chen M.H., Voelker D.R., Vance D.E. 1993, J Biol Chem 268: 16655-63. 303. Bionda C., Portoukalian J., Schmitt D., Rodriguez-Lafrasse C., Ardail D. 2004, Biochem J 382: 527-33. 304. Kornmann B., Currie E., Collins S.R., Schuldiner M., Nunnari J., Weissman J.S., Walter P. 2009, Science 325: 477-81. 305. Birner R., Burgermeister M., Schneiter R., Daum G. 2001, Mol Biol Cell 12: 997-1007. 306. Heo J.M., Rutter J. 2011, Int J Biochem Cell Biol 43: 1422-6. 307. Yang Z., Klionsky D.J. 2010, Nat Cell Biol 12: 814-22. 308. De Duve C., Wattiaux R. 1966, Annu Rev Physiol 28: 435-92. 309. Tanida I. 2011, Microbiol Immunol 55: 1-11. 310. Narendra D.P., Youle R.J. 2011, Antioxid Redox Signal 14: 1929-38.


80

Patricia Renard et al.

311. Novak I., Kirkin V., McEwan D.G., Zhang J., Wild P., Rozenknop A., Rogov V., Lohr F., Popovic D., Occhipinti A., Reichert A.S., Terzic J., Dotsch V., Ney P.A., Dikic I. 2010, EMBO Rep 11: 45-51. 312. Exner N., Treske B., Paquet D., Holmstrom K., Schiesling C., Gispert S., Carballo-Carbajal I., Berg D., Hoepken H.H., Gasser T., Kruger R., Winklhofer K.F., Vogel F., Reichert A.S., Auburger G., Kahle P.J., Schmid B., Haass C. 2007, J Neurosci 27: 12413-8. 313. Mortiboys H., Thomas K.J., Koopman W.J., Klaffke S., Abou-Sleiman P., Olpin S., Wood N.W., Willems P.H., Smeitink J.A., Cookson M.R., Bandmann O. 2008, Ann Neurol 64: 555-65. 314. Gautier C.A., Kitada T., Shen J. 2008, Proc Natl Acad Sci U S A 105: 11364-9. 315. Palacino J.J., Sagi D., Goldberg M.S., Krauss S., Motz C., Wacker M., Klose J., Shen J. 2004, J Biol Chem 279: 18614-22. 316. Narendra D., Tanaka A., Suen D.F., Youle R.J. 2008, J Cell Biol 183: 795-803. 317. Kim Y., Park J., Kim S., Song S., Kwon S.K., Lee S.H., Kitada T., Kim J.M., Chung J. 2008, Biochem Biophys Res Commun 377: 975-80. 318. Narendra D.P., Jin S.M., Tanaka A., Suen D.F., Gautier C.A., Shen J., Cookson M.R., Youle R.J. 2010, PLoS Biol 8: e1000298. 319. Geisler S., Holmstrom K.M., Skujat D., Fiesel F.C., Rothfuss O.C., Kahle P.J., Springer W. 2010, Nat Cell Biol 12: 119-31. 320. Kim P.K., Hailey D.W., Mullen R.T., Lippincott-Schwartz J. 2008, Proc Natl Acad Sci U S A 105: 20567-74. 321. Narendra D., Kane L.A., Hauser D.N., Fearnley I.M., Youle R.J. 2010, Autophagy 6: 1090-106. 322. Van Humbeeck C., Cornelissen T., Hofkens H., Mandemakers W., Gevaert K., De Strooper B., Vandenberghe W. 2011, J Neurosci 31: 10249-61. 323. Gegg M.E., Cooper J.M., Chau K.Y., Rojo M., Schapira A.H., Taanman J.W. 2010, Hum Mol Genet 19: 4861-70. 324. Twig G., Elorza A., Molina A.J., Mohamed H., Wikstrom J.D., Walzer G., Stiles L., Haigh S.E., Katz S., Las G., Alroy J., Wu M., Py B.F., Yuan J., Deeney J.T., Corkey B.E., Shirihai O.S. 2008, EMBO J 27: 433-46. 325. Wang X., Winter D., Ashrafi G., Schlehe J., Wong Y.L., Selkoe D., Rice S., Steen J., Lavoie M.J., Schwarz T.L. 2011, Cell 147: 893-906. 326. Vives-Bauza C., Przedborski S. 2011, Trends Mol Med 17: 158-65. 327. Lee J.Y., Nagano Y., Taylor J.P., Lim K.L., Yao T.P. 2010, J Cell Biol 189: 671-9. 328. Sandoval H., Thiagarajan P., Dasgupta S.K., Schumacher A., Prchal J.T., Chen M., Wang J. 2008, Nature 454: 232-5. 329. Schweers R.L., Zhang J., Randall M.S., Loyd M.R., Li W., Dorsey F.C., Kundu M., Opferman J.T., Cleveland J.L., Miller J.L., Ney P.A. 2007, Proc Natl Acad Sci U S A 104: 19500-5. 330. Aoki Y., Kanki T., Hirota Y., Kurihara Y., Saigusa T., Uchiumi T., Kang D. 2011, Mol Biol Cell 22: 3206-17. 331. Zhang J., Randall M.S., Loyd M.R., Dorsey F.C., Kundu M., Cleveland J.L., Ney P.A. 2009, Blood 114: 157-64.


Mammalian mitochondrial genetics, genomics and turnover

81

332. Novak I. 2011, Antioxid Redox Signal. 333. Zhang J., Ney P.A. 2009, Cell Death Differ 16: 939-46. 334. Rikka S., Quinsay M.N., Thomas R.L., Kubli D.A., Zhang X., Murphy A.N., Gustafsson A.B. 2011, Cell Death Differ 18: 721-31. 335. Ding W.X., Ni H.M., Li M., Liao Y., Chen X., Stolz D.B., Dorn G.W., 2nd, Yin X.M. 2010, J Biol Chem 285: 27879-90. 336. Pattingre S., Tassa A., Qu X., Garuti R., Liang X.H., Mizushima N., Packer M., Schneider M.D., Levine B. 2005, Cell 122: 927-39. 337. Butow R.A., Avadhani N.G. 2004, Mol Cell 14: 1-15. 338. Liao X.S., Small W.C., Srere P.A., Butow R.A. 1991, Mol Cell Biol 11: 38-46. 339. Parikh V.S., Conrad-Webb H., Docherty R., Butow R.A. 1989, Mol Cell Biol 9: 1897-907. 340. Parikh V.S., Morgan M.M., Scott R., Clements L.S., Butow R.A. 1987, Science 235: 576-80. 341. Liu Z., Butow R.A. 2006, Annu Rev Genet 40: 159-85. 342. Haynes C.M., Ron D. 2010, J Cell Sci 123: 3849-55. 343. Biswas G., Adebanjo O.A., Freedman B.D., Anandatheerthavarada H.K., Vijayasarathy C., Zaidi M., Kotlikoff M., Avadhani N.G. 1999, EMBO J 18: 522-33. 344. Arnould T., Vankoningsloo S., Renard P., Houbion A., Ninane N., Demazy C., Remacle J., Raes M. 2002, EMBO J 21: 53-63. 345. Jones A.W., Yao Z., Vicencio J.M., Karkucinska-Wieckowska A., Szabadkai G. 2011, Mitochondrion. 346. Marusich M.F., Robinson B.H., Taanman J.W., Kim S.J., Schillace R., Smith J.L., Capaldi R.A. 1997, Biochim Biophys Acta 1362: 145-59. 347. Wang H., Morais R. 1997, Biochim Biophys Acta 1352: 325-34. 348. von Kleist-Retzow J.C., Hornig-Do H.T., Schauen M., Eckertz S., Dinh T.A., Stassen F., Lottmann N., Bust M., Galunska B., Wielckens K., Hein W., Beuth J., Braun J.M., Fischer J.H., Ganitkevich V.Y., Maniura-Weber K., Wiesner R.J. 2007, Exp Cell Res 313: 3076-89. 349. Brini M., Pinton P., King M.P., Davidson M., Schon E.A., Rizzuto R. 1999, Nat Med 5: 951-4. 350. Rommelaere G., Michel S., Malaisse J., Charlier S., Arnould T., Renard P. 2011, Int J Biochem Cell Biol. 351. Biswas G., Anandatheerthavarada H.K., Zaidi M., Avadhani N.G. 2003, J Cell Biol 161: 507-19. 352. Amuthan G., Biswas G., Ananadatheerthavarada H.K., Vijayasarathy C., Shephard H.M., Avadhani N.G. 2002, Oncogene 21: 7839-49. 353. Arnould T., Mercy L., Houbion A., Vankoningsloo S., Renard P., Pascal T., Ninane N., Demazy C., Raes M. 2003, FASEB J 17: 2145-7. 354. Mercy L., Pauw A., Payen L., Tejerina S., Houbion A., Demazy C., Raes M., Renard P., Arnould T. 2005, FEBS J 272: 5031-55. 355. Guha M., Fang J.K., Monks R., Birnbaum M.J., Avadhani N.G. 2010, Mol Biol Cell 21: 3578-89. 356. Guha M., Pan H., Fang J.K., Avadhani N.G. 2009, Mol Biol Cell 20: 4107-19.


82

Patricia Renard et al.

357. Guha M., Tang W., Sondheimer N., Avadhani N.G. 2010, Biochim Biophys Acta 1797: 1055-65. 358. Droge W. 2002, Physiol Rev 82: 47-95. 359. Jones D.P. 2008, Am J Physiol Cell Physiol 295: C849-68. 360. Suzuki H., Kumagai T., Goto A., Sugiura T. 1998, Biochem Biophys Res Commun 249: 542-5. 361. Perez-de-Arce K., Foncea R., Leighton F. 2005, Biochem Biophys Res Commun 338: 1103-9. 362. Storz P., Doppler H., Toker A. 2005, Mol Cell Biol 25: 8520-30. 363. Gelain D.P., Cammarota M., Zanotto-Filho A., de Oliveira R.B., Dal-Pizzol F., Izquierdo I., Bevilaqua L.R., Moreira J.C. 2006, Cell Signal 18: 1685-94. 364. Felty Q., Xiong W.C., Sun D., Sarkar S., Singh K.P., Parkash J., Roy D. 2005, Biochemistry 44: 6900-9. 365. Hardie D.G. 2007, Nat Rev Mol Cell Biol 8: 774-85. 366. Prigione A., Cortopassi G. 2007, Aging Cell 6: 619-30. 367. Jager S., Handschin C., St-Pierre J., Spiegelman B.M. 2007, Proc Natl Acad Sci U S A 104: 12017-22. 368. Bergeron R., Ren J.M., Cadman K.S., Moore I.K., Perret P., Pypaert M., Young L.H., Semenkovich C.F., Shulman G.I. 2001, Am J Physiol Endocrinol Metab 281: E1340-6. 369. Wallace D.C., Fan W. 2010, Mitochondrion 10: 12-31. 370. Canto C., Auwerx J. 2009, Curr Opin Lipidol 20: 98-105. 371. Jack B.H., Pearson R.C., Crossley M. 2011, Int J Biochem Cell Biol 43: 693-6. 372. Verger A., Quinlan K.G., Crofts L.A., Spano S., Corda D., Kable E.P., Braet F., Crossley M. 2006, Mol Cell Biol 26: 4882-94. 373. Kajimura S., Seale P., Tomaru T., Erdjument-Bromage H., Cooper M.P., Ruas J.L., Chin S., Tempst P., Lazar M.A., Spiegelman B.M. 2008, Genes Dev 22: 1397-409. 374. Van Rechem C., Boulay G., Pinte S., Stankovic-Valentin N., Guerardel C., Leprince D. 2010, Mol Cell Biol 30: 4045-59. 375. Cillero-Pastor B., Carames B., Lires-Dean M., Vaamonde-Garcia C., Blanco F.J., Lopez-Armada M.J. 2008, Arthritis Rheum 58: 2409-19. 376. Nakashima-Kamimura N., Asoh S., Ishibashi Y., Mukai Y., Shidara Y., Oda H., Munakata K., Goto Y., Ohta S. 2005, J Cell Sci 118: 5357-67. 377. Chakrabarti A., Chen A.W., Varner J.D. 2011, Biotechnol Bioeng 108: 2777-93 378. Horibe T., Hoogenraad N.J. 2007, PLoS One 2: e835. 379. Papa L., Germain D. 2011, J Cell Sci 124: 1396-402. 380. Martinus R.D., Garth G.P., Webster T.L., Cartwright P., Naylor D.J., Hoj P.B., Hoogenraad N.J. 1996, Eur J Biochem 240: 98-103. 381. Zhao Q., Wang J., Levichkin I.V., Stasinopoulos S., Ryan M.T., Hoogenraad N.J. 2002, EMBO J 21: 4411-9. 382. Aldridge J.E., Horibe T., Hoogenraad N.J. 2007, PLoS One 2: e874. 383. Haynes C.M., Petrova K., Benedetti C., Yang Y., Ron D. 2007, Dev Cell 13: 467-80. 384. Haynes C.M., Yang Y., Blais S.P., Neubert T.A., Ron D. 2010, Mol Cell 37: 529-40.


Mammalian mitochondrial genetics, genomics and turnover

83

385. von Heijne G. 1986, FEBS Lett 198: 1-4. 386. de Grey A.D. 2005, Bioessays 27: 436-46. 387. Barrey E., Saint-Auret G., Bonnamy B., Damas D., Boyer O., Gidrol X. 2011, PLoS One 6: e20220.


Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology, 2012: 85-97 ISBN: 978-81-308-0487-3 Editors: Phing-How Lou and Natalia Petersen

2. Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice] Per Lindström Department of Integrative Medical Biology, Section for Histology and Cell Biology Umeå University, S-901 87 Umeå, Sweden

Abstract. This review summarises key aspects of what has been

learned about the effect of leptin on mitochondrial function from studies in leptin deficient obese ob/ob mice. Ob/ob mice are grossly overweight and hyperphagic particularly at young ages. The feeding behaviour in combination with leptin deficiency leads to severe insulin resistance with hyperglycaemia and hyperinsulinemia. Mitochondrial metabolism is important in the regulation of insulin release from pancreatic ß-cells. Ob/ob mouse ß-cells respond adequately to most stimuli and ob/ob mice have been used as a rich source of pancreatic islets with high insulin release capacity. Mitochondrial metabolism is altered in leptin deficient animals leading to reduced oxidative capacity in liver and muscle cells. Uncoupling proteins are down regulated in leptin deficient mice and the production of reactive oxygen species is increased.

Introduction Knowledge about the regulation of metabolism and energy balance has increased rapidly during the last several decades. One of the most important findings was the discovery of leptin released from adipose tissue [1, 2]. Leptin reduces food intake and increases energy expenditure. Leptin administration therefore lowers body weight in leptin sensitive experimental Correspondence/Reprint request: Dr. Per Lindström, Department of Integrative Medical Biology, Section for Histology and Cell Biology, Umeå University, S-901 87 Umeå, Sweden. E-mail: per.lindstrom@histocel.umu.se


86

Per Lindstrรถm

animals and humans [3-6]. It was initially suggested that leptin could be used as cure for the obesity epidemic that afflicts most parts of the world today, but it turned out that most obese individuals have very high serum levels of leptin and are leptin-resistant. However, increased knowledge about the physiology of leptin has shown that leptin administration can be of great value in certain conditions such as lipodystrophia [7, 8]. There are functional leptin receptors in the hypothalamus but also in several other brain areas [9-11]. It may be that central effects of leptin are sufficient to explain most of the metabolic and cellular effects observed through stimulation of sympathetic outflow [12-15]. There are leptin receptors also in many peripheral cells and much evidence supports that leptin can regulate cellular mechanisms through peripheral effects in fat [16, 17], muscle [18-20], and liver cells [21]. The effects on peripheral tissues involve mitochondrial metabolism but modulation of mitochondrial metabolism is probably one of the mechanisms involved also in leptin regulation of metabolic functions in the CNS [22]. In addition to metabolic effects, leptin regulates immune functions, reproduction, and growth [27]. With regard to the control of food intake, short term effects of leptin may be initiated in neurons in the arcuate nucleus and from there, relayed to other brain areas [13, 23-24]. When it comes to long-term effects on food intake it may be that leptin affects an interacting network between several areas in the hypothalamus and brainstem [25, 26]. The balance between energy intake and energy expenditure determines body weight and leptin is an important regulator of energy expenditure. Much of our understanding of leptin physiology derives from studies in obese-hyperglycaemic (ob/ob) mice. Leptin deficient obese-hyperglycaemic mice were discovered as a spontaneous mutation in 1949 [28] and have been used as a model for diabetes and obesity in more than a thousand studies. Elegant cross-circulation studies pointed to that the syndrome was caused by the lack of a blood-borne factor [29, 30]. This factor was discovered in 1994 and termed leptin [1, 2]. This opened up a whole new field of research on the importance of adipose tissue as an important hormone-producing organ for the regulation of food consumption and metabolic events as well as for the regulation of tissue growth, inflammation, and reproductive function. Mitochondria play a central role in energy metabolism. Therefore it is not surprising that many studies on leptin function in ob/ob mice and other models have focused on mitochondrial metabolism. The balance between lipid storage and lipid oxidation in mitochondria is regulated by leptin. Leptin increases lipid oxidation and reduces lipid storage. Leptin inhibits gluconeogenesis in isolated hepatocytes and increases glucose oxidation. It has become evident that leptin can also regulate mechanisms for thermogenesis


Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice]

87

and other mechanisms where mitochondrial uncoupling proteins are involved. Production of reactive oxygen species by mitochondria is also influenced by leptin. One of the features of leptin deficient ob/ob mice is that they have very large pancreatic islets comprising a high proportion of insulin producing ß-cells [31-33]. Ob/ob mouse ß-cells respond to nutrient secretagogues for insulin release and they have therefore been used in many studies of glucoseinduced insulin release [34, 35]. However, they are also functionally different from ß-cells in normal lean animals.

1. Leptin and mitochondrial metabolism in pancreatic islets The ß-cells continuously measure blood glucose and release insulin in response to raised blood glucose levels. The trigger for insulin release is coupled to glucose metabolism both in the cytosol and in the mitochondria. When the glucose concentration rises above 4-5 mmol/l, there is an enhancement of the glucose oxidation rate and ATP production in the mitochondria driven by fuel supply rather than energy demand. The ensuing rise in ATP production leads to an increased ratio of ATP/ADP in the cytosol and a closure of ATP sensitive K+ channels. This, in turn, is important for ß-cell depolarisation and opening of plasma membrane voltage sensitive calcium channels. The influx of calcium triggers insulin release [36, 37]. Mitochondrial metabolism is essential for glucose-induced insulin release and mitochondrial dysfunction in the islets can lead to impaired insulin secretion and diabetes mellitus [37]. ß-Cells have full-length leptin receptors and leptin inhibits insulin release in most studies [38, 39]. Lack of leptin could therefore be beneficial for the insulin release capacity. ß-Cells in ob/ob mice are severely insulin resistant. The interaction between insulin and leptin is complex but studies in ob/ob mice show that absence of leptin signalling probably worsens insulin resistance [40, 41]. Absence of leptin could therefore enhance islet function also via insulin resistance. Insulin plays a role for regulating many aspects of metabolism. The close coupling between mitochondrial metabolism and the triggering of insulin secretion might suggest that ob/ob mice ß-cells show large metabolic differences from mice with normal leptin signalling. However, islets from ob/ob mice respond adequately to stimulators and inhibitors of insulin release [34, 35] and ob/ob mice have been used mostly as a rich source of ‘normally functioning’ ß-cells. Ob/ob mouse ß-cells are almost degranulated in the fed state because of the large functional load but they release larger quantities of insulin after fasting when compared with islets from normal mice [42]. The persistent hyperglycaemia can, nevertheless, be a sign of insufficient ß-cell function,


88

Per Lindström

because transplantation of coisogenic (+/+) islets to ob/ob mice lowered blood glucose values to almost normal [43]. The threshold for glucoseinduced insulin release occurs at a lower glucose concentration than in lean mouse islets [42, 44]. We do not know the underlying mechanism for this. Ob/ob mouse ß-cells have an increased Na/K-ATP-ase activity [45]. They may be more sensitive to voltage-dependent events such as opening of calcium channels perhaps caused by a reduced activation of KATP channels in the unstimulated state at low glucose concentrations [44]. The pattern of cytosolic calcium changes after glucose stimulation is different from that in normal islets [46] and we did not find the same type of cell-specific Ca2+ responses as is observed in ß-cells from normal mice [47]. Differences between ob/ob mouse and lean mouse ß-cells with regard to uncoupling proteins and production of reactive oxygen species will be discussed elsewhere. Some studies have demonstrated differences also in other aspects of mitochondrial metabolism between ß-cells from normal and leptin deficient mice. Ob/ob and normal mouse islets showed the same activity of the mitochondrial enzyme FA-linked glycerophosphate dehydrogenase (mGDH) which catalyses a rate limiting step of the glycerol phosphate shuttle and therefore plays a key role in glucose-induced insulin release [48] but ob/ob mouse islets have a lower fatty acid oxidation rate when compared with normal islets [49]. This could lead to an accumulation of lipids and lipotoxic effects. However, metabolic consequences of leptin deficiency related to other cell types may be protective. Ob/ob mice have low VLDL levels and high LDL levels [50]. They also have a large capacity for lipid accumulation in adipose tissue which could protect ob/ob mouse ß-cells from cytolipotoxicity [50].

2. Uncoupling proteins in ob/ob mice Uncoupling proteins are located on the mitochondrial inner membrane. They dissipate the proton gradient to reduce the transmembrane potential and reduce ATP formation while maintaining oxygen consumption, and with no coupling to other energy-consuming processes. Brown adipose tissue (BAT) is important for temperature regulation because of the presence of uncoupling protein-1 (UCP-1) which is a unique feature of adipocytes. Brown adipocytes store lipids in multilocular sites. This lipid plays little role as energy store for the whole organism but can be very important for energy balance because of its role as a fuel for BAT thermogenesis [51]. BAT cells have a large number of mitochondria packed with cristae. Sympathetic stimulation of BAT causes the activation of UCP-1.


Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice]

89

UCP-1 expression is enhanced by cold exposure and leptin [52], probably through increased sympathetic innervation. Brown fat is present throughout life in rodents [53]. It was long thought that BAT disappears soon after birth in larger mammals, including humans [53 ]. However, recent studies show that BAT is present and active in adults. This has opened up the possibilities of using BAT activation as a way to treat obesity and related disorders [54, 55]. It has also been shown that UCP-1 may be induced in white adipose tissue (WAT). This could be exploited to increase the capacity to oxidize fatty acids in white adipocytes, thereby regulating body fat mass in humans [56]. Several lines of evidence suggest that leptin is important for normal BAT activity. Ob/ob mice have low levels of UCP-1 in adipose tissue, probably caused by a reduced sympathetic input [57, 58]. UCP-1 levels could be raised by transplantation to lean animals [57, 58] or by swim training [59] but ob/ob mice increase BAT uncoupling in response to cold much more slowly than lean animals do [60]. BAT activity increases with age in lean animals but not in ob/ob mice [61]. Leptin increased UCP-1 mRNA and UCP-1 protein several fold in both BAT and WAT [62, 63]. UCP-1 expression in BAT was lower also in db/db mice that lack functional leptin receptors but UCP-2 expression in WAT was higher [64]. Leptin is not normally found in brown adipose tissue cells. However, leptin is observed in BAT from db/db mice [52]. This may be viewed as a compensatory effect for the lack of leptin stimulation. GDP binding to BAT can be used as a measure of UCP-1 activation. Leptin has a stimulatory effect of GDP binding in fasted rats but not in fed rats indicating that a decrease in circulating leptin contributes to the down regulation of thermogenic response [65]. Corticosterone can inhibit BAT mitochondrial uncoupling and this effect is more pronounced in ob/ob mice [66]. Ob/ob mice also have a higher central sensitivity to corticosterone, which results in increased food intake and a more marked exaggeration of insulin levels. Adrenalectomy increased BAT activity more in ob/ob mice when compared with lean mice [67, 68]. Leptin levels and UCP-1 are lowered during lactation in rats. The functional significance of this may be to reduce the use of free fatty acids as a fuel source during lactation and to decrease thermogenesis [69]. Several homologues of UCP-1 have been discovered. UCP-2 is found in many cell types including pancreatic Ă&#x;-cells but the physiological role of UCP-2 is not known. UCP-2 does not appear to participate in adaptive thermogenesis [70-72]. Increased UCP-2 activity may reduce the formation of mitochondria-derived reactive oxygen species. Increased expression of UCP-2 results in diminished ATP production and lowered insulin secretion in


90

Per Lindström

pancreatic ß-cells and lack of UCP-2 improved insulin secretion in leptin deficient ob/ob mice [73]. Leptin induces expression of UCP-2 in adipose tissue and pancreatic islets, and the expression of enzymes involved in free fatty acid metabolism [74]. Leptin-sensitive neurons in the hypothalamus express UCP-2 and this was present in axonal processes indicating that axonal uncoupling may be involved in the control of neurotransmission [75]. Mitochondrial dysfunction is involved in the pathogenesis of Parkinson’s disease and other neurodegenerative disorders. The role of UCP-2 in CNS and may have neuroprotective by reducing oxidative stress. Leptin increases UCP-2 in nerve cells and this can be a mechanism for the protective effects of leptin on cell survival [76]. UCP-3 is another member of the uncoupling protein family. UCP-3 appears to be muscle specific. Leptin increases musclespecific UCP-3 in ob/ob mice [77].

3. Reactive oxygen species and ob/ob mice Reactive oxygen species (ROS) are formed as a by-product of mitochondrial oxygen consumption. Superoxide anion (O2●-) and the less reactive hydrogen peroxide (H2O2) are the most common. Superoxide anion can be converted to hydrogen peroxide by superoxide dismutase (SOD) and then to water by catalase and other enzymes. Superoxide anion may also form hydroxyl radical (HO●) and react with nitrogen oxide (NO) to form peroxynitrate (ONOO-). Peroxynitrate can form nitrated proteins. ROS and protein nitration can affect most cellular functions. ROS are usually cytotoxic and can be involved in the pathogenesis of conditions such as diabetes. Superoxide dismutase therefore plays an important role to reduce the cellular amount of ROS. However, there is substantial evidence that ROS are involved in normal regulation of cell functions and H2O2 can be an intracellular messenger in many cell types. H2O2 may, for example, be important as a stimulus for insulin secretion [78, 79]. Leptin protects against hydrogen peroxide-induced cell damage [80]. There have been conflicting results regarding the level of UCP-2 and SOD in ob/ob mouse mitochondria [81-83]. The production of ROS is high in ob/ob hepatocytes [81, 84] and ob/ob mice have increased levels of proteins involved in apoptosis [82] but there are few apoptotic hepatocytes and little oxidative stress [85]. Mitochondrial metabolism is dependent on a normal nitric oxide steady state level [86]. Nitric oxide inhibition of mitochondria results in increased production of reactive oxygen species. Defective leptininduced AMPK activation may be the cause of increased ROS production through increased mitochondrial nitric oxide formation in ob/ob mice [87].


Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice]

91

4. Mitochondrial metabolism in adipocytes, hepatocytes, and muscle cells of ob/ob mice Many studies suggest that leptin has insulin-like effects on glucose metabolism in skeletal muscle cells and that leptin increases insulin sensitivity. Normal leptin levels and function is therefore important for insulin action. Leptin increases glucose uptake and oxidation of glucose and fatty acids. Leptin inhibits lipid deposition in skeletal muscle. Leptin also inhibits gluconeogenesis in isolated hepatocytes and stimulates glucose and fatty acid oxidation in adipocytes. Mitochondria are involved in several of these metabolic events. Most studies addressing the effect of leptin in ob/ob mice muscle mitochondrial function have been on cardiomyocytes. Leptin stimulates cardiac mitochondrial activity through activation of MAPK [89]. Mitochondrial uncoupling was higher in cardiomyocytes from ob/ob mice when compared with lean mice and the mitochondrial oxidative capacity was reduced [90]. Cardiomyocytes from leptin-deficient ob/ob mice show decreased peak shortening and maximal velocity of shortening. This correlated with swelling and disorganisation of mitochondria and a reduced Ca2+ release from intracellular stores [91]. There are have abnormal mitochondrial Ca2+ transients in cardiomyocytes from ob/ob mice which may predispose for arrhythmias [92]. Ob/ob mice cardiomyocytes have increased expression of enzymes involved in lipid transport and higher levels of lipid accumulation and enhanced cardiac fatty acid utilisation is associated with reduced contractile function [93, 94]. Leptin has cardioprotective effects after infarction. This may involve PI3K and MAPK pathways [95]. Leptin also protects cardiomyocytes against apoptosis by inhibition of the pro-apoptotic protein Bax translocation to the mitochondria [96]. The induction of mitochondrial metabolism seen with peripheral administration of leptin is probably caused by the reduced caloric intake rather than on direct independent effects of leptin on liver and muscle mitochondria [97]. Decreased content of contractile protein levels may contribute to the contractile dysfunction in leptin deficient mice [98]. These contractile dysfunctions were improved with leptin administration. Increased mitochondrial respiration was found in one study in skeletal muscle from ob/ob mice and this was reflected by an increased expression of mitochondrial-encoded genes involved in respiration [88]. The prevailing concept is that leptin and insulin share downstream intracellular signalling mechanisms and has mostly similar cellular effects. However, some studies indicate that leptin and insulin can have have


92

Per LindstrĂśm

contrasting effects on fatty acid uptake in ob/ob mouse adipocytes. Insulin increases lipid uptake in adipocytes whereas normal leptin levels probably have an inhibitory effect. A higher lipid accumulation was found in adipocytes from ob/ob mice when compared with WAT from lean animals [99]. Leptin down-regulated proteins involved in fatty acid uptake in ob/ob mice to levels found in normal mice [100]. This may be coupled to activation of AMP-activated protein kinase (AMPK) [101]. Lack of leptin can cause aberrant lipid accumulation indirectly through insulin resistance [102]. It should be pointed out that peripheral effects of leptin on adipose tissue are probably not sufficient to explain the depletion of adipocyte fat seen with leptin administration [103]. It is most likely centrally mediated. Norepinephrine inhibits insulin-stimulated leptin release. This can be coupled to stimulation of lipolysis and an increase in long chain fatty acids [104] but mitochondrial metabolism does not seem to be involved. Non-alcoholic fatty liver disease (Non-alcoholic steatohepatitis, NASH) is commonly seen in patients with insulin resistance [105] and is the most common cause of abnormal liver function tests in the working population [106]. Ob/ob mice show signs of non-alcoholic steatohepatitis and have been used as a model for that syndrome [107]. Ob/ob mice have large hepatocytes with swollen mitochondria and a marked liver accumulation of triglycerides in fat droplets [108]. The hepatocytes have higher levels of proteins involved in lipid transport but lower activity of acyl-CoA synthetase, which predisposes for VLDL production and fat accumulation [109]. Intravenous leptin injection increased mitochondrial palmitate oxidation and lowered hepatic and skeletal muscle triglyceride content [110]. Hepatocytes from ob/ob mice also show an increased susceptibility for induction of apoptosis [111]. Liver mitochondria had a reduced membrane potential [111, 112], respiratory chain activity was decreased and mitochondrial proteins were markedly tyrosine nitrated [113]. Treatment with uric acid that lowers nitration improved the mitochondrial respiratory chain (MRC) activity. Free cholesterol was more abundant in liver mitochondria from ob/ob mice [114]. This could also render them more susceptible to TNF-Îą-induced steatohepatitis. Oxidative stress is believed to play a role in the pathogenesis of NASH [105] and lipid peroxidation is significantly increased in ob/ob mouse hepatocytes [108]. The oxidative capacity of liver mitochondria was eight times higher in ob/ob mice when compared with lean C57Bl/6J control mice [115] and becomes elevated with age [116]. Leptin administration decreased liver metabolism in ob/ob mice together with a reduction in mitochondrial volume density and respiratory chain proteins. The long chain fatty acid production was reduced and the hepatocyte lipid export was increased [117].


Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice]

93

Peroxisome proliferator-activated receptor gamma (PPAR-gamma) is an insulin sensitiser, which is markedly increased in ob/ob mouse hepatocytes [108]. Activation of PPAR-gamma with rosiglitazone or pioglitazone induced a change in unilocular white adipose tissue cells to smaller, multilocular adipocytes in adult ob/ob mice with more numerous mitochondria and increased expression of UCP-1 [119]. MRC activity was decreased in the liver of ob/ob mice by the PPAR-gamma agonist rosiglitazone [108] and mitochondrial Ă&#x;-oxidation of palmitic acid was higher. Impaired MRC activity can slow down the electron flow through the complex and cause overreducion of complex I and III, leading to formation of superoxide radicals [118]. ATP synthesis is not perfectly coupled to oxygen consumption in any cell type. Mitochondria from ob/ob mice have a larger proton leak and this can be restored by leptin in isolated liver mitochondria [112, 120].

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Friedman J.M., Leibel R.L., Siegel D.S., Walsh J., Bahary N. 1991, Genomics 11:1054-1062. Zhang Y., Proenca R., Maffei M., Barone M., Leopold L., Friedman J.M. 1994, Nature 372:425-432. Larcher F., Del Rio M., Serrano F., Segovia J.C., Ramirez A., Meana A., Page A., Abad J.L., Gonzalez M.A., Bueren J., Bernad A., Jorcano J.L. 2001, FASEB J 15:1529-1538. Pelleymounter M.A., Cullen M.J., Baker M.B., Hecht R., Winters D., Boone T., Collins F. 1995, Science 269:540-543. Halaas J.L., Gajiwala K.S., Maffei M., Cohen S.L., Chait B.T., Rabinowitz D., Lallone R.L., Burley S.K., Friedman J.M. 1995, Science 269:543-546. Farooqi I.S. 2011, Eur J Clin Invest 41:451-5. Oral E.A., Simha V., Ruiz E., Andewelt A., Premkumar A., Snell P., Wagner A.J., DePaoli A.M., Reitman M.L., Taylor S.I., Gorden P., Garg A. 2002, N Engl J Med 346:570-578. Petersen K.F., Oral E.A., Dufour S., Befroy D., Ariyan C., Yu C., Cline G.W., DePaoli A.M., Taylor S.I., Gorden P., Shulman G.I. 2002, J Clin Invest 109:1345-1350. Tartaglia L.A. 1997, J Biol Chem 272, 6093-6096. Fei H., Okano H.J., Li C., Lee G.H., Zhao C., Darnell R., Friedman J.M. 1997, Proc Natl Acad Sci USA 94:7001-7005. Gao Q., Horvath T.L. 2008, Am J Physiol Endocrinol Metab 294:E817-E826. de Luca C, et al. 2005, J Clin Invest 115:3484-3493. Coppari R, et al. 2005, Cell Metab 1:63-72. Haynes W.G., Morgan D.A., Walsh S.A., Mark A.L., Sivitz W.I. 1997, J Clin Invest 100:270-278.


94

Per Lindström

15. Buettner C., et al. 2008, Nat Med 14:667-675. 16. Ceddia R.B., William W.N. Jr., Lima F.B., Curi R. 1998, J Endocrinol 158: R7-R9. 17. Moon H.S., Chamberland J.P., Diakopoulos K.N., Fiorenza C.G., Ziemke F., Schneider B., Mantzoros C.S. 2011, Diabetes Care 34:132-138. 18. Bates S.H., Gardiner J.V., Jones R.B., Bloom S.R., Bailey C.J. 2002, Horm Metab Res 34:111-115. 19. Berti L., Kellerer M., Capp E., Haring H.U. 1997, Diabetologia 40:606-609. 20. Ceddia R.B., William W.N. Jr., Curi R. 1999, Int J Obes Relat Metab Disord 23:75-82. 21. Wang Y., Kuropatwinski K.K., White D.W., Hawley T.S., Hawley R.G., Tartaglia L.A., Baumann H. 1997, J Biol Chem 272:16216-16223. 22. Abizaid A., Horvath T.L. 2008, Regul Pept 149:3-10. 23. Hill J.W., et al. 2008, J Clin Invest 118:1796-1805. 24. Reed A.S., Unger E.K., Olofsson L.E., Piper M.L., Myers M.G. Jr., Xu A.W. 2010, Diabetes 59:894-906. 25. Williams D.L., Baskin D.G., Schwartz M.W. 2009, Am J Physiol Regul Integr Comp Physiol 297:R1238-R1246. 26. Hayes M.R., et al. 2010, Cell Metab 11:77-83. 27. Mantzoros C.S., Magkos F., Brinkoetter M., Sienkiewicz E., Dardeno T.A., Kim S.Y., Hamnvik O.P., Koniaris A. 2011, Am J Physiol Endocrinol Metab 301:E567-84. 28. Ingalls A.M., Dickie M.M., Snell G.D. 1950, J Hered 41:317-318. 29. Coleman D.L. 1978, Diabetologia 14:141-148. 30. Coleman D.L. 1973, Diabetologia 9:294-298. 31. Bleisch V.R., Mayer J., Dickie M.M. 1952, Am J Pathol 28:369-385. 32. Gepts W., Christophe J., Mayer J. 1960, Diabetes 9:63-69. 33. Westman S., 1968, Acta Med Upsal 73:81-89. 34. Hahn H.J., Hellman B., Lernmark Å., Sehlin J., Täljedal I-B. 1974, J Biol Chem 249:5275-5284. 35. Hellman B., Idahl L-Å., Lernmark Å., Sehlin J., Täljedal I-B. 1974, Arch Biochem Biophys 162:448-457. 36. Wiederkehr A., Wollheim C.B. 2012, Mol Cell Endocrinol 353:128-137. 37. Mulder H., Ling C. 2009, Mol Cell Endocrinol 297:34-40. 38. Baetens D., Stefan Y., Ravazzola M., Malaisse-Lagae F., Coleman D.L., Orci L. 1978, Diabetes 27:1-7. 39. Chen L., Komiya I., Inman L., McCorkle K., Alam T., Unger R.H. 1989, Proc Natl Acad Sci USA 86:1367-1371. 40. Lulu Strat A., Kokta T.A., Dodson M.V., Gertler A., Wu Z., Hill R.A. 2005, Biochim Biophys Acta 1744:164-175. 41. Rattarasarn C. 2006, Acta Physiol 186:87-101. 42. Lavine R.L., Voyles N., Perrino P.V., Recant L. 1977, Am J Physiol 233: E86-E90. 43. Barker C.F., Frangipane L.G., Silvers W.K. 1977, Ann Surg 186:401-410. 44. Chen N.G., Tassava T.M., Romsos D.R. 1993, J Nutr 123:1567-1574.


Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice]

45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75.

95

Fournier L.A., Heick H.M., Begin-Heick N. 1990, Biochem Cell Biol 68:243-248. Ravier M.A., Sehlin J., Henquin J.C. 2002, Diabetologia 45:1154-1163. Gustavsson N., Larsson-Nyren G., Lindström P. 2006, J Endocrinol 190:461-470. Sener A., Anak O., Leclercq-Meyer V., Herberg L., Malaisse W.J. 1993, Biochem Mol Biol Int 30:397-402. Berne C. 1975, Biochem J 152:661-666. Camus M.C., Aubert R., Bourgeois F., Herzog J., Alexiu A., Lemonnier D. 1988, Biochim Biophys Acta 961:53-64. Sell H., Deshaies Y., Richard D. 2004, Int J Biochem Cell Biol 36:2098-2104. Cinti S., Frederich R.C., Zingaretti M.C., De Matteis R., Flier J.S., Lowell B.B. 1997, Endocrinology 138:797-804. Nicholls D.G., Locke R.M. 1984, Physiol Rev 64:1-64. Enerbäck S. 2010, Int J Obes (Lond). 34 Suppl 1:S43-6. Ravussin E., Galgani J.E. 2011, Annu Rev Nutr 31:33-47. Si Y., Palani S., Jayaraman A., Lee K . 2007, J Lipid Res 48(4):826-836. Ashwell M., Wells C., Dunnett S.B. 1986, Am J Physiol 262(1 Pt 1):E110-E117. Chen M.D., Lin P.Y., Chen P.S., Cheng V., Lin W.H. 1997, Biol Trace Elem Res 57(2):139-145. Ueno N., Oh-ishi S., Kizaki T., Nishida M., Ohno H. 1997, Res Commun Mol Pathol Pharmacol 95(1):92-104. Milner R.E., Trayhurn P. 1989, Am J Physiol 257(2 Pt 2):R292-R299. Ueno N., Oh-ishi S., Segawa M., Nishida M., Fukuwatari Y., Kizaki T., Ookawara T., Ohno H. 1998, Mech Ageing Dev 100(1):67-76. Commins S.P., Watson P.M., Padgett M.A., Dudley A., Argyropoulos G., Gettys T.W. 1999, Endocrinology 140(1):292-300. Sarmiento U., Benson B., Kaufman S., Ross L., Qi M., Scully S., DiPalma C. 1997, Lab Invest 77(3):243-256. Masaki T., Yoshimatsu H., Chiba S., Sakata T. 2000, Am J Physiol Regul Integr Comp Physiol 279(4):R1305-R1309. Surmely J.F., Voirol M.J., Stefanoni N., Assimacopoulos-Jeannet F., Giacobino J.P., Jéquier E., Gaillard R.C., Tappy L. 1998, Int J Obes Relat Metab Disord 22(9):923-926. Kim H.K., Romsos D.R. 1987, Am J Physiol 253(2 Pt 1):E149-E157. Shargill N.S., Lupien J.R., Bray G.A. 1989, Horm Metab Res 21(9):463-467. Kim H.K., Romsos D.R. 1990, Am J Physiol 259(3 Pt 1):E362-E369. Xiao X.Q., Grove K.L., Grayson B.E., Smith M.S. 2004, Endocrinology 145(2):830-838. Krauss S., Zhang C.Y., Lowell B.B. 2002, Proc Natl Acad Sci USA 99(1):118-22. Rousset S., Alves-Guerra M.C., Mozo J., Miroux B., Cassard-Doulcier A.M., Bouillaud F., Ricquier D. 2004, Diabetes 53 Suppl 1:S130-5. Brand M.D., Esteves T.C. 2005, Cell Metab 2(2):85-93. Langin D. 2003, Drugs Today 39(4):287-295. Zhou Y.T., Shimabukuro M., Koyama K., Lee Y., Wang M.Y., Trieu F., Newgard C.B., Unger R.H. 1997, Proc Natl Acad Sci USA 94(12):6386-6390. Horvath T.L., Warden C.H., Hajos M., Lombardi A., Goglia F., Diano S. 1999, J Neurosci 19(23):10417-10427.


96

Per Lindström

76. Ho P.W., Liu H.F., Ho J.W., Zhang W.Y., Chu A.C., Kwok K.H., Ge X., Chan K.H., Ramsden D.B., Ho S.L. 2010, Neurotox Res 17(4):332-343. 77. Liu Q., Bai C., Chen F., Wang R., MacDonald T., Gu M., Zhang Q., Morsy M.A., Caskey C.T. 1998, Gene 207(1):1-7. 78. Pi J., Collins S. 2010, Diabetes Obes Metab 12 (Suppl 2):141-8. 79. Drews G., Krippeit-Drews P., Düfer M. 2010, Pflugers Arch 460(4):703-18. 80. Chavin K.D., Yang S., Lin H.Z., Chatham J., Chacko V.P., Hoek J.B., WalajtysRode E., Rashid A., Chen C.H., Huang C.C., Wu T.C., Lane M.D., Diehl A.M. 1999, J Biol Chem 274(9):5692-5700. 81. Rashid A., Wu T.C., Huang C.C., Chen C.H., Lin H.Z., Yang S.Q., Lee F.Y., Diehl A.M. 1999, Hepatology 29(4):1131-1138. 82. Welsh J.J., Narbaitz R., Begin-Heick N. 1985, J Nutr 115(7):919-928. 83. Laurent A., Nicco C., Tran Van Nhieu J., Borderie D., Chéreau C., Conti F., Jaffray P., Soubrane O., Calmus Y., Weill B., Batteux F. 2004, Hepatology 39(5):1277-1285. 84. Robin M.A., Demeilliers C., Sutton A., Paradis V., Maisonneuve C., Dubois S., Poirel O., Lettéron P., Pessayre D., Fromenty B. 2005, Hepatology 42(6): 1280-1290. 85. Poderoso J.J. 2009, Arch Biochem Biophys 484(2):214-220. 86. Finocchietto P.V., Holod S., Barreyro F., Peralta J.G., Alippe Y., Giovambattista A., Carreras M.C., Poderoso J.J. 2011, Antioxid Redox Signal 15(9):2395-2406. 87. Antonetti D.A., Reynet C., Kahn C.R. 1995, J Clin Invest 95(3):1383-138880. 88. Eguchi M., Liu Y., Shin E.J., Sweeney G. 2008, FEBS J 275(12):3136-3144. 89. Sharma V., Mustafa S., Patel N., Wambolt R., Allard M.F., McNeill J.H. 2009, Eur J Pharmacol 617(1-3):113-117. 90. Boudina S., Sena S., O'Neill B.T., Tathireddy P., Young M.E., Abel E.D. 2005, Circulation 112(17):2686-2695. 91. Dong F., Zhang X., Yang X., Esberg L.B., Yang H., Zhang Z., Culver B., Ren J. 2006, J Endocrinol 188(1):25-36. 92. Fauconnier J., Lanner J.T., Zhang S.J., Tavi P., Bruton J.D., Katz A., Westerblad H. 2005, Diabetes 54(8):2375-2381. 93. Christoffersen C., Bollano E., Lindegaard M.L., Bartels E.D., Goetze J.P., Andersen C.B., Nielsen L.B. 2003, Endocrinology 144(8):3483-3490. 94. Carley A.N., Severson D.L. 2005, Biochim Biophys Acta 1734(2):112-126. 95. Smith C.C., Mocanu M.M., Davidson S.M., Wynne A.M., Simpkin J.C., Yellon D.M. 2006, Br J Pharmacol 149(1):5-13. 96. Shin E.J., Schram K., Zheng X.L., Sweeney G. 2009, J Cell Physiol 221(2): 490-497. 97. Barazzoni R., Zanetti M., Bosutti A., Biolo G., Vitali-Serdoz L., Stebel M., Guarnieri G. 2005, Endocrinology 146(4):2098-2106. 98. Fan X., Bradbury M.W., Berk P.D. 2003, J Nutr 133(9):2707-2715. 99. Essop M.F., Chan W.A., Hattingh S. 2011, Cardiovasc J Afr 22(4):175-178. 100. Fan X., Bradbury M.W., Berk P.D. 2003, J Nutr 133(9):2707-2715. 101. Ruderman N.B., Saha A.K., Kraegen E.W. 2003, Endocrinology 144(12): 5166-5171.


Mitochondrial function in leptin-deficient obese-hyperglycaemic mice [ob/ob mice]

97

102. McClelland G.B., Kraft C.S., Michaud D., Russell J.C., Mueller C.R., Moyes C.D. 2004, Biochim Biophys Acta 1688(1):86-93. 103. Park B.H., Wang M.Y., Lee Y., Yu X., Ravazzola M., Orci L., Unger R.H. 2006, J Biol Chem 281(52):40283-40291. 104. Cammisotto P.G., Gélinas Y., Deshaies Y., Bukowiecki L.J. 2003, Am J Physiol Endocrinol Metab 285(3):E521-E526. 105. Larter C.Z., Chitturi S., Heydet D., Farrell G.C. 2010, J Gastroenterol Hepatol 25(4):672-690. 106. Watanabe S., Yaginuma R., Ikejima K., Miyazaki A. 2008, J Gastroenterol 43(7):509-518. 107. Koteish A., Diehl A.M. 2001, Semin Liver Dis 21(1):89-104. 108. García-Ruiz I., Rodríguez-Juan C., Díaz-Sanjuán T., Martínez M.A., MuñozYagüe T., Solís-Herruzo J.A. 2007, Hepatology 46(2):414-423. 109. Memon R.A., Fuller J., Moser A.H., Smith P.J., Grunfeld C., Feingold K.R. 1999, Diabetes 48(1):121-127. 110. Wein S., Ukropec J., Gasperíková D., Klimes I., Seböková E. 2007, Exp Clin Endocrinol Diabetes 115(4):244-251. 111. Siebler J., Schuchmann M., Strand S., Lehr H.A., Neurath M.F., Galle P.R. 2007, Dig Dis Sci 52(9):2396-2402. 112. Melia H.P., Andrews J.F., McBennett S.M., Porter R.K. 1999, FEBS Lett 458(2):261-264. 113. García-Ruiz I., Rodríguez-Juan C., Díaz-Sanjuan T., del Hoyo P., Colina F., Muñoz-Yagüe T., Solís-Herruzo J.A. 2006, Hepatology 44(3):581-591. 114. Marí M., Caballero F., Colell A., Morales A., Caballeria J., Fernandez A., Enrich C., Fernandez-Checa J.C., García-Ruiz C. 2006, Cell Metab 4(3):185-198. 115. Rogers K.S., Higgins E.S., Loria R.M. 1986, Biochem Med Metab Biol 35(1): 72-76. 116. Brady L.J., Brady P.S., Romsos D.R., Hoppel C.L. 1985, Biochem J 231(2): 439-444. 117. Singh A., Wirtz M., Parker N., Hogan M., Strahler J., Michailidis G., Schmidt S., Vidal-Puig A., Diano S., Andrews P., Brand M.D., Friedman J. 2009, Proc Natl Acad Sci USA 106(31):13100-13105. 118. Young T.A., Cunningham C.C., Bailey S.M. 2002, Arch Biochem Biophys 405(1):65-72. 119. Koh Y.J., Park B.H., Park J.H., Han J., Lee I.K., Park J.W., Koh G.Y. 2009, Exp Mol Med 41(12):880-895. 120. Porter R.K., Joyce O.J., Farmer M.K., Heneghan R., Tipton K.F., Andrews J.F., McBennett S.M., Lund M.D., Jensen C.H., Melia H.P. 1999, Int J Obes Relat Metab Disord 23 Suppl 6:S12-S18.


Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology, 2012: 99-114 ISBN: 978-81-308-0487-3 Editors: Phing-How Lou and Natalia Petersen

3. Defects in the biogenesis and respiratory function of mitochondria in insulin insensitivity and type 2 diabetes 1

Chih-Hao Wang1, Hsin-Chang Huang1 and Yau-Huei Wei1,2

Institute of Biochemistry and Molecular Biology, National Yang-Ming University, Taipei 112, Taiwan 2 Department of Medicine, Mackay Medical College, Sanjhih, New Taipei City 252, Taiwan

Abstract. Type 2 diabetes has received a great deal of attention from researchers in the biomedical field because of the rapid increase in the number of patients and costs in medical treatments. Many studies have suggested that mitochondrial dysfunction is associated with type 2 diabetes. Recent findings from this and other laboratories have supported the notion that mitochondrial dysfunction is a cause of insulin insensitivity in myocytes and adipocytes due to insufficient supply of energy or overproduction of ROS that perturb the insulin signaling pathway. In addition, plenty of studies have shown that hormones secreted from the adipose tissue play important roles in the homeostasis of energy metabolism. Adiponectin is one of such hormones that have been reported to ameliorate insulin resistance by regulating glucose and lipid metabolism in the skeletal muscle. Moreover, defective mitochondria also decrease adiponectin secretion which leads to decreased glucose utilization in muscle and other tissues. Recent studies demonstrated that adiponectin can up-regulate PGC-1Îą after binding to its receptor to promote the biogenesis and function Correspondence/Reprint request: Prof. Yau-Huei Wei, Department of Biochemistry and Molecular Biology National Yang-Ming University, No. 155, Sec. 2, Li-Nong St., Taipei 112, Taiwan E-mail: joeman@ym.edu.tw or joeman@mmc.edu.tw


100

Chih-Hao Wang et al.

of mitochondria, which in turn confers increased insulin sensitivity of the muscle tissue. Furthermore, it has been documented that exercise can increase mitochondrial biogenesis and alleviate the symptoms of patients with insulin insensitivity or type 2 diabetes. On the other hand, resveratrol was found to increase the activity of PGC-1Îą by deacetylation through activation of Sirt1 and thereby promote mitochondrial biogenesis. Moreover, resveratrol could increase insulin sensitivity in the muscle and protected mice from obesity and insulin resistance induced by high-fat diet. Taking these findings into account, we suggest that mitochondrial dysfunction plays a role in the pathogenesis of insulin insensitivity, and that activation of the biogenesis and bioenergetic function of mitochondria may be an effective strategy for the prevention and treatment of patients with insulin resistance or type 2 diabetes.

Introduction Diabetes mellitus is a metabolic disease which is often characterized by prolonged high blood sugar levels in patients. The disease has been classified into two major types, type 1 or type 2 diabetes, each with its own distinct pathophysiological. Type 1 diabetes is usually diagnosed in children, and is therefore also termed juvenile diabetes. Most of the patients with type 1 diabetes have defects in insulin-producing β cells in the pancreas, which are damaged by immune-mediated mechanisms [1]. Type 2 diabetes occurs more commonly than type 1 diabetes throughout the world, and is closely related to high sugar and high fat in the diet. Type 2 diabetes is often characterized by insulin resistance in insulin-responsive tissues such as muscle, adipocytes, and liver. In skeletal muscle cells and adipocytes, insulin resistance reduces glucose uptake, whereas in liver cells insulin resistance decreases glycogen storage and results in the inability of liver to suppress glucose production and release into the blood. Type 2 diabetes is one of the important diseases in most developed countries because of its high prevalence and economic impacts. The accelerating increase in the number of diabetic patients has led biomedical researchers and clinicians to make great efforts to better understand the pathophysiology of insulin resistance and to find new avenues for the therapy of type 2 diabetes [2].

1. Mitochondrial role in the regulation of cellular metabolism Mitochondria are membrane-enclosed organelles distributed in a network structure within the cytosol of animal and human cells. They are considered cellular power plants because they generate the majority of the ATP required to meet the energy need of the cells. They use the reduced metabolites and coenzymes such as NADH and FADH2, which are produced from oxidative metabolism, to execute respiration and oxidative phosphorylation (OXPHOS)


Mitochondrial dysfunction and insulin insensitivity

101

to generate ATP to support various cellular functions. Besides, mitochondria are the most important organelles in the human and animal cells where many vital biochemical reactions take place (e.g., tricarboxylic acid cycle and β-oxidation) [3]. They contain their own genomes and transcriptional and translational machineries. Each mitochondrion contains 2-10 copies of mitochondrial DNA (mtDNA), which is a 16.6 kb long, circular, and doublestranded DNA molecule [4]. The mtDNA is naked, compact and lacks efficient DNA repair systems, which renders it prone to oxidative damage, thereby increasing its risk of acquiring DNA mutations. Mutations in mtDNA have been reported to be responsible for or associated with many diseases. The biogenesis of mitochondria requires a tight coordination between the expression of genes encoded by mtDNA and nuclear DNA, respectively. Nuclear regulatory proteins involved in this coordination include nuclear respiratory factor 1 (NRF1) and NRF2, mitochondrial transcription factor A (mtTFA) and B (mtTFB), which coordinately regulate the expression of nuclear DNA-encoded polypeptides constituting the respiratory enzyme complexes. Most importantly, the peroxisome proliferator-activated receptor gamma coactivactor 1α (PGC-1α), has been established to be able to modulate the expression of these nuclear regulatory proteins and is important in the regulation of a number of mitochondrial genes coding for the subunits of respiratory enzymes and antioxidant enzymes [5, 6]. Through the abovementioned mechanisms, energy metabolism of mitochondria in the animal and human cells is tightly regulated to supply sufficient energy to respond to different physiological and environmental conditions.

2. ROS and antioxidant enzyme system in mitochondria Mitochondria are also the major source of endogenous reactive oxygen species (ROS) in human cells. This is because of the fact that electrons leaked out from the respiratory chain can react with O2 to produce superoxide ions (O2•-), which can be converted to other ROS, such as hydroxyl radicals (.OH) and hydrogen peroxide (H2O2) through a series of redox reactions. To overcome the effects of ROS, there is an array of antioxidant defense system including enzymatic and non-enzymatic antioxidants to protect cells from damage inflicted by ROS [7]. By catalysis of superoxide dismutase (SOD), superoxide anions are converted to H2O2, which can be further decomposed to H2O and O2 by catalase (CAT) or reduced to H2O by glutathione peroxidase (GPx) [8, 9]. Nevertheless, overproduction of ROS by defective mitochondria and inefficient free radical scavenging system is harmful to cells because it may cause oxidative damage to proteins, lipids and DNA, and especially mtDNA, and result in a variety of human diseases such as


102

Chih-Hao Wang et al.

cardiovascular diseases, ischemia/reperfusion injuries, neurodegenerative diseases, cancer and metabolic diseases [10].

3. Mitochondrial dysfunction leads to insulin insensitivity During the 1980s and early 1990s, rearrangements of mtDNA or point mutations in mtDNA have been associated with a wide spectrum of mitochondrial diseases in the affected tissues. Diabetes is one of the clinical manifestations that are associated with mitochondrial diseases such as mitochondrial encephalopathy, lactic acidosis and stroke-like episodes (MELAS) and maternally inherited diabetes and deafness (MIDD) syndrome. Both syndromes were found to be associated with A to G transition at nucleotide position 3243 of mtDNA [11]. However, while diabetes is usually thought as a secondary clinical manifestation of aging or age-related diseases in the subjects with mtDNA mutations, there is no direct linkage between alterations in mtDNA and mitochondrial function with the occurrence of diabetes. In the past decade, many studies provided evidence to support the notion that patients with type 2 diabetes exhibit mitochondrial dysfunction in skeletal muscle or white adipose tissue (Table 1). The notable ones include decreased expression of genes encoding protein subunits constituting the respiratory enzymes [12], decrease in the activities of respiratory enzyme complexes [13], decreased expression of genes involved in mitochondrial biogenesis [14, 15], mtDNA mutation or deletion [16, 17], decrease in bioenergetic capacity [18-20], and defects in β-oxidation of fatty acids [21, 22]. In these reports, impaired mitochondrial function was usually observed in insulin-responsive tissues such as muscle and adipose tissues. In addition, the decline of mitochondrial function was more pronounced when insulin insensitivity became more severe. These findings suggest that mitochondrial defects are highly correlated with insulin insensitivity of type 2 diabetes. However, the mechanism underlying insulin resistance in affected tissues with mitochondrial dysfunction is still poorly understood. Most studies have focused on the cause-effect relationship between mitochondrial dysfunction and the development of insulin resistance. Genetic manipulation and chemical treatment approach were used to induce mitochondrial defects such as alteration of the copy number [23] or sequence of mtDNA [24], and inhibition of respiratory enzymes [25]. Park and colleagues demonstrated that chronic treatment with ethidium bromide (EtBr) caused the decrease of insulin sensitivity and impaired activation of insulin signaling in mtDNA-depleted muscle cells, which were then restored when the cells had been repopulated with mtDNA after the removal of EtBr [23].


Mitochondrial dysfunction and insulin insensitivity

103

Table 1. Mitochondrial defects in tissues of the mammals with type 2 diabetes Mitochondrial DNA

A3243G transition 4,977 bp deletion Depletion

S.M. in T2DM [11, 16] S.M. in T2DM [17] S.M. in diabetic mice [15] WAT in diabetic mice [22]

Expression of mitochondrial genes

Down-regulation

S.M. in T2DM [12, 14] S.M. in diabetic mice [15]

Respiratory chain function

Decline

S.M. in T2DM [13]

Oxidative phosphorylation

Decrease

S.M. in T2DM [12, 18-20] S.M. in diabetic mice [15] WAT in diabetic mice [22]

Fatty acids β oxidation

Decrease

S.M. in T2DM [20, 21] WAT in diabetic mice [22]

S.M., skeletal muscle; WAT, white adipose tissue; T2DM, type 2 diabetes mellitus

Besides, Pravenec et al. [24] established conplastic strains of rats with different mitochondrial genomes and demonstrated that variation in mtDNA, even a single nucleotide substitution, could directly lead to metabolic dysregulation including glucose intolerance and insulin insensitivity in these conplastic strains. In addition, mitochondrial defects in murine C2C12 myotube cells caused by treatment of respiratory inhibitors also resulted in a decline of insulin-stimulated glucose uptake and inactivation of AKT and IRS-1 in the insulin signaling pathway [25]. Recently, ROS produced from mitochondria has been considered as one of the possible unifying factors leading to insulin resistance. Insulin resistance caused by tumor necrosis factor α (TNF-α) and dexamethasone was observed to be related to the increase of the intracellular ROS levels, although they act through very different mechanisms [26, 27]. The effect of ROS overproduction on insulin signaling pathways has been extensively studied [28]. ROS or oxidative stress may induce activation of serine/threonine kinase signaling cascades, including p38 MAPK and JNK. These activated kinases can act on a number of potential targets in the insulin signaling pathway, including the insulin receptor and the family of IRS proteins. After treatment of cells with ROS-scavenging enzymes, insulin resistance caused by TNFα and dexamethasone could be recovered by 25%


104

Chih-Hao Wang et al.

to 60%. This indicates that ROS plays an important role in the development of insulin resistance [29]. It was demonstrated that the amount of superoxide anions produced from mitochondria was increased, and that a mitochondriatargeting superoxide dismutase mimetic reversed insulin resistance in four animal models of insulin resistance [30]. This further suggests that increased mitochondrial production of superoxide anions is a common phenomenon in different animal models of insulin resistance, and precedes the disruption of insulin signaling pathways. On the other hand, it has been shown that impaired β-oxidation of fatty acids due to decreased activities of carnitine palmitoyl transferase (transports long-chain fatty acids into mitochondria) and long-chain acyl-CoA dehydrogenase (LCAD, an enzyme involved in β-oxidation of fatty acids), results in the accumulation of intracellular lipids, which then lead to insulin resistance in insulin-targeting cells [31]. It has been proposed that an increase in the accumulation of fatty acid metabolites, such as diacylglycerol and fatty acyl-CoA and ceramides, could activate some serine/threonine kinases such as PKCβ and PKCδ in human cells [32]. In turn, the activation of PKCs phosphorylates IRS-1 and IRS-2 to increase serine/threonine phosphorylation and inhibit the function of these two proteins. Subsequently, this leads to the blockade of the downstream PI3K/AKT signaling cascade, and glucose transport activity and other events downstream of insulin receptor signaling are diminished ultimately. Together, these observations suggest that mitochondrial dysfunction-elicited ROS production and oxidative damage may play a role in the dysregulation of insulin signaling pathway, which in turn leads to insulin insensitivity in tissue cells (Fig. 1).

4. Upregulation of mitochondrial function and antioxidant defense ameliorate insulin resistance In light of the observations of mitochondrial impairment in patients with type 2 diabetes, it has been thought that the increase of mitochondrial function or elimination of ROS may be effective therapies for these patients. It was reported that thiazolidinediones (TZDs), drugs have already used for treatment of diabetes clinically, not only increased insulin sensitivity of cells but also improved the biogenesis, functions and morphology of mitochondria in vitro and in vivo through the upregulation of PGC-1α expression [33-34]. This new mechanism of action of these anti-diabetic drugs further support the notion that manipulating mitochondrial function may be a new means for treatment of insulin insensitivity or type 2 diabetes. Thus, many studies have made considerable efforts in looking for possible ways to improve insulin sensitivity in target cells and tissues through upregulation of mitochondrial function or antioxidant defense system (Table 2).


Mitochondrial dysfunction and insulin insensitivity

105

Figure 1. Mechanisms underlying mitochondrial dysfunction-induced insulin insensitivity. Binding of insulin to the insulin receptor (IR) induces glucose uptake by a series of signaling cascades. Activation of IR can phosphorylate insulin receptor substrate 1/2 (IRS1/2) at tyrosine residues and then phosphoinositide-3-kinase (PI3K) can be activated by IRS, which in turn, induces the activation of downstream target, Akt, the key regulator in insulin signaling pathway. Akt activation can promote translocation of GLUT4 from intracellular vesicles to the plasma membrane and execute glucose uptake efficiently. Mitochondrial dysfunction leads to overproduction of ROS or decrease in fatty acid β-oxidation and subsequently activates some serine/threonine kinases, such as JNK and PKCs. Increase of serine phosphorylation on IRS by these kinases inhibits the tyrosine phosphorylation on IRS and blocks the downstream events of insulin signaling pathway.

It is worth mentioning that mitochondrial biogenesis can be up-regulated by regular exercise through the activation of PGC-1Îą and its downstream targets including mitochondrial transcription factor, mtTFA, and nuclear respiratory factors, NRF1 and NRF2, which regulate the expression of some polypeptides constituting respiratory enzyme complexes [35-37]. These molecular events will culminate in the up-regulation of the bioenergetic function of mitochondria and thereby improve the insulin sensitivity of the animals or human subjects doing regular exercise [38, 39]. In light of these findings and documentations that exercise can alleviate the symptoms of patients with type 2 diabetes [40], we have suggested that increasing biogenesis of mitochondria is the key to reaping the benefits of exercise in


106

Chih-Hao Wang et al.

the management of insulin insensitivity and type 2 diabetes. On the other hand, resveratrol (3,5,4'- trihydroxystilbene), a well-known polyphenolic antioxidant, has been demonstrated to be able to promote mitochondrial biogenesis as well as insulin sensitivity in a mouse model [41]. Resveratrol treatment led to the decrease of PGC-1Îą acetylation through activation of Sirt1 (a deacetylase), thereby increasing the activity of PGC-1Îą. In turn, a set of oxidative metabolism-related genes were up-regulated and mitochondrial OXPHOS was also elevated in the muscle tissues of the mice treated with resveratrol. Moreover, resveratrol increased insulin sensitivity in the muscle and protected mice from obesity or insulin resistance induced by high-fat diet [41]. On the other hand, treatment of lipoic acid, an antioxidant, could not only decrease intracelluar ROS but also enhance insulin-stimulated glucose utilization in skeletal muscle of diabetic animals with insulin resistance [42]. Moreover, Yaworsky et al. [43] demonstrated that adipocytes treated with lipoic acid significantly increased insulin-stimulated glucose uptake through rapid translocation of Glut1 and Glut4 to the plasma membrane via the activation of insulin signaling pathway, which includes an increase in tyrosine phosphorylation of IR and IRS-1, and activation of PI3K and AKT [43]. In addition to its antioxidant function, lipoic acid was found to significantly increase the biogenesis and function of mitochondria, including oxygen consumption and fatty acid oxidation in adipocytes [44]. All of these lipoic acid effects were enhanced upon co-treatment of adipocytes with another mitochondrial nutrient, acetyl L-carnitine (ALCAR) [45]. These findings suggest that lipoic acid improves insulin sensitivity of adipocytes through its ability to elevate antioxidant capacity and enhance mitochondrial function. In addition, several studies demonstrated that increasing the capacity of antioxidant enzymes by addition of cell-permeablized MnSOD [29, 30], and overexpression of catalase [46, 47] and GPx3 [48], respectively, to reduce ROS could improve insulin sensitivity in targeted cells or mice. These findings clearly indicate that therapeutic agents targeting mitochondria or antioxidant defense system could ameliorate the symptoms of insulin insensitivity in cells and animals, and perhaps also in human subjects. These findings have also substantiated the notion that normal mitochondrial function is essential for the maintenance of insulin sensitivity and homeostasis of glucose metabolism in the human body.

5. The emerging role of adipokines in obesity and insulin resistance Adipose tissue is known to be the storage form of energy of mammals. However, it is not exactly the only work that the adipose tissue is engaged in.


Mitochondrial dysfunction and insulin insensitivity

107

Table 2. Improvement of insulin sensitivity by upregulation of mitochondrial function and the antioxidant defense system. Approach or treatment Rosiglitazone

Effect on mitochondrial function or ROS levels Mitochondrial biogenesis ↑ Oxygen consumption rate ↑ Respiratory chain function ↑ Fatty acids β-oxidation ↑

Effect on glucose metabolism Plasma glucose ↓ Insulin-stimulated glucose uptake ↑ Translocation of Glut4 ↑

References

Regular exercise

Mitochondrial biogenesis ↑ Oxidative phosphorylation ↑

Glucose tolerance ↑ Insulin-stimulated glucose uptake ↑

[38-40]

RSV

Activity of PGC-1α ↑ Mitochondrial biogenesis ↑ Oxidative phosphorylation ↑

Plasma insulin ↓ Glucose infusion rate ↑ Glucose disposal rate ↑

[41]

Lipoic acid

ROS production ↓ Mitochondrial biogenesis ↑ Oxygen consumption rate ↑ Fatty acid β-oxidation ↑

Plasma insulin ↓ Insulin- stimulated glucose uptake↑ Translocation of Glut1 and Glut4 ↑ Activation of insulin signaling ↑

[42-45]

MnSOD, MnTBAP

ROS production ↓

Insulin-stimulated glucose uptake ↑ Insulin tolerance ↑ Glucose tolerance ↑ Activation of insulin signaling ↑

[29, 30]

Catalase

ROS production ↓ mtDNA damage ↓ Lipid peroxidation ↓ Oxygen consumption rate ↑

Glucose infusion rate ↑ Insulin tolerance ↑ Glucose tolerance ↑ Activation of insulin signaling ↑

[46, 47]

GPx3

ROS production ↓

Activation of insulin signaling ↑

[48]

[33, 34]

RSV, resveratrol (3,5,4'-trihydroxystilbene) GPx3, glutathione peroxidase 3

Researchers working in the field of metabolism also consider the adipose tissue as an endocrine system, owing to the findings that hormones secreted by adipocytes also regulate the energy metabolism [49]. Hormones secreted


108

Chih-Hao Wang et al.

by adipocytes, called adipokines, include adiponectin, adipsin, IL-6, leptin, resistin, TNFÎą, and plasminogen-activator inhibitor type 1 (PAI-1). Through the release of various adipokines, adipocytes can regulate glucose metabolism in peripheral tissues such as the liver and muscle to maintain glucose homeostasis [50]. Among these adipokines, adiponectin has been considered the most important due to its higher concentration than the others [51]. Besides, several clinical studies have demonstrated that the plasma level of adiponectin in obese subjects or patients with type 2 diabetes was significantly decreased compared with normal subjects, and that blood glucose was higher and insulin sensitivity was decreased in mice with adiponectin deficiency [52, 53]. Thus, adiponectin has become the most attractive adipokine and its function is gradually unraveled.

5.1. Adiponectin improves mitochondrial function and insulin sensitivity Adiponectin is known as an anti-hyperglycemic adipokine because it improves insulin sensitivity of insulin-responsive tissues. In the mid-1990s, four different groups studied the structure and function of adiponectin. After further characterization, they named adiponectin as adipocyte complementrelated protein of 30 kDa (Acrp30) [54], adipoQ [55], adipose most abundant gene transcript 1 (Apm1) [56], and gelatin binding protein of 28 kDa (Gbp28) [57], respectively. Adiponectin exists as multiple forms in human blood circulation. While the full-length form is the most common type in circulation, adiponectin also exists as multimers from low molecular weight (LMW), medium molecular weight (MMW), to high molecular weight (HMW) forms. Lodish and coworkers were the first to report the physiological function of adiponectin. They found that acute treatment with purified C-terminal globular domain of adiponectin (gAd) significantly decreased the levels of plasma free fatty acids in mice fed with high-fat diet. Compared to the fulllength adiponectin, gAd was found to be a much more potent agent in clearing the circulating system from overloaded fatty acids and increasing β oxidation of fatty acids in muscle cells [58]. This study demonstrated that gAd may regulate energy balance and body weight by stimulating fatty acids oxidation by mitochondria in skeletal muscle. Kadowaki’s group also discovered similar physiological functions of adiponectin in vivo. They showed that the serum level of adiponectin was decreased in mice fed with high-fat diet compared to the mice fed with high-carbohydrate diet [59]. Importantly, mice fed with the high-fat diet had symptoms of diabetes, hyperglycemia and hyperinsulinemia. Administration of adiponectin to the


Mitochondrial dysfunction and insulin insensitivity

109

mice fed with high-fat diet could ameliorate hyperglycemia and hyperinsulinemia as revealed by the glucose tolerance test. The gAd was found to display more significant improvement of diabetic symptoms than with full-length adiponectin [59]. These results explain the physiological function of adiponectin and its potential in the development of therapeutic agents for the treatment of diabetes. The underlying mechanism of adiponectin was revealed by Kadowaki’s group and is illustrated in Figure 2. They showed that phosphorylation and activation of AMPK are stimulated by globular and full-length adiponectins in the skeletal muscle but only by full-length adiponectin in the liver [60]. AMPK has been reported to stimulate glucose uptake [61] and fatty acid oxidation [62] of muscle by independent pathways. They further confirmed that both Ad and gAd stimulated glucose uptake and fatty acids oxidation in skeletal muscle via the activation of AMPK in vivo and in vitro. Even though both Ad and gAd increase the glucose uptake and fatty acids oxidation in muscle cells, gAd has a higher binding affinity to the membrane fractions of muscle cells, whereas the full-length Ad has a higher binding affinity to the membrane fractions of hepatocytes [60]. The difference in the affinity of skeletal muscle and liver cells for adiponectin was due to the difference in adiponectin receptors, called AdipoR1 and AdipoR2. AdipoR1 is ubiquitously expressed and was found most abundantly in skeletal muscle, whereas AdipoR2 is mostly expressed in liver. Both AdipoR1 and AdipoR2 are seven-transmembrane domain-containing proteins. The structure of AdipoR1 or AdipoR2 predicts that the N terminus resides inside the membrane while the C terminus is located on the outside [63]. The orientation of AdipoR1 or AdipoR2 is the inverse of that in G proteincoupled receptors. AdipoR1 is conserved from yeast to human, and the yeast homolog plays a key role in the regulation of lipid metabolism in yeast [64], further implying the importance of AdipoR1. AdipoR2 shares 67.5% identity of protein sequence of AdipoR1, and is also conserved from yeast to human [63]. AdipoR1 is a receptor specific for the globular form of adiponectin, whereas AdipoR2 is a receptor for full-length adiponectin, which is consistent with the results obtained from a previous study [60]. The mechanism by which that AdipoR1 dominantly acts is different from AdipoR2. Adenovirus-mediated overexpression of AdipoR1 in liver showed an increase in the activation of AMPK, which was not observed in animals that overexpressed AdipoR2. On the other hand, overexpression of AdipoR2 enhanced the activation of PPAR and its target genes. These findings clearly indicated that the biochemical effects of adiponectin exerted by AdipoR1 and AdipoR2 are executed through different signaling pathways [65].Â


110

Chih-Hao Wang et al.

Figure 2. Molecular mechanisms of adiponectin action in skeletal muscle cells. In skeletal muscle, AdipoR1 is the seven-transmembrane receptor that specifically binds to the globular form of adiponectin (gAd). It activates the phosphorylation of AMPKα, which is the key molecule in gAd signal pathway. AMPKα stimulate GLUT4 translocation to increase glucose uptake in skeletal muscle cells via unknown pathway. On the other hand, phosphorylated AMPKα also activate acetyl-CoA carboxylase to increase fatty acid β-oxidation. The new linkage between gAd signaling and mitochondria in muscle cells has recently been found. Through AMPKα, PGC1α is activated by phosphorylation and deactylation, which then activates several genes related to mitochondrial biogenesis and ROS defense system – the new possible underlying mechanism of how gAd improves insulin resistance.

Conclusion Strong relationship between mitochondrial dysfunction and insulin insensitivity or type 2 diabetes have been documented in clinical observations and animal studies. The possible mechanisms underlying the involvement of mitochondrial dysfunction in the pathogenesis of type 2 diabetes are illustrated in Figure 1. Muscle cells and adipocytes become insulininsensitive and inefficient in the insulin-stimulated glucose utilization as a result of defects in the insulin signaling pathway resulting from excess intracellular ROS or lipids in the cells with mitochondrial dysfunction. In


Mitochondrial dysfunction and insulin insensitivity

111

addition to the above-mentioned experimental findings, several recent studies showed that insulin sensitivity and glucose utilization can be improved by exercise and therapeutic agents that are able to up-regulate the biogenesis and respiratory function of mitochondria. Another strategy is to boost the antioxidant defense by treatment of diabetic patients with antioxidants or overexpression of antioxidant enzymes. These manipulations have been shown to be effective in the activation of insulin signaling pathway to improve insulin response of the muscle and adipose tissues in diabetic mice. These observations together with clinical findings of mitochondrial defects in diabetic patients suggest that it is of great potential to develop mitochondriatargeting drugs for the prevention or treatment of insulin resistance and type 2 diabetes.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

13. 14.

15.

Rother K.I. 2007, N Engl J Med 356:1499. Wild S., Roglic G., Green A., Sicree R., and King H. 2004, Diabetes Care 27:1047. Wallace D.C. 2005, Annu Rev Genet 39:359. Wiesner R.J., Ruegg J.C., and Morano I. 1992, Biochim Biophys Acta, 183:553. Wu Z., Puigserver P., Andersson U., Zhang C., Adelmant G., Mootha V., Troy A., Cinti S., Lowell B., Scarpulla R.C., and Spiegelman B.M. 1999, Cell 98:115. St-Pierre J., Lin J., Krauss S., Tarr P.T., Yang R., Newgard C.B., and Spiegelman B.M. 2003, J Biol Chem 278:26597. Valko M., Leibfritz D., Moncol J., Cronin M.T., Mazur M., and Telser J. 2007, Int J Biochem Cell Biol 39:44. Cadenas E., and Davies K.J. 2000, Free Radic Biol Med 29:222. Balaban R.S., Nemoto S., and Finkel T. 2005, Cell 120:483. Turrens J.F. 2003, J Physiol 552:335. van den Ouweland J.M., Lemkes H.H., Ruitenbeek W., Sandkuijl L.A., de Vijlder M.F., Struyvenberg P.A., van de Kamp J.J., and Maassen J.A. 1992, Nat Genet 1:368. Mootha V.K., Lindgren C.M., Eriksson K.F., Subramanian A., Sihag S., Lehar J., Puigserver P., Carlsson E., Ridderstrale M., Laurila E., Houstis N., Daly M.J., Patterson N., Mesirov J.P., Golub T.R., Tamayo P., Spiegelman B., Lander E.S., Hirschhorn J.N., Altshuler D., and Groop L.C. 2003, Nat Genet 34:267. Kelley D.E., He J., Menshikova E.V., Ritov V.B. 2002, Diabetes 51:2944. Patti M.E., Butte A.J., Crunkhorn S., Cusi K., Berria R., Kashyap S., Miyazaki Y., Kohane I., Costello M., Saccone R., Landaker E.J., Goldfine A.B., Mun E., DeFronzo R., Finlayson J., Kahn C.R., and Mandarino L.J. 2003, Proc Natl Acad Sci USA 100:8466. Pagel-Langenickel I., Bao J., Joseph J.J., Schwartz D.R., Mantell B.S., Xu X., Raghavachari N., and Sack M.N. 2008, J Biol Chem 283:22464.


112

Chih-Hao Wang et al.

16. Maassen J.A., Jahangir Tafrechi R.S., Janssen G.M., Raap A.K., Lemkes H.H., and ’t Hart L.M. 2006, Endocrinol Metab Clin North Am 35:385. 17. Liang P., Hughes V., and Fukagawa N.K. 1997, Diabetes 46:920. 18. Scheuermann-Freestone M., Madsen P.L., Manners D., Blamire A.M., Buckingham R.E., Styles P., Radda G.K., Neubauer S., and Clarke K. 2003, Circulation 107:3040. 19. Mogensen M., Sahlin K., Fernström M., Glintborg D., Vind B.F., Beck-Nielsen H., and Højlund K. 2007, Diabetes 56:1592. 20. Petersen K.F., Dufour S., Befroy D., Garcia R., and Shulman G.I. 2004, N Engl J Med 350:664. 21. Gaster M., Rustan A.C., Aas V., and Beck-Nielsen H. 2004, Diabetes 53:542. 22. Choo H.J., Kim J.H., Kwon O.B., Lee C.S., Mun J.Y., Han S.S., Yoon Y.S., Yoon G., Choi K.M., and Ko Y.G. 2006, Diabetologia 49:784. 23. Park S.Y., and Lee W. 2007, Diabetes Res Clin Pract 77 (Suppl 1):S165. 24. Pravenec M., Hyakukoku M., Houstek J., Zidek V., Landa V., Mlejnek P., Miksik I., Dudová-Mothejzikova K., Pecina P., Vrbacky M., Drahota Z., Vojtiskova A., Mracek T., Kazdova L., Oliyarnyk O., Wang J., Ho C., Qi N., Sugimoto K., and Kurtz T. 2007, Genome Res 17:1319. 25. Lim J.H., Lee J.I., Suh Y.H., Kim W., Song J.H., and Jung M.H. 2006, Diabetologia 49:1924. 26. Uysal K.T., Wiesbrock S.M., Marino M.W., and Hotamisligil G.S. 1997, Nature 389:610. 27. Kusunoki M., Cooney G.J., Hara T., and Storlien L.H. 1995, Diabetes 44:718. 28. Erol A. 2007, Bioessays 29:811. 29. Houstis N., Rosen E.D., and Lander E.S. 2006, Nature 440:944. 30. Hoehn K.L., Salmon A.B., Hohnen-Behrens C., Turner N., Hoy A.J., Maghzal G.J., Stocker R., van Remmen H., Kraegen E.W., Cooney G.J., Richardson A.R., and James D.E. 2009, Proc Natl Acad Sci USA 106:17787. 31. Zhang D., Liu Z.X., Choi C.S., Tian L., Kibbey R., Dong J., Cline G.W., Wood P.A., and Shulman G.I. 2007, Proc Natl Acad Sci USA 104:17075. 32. Itani S.I., Pories W.J., Macdonald K.G., and Dohm G.L. 2001, Metabolism 50:553. 33. Wilson-Fritch L., Nicoloro S., Chouinard M., Lazar M.A., Chui P.C., Leszyk J., Straubhaar J., Czech M.P., and Corvera S. 2004, J Clin Invest 114:1281. 34. Mensink M., Hesselink M.K., Russell A.P., Schaart G., Sels J.P., and Schrauwen P. 2007, Int J Obes (London) 31:1302. 35. Geng T., Li P., Okutsu M., Yin X., Kwek J., Zhang M., and Yan Z. 2010, Am J Physiol Cell Physiol 298:C572. 36. Lira V.A., Benton C.R., Yan Z., and Bonen A. 2010, Am J Physiol Endocrinol Metab 299:E145. 37. Li L., Pan R., Li R., Niemann B., Aurich A.C., Chen Y., and Rohrbach S. 2011, Diabetes 60:157. 38. Henriksen E.J. 2002, J Appl Physiol 93:788. 39. Hernández-Alvarez M.I., Thabit H., Burns N., Shah S., Brema I., Hatunic M., Finucane F., Liesa M., Chiellini C., Naon D., Zorzano A., and Nolan J.J. 2010, Diabetes Care 33:645.


Mitochondrial dysfunction and insulin insensitivity

113

40. Phielix E., Meex R., Moonen-Kornips E., Hesselink M.K., and Schrauwen P. 2010, Diabetologia 53:1714. 41. Lagouge M., Argmann C., Gerhart-Hines Z., Meziane H., Lerin C., Daussin F., Messadeq N., Milne J., Lambert P., Elliott P., Geny B., Laakso M., Puigserver P., and Auwerx J. 2006, Cell 127:1109. 42. Jacob S., Streeper R.S., Fogt D.L., Hokama J.Y., Tritschler H.J., Dietze G.J., and Henriksen E.J. 1996, Diabetes 45:1024. 43. Yaworsky K., Somwar R., Ramlal T., Tritschler H.J., and Klip A. 2000, Diabetologia 43:294. 44. Shen W., Hao J., Feng Z., Tian C., Chen W., Packer L., Shi X., Zang W., and Liu J. 2011, Br J Pharmacol 162:1213. 45. Shen W., Liu K., Tian C., Yang L., Li X., Ren J., Packer L., Cotman C.W., and Liu J. 2008, Diabetologia 51:165. 46. Ikemura M., Nishikawa M., Hyoudou K., Kobayashi Y., Yamashita F., and Hashida M. 2010, Mol Pharm 7:2069. 47. Lee H.Y., Choi C.S., Birkenfeld A.L., Alves T.C., Jornayvaz F.R., Jurczak M.J., Zhang D., Woo D.K., Shadel G.S., Ladiges W., Rabinovitch P.S., Santos J.H., Petersen K.F., Samuel V.T., and Shulman G.I. 2010, Cell Metab 12:668. 48. Chung S.S., Kim M., Youn B.S., Lee N.S., Park J.W., Lee I.K., Lee Y.S., Kim J.B., Cho Y.M., Lee H.K., and Park K.S. 2009, Mol Cell Biol 29:20. 49. Kershaw E.E., and Flier J.S. 2004, J Clin Endocrinol Metab 89:2548. 50. Rosen E.D. and Spiegelman B.M. 2006, Nature 444:847. 51. Dyck D.J. 2009, Appl Physiol Nutr Metab 34:396. 52. Hotta K., Funahashi T., Arita Y., Takahashi M., Matsuda M., Okamoto Y., Iwahashi H., Kuriyama H., Ouchi N., Maeda K., Nishida M., Kihara S., Sakai N., Nakajima T., Hasegawa K., Muraguchi M., Ohmoto Y., Nakamura T., Yamashita S., Hanafusa T., and Matsuzawa Y. 2000, Arterioscler Thromb Vasc Biol 20:1595. 53. Li K., Li L., Yang G.Y., Liu H., Li S.B., and Boden G. 2010, J Endocrinol Invest 33:96. 54. Scherer P.E., Williams S., Fogliano M., Baldini G., and Lodish H.F. 1995, J Biol Chem 270:26746. 55. Hu E., Liang P., and Spiegelman B.M. 1996, J Biol Chem 271:10697. 56. Maeda K., Okubo K., Shimomura I., Funahashi T., Matsuzawa Y., and Matsubara K. 1996, Biochem Biophys Res Commun 221:286. 57. Nakano Y., Tobe T., Choi-Miura N.H., Mazda T., and Tomita M. 1996, J Biochem (Tokyo) 120:803. 58. Fruebis J., Tsao T.S., Javorschi S., Ebbets-Reed D., Erickson M.R., Yen F.T., Bihain B.E., and Lodish H.F. 2001, Proc Natl Acad Sci USA 98:2005. 59. Yamauchi T., Kamon J., Waki H., Terauchi Y., Kubota N., Hara K., Mori Y., Ide T., Murakami K., Tsuboyama-Kasaoka N., Ezaki O., Akanuma Y., Gavrilova O., Vinson C., Reitman M.L., Kagechika H., Shudo K., Yoda M., Nakano Y., Tobe K., Nagai R., Kimura S., Tomita M., Froguel P., and Kadowaki T. 2001, Nat Med 7: 941. 60. Yamauchi T., Kamon J., Minokoshi Y., Ito Y., Waki H., Uchida S., Yamashita S., Noda M., Kita S., Ueki K., Eto K., Akanuma Y., Froguel P., Foufelle F., Ferre


114

61. 62. 63.

64. 65.

Chih-Hao Wang et al.

P., Carling D., Kimura S., Nagai R., Kahn B.B., and Kadowaki T. 2002, Nat Med 8:1288. Mu J., Brozinick J.T. Jr., Valladares O., Bucan M., and Birnbaum M.J. 2000, Mol Cell 7:1085. Winder W.W., and Hardie D.G. 1999, Am J Physiol 277:E1. Yamauchi T., Kamon J., Ito Y., Tsuchida A., Yokomizo T., Kita S., Sugiyama T., Miyagishi M., Hara K., Tsunoda M., Murakami K., Ohteki T., Uchida S., Takekawa S., Waki H., Tsuno N.H., Shibata Y., Terauchi Y., Froguel P., Tobe K., Koyasu S., Taira K., Kitamura T., Shimizu T., Nagai R., and Kadowaki T. 2003, Nature 423:762. Karpichev I.V., Cornivelli L., and Small G.M. 2002, J Biol Chem 277:12152. Yamauchi T., Nio Y., Maki T., Kobayashi M., Takazawa T., Iwabu M., OkadaIwabu M., Kawamoto S., Kubota N., Kubota T., Ito Y., Kamon J., Tsuchida A., Kumagai K., Kozono H., Hada Y., Ogata H., Tokuyama K., Tsunoda M., Ide T., Murakami K., Awazawa M., Takamoto I., Froguel P., Hara K., Tobe K., Nagai R., Ueki K., and Kadowaki T. 2007, Nat Med 13:332.


Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology, 2012: 115-147 ISBN: 978-81-308-0487-3 Editors: Phing-How Lou and Natalia Petersen

4. Mitochondria and cancer 1

Jean-François Dumas1,2, Damien Roussel3 and Stéphane Servais1,2,4

INSERM U921 « Nutrition, Growth and Cancer », 37032 Tours, France; 2Université François Rabelais, 37032 Tours, France; 3Université de Lyon, CNRS, UMR5023, 69622 Villeurbanne, France; 4IUT, Tours, F-37082, France

Abstract. It was first discovered that glycolysis was increased in tumor cells even in the presence of oxygen, suggesting that mitochondrial oxidative phosphorylation was altered in cancer cells. This is not true for all tumor cells since in some cancer cells ATP is mainly produced by mitochondrial oxidative phosphorylation. A lower mitochondrial oxidative phosphorylation is therefore not necessarily a hallmark of tumor cells. However, mitochondria remain an important point in tumorigenesis in cancer cells. Moreover, it was shown that any alterations in mitochondrial oxidative phosphorylation could be implicated in the adverse nutritional status (cachexia) that is present in some patients suffering from cancer and at the origin of a lower resistance to anticancer therapy. Mitochondria, therefore, present a promising strategy for cancer therapy.

Abbreviations ANT αKG BH3 COX HIF-1α

: : : : :

adenine nucleotide translocator alpha-ketoglutarate Bcl-2 homology 3 cytochrome c oxidase, hypoxia-inducible factor 1α

Correspondence/Reprint request: Dr. Jean-François Dumas, INSERM U921 « Nutrition, Growth and Cancer » 37032 Tours, France. E-mail: jean-francois.dumas@univ-tours.fr


116

mPTP : MCT : NADPH : NMR : OAA : OPA1 : PGC-1 : PPP : ROS : TB : TCA : TNF-α : UCP :

Jean-François Dumas et al.

mitochondrial permeability transition pore monocarboxylate transporter nicotinamide adenine dinucleotide phosphate nuclear magnetic resonance oxaloacetate Optic Atrophy 1 PPARγ coactivator-1 pentose phosphate pathway reactive oxygen species, transplanted-tumour bearing, tricarboxylic cycle tumour necrosis factor-alpha uncoupling protein

Introduction Cancers are among the leading causes of death in the world and as such, represent a major problem for public health managemant. Moreover, the majority of cancer patients suffer also from cachexia. This complex syndrome is characterized, among other things, by a loss of muscle mass that provokes severe reduction in autonomy, quality of life and survival since cachectic cancer patients are less resistant to anticancer therapy. Mitochondrial energy metabolism plays an especially important role in cancer and its associated pathology, cachexia. Mitochondria, the power plants of cells, use oxidative phosphorylation to couple substrate oxidation and oxygen consumption to the production of ATP, the main form of useable energy that cells allocate towards cellular maintenance, proliferation, and work depending on physiological circumstances (Figure 1). The oxidation of substrates is coupled to the reduction of NAD and FAD in the mitochondrial matrix. Reducing power of NADH and FADH2 is transferred to a chain of electron-driven proton pumps in the mitochondrial inner membrane, which creates a transmembrane electrochemical proton gradient (protonmotive force). The energy stored in this proton motive force is then coupled to the “downhill” re-entering of these protons through the ATP synthase to synthesize ATP or used by other proteins to drive ion and substrate transport. In this coupled system, the maximum amount of ATP that mitochondria make per energetic substrate depends upon the coupling efficiency of oxidative phosphorylation which is known as the ATP/O ratio. This ratio, in turn, depends upon the nature of reducing equivalents: NADH or FADH2. Using the more recent data in the field [1], it appears that the maximum amount of ATP from the complete


Mitochondria and cancer

117

Amino acids Fatty acids Glucose

Oxygen

NAD FAD

ADP

 Nutrients catabolism NADH FADH 2

CO 2

ATP H2 O

Mitochondrial Intermembrane

Oxidative

space

+

H+ H+ H H+

H H+ H+ H+

e-

Q

e-

Phosphorylation

Proton motive force H+

+

c

I

Cellular works

Oxidative Phosphorylation

H+

IV UCP

e-

H+ + H+H

III FADH

2

Oxygen

H+

NADH N AD matrix

+

H 2O ADP + Pi

ATP

Figure 1. Mitochondrial energy metabolism. Top: Catabolic metabolism oxidizes nutrients (mainly fatty acids, glucose, but also amino acids) to generate NADH and FADH2. Reduced coenzymes are re-oxidized in the presence of oxygen by mitochondrial oxidative phosphorylation processes, which represent a series of reactions that couple oxygen consumption and substrate oxidation with the production of ATP. ATP is then used to perform biological work within the cell. Bottom: The electrons from NADH and FADH2 pass through the four membrane-bound protein complexes (complexes I to IV, orange boxes) of the electron-transport chain (yellow arrows) with the aid of the two soluble electron carriers: ubiquinone (Q), which carries two electrons within the inner membrane from complexes I and II to complex III and cytochrome c,which carries one electron in the intermembrane space from complex III to complex IV. Molecular oxygen is finally reduced to water by complex IV. The transfer of electrons allow complexes I, III and IV to pump protons out of the mitochondrial matrix (grey arrows), which generates a proton electrochemical potential gradient across the inner mitochondrial membrane (proton motive force). The energy stored in the proton motive force then drives the synthesis of ATP as protons re-enter the mitochondrial matrix though ATP synthase (green boxes). The proton motive force can also drive proton leak reactions (red arrows) through either the basal or UCP-inducible proton conductance pathways, which dissipates part of the initial energy content of fuels into heat (energy wasting processes).


118

Jean-Franรงois Dumas et al.

oxidation of glucose in a mammalian cell that uses the malate-aspartate shuttle for the transfer of glycolytic NADH into the mitochondrial matrix would be nearly 35 ATP molecules per glucose (for a comprehensive review of this calculation see [2]). This makes the mitochondrial aerobic oxidation of glucose a far more efficient energy-converting pathway than glycolysis, producing 2 ATP molecules per glucose. However, oxidative phosphorylation is not a hundred percent efficient process, which means that not all the oxygen consumed by mitochondria (i.e. not all the substrate oxidized by the respiratory chain) is coupled to the mitochondrial production of ATP. It has been estimated that 1 to 5% of oxygen consumed by mitochondria is reduced into reactive oxygen species (ROS) as a by-product of electron transport chain activity and up to 20% are used to counteract energy wasting processes (proton leak and redox slip) [3]. The dissipation of the electrochemical proton gradient by proton leak reactions is a ubiquitous phenomenon that diverts energy from ATP synthesis to thermogenesis, thereby modulating the yield of ATP synthesis in the mitochondria and contributing to the whole body resting energy expenditure. This mitochondrial proton leak has been shown to regulate important cellular metabolic functions such as calcium signalling, lipid/glucose metabolism and ROS production [4-6]. The role of mitochondria as the keystone element in the intrinsic pathway of apoptosis that lead to the programmed death of cells is becoming more evident. Last but not the least, cardiolipin (a phospholipid specifically found in the mitochondrial inner membrane), plays an essential role in the optimal functioning of oxidative phosphorylation enzymes such as ATP synthase and respiratory chain complexes, and also in the energy wasting processes, i.e. proton leak and redox slip. In this context, there is increasing evidence that mitochondrial bioenergetics play a role in the reprogramming of cancer cell metabolism, and in the development of resistance against mitochondria-induced apoptosis. Nevertheless, the role of mitochondrial energy metabolism in malignant transformation is still not well understood [7]. This chapter is devoted to the role of mitochondria in both tumor cells and non cancer cells of the cancerous host.

1. Mitochondrial bioenergetics in cancer cells 1.1. Energy production Tumor cells need to generate an ample amount of ATP for energy and de novo synthesis of nucleotides, lipids, and proteins for rapid proliferation.

1.1.1. Warburg effect In normal tissues, mitochondrial oxidative phosphorylation accounts for 90% of ATP production with glycolysis only accounting for 10% [3].


Mitochondria and cancer

119

Otto Warburg has been the first to observe that proliferating tumor cells consume glucose at a high rate and release lactate and not CO2 [8-9]. This observation means that tumors use less of the highly efficient oxidative phosphorylation, producing 50% of the ATP from oxidative phosphorylation and 50% from glycolysis (Figure 2). Moreover, this shift occurs even though there was sufficient oxygen to support mitochondrial energy metabolism and so, this phenomenon has been called “aerobic glycolysis” or the “Warburg effect”. While the increase in glycolysis is a hallmark of tumorigenicity, bioenergetic measurements have shown that global ATP concentration and adenylate energy charge change only marginally in tumors compared with normal tissues [10-11], suggesting that mitochondrial metabolism is not necessarily shut down. Glucose

Glucose

Glucose

Glucose

ATP

glycolysis

lactate

glycolysis

pyruvate

lactate

pyruvate

oxygen ADP

acetyl-coA

ATP

acetyl-coA

TCA cycle + OxPhos

TCA cycle + OxPhos

ATP

mitochondrion

normal cell

ATP

oxygen ADP

mitochondrion

cancer cell

Figure 2. In normal cells, ATP is mainly synthesized by mitochondria through the tricarboxylic cycle (TCA) and then oxidative phosphorylation (OxPhos). In contrast, from the hypothesis postulated by Warburg, cancer cells rely on high levels of anaerobic glycolysis to produce ATP.

1.1.2. Why is glycolysis increased in cancer cells? Enhanced glucose use is advantageous to cancer cells for several reasons [12]: First, it allows cells to use the most abundant extracellular nutrient, glucose, to produce abundant ATP. The yield of ATP per glucose consumed is low. However, if the glycolytic flux is high enough, the percentage of cellular ATP produced from glycolysis can exceed that produced from oxidative phosphorylation [13-14]. Second, with anaerobic glycolysis, cells may adapt to conditions of varying oxygen concentration (due to inconstant hemodynamics of distant


120

Jean-François Dumas et al.

blood vessels) that would be lethal for cells that rely on oxidative phosphorylation to synthetize ATP [15]. Third, during anaerobic glycolysis, cancer cells generate lactic acid, creating a favorable acidic condition for tumor invasion [16]. Fourth, and most importantly, cancer cells use intermediates of the glycolytic pathway for biosynthetic pathways (Figure 3). Thus, tumors can metabolize glucose through the pentose phosphate pathway (PPP) to generate nicotinamide adenine dinucleotide phosphate (NADPH) that can contribute to fatty acid synthesis and, together with ribose 5-phosphate, to nucleotide synthesis [7]. For instance, fatty acid synthase, which synthesizes long-chain fatty acids from acetyl-CoA, malonyl-CoA, and NADPH, is upregulated or activated in many cancers and in contrast to normal cells, de novo fatty acid synthesis occurs at high rates in tumor cells [17-18]. Moreover, cancer cells use intermediates of the glycolytic pathway as glucose 6-phosphate for glycogen and ribose 5-phosphate synthesis, dihydroxyacetone phosphate for lipid synthesis, and pyruvate for alanine and malate synthesis [19]. Glucose

Glucose Glucose 6-P PPP

Fatty acids

NADP+

NADPH ribose-5-phosphate

pyruvate citrate OAA

acetyl-CoA

acetyl-CoA

Nucleotides

OAA

Amino acids

citrate TCA cycle Glutamine αKG Glutamate

Glutamine

mitochondrion

Figure 3. In cancer cells, anaerobic glycolysis provides glycolytic intermediates to the pentose phosphate pathway (PPP) for nucleotide synthesis while mitochondria, especially tricarboxylic (TCA) cycle provides proliferating tumor cells with biosynthetic precursors for fatty acids and amino acids synthesis (cataplerosis), instead of ATP. NADPH contributes to fatty acids synthesis and, together with ribose5-phosphate, to nucleotides synthesis. Moreover, NADPH also ensures the cell’s glutathione-mediated antioxidant defenses against the microenvironment and chemotherapeutic agents. Glutamine, the most abundant amino acid in mammals can serve as a substrate to maintain the TCA cycle (anaplerosis). αKG, alphaketoglutarate; NADPH, nicotinamide adenine dinucleotide phosphate; OAA, oxaloacetate.


Mitochondria and cancer

121

Finally, the switch from oxidative phosphorylation to glycolysis can avoid oxidative stress in tumor cells. Indeed, mitochondrial oxidative phosphorylation is the major cellular source of ROS. Recently, it was reported that oxidative phosphorylation is required for an efficient execution of programmed cell death through the generation of ROS in cancer cell [20]. Therefore, glycolytic metabolism diminishes the potential for ROS production and oxidative damage and therefore, increases the threshold for programmed cell death induction. Moreover, NADPH that is derived from PPP can participate in the antioxidant defense of tumor cells. Various studies have reported that NADPH is essential in the regeneration of the reduced glutathione in the cellular defense against a hostile microenvironment and chemotherapeutic agents to promote cancer cell growth and survival [21-23].

1.1.3. Mechanisms of the Warburg effect Warburg hypothesized that the increase in glycolysis under normal oxygen conditions arose from a deficiency in oxidative phosphorylation [9]. Several studies have described changes in the mitochondrial energy metabolism in tumor cells as a decrease in the number of mitochondria; modifications of their ultrastructure, in the content and composition of the oxidative phosphorylation protein complexes and in the respiratory chain activity; and presence of somatic mutations in mitochondrial DNA [24-32]. Interestingly, it has been shown that the observed upregulation of glycolysis and the linked attenuation in the oxidative phosphorylation capacity in two human cancer cell lines appeared to be mediated by the AMPK pathway [33]. However, collectively these studies reveal no consistent pattern and do not unambiguously confirmed the hypothesis of Warburg. Moreover, multiple studies have demonstrated that mitochondrial energy metabolism is not altered in all tumor cells [34-35]. Therefore, several other mechanisms for the enhanced glycolysis in tumor cells have been described and there is now a growing body of molecular and biochemical evidence indicating that oncogenes most likely underlie the mechanisms that drive the switch to anaerobic glycolysis especially through stabilization of hypoxia-inducible factor 1Îą (HIF-1Îą) [36-38]. Thus, oncogenes, such Ras or Myc have been shown to stimulate glycolysis through induction of glycolytic enzymes such as hexokinase or phosphofructokinase and glucose transporters [37]. Interestingly, it has been shown that the above-mentioned cancer-gene concept is fully concordant with the Warburg effect but, presumably, with oncogenes as the cause and metabolic alterations as the consequence [39]. In addition to oncogenes, the PI3K/Akt/mTOR pathway is likely the most important signaling event in terms of cell metabolism since it is sufficient to


122

Jean-François Dumas et al.

increase the uptake of glucose and to stimulate anaerobic glycolysis in tumor cells [36, 40-42]. Moreover, Akt is known to aberrantly activate HIF-1α under normoxia [43]. HIF-1α, that generally is only stabilized and functional at low oxygen consumption, enhances glycolysis by increasing the glycolytic enzymes and glucose transporters [44]. HIF-1α is also known to decrease mitochondrial oxygen consumption by increasing the expression of the pyruvate dehydrogenase kinase that inactivates pyruvate dehydrogenase, the enzyme responsible for the conversion of pyruvate into acetyl-coA and therefore the functioning of the tricarboxylic acid (TCA) cycle [45-46]. In contrast to oncogenes, the activation of the tumor suppressor p53 leads to a decrease in glycolysis and an increase in mitochondrial energy metabolism [47-49]. Therefore, the loss of p53 is able to enhance the glycolytic flux in cancer cells [50]. Several studies have reported that ROS are involved in the stabilization and activation of HIF under hypoxic conditions [51-53]. Interestingly, ROS also appear to act downstream of some oncogenes to stabilize HIF under conditions of normoxia, leading to an aberrant activation of HIF and the promotion of tumorigenesis [54]. The Warburg theory could be also explained by the Crabtree effect i.e. the inhibition of respiration by glucose [55]. Such an effect of glucose on the oxidative phosphorylation is well-documented in tumor cells [56-60]. On the contrary, when Hela cells were grown in galactose and not in glucose medium, ATP was maximally synthesized by oxidative phosphorylation and not glycolysis suggesting that the source of energy substrate modulates oxidative capacity in cancer cells [61]. Interestingly, it was shown that glucose exerts more potent effects than oxygen limitation on oxidative phosphorylation in cancer cells [62]. Finally, more recently, it has been discovered that some mitochondrial proteins can be tumor suppressors whose mutation indirectly engenders anaerobic glycolysis. Four genes are involved, the succinate dehydrogenase (SDH) genes SDHB, SDHC and SDHD and the fumarase gene FH [63]. The biochemical mechanism that links this mitochondrial dysfunction with tumorigenesis is redox stress that results from increased ROS production in mitochondria or metabolic signaling involving metabolites of TCA cycle as intracellular messengers.

1.1.4. Re-evaluation of mitochondrial energy metabolism in cancer cells There are now several works showing that mitochondrial oxidative phosphorylation is not impaired in all cancer cells and various tumors synthesize a major part of their ATP by oxidative phosphorylation [33-35]. Therefore, this implies the existence of two classes of tumors: the glycolytic


Mitochondria and cancer

123

and the oxidative phosphorylation classes [64]. Interestingly, a recent study demonstrated the existence of a metabolic symbiosis between hypoxic and oxygenated tumor cells [65] (Figure 4). Thus, oxygenated tumor cells (i.e. near of blood vessels) oxidize lactate produced by hypoxic cells to synthesize energy and spare glucose and make it available for hypoxic tumor cells (located far from blood vessels). Oxygenated tumor cells import lactate via monocarboxylate transporter 1 (MCT-1), a protein that participates in the bidirectional transmembrane exchange of lactate and pyruvate [66]. Overexpression of MCT-1 has been reported in several tumors [67-68]. In the oxidative phosphorylation process, lactate is the preferred substrate over glucose [65]. Such metabolic symbiosis between hypoxic and oxygenated tumor cells represents a mechanism that has previously been described as a process exploited by stromal cells to buffer products of the anaerobic metabolism of cancer cells [67]. Existence of this metabolic symbiosis could provide several advantages to tumor cells. First, the yield of oxidative phosphorylation with Blood vessels

aerobiosis oxygen ADP

acetyl-coA

oxygen gradient

pyruvate lactate

TCA cycle + OxPhos mitochondrion

ATP

MCT-1 lactate

lactate

Glucose

MCT-4

lactate pyruvate

Glucose

glycolysis

ATP

hypoxia mitochondrion

Figure 4. Aerobic cancer cells express monocarboxylate transporter 1 (MCT1) and used lactate for oxidative phosphorylation. Therefore, glucose is spared and can be used by hypoxic tumor cells to fuel glycolysis. In turn, the resulting lactate is exported from hypoxic cancer cells via monocarboxylate transporter 4 (MCT4) and can be used by aerobic tumor cells.


124

Jean-Franรงois Dumas et al.

lactate is more concise and more effective than with glucose. Second, oxidation of lactate into pyruvate by lactate dehydrogenase results in a sustained production of reduced equivalents that could buffer tumor oxidative stress and activate prosurvival pathways [65]. Finally, this arrangement buffers and recycles products of anaerobic metabolism to sustain cancer cell survival and growth. The conclusion that tumor cells exhibit a lower mitochondrial oxidative phosphorylation cannot be applied to all cancer cells. Therefore the oxidative phosphorylation should be experimentally determined for each particular cancer cell line, especially in the goal to develop strategies for cancer therapy.

1.2. Apoptosis and life-supporting roles In both normal and cancer cells, mitochondria not only function to produce ATP, but also play critical life-supporting roles in cellular homeostasis including calcium signaling, ROS production and redox balance and in key metabolic pathways such as fatty acid and amino acid production (cataplerosis). ATP synthesis and cataplerosis are coordinated through the shared TCA cycle in the mitochondria. In order to avoid alteration of the mitochondrial integrity due to cataplerosis, influx of biosynthesis intermediates (anaplerosis) is therefore necessary to maintain the cycle function. Glutamine was identified as a main factor in this phenomenon [69]. Contrary to normal cells, glutamine becomes an important substrate for growth and proliferation in cancer cells [70-72]. Mitochondria are also central in the intrinsic pathway of apoptosis that lead to a programmed death of cells (Figure 5). In response to a variety of stimuli that lead to the permeabilization of the outer mitochondrial membrane, mitochondria release several soluble proteins from their intermembrane space to the cytosol that are able to activate cellular apoptotic programs directly [73]. These vital and lethal functions are intricately linked to some key components of the mitochondrial bioenergetic metabolism, such as cytochrome c, cardiolipin, ROS generation and membrane permeability. The mitochondrial cytochrome c is a soluble nuclear-encoded protein that functions as a single electron shuttle from the cytochrome c reductase (complex III) to the cytochrome c oxidase (complex IV) of the respiratory chain. However, when mitochondrial cytochrome c is released into the cytosol, it becomes a key participant in the formation of the apoptosome and the progression of apoptosis [74-75]. Under physiological conditions, most of the cytochrome c (85%) resides within the cristae, which are believed to form a barrier against its diffusion into the intermembrane space. In this context,


Mitochondria and cancer

125

Normal mitochondria

Mitochondrial pro-apoptotic pathways ↑ ROS

OPA1 ↑ ROS

Cardiolipin Peroxidized cardiolipin

↑ ROS

Pro-apoptotic Bcl-2 members Cytochrome c

Figure 5. Mitochondrial apoptosis. Top. Under physiological conditions, most of the cytochrome c (yellow circles) resides within cristae. Loosely bound to the outer surface of the inner mitochondrial membrane by cardiolipin (black triangles), cytochrome c participates in the transfer of electrons within the mitochondrial respiratory chain. OPA1 hetero-oligomer (blue semi circles) maintains normal tight cristae morphology, forming a barrier for cytochrome c diffusion towards the intermembrane space. In this context, permeabilization of the mitochondrial outer membrane would not release sufficient amount of cytochrome c to induce apoptotic cell death program. Bottom. Mitochondria are central in the intrinsic pathway of apoptosis that lead to the permeabilization of the outer mitochondrial membrane and the release of cytochrome c and several soluble others proapoptotic proteins from their intermembrane space to the cytosol. Early steps of this mitochondrial permeabilization are the peroxidation of cardiolipin (red triangles) and the remodeling of cristae. Oxidation of cardiolipin by reactive oxygen species (ROS) or by a tight association with cytochrome c initiates cytochrome c detachment from the inner membrane. Then, peroxidized cardiolipin relocated on the contact sites of mitochondrial membranes, where it serves as a docking platform for the oligomerization of the proapoptotic Bcl-2 proteins in the outer membrane (grey trapezoid). In this scheme, cardiolipin assists the formation of the mitochondrial apoptosis-induced channel and the release of cytochrome c. At the same time, the proteolytic disassembly of the OPA1 hetero-oligomer induces cristae remodeling, making the cytochrome c cristae pool releasable after its redistribution in the intermembrane space.


126

Jean-Franรงois Dumas et al.

the release of cytochrome c from mitochondria would require at least two steps during apoptosis. The first step involves an early remodeling of cristae configuration [76-77] which would lead to an enlargement of the narrow tubular cristae junction, making the cytochrome cristae pool releasable after its redistribution in the intermembrane space [77-78]. Studies indicate that cristae remodeling is associated with the disassembly of the Optic Atrophy 1 (OPA1) oligomer [79-80] and is required for a sufficient amount of cytochrome c to be released for the progression of the apoptotic cascade [81]. This hypothesis is further supported by evidence suggesting that a certain threshold of released cytochrome c must be reached to activate caspases and produce apoptosis [81-83]. In other words, the pool size of released cytochrome c does matter before cells commit themselves to die. Recently, it has been reported that formation of normal cristae morphology by OPA1 is a process regulated by prohibitins that form large multimeric ring complexes in the inner membrane and would play essential role in controlling proliferation and survival of normal and cancer cells [84-87]. The second step of cytochrome c release involves the movement through openings in the outer membrane of mitochondria. The early movement of the mobilized cytochrome c through the outer mitochondrial membrane to the cytosol is tightly regulated by Bcl-2 family proteins that control the oligomerization of pro-apoptotic members Bax/Bak in the outer mitochondrial membrane [88-90] and the formation of the mitochondrial apoptosis-induced channel [91-93]. The mitochondrial apoptosis-induced channel is a voltageindependent channel that forms a giant pore with diameters >3nm, big enough to allow the passage of 12.5 kDa cytochrome c [94]. Besides this route of cytochrome c release, the mitochondrial permeability transition pore (mPTP) is another channel that plays a central role in cell death following ischemic injury and necrosis, but would be secondarily involved in developmental and other forms of apoptosis [95-97]. With mPTP most likely assembled at the contact sites of mitochondrial inner and outer membranes, it opens a large pore with low ion selectivity that increases the permeability of the inner mitochondrial membrane for molecules up to 1.5 kDa [98-99]. Its sustained opening causes mitochondrial swelling and depolarization, leading to the rupture of outer mitochondrial membrane and subsequent release of intermembrane space proteins, including cytochrome c, into the cytosol [98-99]. The most notable activators of mPTP opening are the mitochondrial calcium, phosphate and ROS [95]. During the progression of apoptotic cell death processes, mPTP might participate in a deadly amplification loop that involves cytochrome c triggering calcium mobilization from the endoplasmic reticulum [100]. Hence, as apoptosis is initiated, the formation of mitochondrial apoptosis-induced channel releases cytochrome c that might


Mitochondria and cancer

127

bind to and relieve the calcium-dependent inactivation of inositol (1,4,5) triphosphate receptors, releasing calcium from the endoplasmic reticulum. Calcium would then activate the mPTP, leading to mitochondrial swelling, outer membrane disruption and more extensive cytochrome c release from intermembrane space [100]. Bcl-2 protein family members would also operate on the endoplasmic reticulum to regulate calcium release and also calcium-dependent signaling in apoptosis and cell survival [101]. Because a significant proportion of cytochrome c seems to be attached to the inner membrane phospholipid cardiolipin, its mobilization would first require its dissociation from cardiolipin [102]. Cardiolipin is a phospholipid essentially located in mitochondrial membranes where it interacts with a number of inner mitochondrial membrane proteins, including the electron transport chain complexes, cytochrome c and several mitochondrial substrate carriers [103-104]. Thus, cardiolipin is required for the optimal activity of mitochondrial energy metabolism and inner membrane integrity [105], and its alterations have been reported in a variety of pathological settings [103, 106]. However, its unique fatty acid composition (essentially restricted to highly unsaturated fatty acids), and its close association with mitochondria (a major source of ROS), makes mammalian cardiolipin a vulnerable target of ROS [104]. Oxidation of cardiolipin either by ROS directly or due to its proximity to cytochrome c may initiate cytochrome c detachment from the inner membrane [102, 107-108] and decrease the activities of mitochondrial respiratory chain complexes [109]. Cardiolipin peroxidation may also account for its redistribution on the contact sites of mitochondrial membranes, providing access for tBid, leading to subsequent destabilization of mitochondrial bioenergetics [110-112]. Thus, the peroxidation of cardiolipin is thought to play an important and active role in mitochondriadependent apoptosis by promoting the detachment of cytochrome c and by providing a docking site for tBid and Bax, initiating the formation of mitochondrial outer membrane pore [103]. Although controversial [113], it has been shown that cardiolipin deficiency may prevent cardiolipin peroxidation and increase resistance to apoptosis [114]. Oxidative stress and ROS produced by the mitochondria appear to participate in early and late steps of the regulation of apoptosis [115]. In mitochondria, the generation of free ROS depends critically on the mitochondrial membrane potential and the degree of reduction of the electron carriers (especially coenzyme Q) within the respiratory chain [116-117]. In this specific field, emerging evidence indicates that uncoupling protein-2 (UCP2) may trigger some of the mitochondrial adaptations seen in cancer cells and promote chemoresistance [118]. UCP2 belongs to the UCP subfamily of the mitochondrial transporter superfamily and increases


128

Jean-François Dumas et al.

mitochondrial inner membrane conductance to protons when activated by ROS in the presence of low amount of free fatty acids [119]. Hence, UCP2 would respond to mitochondrial production of ROS by increasing proton leakage across the inner membrane, which would lower membrane potential and decrease superoxide production from the electron transport chain. In this model, UCP2 would function as an antioxidant system, by decreasing mitochondrial ROS production and subsequent cell damages [120]. Interestingly, the cost of such mitochondrial mild uncoupling is a lower efficiency of oxidative phosphorylation, resulting in mitochondrial metabolic adaptation within cells [121]. Given that the expression of UCP2 has been found to be upregulated in various cancer cells [118], UCP2-induced proton leak might participate in the metabolic reprogramming that may favor a decrease in or a protection against ROS generation while sustaining ATP synthesis through the disproportionate dependence of glycolysis [35, 122-124]. Interestingly, a recent study reports that UCP2 expression is stimulated by glutamine, an amino acid highly oxidized by cancer cells [125].

1.3. Targeting mitochondria in tumor cells for cancer therapy Even if the role of mitochondrial energy metabolism remains debatable in tumor cells, the involvement of mitochondria in tumor cell proliferation exists. For instance, the downregulation of certain mitochondrial protein correlated with disease prognosis [31-32]. Therefore, mitochondria appear as a potential metabolic target for the treatment of cancer. Of course, it is essential to target pathways that appear to be different between tumor cells and healthy host cells, since the energy metabolism of tumors and the host cells also depend on the same essential pathways for ATP supply. Among the potential therapeutic targets in mitochondria that could prevent tumor growth was glutaminase or aminotransferases, enzymes that permit glutamine to fuel the TCA cycle to ensure anaplerosis. Indeed, blocking aminotransferases by aminooxyacetic acid prevents xenograft tumor growth of MDA-MB-231 breast cancer cells [126]. Fatty acid synthesis is another possible mitochondrial target for the treatment of cancer. Thus, inhibition of fatty acid oxidation by etomoxir is shown to induce cell death in human glioblastoma cells by impairing NADH production, increasing ROS production and depleting ATP [127]. Moreover, growth of human ovarian cancer cell in SCID mice was markedly reduced by inhibition of the fatty acid synthase activity [18]. Such “anti-cancer� effects have also been reported when ATP citrate lyase, choline kinase and acetyl-CoA carboxylase activities were suppressed [128-130]. Another possibility is to directly modify the oxidative phosphorylation in tumor cells. Thus, overexpression of mitochondrial


Mitochondria and cancer

129

frataxin reduced the growth and the tumorigenic capacity of cancer cells by induction of oxidative phosphorylation [131]. Dichloroacetate indirectly activates mitochondrial generation of ATP by inhibiting pyruvate dehydrogenase kinase and therefore activating pyruvate dehydrogenase [132]. Dichloroacetate has been shown to decrease in vivo and in vitro tumor cell growth [133-134]. Recently, it was found that inhibition of MCT-1 leads to the disruption of the metabolic symbiosis between hypoxic and oxygenated tumor cells by blocking the capacity of aerobic cells to use lactate for oxidative phosphorylation and forcing them to use glucose. Therefore hypoxic tumor cells are deprived of glucose and died. This retarded tumor growth and rendered the remaining cell (located at the vicinity of blood vessels) sensitive to radiotherapy [65]. Typical oxidative phosphorylation inhibitors, such as rotenone and oligomycin have already been tested. Oligomycin, at low doses (0.06-0.7 ÎźM) that do not affect normal cells, stopped the cell cycle progression in human promyelocytic leukeumia cells and in Jurkat T cells [135]. Similarly, rotenone (0.1-1 ÎźM) was also shown to inhibit cell proliferation in human lymphoma WP and 134 osteosarcoma [136]. Among therapeutic strategies, mitochondrial apoptosis is also a possible target (see review in [137]). To date, therapy targeting the intrinsic apoptosis pathway is one of the most exciting areas of cancer research. In this regard, mPTP is a target of choice. It was shown that lonidamine, a putative adenine nucleotide translocator (ANT, one of the component of mPTP) ligand, exerts a cytostatic effect on tumour growth in a Phase II clinical study [138]. In addition, clodronate that acts as competitive ANT inhibitor has recently been shown to improve the overall survival of patients with primary breast cancer [139]. To induce apoptosis in cancer cells, it is conceivable to therapeutically induce mitochondrial outer membrane permeabilization by targeting mitochondrial membranes, members from the Bcl-2 family and components of the permeability transition pore complex [140]. For instance, the in vivo antitumor activity of ABT-737 that acts as a BH3 mimetic, an essential initiator of programmed cell death, has been tested in several preclinical models of human malignancies. The involvement of mitochondrial ROS in tumorigenic pathways suggests that the inhibition of ROS production may be a good strategy for cancer therapy. Studies using antioxidants targeted specifically to mitochondria could represent a very interesting way. However, the use of antioxidant diet in human cancer prevention and during cancer treatment is controversial especially because of the possibility of tumor protection and reduced survival (see review in [141]). On the contrary, ROSpromoting treatments such as doxorubicin and vinblastine also represent a strategy for cancer therapy. Interestingly, it was reported that peroxidation of cardiolipin might be the primary event that triggers the release of cytochrome


130

Jean-Franรงois Dumas et al.

c from mitochondria in the apoptotic process [142]. Moreover, data have shown that sufficient cardiolipin content is required for apoptosis [143]. Collectively, these results suggest that modulation of cardiolipin metabolism could be another strategy for cancer therapy. The efficacy of the above-mentioned strategies for cancer therapy could be greatly increased if the agent specifically targets mitochondria from cancer cells. This can be done by using: (1) lipophilic cations such as triphenylphosphonium groups that are attracted to the negatively-charged mitochondrial matrix and can readily cross mitochondrial membranes [144], (2) mitochondria-penetrating peptides [145], (3) liposome-based carrier that delivers its macromolecular cargo to the mitochondrial interior via membrane fusion [146]. In conclusion, mitochondria represent a very promising target for cancer therapy. Moreover, in addition to specifically target mitochondria, the use of strategies targeting the anaerobic glycolysis such as hexokinase and oncogenes will greatly reinforce these anti-cancer therapies.

2. Mitochondrial bioenergetics in the host 2.1. Introduction Whereas the majority of studies are focussed on the role of mitochondria in cancer in order to find a way to eradicate cancer cells, there are only a few studies reporting data on mitochondrial bioenergetics in non-cancerous tissues in the host. Potential alterations of mitochondrial oxidative phosphorylation in the host tissues could also play a significant role in the alteration of the cancer patient nutritional status. The majority of cancer patients undergo cancer cachexia. Cancer cachexia is the consequence of whole body negative energy balance in cancer patients induced by reduced calorie intake (anorexia) and increased energy expenditure [147] with a systemic inflammation status (for review see [148-149]) (Figure 6). The disturbance of the energy balance induces reduction of body weight due to adipose tissue and skeletal muscle losses. Thus, cancer cachexia is a systemic multifactorial syndrome that provokes severe reduction in autonomy, quality of life and survival in patients [150]. In fact, cachectic cancer patients are less resistant to secondary effects induced by anticancer therapy [151-154]. Cancer cachexia is frequent in cancer patient (~ 60%) and is dependent upon tumour type. Indeed, the highest risk of cachexia


Mitochondria and cancer

131

Cancer cachexia Systemic Inflammation status

Anorexia Calorie intake

Hypermetabolism Energy expenditure

Adipose tissue and skeletal muscle loss

Ă” Autonomy, mobility, quality of life & survival

Figure 6. Cancer cachexia is characterized by a negative energy balance due to a reduction in energy intake (anorexia) and an increase in energy expenditure (hypermetabolism) and a systemic inflammation. This results in weight loss (adipose tissue and skeletal muscle) and then a reduction in quality of life and survival of patients.

is found in pancreatic, lung and digestive cancer patients [154-155]. Most patients with these types of cancer are already undernourished at the time of cancer diagnosis. This poor nutritional status is also responsible for 20% of death per se (immobility, cardiac and respiratory failure) in cancer patients [156]. Because cancer cachexia is due to negative energy balance (inadequate energy intake compared to energy expenditure), it is logical to implicate the significance of mitochondrial bioenergetics in cachexia. Indeed mitochondria through oxidative phosphorylation produce 90% of ATP as energy source in the cell [3]. Studies that investigated the role of mitochondria in cancer cachexia focussed on liver and skeletal muscle for different reasons: 1) Liver and skeletal muscle are rich in mitochondria and contribute largely to total energy expenditure (> 50%) [3]. 2) Liver is the controller of whole body metabolism, and is specifically implicated in cancer cachexia since liver recycle lactate produced by the tumor cells (Cori cycle; an energy-consuming process) and produce acute-phase protein in cancer patient [149]. 3) Skeletal muscle is the principal target for cancer cachexia since muscle loss is an important factor affecting cancer patient survival. Muscle loss


132

Jean-Franรงois Dumas et al.

is due to an upregulation of proteolytic systems, mainly the ubiquitinproteasome-dependent proteolysis (ATP requiring process) [157] with a parallel downregulation of muscle anabolism [149, 158]. In this way, muscle cells of cachectic cancer patients have to deal with high energy need for protein breakdown in cancer cachexia. Meeting such a high ATP demand would be a challenging feat for the skeletal mitochondrial oxidative phosphorylation. Most of the data investigating mitochondrial bioenergetics in cancer cachexia were performed in vitro and in preclinical models, with only few studies in human. This is due to the difficult access to biological material in human as skeletal muscle and liver biopsies.

2.2. Bioenergetics of liver mitochondria in cancer cachexia Pioneer studies investigating hepatic mitochondria bioenergetics in preclinical model of cancer cachexia have reported no alterations in the yield of oxidative phosphorylation [159-161]. They found neither a difference in P/O ratio nor in respiratory control ratio in liver mitochondria from solid transplanted-tumour bearing rat (TBR). However, more recent studies have clearly demonstrated a reduction in ATP concentration in liver of TBR compared to control [162-163]. Interestingly, these alterations were reversed by resectioning of the tumour. At the same time, Makino and colleagues. have shown an alteration of mitochondrial ultrastructure in a preclinical model of cancer cachexia [164]. Liver mitochondria from TBR were significantly enlarged in comparison to control rats. In 2005, it was found that liver mitochondria from mammary carcinoma bearing rat presented a two times lower cytochrome c oxidase (complex IV) activity compared to control [165]. In this study, the authors have clearly reversed this alteration by energy modulating vitamin supplementation (niacine, riboflavin and coenzyme Q10). Since this benefit is related to the fact that these vitamins are cofactors for electron transport chain function and Krebs cycle, Perumal and colleagues. have demonstrated for the first time a specific alteration in electron transport chain in preclinical model of cancer cachexia. Since all these data are in favour of oxidative phosphorylation alteration in cancer cachexia, our group have recently performed a convincing study demonstrating liver mitochondrial oxidative phosphorylation dysfunction in a preclinical model of cancer cachexia [166]. Indeed, we have shown an important reduction in ATP synthesis efficiency as the relationship between oxygen consumption and ATP synthesis was shifted to the right in TBR compared to control rats (Figure 7). Thus, for the same ATP synthesis rate,


Mitochondria and cancer

133

Figure 7. Effect of cancer cachexia on oxidative phosphorylation in liver mitochondria. Relationship between the ATP synthesis rate and the oxygen consumption rate. Measurements were conducted in isolated liver mitochondria from peritoneal carcinosis rats suffering of cachexia (PC) and controls rats (control).

liver mitochondria from TBR would need to consume more oxygen (~ + 25%) in comparison to control. This reduction in the ATP synthesis efficiency was neither linked to a reduction in ATP synthase activity, nor to ANT protein content. By analyzing the relationship between oxygen consumption and membrane potential in non-phosphorylating state, we determined that the reduction in ATP synthesis yield was associated to an increase in energy wasting in liver mitochondria from TBR (Figure 8). Actually, the curve of TBR was shifted to the left in comparison to control, meaning that for the same membrane potential (> 155mV), oxygen consumption is higher in liver mitochondria from TBR (+22%) than in control. Therefore our data demonstrated that energy wasting was increased in liver mitochondria of TBR, contributing to a reduction in membrane potential and in turn to the decrease in efficiency of oxidative phosphorylation [166]. This energy wasting process was not linked to UCP activity since GDP (inhibitor of UCP) was not able to restore ATP synthesis yield. Interestingly, we found that this increase in energy wasting was associated to modifications in phospholipids membrane composition. Our data highlight the implication of cardiolipin in this process since liver mitochondria from TBR have a 55% increase in cardiolipin content in comparison to control. Besides, the involvement of


134

Jean-Franรงois Dumas et al.

Figure 8. Effect of cancer cachexia on energy wasting in liver mitochondria. Relationship between the oxygen consumption rate and the membrane potential under non-phosphorylating condition. Measurements were conducted in isolated liver mitochondria from peritoneal carcinosis rats suffering of cachexia (PC) and controls rats (control).

cardiolipin in energy wasting was accentuated by the positive correlation between energy wasting and cardiolipin content in liver mitochondria (Figure 9). Moreover, the fatty acid composition of cardiolipin is a significant factor influencing energy wasting in TBR liver mitochondria. For example, we found that the docosahexaenoic acid content in cardiolipin was significantly and positively correlated to energy wasting (R2= 0.23), whereas palmitoleic acid content in cardiolipin was negatively correlated (R2= 0.4). Most importantly, in this preclinical model of cancer cachexia, liver mass is maintained in TBR compared to the drastic reduction in liver mass observed in response to caloric restriction alone [167] in agreement with a study involving patients with advanced colorectal cancer [168]. Thus, it can be expected that energy wasting at mitochondrial level can significantly increase liver energy consumption leading to whole body hypermetabolism commonly associated to cancer cachexia (Figure 10). All these studies clearly prove that liver mitochondrial oxidative phosphorylation is altered in preclinical model of cancer cachexia. There is no clinical study reporting data on liver mitochondria in cachectic cancer


Mitochondria and cancer

135 R2 = 0.4 P< 0.01

-1

-1

O2 consumption (natoms O . min . mg prot )

40

35

30

25 100

150

200

250

300 -1

Cardiolipin conte nt (a.u. . mg prot )

Figure 9. Relationship between the energy wasting-related oxygen consumption rate and the cardiolipin content in isolated liver mitochondria from peritoneal carcinosis rats suffering of cachexia and controls rats. Inflammatory cytokines from tumour and host

Liver mitochondria

cardiolipin Oxidative phosphorylation system in inner membrane

ATP

ATP/O

Energy wasting

Anorexia Calorie intake

Hypermetabolism Energy expenditure

Adipose tissue and skeletal muscle loss

Figure 10. Implication of the energy metabolism alteration observed in liver mitochondria in the cancer cachexia syndrome.


136

Jean-Franรงois Dumas et al.

patient. The implication of liver mitochondrial energy wasting in hypermetabolism during cancer cachexia needs to be further investigated in animal models and human.

2.3. Bioenergetics of skeletal muscle mitochondria in cancer cachexia The first indirect data supporting alteration in oxidative phosphorylation in skeletal muscle mitochondria were reported in 1998 [169]. The authors have shown a significant increase in UCP2 and 3 gene expressions in TBR muscle compared to control. Since then numerous studies confirmed the skeletal muscle overexpression of UCP2 and 3 in tumour bearing animals [170-173] and cancer patients [174]. Some authors have shown that addition of zinc-alpha2-glycoprotein in culture medium of C2C12 myotube induces a dose-dependent increase in UCP2 and 3 [175]. However the implication of UCP2 and 3 in mitochondrial uncoupling is a subject of intense debate [176], and therefore the precise role of skeletal muscle UCPs in cancer cachexia needs to be elucidated. The study of Ushmorov and colleagues. was the first to investigate mitochondrial oxidative phosphorylation in skeletal muscle using a mouse model of cancer cachexia [177]. They have shown that muscle mitochondria from TB mice (TBM) displayed a 25% reduction in oxygen consumption with succinate as substrate. This study highlights a reduction in cytochrome c oxidase (complex IV) activity in skeletal muscle of TBM. Furthermore these alterations were correlated to plasma cystine/thiol ratio, essential for antioxidant defences. The authors were able to reverse the mitochondrial reduction of complex IV activity by a supplementation in cysteine and ornithine [177]. By this way, this study demonstrated that oxidative stress is implicated in skeletal muscle alteration of oxidative phosphorylation in cachectic mice. Recently, White and colleagues. confirmed the specific alteration of complex IV in skeletal muscle of TBM by showing a significant reduction in complex IV protein expression [178]. Our group confirmed reduction in complex IV activity without modification in protein expression in skeletal muscle mitochondria from TBR (unpublished data). An in vivo study, using 31P NMR in mice implanted with Lewis lung carcinomas, demonstrated a significant reduction in hindlimb skeletal muscle ATP synthesis rate [173]. However, these data are not in agreement with human study. Actually, a human study using 31P and 1H magnetic resonance spectroscopy on Vastus lateralis muscle in healthy volunteers and cancerrelated cachectic patients demonstrated that the loss of skeletal muscle was not associated with an alteration in high-energy phosphates [179]. Interestingly,


Mitochondria and cancer

137

these authors found an increase in intramuscular lipid (+35%) in cancer patients experiencing weight loss. This finding is consistent with the increase in lipid droplet associated to mitochondria in rectus abdominis muscle from cancer patients [180]. In non pathologic situation, lipid droplets associated with muscle mitochondria are expected to provide substrate for energy production. Alteration in the lipid droplet/mitochondria physical interaction and increase in lipid droplets number are associated with mitochondrial dysfunction during ageing [181]. Stephens and colleagues. reinforced the possible implication of lipid droplet and mitochondria interaction in cancer cachexia by reporting a significant positive correlation between lipid droplet number and weight loss in muscle from cancer patient [180]. The authors concluded that the next step must be to study mitochondrial oxidative phosphorylation. It is clear that data on muscle mitochondrial bioenergetics are lacking and need more in vitro and in vivo studies, in human and animal model, to clearly establish the nature of the alteration in skeletal muscle during cancer cachexia.

2.4. Mechanisms implicated and possible strategies to fight cancer cachexia through mitochondrial bioenergetics Mechanisms by which cancer cachexia can affect liver and muscle mitochondrial oxidative phosphorylation are largely unknown. Because host inflammation response to cancer is a major trend of cancer cachexia, proinflammatory cytokines could be the effectors of mitochondrial bioenergetics alterations. Tumour Necrosis Factor-alpha (TNF-α) is one of the major cytokine implicated in cancer cachexia. Pioneer in vitro study demonstrated that addition of TNF-α in culture medium of different cell lines induce inhibition of cell mitochondrial chain electron transfer. The authors suggested that these bioenergetics alterations may be involved in the cytotoxic effect of TNF-α [182]. Furthermore, a TNF mitochondrial-binding protein has been identified [183]. More recently the group of Argiles investigated effects of TNF-α on oxygen consumption of isolated liver mitochondria. They found a significant reduction in respiratory control ratio with TNF-α concentration from 10-6 U/µL to 10-1 U/µL [184]. However, even if TNF-α can affect oxidative phosphorylation in vitro, the role of the cytokine in liver and skeletal muscle mitochondrial disorders in cancer cachexia is not yet demonstrated. Oxidative stress is another pathway to induce mitochondrial bioenergetics alterations in cancer cachexia. Several studies reported an increase in oxidative stress markers in TB animals [177, 185-187] and in cachectic cancer patients [188-190]. At the mitochondrial scale, it was shown


138

Jean-François Dumas et al.

that mitochondrial proteins invovled in oxidative phosphorylation processes were damaged by oxidative stress in TBR skeletal muscle [191]. Indeed, reactive carbonyl group and malondialdehyde adduct of ATP synthase were significantly increased in gastrocnemius muscle of TBR without modification of Mn-superoxide dismutase as antioxidant defence. In those conditions, oxidized mitochondrial membrane lipids (such as cardiolipin) participate in oxidative phosphorylation alterations. Therefore, antioxidant supplementation can be a way to limit oxidative damages in muscle cells. Alpha-tocopherol was shown to slow down muscle loss in TBR [187], but there is no data on antioxidant effects on mitochondrial function in cancer cachexia. Clinical studies on the efficacy of antioxidant therapy in cancer cachexia treatment are encouraging. We can hypothesize that among the large spectrum of targets, antioxidant strategies would certainly improve mitochondrial oxidative stress status in cachectic patients. Activin-β proteins are closely related to the TGF-beta family and play an important role in physiological functions such as body weight composition, and energy metabolism. It was shown that mice lacking inhibin-α (activin antagonist) experience cachexia-like syndrome [192]. Mice having the mutated version of activin (InhbaBK/-) have a higher whole body metabolic rate associated to an increase in liver mitochondrial oxygen consumption in non-phosphorylating state, i.e. an increase in energy wasting [193]. The link between mitochondria bioenergetics alterations and activin signalling is not totally elucidated. PPARγ coactivator-1 (PGC-1) is another master regulator of oxidative metabolism inducing gene expression linked to mitochondrial electron transport chain. It has been demonstrated that cytokines such as TNF-α, can activate the transcriptional PGC-1 in C2C12 myotubes. This higher expression of PGC-1 was associated to an increase in total oxygen consumption of cells linked to overexpression of mitochondrial genes such as ATP synthase β subunit and COX-II [194]. These data highlighted PGC-1 as a molecular target linking cytokines and stimulation of oxidative metabolism via mitochondria. Finally, metabolism of cardiolipin is a key element for mitochondrial oxidative phosphorylation [195]. Our findings highlighted the significance of cardiolipin (quantity and fatty acids composition) in liver mitochondrial energy wasting in preclinical model of cancer cachexia [166]. Thus, pharmacological modulation of cardiolipin metabolism could be an interesting way to limit energy wasting in liver of TBR. This will need further in vitro and in vivo investigation in animal models of cachexia and in cancer patients. Nutritional intervention can also modulate cardiolipin composition [196]. As we have identified that cardiolipin fatty acids composition is associated with reduction in energy wasting in liver


Mitochondria and cancer

139

mitochondria, we can expect that specific fatty acid supplementation of TBR will prevent energy wasting and slow down cancer cachexia. This nutritional approach will need large scale investigation of cardiolipin metabolism that is still not completely established. In conclusion, there are clearly experimental and clinical data proving mitochondrial oxidative phosphorylation alteration in liver and skeletal muscle in cancer cachexia. However, to have a clear scheme of mitochondrial bioenergetics dysfunction in cancer cachexia, large scale human studies, both in vitro and in vivo (preclinical models) settings, need to be conducted.

Conclusion All cancer cell types show a high glycolytic flux but not all have decreased mitochondrial oxidative phosphorylation. Therefore, in contrast to the theory of Warburg, not all tumor cells depend on glycolysis for ATP supply and the increased glycolysis is not the consequence of an altered mitochondrial oxidative phosphorylation in all cancer cells. However, mitochondria remain at the center of essential physiological processes in cancer cells and play an essential role in tumorigenesis. On the other hand, in addition to the role played in cancer cells, many studies suggest that mitochondria from normal cells in the cancerous host could also play an important role in cancer cachexia. Because of the presence of this metabolic syndrome, cancer patients are less resistant to anticancer therapy. Whatever the nature of cells in patients (cancer or normal) it appears essential to study the mitochondrial energy metabolism since mitochondria represent a very interesting tool for cancer therapy.

References 1.

Watt I.N., Montgomery M.G., Runswick M.J., Leslie A.G., Walker J.E. 2010, Proc Natl Acad Sci USA 107: 16823-7. 2. Brand M.D. 2005, Biochem Soc Trans 33: 897-904. 3. Rolfe D.F., Brown G.C. 1997, Physiol Rev 77: 731-58. 4. Harper M.E., Green K., Brand M.D. 2008, Annu Rev Nutr 28: 13-33. 5. Brookes P.S. 2005, Free Radic Biol Med 38: 12-23. 6. Hudman D., Rainbow R.D., Lawrence C.L., Standen N.B. 2002, J Mol Cell Cardiol 34: 859-71. 7. DeBerardinis R.J., Lum J.J., Hatzivassiliou G., Thompson C.B. 2008, Cell Metab 7: 11-20. 8. Warburg O. 1956, Science 124: 269-70. 9. Warburg O. 1956, Science 123: 309-14. 10. Walenta S., Snyder S., Haroon Z.A., Braun R.D., Amin K., Brizel D., Mueller-Klieser W., Chance B., Dewhirst M.W. 2001, Int J Radiat Oncol Biol Phys 51: 840-8.


140

11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39.

Jean-Franรงois Dumas et al.

Vaupel P. 1996, Experientia 52: 464-8. Kroemer G., Pouyssegur J. 2008, Cancer Cell 13: 472-82. Guppy M., Greiner E., Brand K. 1993, Eur J Biochem 212: 95-9. Pfeiffer T., Schuster S., Bonhoeffer S. 2001, Science 292: 504-7. Pouyssegur J., Dayan F., Mazure N.M. 2006, Nature 441: 437-43. Swietach P., Vaughan-Jones R.D., Harris A.L. 2007, Cancer Metastasis Rev 26: 299-310. Kuhajda F.P. 2000, Nutrition 16: 202-8. Wang H.Q., Altomare D.A., Skele K.L., Poulikakos P.I., Kuhajda F.P., Di Cristofano A., Testa J.R. 2005, Oncogene 24: 3574-82. Gatenby R.A., Gillies R.J. 2004, Nat Rev Cancer 4: 891-9. Santamaria G., Martinez-Diez M., Fabregat I., Cuezva J.M. 2006, Carcinogenesis 27: 925-35. Jo S.H., Son M.K., Koh H.J., Lee S.M., Song I.H., Kim Y.O., Lee Y.S., Jeong K.S., Kim W.B., Park J.W., Song B.J., Huh T.L. 2001, J Biol Chem 276: 16168-76. Kil I.S., Chung K.H., Park J.W. 2010, Free Radic Res 44: 332-9. Kil I.S., Kim S.Y., Lee S.J., Park J.W. 2007, Free Radic Biol Med 43: 1197-207. Polyak K., Li Y., Zhu H., Lengauer C., Willson J.K., Markowitz S.D., Trush M.A., Kinzler K.W., Vogelstein B. 1998, Nat Genet 20: 291-3. Boitier E., Merad-Boudia M., Guguen-Guillouzo C., Defer N., Ceballos-Picot I., Leroux J.P., Marsac C. 1995, Cancer Res 55: 3028-35. Stocco D.M., Hutson J.C. 1980, Cancer Res 40: 1486-92. Senior A.E., McGowan S.E., Hilf R. 1975, Cancer Res 35: 2061-7. Irwin C.C., Malkin L.I., Morris H.P. 1978, Cancer Res 38: 1584-8. Simonnet H., Alazard N., Pfeiffer K., Gallou C., Beroud C., Demont J., Bouvier R., Schagger H., Godinot C. 2002, Carcinogenesis 23: 759-68. Springer E.L. 1980, Cancer Res 40: 803-17. Cuezva J.M., Krajewska M., de Heredia M.L., Krajewski S., Santamaria G., Kim H., Zapata J.M., Marusawa H., Chamorro M., Reed J.C. 2002, Cancer Res 62: 6674-81. Isidoro A., Casado E., Redondo A., Acebo P., Espinosa E., Alonso A.M., Cejas P., Hardisson D., Fresno Vara J.A., Belda-Iniesta C., Gonzalez-Baron M., Cuezva J.M. 2005, Carcinogenesis 26: 2095-104. Wu M., Neilson A., Swift A.L., Moran R., Tamagnine J., Parslow D., Armistead S., Lemire K., Orrell J., Teich J., Chomicz S., Ferrick D.A. 2007, Am J Physiol Cell Physiol 292: C125-36. Zu X.L., Guppy M. 2004, Biochem Biophys Res Commun 313: 459-65. Moreno-Sanchez R., Rodriguez-Enriquez S., Marin-Hernandez A., Saavedra E. 2007, FEBS J 274: 1393-418. Elstrom R.L., Bauer D.E., Buzzai M., Karnauskas R., Harris M.H., Plas D.R., Zhuang H., Cinalli R.M., Alavi A., Rudin C.M., Thompson C.B. 2004, Cancer Res 64: 3892-9. Dang C.V., Semenza G.L. 1999, Trends Biochem Sci 24: 68-72. Almeida A., Moncada S., Bolanos J.P. 2004, Nat Cell Biol 6: 45-51. Ramanathan A., Wang C., Schreiber S.L. 2005, Proc Natl Acad Sci U S A 102: 5992-7.


Mitochondria and cancer

40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68.

141

Plas D.R., Rathmell J.C., Thompson C.B. 2002, Nat Immunol 3: 515-21. Wieman H.L., Wofford J.A., Rathmell J.C. 2007, Mol Biol Cell 18: 1437-46. Edinger A.L., Thompson C.B. 2002, Mol Biol Cell 13: 2276-88. Weinberg F., Chandel N.S. 2009, Ann N Y Acad Sci 1177: 66-73. Semenza G.L., Jiang B.H., Leung S.W., Passantino R., Concordet J.P., Maire P., Giallongo A. 1996, J Biol Chem 271: 32529-37. Papandreou I., Cairns R.A., Fontana L., Lim A.L., Denko N.C. 2006, Cell Metab 3: 187-97. Kim J.W., Tchernyshyov I., Semenza G.L., Dang C.V. 2006, Cell Metab 3: 177-85. Bensaad K., Tsuruta A., Selak M.A., Vidal M.N., Nakano K., Bartrons R., Gottlieb E., Vousden K.H. 2006, Cell 126: 107-20. Schwartzenberg-Bar-Yoseph F., Armoni M., Karnieli E. 2004, Cancer Res 64: 2627-33. Matoba S., Kang J.G., Patino W.D., Wragg A., Boehm M., Gavrilova O., Hurley P.J., Bunz F., Hwang P.M. 2006, Science 312: 1650-3. Levine A.J., Oren M. 2009, Nat Rev Cancer 9: 749-58. Brunelle J.K., Bell E.L., Quesada N.M., Vercauteren K., Tiranti V., Zeviani M., Scarpulla R.C., Chandel N.S. 2005, Cell Metab 1: 409-14. Guzy R.D., Hoyos B., Robin E., Chen H., Liu L., Mansfield K.D., Simon M.C., Hammerling U., Schumacker P.T. 2005, Cell Metab 1: 401-8. Chandel N.S., Maltepe E., Goldwasser E., Mathieu C.E., Simon M.C., Schumacker P.T. 1998, Proc Natl Acad Sci U S A 95: 11715-20. Gao P., Zhang H., Dinavahi R., Li F., Xiang Y., Raman V., Bhujwalla Z.M., Felsher D.W., Cheng L., Pevsner J., Lee L.A., Semenza G.L., Dang C.V. 2007, Cancer Cell 12: 230-8. Crabtree H.G. 1929, Biochem J 23: 536-45. Gauthier T., Denis-Pouxviel C., Murat J.C. 1990, Int J Biochem 22: 411-7. Gabai V.L. 1992, FEBS Lett 313: 126-8. Sener A., Blachier F., Malaisse W.J. 1988, J Biol Chem 263: 1904-9. Sussman I., Erecinska M., Wilson D.F. 1980, Biochim Biophys Acta 591: 209-23 Sauer L.A. 1977, J Cell Physiol 93: 313-6. Rossignol R., Gilkerson R., Aggeler R., Yamagata K., Remington S.J., Capaldi R.A. 2004, Cancer Res 64: 985-93. Smolkova K., Bellance N., Scandurra F., Genot E., Gnaiger E., Plecita-Hlavata L., Jezek P., Rossignol R. 2010, J Bioenerg Biomembr 42: 55-67. Gottlieb E., Tomlinson I.P. 2005, Nat Rev Cancer 5: 857-66. Bellance N., Lestienne P., Rossignol R. 2009, Front Biosci 14: 4015-34. Sonveaux P., Vegran F., Schroeder T., Wergin M.C., Verrax J., Rabbani Z.N., De Saedeleer C.J., Kennedy K.M., Diepart C., Jordan B.F., Kelley M.J., Gallez B., Wahl M.L., Feron O., Dewhirst M.W. 2008, J Clin Invest 118: 3930-42. Halestrap A.P., Price N.T. 1999, Biochem J 343 Pt 2: 281-99. Koukourakis M.I., Giatromanolaki A., Harris A.L., Sivridis E. 2006, Cancer Res 66: 632-7. Froberg M.K., Gerhart D.Z., Enerson B.E., Manivel C., Guzman-Paz M., Seacotte N., Drewes L.R. 2001, Neuroreport 12: 761-5.


142

69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93.

Jean-Franรงois Dumas et al.

Kovacevic Z., McGivan J.D. 1983, Physiol Rev 63: 547-605. Bustamante E., Pedersen P.L. 1977, Proc Natl Acad Sci U S A 74: 3735-9. Sheid B., Morris H.P., Roth J.S. 1965, J Biol Chem 240: 3016-22. Reitzer L.J., Wice B.M., Kennell D. 1979, J Biol Chem 254: 2669-76. Saelens X., Festjens N., Vande Walle L., van Gurp M., van Loo G., Vandenabeele P. 2004, Oncogene 23: 2861-74. Wang X. 2001, Genes Dev 15: 2922-33. Ow Y.P., Green D.R., Hao Z., Mak T.W. 2008, Nat Rev Mol Cell Biol 9: 532-42. Sun M.G., Williams J., Munoz-Pinedo C., Perkins G.A., Brown J.M., Ellisman M.H., Green D.R., Frey T.G. 2007, Nat Cell Biol 9: 1057-65. Scorrano L., Ashiya M., Buttle K., Weiler S., Oakes S.A., Mannella C.A., Korsmeyer S.J. 2002, Dev Cell 2: 55-67. Germain M., Mathai J.P., McBride H.M., Shore G.C. 2005, EMBO J 24: 1546-56. Frezza C., Cipolat S., Martins de Brito O., Micaroni M., Beznoussenko G.V., Rudka T., Bartoli D., Polishuck R.S., Danial N.N., De Strooper B., Scorrano L. 2006, Cell 126: 177-89. Cipolat S., Rudka T., Hartmann D., Costa V., Serneels L., Craessaerts K., Metzger K., Frezza C., Annaert W., D'Adamio L., Derks C., Dejaegere T., Pellegrini L., D'Hooge R., Scorrano L., De Strooper B. 2006, Cell 126: 163-75. Yamaguchi R., Lartigue L., Perkins G., Scott R.T., Dixit A., Kushnareva Y., Kuwana T., Ellisman M.H., Newmeyer D.D. 2008, Mol Cell 31: 557-69. Clayton R., Clark J.B., Sharpe M. 2005, J Neurochem 92: 840-9. Khodjakov A., Rieder C., Mannella C.A., Kinnally K.W. 2004, Mitochondrion 3: 217-27. Sato S., Murata A., Orihara T., Shirakawa T., Suenaga K., Kigoshi H., Uesugi M. 2011, Chem Biol 18: 131-9. Sievers C., Billig G., Gottschalk K., Rudel T. 2010, PLoS One 5: e12735. Osman C., Merkwirth C., Langer T. 2009, J Cell Sci 122: 3823-30. Merkwirth C., Dargazanli S., Tatsuta T., Geimer S., Lower B., Wunderlich F.T., von Kleist-Retzow J.C., Waisman A., Westermann B., Langer T. 2008, Genes Dev 22: 476-88. Kluck R.M., Bossy-Wetzel E., Green D.R., Newmeyer D.D. 1997, Science 275: 1132-6. Yang J., Liu X., Bhalla K., Kim C.N., Ibrado A.M., Cai J., Peng T.I., Jones D.P., Wang X. 1997, Science 275: 1129-32. Wei M.C., Zong W.X., Cheng E.H., Lindsten T., Panoutsakopoulou V., Ross A.J., Roth K.A., MacGregor G.R., Thompson C.B., Korsmeyer S.J. 2001, Science 292: 727-30. Martinez-Caballero S., Dejean L.M., Kinnally M.S., Oh K.J., Mannella C.A., Kinnally K.W. 2009, J Biol Chem 284: 12235-45. Dejean L.M., Martinez-Caballero S., Guo L., Hughes C., Teijido O., Ducret T., Ichas F., Korsmeyer S.J., Antonsson B., Jonas E.A., Kinnally K.W. 2005, Mol Biol Cell 16: 2424-32. Pavlov E.V., Priault M., Pietkiewicz D., Cheng E.H., Antonsson B., Manon S., Korsmeyer S.J., Mannella C.A., Kinnally K.W. 2001, J Cell Biol 155: 725-31.


Mitochondria and cancer

143

94. Dejean L.M., Ryu S.Y., Martinez-Caballero S., Teijido O., Peixoto P.M., Kinnally K.W. 2010, Biochim Biophys Acta 1797: 1231-8. 95. Halestrap A.P. 2009, J Mol Cell Cardiol 46: 821-31. 96. Baines C.P., Kaiser R.A., Purcell N.H., Blair N.S., Osinska H., Hambleton M.A., Brunskill E.W., Sayen M.R., Gottlieb R.A., Dorn G.W., Robbins J., Molkentin J.D. 2005, Nature 434: 658-62. 97. Nakagawa T., Shimizu S., Watanabe T., Yamaguchi O., Otsu K., Yamagata H., Inohara H., Kubo T., Tsujimoto Y. 2005, Nature 434: 652-8. 98. Kinnally K.W., Peixoto P.M., Ryu S.Y., Dejean L.M. 2011, Biochim Biophys Acta 1813: 616-22. 99. Kroemer G., Galluzzi L., Brenner C. 2007, Physiol Rev 87: 99-163. 100. Boehning D., Patterson R.L., Sedaghat L., Glebova N.O., Kurosaki T., Snyder S.H. 2003, Nat Cell Biol 5: 1051-61. 101. Rong Y., Distelhorst C.W. 2008, Annu Rev Physiol 70: 73-91. 102. Ott M., Robertson J.D., Gogvadze V., Zhivotovsky B., Orrenius S. 2002, Proc Natl Acad Sci U S A 99: 1259-63. 103. Gonzalvez F., Gottlieb E. 2007, Apoptosis 12: 877-85. 104. Schlame M., Rua D., Greenberg M.L. 2000, Prog Lipid Res 39: 257-88. 105. Jiang F., Ryan M.T., Schlame M., Zhao M., Gu Z., Klingenberg M., Pfanner N., Greenberg M.L. 2000, J Biol Chem 275: 22387-94. 106. Chicco A.J., Sparagna G.C. 2007, Am J Physiol Cell Physiol 292: C33-44. 107. Kagan V.E., Tyurin V.A., Jiang J., Tyurina Y.Y., Ritov V.B., Amoscato A.A., Osipov A.N., Belikova N.A., Kapralov A.A., Kini V., Vlasova, II, Zhao Q., Zou M., Di P., Svistunenko D.A., Kurnikov I.V., Borisenko G.G. 2005, Nat Chem Biol 1: 223-32. 108. Petrosillo G., Ruggiero F.M., Pistolese M., Paradies G. 2001, FEBS Lett 509: 435-8. 109. Paradies G., Petrosillo G., Paradies V., Ruggiero F.M. 2009, Cell Calcium 45: 643-50. 110. Gonzalvez F., Pariselli F., Dupaigne P., Budihardjo I., Lutter M., Antonsson B., Diolez P., Manon S., Martinou J.C., Goubern M., Wang X., Bernard S., Petit P.X. 2005, Cell Death Differ 12: 614-26. 111. Garcia Fernandez M., Troiano L., Moretti L., Nasi M., Pinti M., Salvioli S., Dobrucki J., Cossarizza A. 2002, Cell Growth Differ 13: 449-55. 112. Lutter M., Fang M., Luo X., Nishijima M., Xie X., Wang X. 2000, Nat Cell Biol 2: 754-61. 113. Choi S.Y., Gonzalvez F., Jenkins G.M., Slomianny C., Chretien D., Arnoult D., Petit P.X., Frohman M.A. 2007, Cell Death Differ 14: 597-606. 114. Huang Z., Jiang J., Tyurin V.A., Zhao Q., Mnuskin A., Ren J., Belikova N.A., Feng W., Kurnikov I.V., Kagan V.E. 2008, Free Radic Biol Med 44: 1935-44. 115. Fleury C., Mignotte B., Vayssiere J.L. 2002, Biochimie 84: 131-41. 116. Korshunov S.S., Skulachev V.P., Starkov A.A. 1997, FEBS Lett 416: 15-8. 117. Boveris A., Chance B. 1973, Biochem J 134: 707-16. 118. Baffy G. 2010, Mitochondrion 10: 243-52. 119. Echtay K.S., Roussel D., St-Pierre J., Jekabsons M.B., Cadenas S., Stuart J.A., Harper J.A., Roebuck S.J., Morrison A., Pickering S., Clapham J.C., Brand M.D. 2002, Nature 415: 96-9.


144

Jean-Franรงois Dumas et al.

120. Brand M.D., Affourtit C., Esteves T.C., Green K., Lambert A.J., Miwa S., Pakay J.L., Parker N. 2004, Free Radic Biol Med 37: 755-67. 121. Desquiret V., Loiseau D., Jacques C., Douay O., Malthiery Y., Ritz P., Roussel D. 2006, Biochim Biophys Acta 1757: 21-30. 122. Lemarie A., Grimm S. 2011, Oncogene. 123. Baffy G., Derdak Z., Robson S.C. 2011, Br J Cancer 105: 469-74. 124. Hervouet E., Simonnet H., Godinot C. 2007, Biochimie 89: 1080-8. 125. Hurtaud C., Gelly C., Chen Z., Levi-Meyrueis C., Bouillaud F. 2007, Cell Mol Life Sci 64: 1853-60. 126. Thornburg J.M., Nelson K.K., Clem B.F., Lane A.N., Arumugam S., Simmons A., Eaton J.W., Telang S., Chesney J. 2008, Breast Cancer Res 10: R84. 127. Pike L.S., Smift A.L., Croteau N.J., Ferrick D.A., Wu M. 2011, Biochim Biophys Acta 1807: 726-34. 128. Hatzivassiliou G., Zhao F., Bauer D.E., Andreadis C., Shaw A.N., Dhanak D., Hingorani S.R., Tuveson D.A., Thompson C.B. 2005, Cancer Cell 8: 311-21. 129. Al-Saffar N.M., Troy H., Ramirez de Molina A., Jackson L.E., Madhu B., Griffiths J.R., Leach M.O., Workman P., Lacal J.C., Judson I.R., Chung Y.L. 2006, Cancer Res 66: 427-34. 130. Beckers A., Organe S., Timmermans L., Scheys K., Peeters A., Brusselmans K., Verhoeven G., Swinnen J.V. 2007, Cancer Res 67: 8180-7. 131. Schulz T.J., Thierbach R., Voigt A., Drewes G., Mietzner B., Steinberg P., Pfeiffer A.F., Ristow M. 2006, J Biol Chem 281: 977-81. 132. Gudi R., Bowker-Kinley M.M., Kedishvili N.Y., Zhao Y., Popov K.M. 1995, J Biol Chem 270: 28989-94. 133. Sun R.C., Fadia M., Dahlstrom J.E., Parish C.R., Board P.G., Blackburn A.C. 2010, Breast Cancer Res Treat 120: 253-60. 134. Chen Y., Cairns R., Papandreou I., Koong A., Denko N.C. 2009, PLoS One 4: e7033. 135. Sweet S., Singh G. 1995, Cancer Res 55: 5164-7. 136. Armstrong J.S., Hornung B., Lecane P., Jones D.P., Knox S.J. 2001, Biochem Biophys Res Commun 289: 973-8. 137. Fulda S., Galluzzi L., Kroemer G. 2010, Nat Rev Drug Discov 9: 447-64. 138. Oudard S., Carpentier A., Banu E., Fauchon F., Celerier D., Poupon M.F., Dutrillaux B., Andrieu J.M., Delattre J.Y. 2003, J Neurooncol 63: 81-6. 139. Diel I.J., Jaschke A., Solomayer E.F., Gollan C., Bastert G., Sohn C., Schuetz F. 2008, Ann Oncol 19: 2007-11. 140. Debatin K.M., Poncet D., Kroemer G. 2002, Oncogene 21: 8786-803. 141. Lawenda B.D., Kelly K.M., Ladas E.J., Sagar S.M., Vickers A., Blumberg J.B. 2008, J Natl Cancer Inst 100: 773-83. 142. Nomura K., Imai H., Koumura T., Kobayashi T., Nakagawa Y. 2000, Biochem J 351: 183-93. 143. Gonzalvez F., Schug Z.T., Houtkooper R.H., MacKenzie E.D., Brooks D.G., Wanders R.J., Petit P.X., Vaz F.M., Gottlieb E. 2008, J Cell Biol 183: 681-96. 144. Murphy M.P. 1997, Trends Biotechnol 15: 326-30. 145. Yousif L.F., Stewart K.M., Horton K.L., Kelley S.O. 2009, Chembiochem 10: 2081-8.


Mitochondria and cancer

145

146. Yamada Y., Akita H., Kamiya H., Kogure K., Yamamoto T., Shinohara Y., Yamashita K., Kobayashi H., Kikuchi H., Harashima H. 2008, Biochim Biophys Acta 1778: 423-32. 147. Cao D.X., Wu G.H., Zhang B., Quan Y.J., Wei J., Jin H., Jiang Y., Yang Z.A. 2009, Clin Nutr 29: 72-7. 148. Donohoe C.L., Ryan A.M., Reynolds J.V. 2011, Gastroenterol Res Pract 2011: 601434. 149. Tisdale M.J. 2009, Physiol Rev 89: 381-410. 150. Fearon K., Strasser F., Anker S.D., Bosaeus I., Bruera E., Fainsinger R.L., Jatoi A., Loprinzi C., MacDonald N., Mantovani G., Davis M., Muscaritoli M., Ottery F., Radbruch L., Ravasco P., Walsh D., Wilcock A., Kaasa S., Baracos V.E. 2011, Lancet Oncol 12: 489-95. 151. Paccagnella A., Morello M., Da Mosto M.C., Baruffi C., Marcon M.L., Gava A., Baggio V., Lamon S., Babare R., Rosti G., Giometto M., Boscolo-Rizzo P., Kiwanuka E., Tessarin M., Caregaro L., Marchiori C. 2010, Support Care Cancer 18: 837-45. 152. Ravasco P., Monteiro-Grillo I., Vidal P.M., Camilo M.E. 2004, Support Care Cancer 12: 246-52. 153. Arrieta O., Michel Ortega R.M., Villanueva-Rodriguez G., Serna-Thome M.G., Flores-Estrada D., Diaz-Romero C., Rodriguez C.M., Martinez L., Sanchez-Lara K. 2010, BMC Cancer 10: 50. 154. Pressoir M., Desne S., Berchery D., Rossignol G., Poiree B., Meslier M., Traversier S., Vittot M., Simon M., Gekiere J.P., Meuric J., Serot F., Falewee M.N., Rodrigues I., Senesse P., Vasson M.P., Chelle F., Maget B., Antoun S., Bachmann P. 2010, Br J Cancer 102: 966-71. 155. Dewys W.D., Begg C., Lavin P.T., Band P.R., Bennett J.M., Bertino J.R., Cohen M.H., Douglass H.O., Jr., Engstrom P.F., Ezdinli E.Z., Horton J., Johnson G.J., Moertel C.G., Oken M.M., Perlia C., Rosenbaum C., Silverstein M.N., Skeel R.T., Sponzo R.W., Tormey D.C. 1980, Am J Med 69: 491-7. 156. Morley J.E., Thomas D.R., Wilson M.M. 2006, Am J Clin Nutr 83: 735-43. 157. Polge C., Heng A.E., Jarzaguet M., Ventadour S., Claustre A., Combaret L., Bechet D., Matondo M., Uttenweiler-Joseph S., Monsarrat B., Attaix D., Taillandier D. 2011, Faseb J 25: 3790-802. 158. Lee S.J., Glass D.J. 2011, Skelet Muscle 1: 2. 159. Greene A.A. 1960, Cancer Res 20: 233-6. 160. Baldwin P.E., George D.T., Cunningham C.C. 1975, Experientia 31: 1333-4. 161. Bland K.I., Adcock R.A., Ratcliffe D.J., Fry D.E. 1980, J Surg Res 28: 416-20. 162. Tsuburaya A., Blumberg D., Burt M., Brennan M.F. 1995, J Surg Res 59: 421-7. 163. Hochwald S.N., Harrison L.E., Port J.L., Blumberg D., Brennan M.F., Burt M. 1996, Surgery 120: 534-41. 164. Makino T., Noguchi Y., Ito T., Matsumoto A. 1994, Int J Exp Pathol 75: 433-40. 165. Perumal S.S., Shanthi P., Sachdanandam P. 2005, Br J Nutr 93: 901-9. 166. Dumas J.F., Goupille C., Julienne C.M., Pinault M., Chevalier S., Bougnoux P., Servais S., Couet C. 2011, J Hepatol 54: 320-7. 167. Dumas J.F., Goupille C., Pinault M., Fandeur L., Bougnoux P., Servais S., Couet C. 2010, Nutr Cancer 62: 343-50.


146

Jean-Franรงois Dumas et al.

168. Lieffers J.R., Mourtzakis M., Hall K.D., McCargar L.J., Prado C.M., Baracos V.E. 2009, Am J Clin Nutr 89: 1173-9. 169. Sanchis D., Busquets S., Alvarez B., Ricquier D., Lopez-Soriano F.J., Argiles J.M. 1998, FEBS Lett 436: 415-8. 170. Bing C., Brown M., King P., Collins P., Tisdale M.J., Williams G. 2000, Cancer Res 60: 2405-10. 171. Bing C., Russell S.T., Beckett E.E., Collins P., Taylor S., Barraclough R., Tisdale M.J., Williams G. 2002, Br J Cancer 86: 612-8. 172. Busquets S., Almendro V., Barreiro E., Figueras M., Argiles J.M., Lopez-Soriano F.J. 2005, FEBS Lett 579: 717-22. 173. Constantinou C., Fontes de Oliveira C.C., Mintzopoulos D., Busquets S., He J., Kesarwani M., Mindrinos M., Rahme L.G., Argiles J.M., Tzika A.A. 2011, Int J Mol Med 27: 15-24. 174. Collins P., Bing C., McCulloch P., Williams G. 2002, Br J Cancer 86: 372-5. 175. Sanders P.M., Tisdale M.J. 2004, Cancer Lett 212: 71-81. 176. Cioffi F., Senese R., de Lange P., Goglia F., Lanni A., Lombardi A. 2009, Biofactors 35: 417-28. 177. Ushmorov A., Hack V., Droge W. 1999, Cancer Res 59: 3527-34. 178. White J.P., Baltgalvis K.A., Puppa M.J., Sato S., Baynes J.W., Carson J.A. 2011, Am J Physiol Regul Integr Comp Physiol 300: R201-11. 179. Weber M.A., Krakowski-Roosen H., Schroder L., Kinscherf R., Krix M., KoppSchneider A., Essig M., Bachert P., Kauczor H.U., Hildebrandt W. 2009, Acta Oncol 48: 116-24. 180. Stephens N.A., Skipworth R.J., Macdonald A.J., Greig C.A., Ross J.A., Fearon K.C. 2011, J Cachex Sarcopenia Muscle 2: 111-7. 181. Crane J.D., Devries M.C., Safdar A., Hamadeh M.J., Tarnopolsky M.A. 2010, J Gerontol A Biol Sci Med Sci 65: 119-28. 182. Lancaster J.R., Jr., Laster S.M., Gooding L.R. 1989, FEBS Lett 248: 169-74. 183. Ledgerwood E.C., Prins J.B., Bright N.A., Johnson D.R., Wolfreys K., Pober J.S., O'Rahilly S., Bradley J.R. 1998, Lab Invest 78: 1583-9. 184. Busquets S., Aranda X., Ribas-Carbo M., Azcon-Bieto J., Lopez-Soriano F.J., Argiles J.M. 2003, Cytokine 22: 1-4. 185. Gomes-Marcondes M.C., Tisdale M.J. 2002, Cancer Lett 180: 69-74. 186. Barreiro E., de la Puente B., Busquets S., Lopez-Soriano F.J., Gea J., Argiles J.M. 2005, FEBS Lett 579: 1646-52. 187. Guarnier F.A., Cecchini A.L., Suzukawa A.A., Maragno A.L., Simao A.N., Gomes M.D., Cecchini R. 2010, Muscle Nerve 42: 950-8. 188. Fortunati N., Manti R., Birocco N., Pugliese M., Brignardello E., Ciuffreda L., Catalano M.G., Aragno M., Boccuzzi G. 2007, Oncol Rep 18: 1521-7. 189. Laviano A., Meguid M.M., Preziosa I., Rossi Fanelli F. 2007, Curr Opin Clin Nutr Metab Care 10: 449-56. 190. Mantovani G., Madeddu C. 2008, Curr Opin Support Palliat Care 2: 275-81. 191. Marin-Corral J., Fontes C.C., Pascual-Guardia S., Sanchez F., Olivan M., Argiles J.M., Busquets S., Lopez-Soriano F.J., Barreiro E. 2010, Antioxid Redox Signal 12: 365-80.


Mitochondria and cancer

147

192. Coerver K.A., Woodruff T.K., Finegold M.J., Mather J., Bradley A., Matzuk M.M. 1996, Mol Endocrinol 10: 534-43. 193. Li L., Shen J.J., Bournat J.C., Huang L., Chattopadhyay A., Li Z., Shaw C., Graham B.H., Brown C.W. 2009, Endocrinology 150: 3521-9. 194. Puigserver P., Rhee J., Lin J., Wu Z., Yoon J.C., Zhang C.Y., Krauss S., Mootha V.K., Lowell B.B., Spiegelman B.M. 2001, Mol Cell 8: 971-82. 195. Schlame M., Ren M. 2009, Biochim Biophys Acta 1788: 2080-3. 196. Pepe S. 2005, Exp Gerontol 40: 751-8.


Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology, 2012: 149-194 ISBN: 978-81-308-0487-3 Editors: Phing-How Lou and Natalia Petersen

5. Modified mitochondrial dynamics, turnover and function in neurodegeneration: A focus on Huntington’s disease 1

Tatiana R. Rosenstock1 and A. Cristina Rego1,2

CNC-Center for Neuroscience and Cell Biology, University of Coimbra and; 2Faculty of Medicine, University of Coimbra, Coimbra, Portugal

Abstract. Mitochondria are central organelles in the maintenance

of cellular and metabolic homeostasis that are affected in several neurodegenerative disorders, including Huntington’s disease (HD). Mitochondria are susceptible to changes in nutrient factors, toxic agents, misfolded proteins and changes in cytoplasmic clearance, leading to mitochondrial dysfunction. Alterations of cellular mechanisms regulating mitochondrial fission/fusion and mitochondrial axonal transport (dynamics) and organelle biogenesis versus mitochondrial degradation occurring through macroautophagy (together with protein aggregates) or mitophagy may occur in parallel, promoting each other, and converging to cellular dysfunction and cell death by apoptosis. These mechanisms may determine the role of mitochondria in keeping some vital functional mechanisms, such as calcium handling and ATP synthesis. Thus, in this review, we describe the modifications of molecular and cellular pathways that regulate mitochondrial dynamics, turnover and autophagic degradation that influence mitochondrial function as well as neuronal survival in different aspects of neurodegeneration and particularly in HD pathogenesis. Correspondence/Reprint request: Dr. Ana Cristina Rego, Center for Neuroscience and Cell Biology and Faculty of Medicine, Rua Larga, University of Coimbra (pólo I), 3004-504 Coimbra, Portugal E-mail: a.cristina.rego@gmail.com; arego@fmed.uc.pt; acrego@cnc.uc.pt


150

Tatiana R. Rosenstock & A. Cristina Rego

Introduction Mitochondria play an essential role in cells because they control ATP production via oxidative phosphorylation, heme biosynthesis, calcium homeostasis and cellular proliferation [1, for review]. These functions, as well as others, are directly or indirectly related to cell metabolism, which requires the integration and coordination of mitochondria with other cellular organelles, such as the endoplasmic reticulum (ER) and the nucleus. In addition, mitochondria are dynamic organelles which actively divide and fuse, to mix metabolites and mitochondrial DNA (mtDNA) copies, as an effort to adjust changes in the energy demands [2], and to contribute to modifications in mitochondrial shapes and sizes. Thus, disruption of the carefully orchestrated balance between these essential processes can negatively affect mitochondrial function and cell viability. Indeed, abnormalities in mitochondrial function and dynamics contribute to neuronal death and have been reported in several models of different neurodegenerative diseases. Since mitochondria cannot be synthesized de novo, they must proliferate from preexisting ones to keep their biogenesis [3, for review]. Mitochondrial biogenesis requires the coordination of several distinct processes: 1) Synthesis of inner and outer mitochondrial membranes (IMM and OMM, respectively); 2) Synthesis of mitochondrial proteins; 3) Synthesis and import of proteins encoded by the nuclear genome; 4) Replication of mtDNA; and 5) Mitochondrial fusion and fission, in addition to the regulation of multiple processes including lipid import and oxidative phosphorylation, being the peroxisome proliferator-activated receptor γ (PPAR γ) co-activator 1α (PGC1α) the master transcriptional regulator for mitochondrial biogenesis in vertebrates [4-5]. Furthermore, degradation of damaged or excess mitochondria is thought to occur constitutively and in the presence of exogenous stimuli, which is critical for mitochondrial quality and quantity control [6-8]. In an ideal situation of mitochondrial turnover, mitochondrial recycling should provide for removal of damaged mitochondria and their replacement by normal, replicating mitochondria [9]. However, this is not always the case, and the accumulation of defective mitochondria, containing little or no mtDNA, low respiratory activity, or reduced mitochondrial membrane potential (MMP) in postmitotic cells such as neurons is frequently observed [9]. It is proposed that mitochondria turnover occurs as a unit, since inner membrane proteins such as cytochrome aa3, b, and c, the IMM lipid cardiolipin and mtDNA have the same half-life [10]. The first reports, from the 1970s, suggest that under normal conditions, mitochondria of non-proliferating tissues (e.g. brain) would turnover with a half-life of 10–25 days [11-12].


Modified mitochondrial dynamics in neurodegeneration

151

In this review we describe the regulation of cellular and molecular determinants of mitochondrial fission/fusion, turnover and degradation occurring through autophagy and/or mitophagy in several neurodegenerative conditions. Indeed, abnormalities in mitochondrial function can contribute to neuronal cell death, as reported in many neurodegenerative disorders, including HD. The later is an autosomal dominant neurodegenerative disease and the most prevalent polyglutamine (polyQ) disorder, caused by an unstable expansion of CAG repeats (>39) within the coding region of the HD gene, encoding for mutant huntingtin (mHtt) that retains an expanded stretch of polyQ in its N-terminal [reviewed in 13]. Thus, for each section we describe the changes in mitochondrial dynamics, function and autophagy occurring in cellular and animal models of Huntington’s disease (HD) and in HD patient’s brains, mostly affecting the striatum and the cortex.

1. Mitochondrial dynamics Mitochondria are dynamic organelles that frequently change their size, shape, number, and distribution [14]. Such dynamic membrane remodeling takes place in a manner that is not random but rather on purpose to optimize their structure and function for cellular needs and/or in response to intra- and extracellular stimuli.

1.1. Fission and fusion mechanisms Fission and fusion are two opposing processes that control mitochondrial number, size, shape, and distribution, and disturbances in any of these mechanisms, either by an excessive fusion or fission, can negatively affect mitochondrial function and cell viability [15]. Indeed, fission and fusion defects can lead to neuronal death by limiting osmotic homeostasis, metabolic states [16] and mitochondrial motility, decreasing energy production, promoting oxidative stress, and further leading to mtDNA deletion and impaired Ca2+ buffering [2]. Additionally, fission deficiency also causes a reduced rate of mitochondrial ATP synthesis due to a decrease in complex IV activity and an inefficient oxidative phosphorylation system [17]. Fusion deficient cells demonstrate reduced endogenous and uncoupled respiratory rates. Both fission and fusion are controlled by large GTPases that belong to the family of dynamin [18]. Mitochondrial fission or fragmentation is controlled and regulated by dynamin-related protein (Drp1) and mitochondrial fission 1 (Fis1) [15]. Drp1 in mammals and flies is the homolog of Dnm1 in yeast [15; 19]. Drp1 is most localized in the cytoplasm


152

Tatiana R. Rosenstock & A. Cristina Rego

and at the OMM [15]. Fis1, in yeast, and its mammalian homologue hFis1, are involved in the recruitment of Drp1 to mitochondria [20], and is localized on OMM [15]. The favored model of mitochondrial fission suggests that Drp1 assembles into rings or spirals surrounding the OMM with the help of Fis1 [21]. GTP hydrolysis is thought to cause a conformational change in Drp1 that drives the OMM fission event. In mammalian cells, knockdown of Fis1 blocks mitochondrial fission without affecting Drp1 localization to mitochondria [22]. Mitochondrial fusion is controlled by three different proteins, two localized in the OMM, mitofusin 1 and 2 (Mfn1 and Mfn2), and one in IMM, the protein optic atrophy 1 (Opa1) [15; 18; 23]. Mfn1 and 2 facilitate fusion of the OMM in mammals likely through trans-interactions that promote membrane curvature and fusion [24]. Previous studies suggest that the GTPase Opa1 is the main mediator of IMM fusion, thus maintaining mtDNA and cristae morphology in mammals [25-27] and mitochondrial function. Indeed, mitochondria lacking OPA1 have altered cristae structures [23] as demonstrated by the decrease of this protein by RNAi knockdown that converts the tubular mitochondrial network in cell lines into many small and fragmented organelles [25]. Moreover, OPA1 appears to be a chaperone for ATP synthase [28] and seems to precipitate mitochondrial fragmentation during apoptosis [29]. Mitochondria can further undergo fission in the presence of apoptotic stimulus, and this is often an early event [22], despite mitochondrial fission has been considered a non-apoptotic, caspase-independent pathway. Indeed, this has been more described as a ‘necrosis-like’ cell death pathway, which produces slight mitochondrial swelling and a transition of the organelle to a condensed state [30]. One of the most common change in mitochondrial structure is swelling, which typically accompanies the mitochondrial permeability transition, and the ruptured of OMM and cristae [31]. Because electron transport chain (ETC) molecules reside on the cristae membranes [32], the ratio of cristae/mitochondrion surface area can be viewed as the ATP synthesizing capacity of the mitochondrion. Although the relationship between mitochondrial fission and cell death is not fully understood, it is known that Bax and Bak (two pro-apoptotic proteins) colocalize with mitochondrial fission and fusion GTPases [33]. In addition, Bak appears as a facilitator of mitochondrial fission [34], interacting with Mfns, and blocking mitochondrial fusion. It is also known that inhibiting Drp1 delays fission, Bax foci formation, and the loss of neurons [35-36]. Interestingly, loss of Mfn2 has profound effects on mitochondrial structure and function, including increase in mitochondrial fragmentation and swelling, loss of mtDNA, and cristae disorganization [37]. It is important to stress,


Modified mitochondrial dynamics in neurodegeneration

153

however, that there is not a direct causative relation between mitochondrial fragmentation and apoptosis [38-39] since mitochondrial fragmentation per se might not trigger apoptosis [35-36]. Remarkably, expression of either dominant-negative Drp1 or Mfn2 not only prevents changes in mitochondrial morphology, but also restores ATP levels and attenuates cell death. Additionally, the mitochondrial fission inhibitors, such as Mdivi-1 (mitochondrial division inhibitor-1), a selectively blocker of Drp1 activity that retards apoptosis by inhibiting OMM permeability, has important therapeutic potential for future treatment of human neurodegenerative diseases [40]. Oxidative and nitrosative stress [35; 41], DNA damage [41] and high levels of glucose may also stimulate mitochondrial fission [42]. According to these hypotheses, Jahani-Asl and coworkers (2007) reported that hydrogen peroxide treatment of cerebellar granule neurons induced mitochondrial fragmentation within one hour [41]. Furthermore, it was shown that nitric oxide leads to mitochondrial fragmentation prior to the onset of neuronal loss in a mouse model of stroke [35]. Interestingly, expression of Mfn or dominant negative Drp1 in cultured neurons is protective against oxidative insults [35; 41]. In the presence of the topoisomerase camptothecin, which mimics physiological DNA damage, there is increased mitochondrial fragmentation and altered mitochondrial pool [41]. Interestingly, mitochondrial fragmentation begins well before nuclear degradation and neuronal death, indicating that mitochondrial fission (triggered by DNA damage) precedes and may contribute to neuronal death. It was also demonstrated that aberrant activation of cell cycle components in post-mitotic neurons may play an important role in the regulation of the mitochondrial fission machinery, such as Drp1. As one might expect, mitochondrial fission is a regular event during cell division [43] allowing dividing cells to maintain an adequate supply of the energy-producing organelles. Thus, it should come as no surprise that components of the cell cycle machinery help regulate mitochondrial fission. For example, mitosis-promoting factor, such as cyclindependent kinases 1 (Cdk1)/cyclin B, phosphorylates Drp1 and seems to stimulate mitochondrial fragmentation [44]. Interestingly, several studies indicate that cell cycle proteins such as Cdks are upregulated in neurodegenerative disease [e.g. 45-46]. Taken together, these findings indicate some correlation between processes regulating mitochondrial dynamics in neurodegenerative diseases and increased activity of cell cycle proteins. However, whether this correlation represents aberrant activation of the cell cycle or a simple dysfunction in protein expression is unclear. However, not only changes in large GTPases that belong to the family of dynamin are responsible for changes in the fission-fusion machinery.


154

Tatiana R. Rosenstock & A. Cristina Rego

Recent work provides evidence that PINK1 (PTEN-induced putative kinase 1), a protein that stabilizes mitochondrial integrity and function, and is important for the maintenance of mitochondrial networks [47], and Parkin, which is selectively recruited from the cytoplasm to damaged mitochondria by PINK1 and senses damaged mitochondria for compensatory clearance by autophagy, are also involved [48] (see section 4.1.4 for details). Studies revealed that removing a single copy of the fission-promoting factor Drp1 in PINK1 or Parkin mutants dramatically reduces their viability. In contrast, PINK1 and Parkin mutant phenotypes are suppressed by over-expressing Drp1 to enhance mitochondrial fission, or by introducing loss-of-function mutations in genes encoding the fusion-promoting factors Opa1 and Mfn [49].

1.2. Mitochondrial motility Another aspect of mitochondrial dynamics besides fusion and fission is their motility. These dynamic processes regulate mitochondrial function by enabling mitochondrial recruitment to critical sub-cellular compartments, content exchange between mitochondria, mitochondrial shape control, mitochondrial communication with the cytosol and mitochondrial quality control. This aspect is critically important in highly polarized cells, such as neurons [50], which require mitochondria at sites distant from the cell body, as the axonal and dendritic processes [51]. In a healthy neuron, fission and fusion work together changing shapes and size of mitochondria and also moving them along the cells. This implies the mitochondrial movement back and forward from the cell body to axons, dendrites and synapses. Defects in both fusion and fission have been shown to decrease mitochondrial movement. Loss-of-function mutations in Drosophila Drp1 result in a failure to efficiently traffic mitochondria to presynaptic terminals in neurons, which in turn impairs calcium buffering and synaptic transmission [52]. Presumably, the large tangle of highly interconnected mitochondria in fission-deficient cells prevents efficient movement, especially into small pathways such as neuronal processes [52]. Accordingly, lack of mitochondrial transport results in neurotransmission defects during prolonged stimulation [53]. Recent studies demonstrated specific interactions between Mfn2 and Miro and Milton, members of the molecular complex that links mitochondria to kinesin motors [54]. In fusiondeficient cells, mitochondria display loss of directed movement and possibly decreased interaction with motor proteins. In neurons lacking mitochondrial fusion, both increased mitochondrial diameter due to swelling and aggregation of mitochondria seem to block efficient entry into neurites,


Modified mitochondrial dynamics in neurodegeneration

155

resulting in a deficiency of mitochondria in axons and dendrites [37]. These defects result in improperly developed neurons or gradual neurodegeneration.

1.3. Altered mitochondrial dynamics in neurodegenerative disorders Changes in mammalian proteins required for mitochondrial dynamics have been related to mitochondrial dysfunction and different degenerative disorders. It is crucial to recall that perturbations in mitochondrial turnover, fission, fusion, and motility may lead to distinctive defects in neurons since these four processes are interdependent and may overlap [55]. Mutations in Mfn2 cause Charcot-Marie-Tooth type 2A (CMT2A), a peripheral neuropathy affecting sensory and motor neurons of the distal extremities [56]. Mutations in Opa1 are the predominant cause of autosomal dominant optic atrophy (DOA), a degeneration of retinal ganglia cells that leads to optic nerve atrophy [57]. Furthermore, one case of neonatal lethality has been attributed to a defect in Drp1 [58]. This patient carried a dominant-negative allele that caused perinuclear tangles of elongated, large-diameter mitochondria. Symptoms included optic atrophy, a notably small head with abnormal brain development and hypoplasia (underdevelopment of an organ because of a decrease in the number of cells). Lactic acid levels were elevated in the blood and even more so in the cerebral spinal fluid (CSF). Muscle and fibroblasts showed normal respiratory function, but it seems likely that neuronal cells had ETC abnormalities, given the extreme lactic acid concentrations in the CSF and the brain structural defects. In cell culture studies, down-regulation of Drp1 in HeLa cells caused decreased cell growth, loss of mtDNA, uncoupling of the ETC, decreased cellular respiration and increased ROS levels [59]. In addition, mitochondrial fragmentation and the consequent fragmentation of other organelles, as Golgi and ER, may play a causal role in initiation of neuronal cell death [60] in other neurodegenerative disorders such as Alzheimer's Disease (AD) and amyotrophic lateral sclerosis (ALS) [61].

1.3.1. Modified mitochondrial dynamics in HD Altered mitochondrial axonal transport and mitochondrial fragmentation may also contribute to HD pathology. It was previously shown that transgenic mice containing the mutated form of huntingtin, encoded by HD gene, with 72 polyglutamines (polyQ), Htt72Q transgenic mice, display a generalized transport defect in neurons [62], including abnormal movement of mitochondria [62-64]. In fact, mHtt may affect Drp1, modifying mitochondrial morphology and movements [1]. It was previously shown that


156

Tatiana R. Rosenstock & A. Cristina Rego

mHtt affects directly mitochondrial transport along neuronal processes, resulting in mitochondria accumulation and immobilization close to mHtt aggregates. In HD patient’s neurons, mitochondria translocate in general 70% less than in normal neurons [65]. The mitochondria move more slowly, stop more frequently and travel shorter distances. In addition, mHtt impair mitochondrial movement indirectly by the sequestration of machinery components and Htt, which is essential for axonal transport [62] or by physical blockage of axonal transport [63]. Chang et al. (2006) reported that mHtt aggregates act as physical roadblocks for mitochondrial transport in cortical neurons; consequently, in the narrow neuronal projections these aggregates prevent passage of mitochondria and fragmented mitochondria accumulate around mHtt. The authors proposed that this impairment in mitochondrial movement was an early pathogenic event, occurring before mitochondrial and cellular dysfunction in cortical neurons [63]. More recently, another study demonstrated that mHtt associates with microtubule based transport proteins, decreasing mitochondrial transport in striatal neurons [64]. This mechanism may be underlying the vulnerability of striatal neurons in HD disease. This loss of mobility may occur concomitantly with excitotoxicity, the loss of calcium homeostasis and ATP deficiency [63]. Despite of these, decreased axonal transport of brain-derived neurotrophic factor (BDNF)-containing vesicles in HD was not associated with altered transport of mitochondria [37]. HeLa cells overexpressing a mHtt with a 74 glutamine repeat (Htt74Q) exhibited fragmented mitochondria, reduced mitochondrial fusion, reduced ATP and increased cell death [66]. Likewise, rat cortical neurons treated with 3-nitropropionic acid (3NP) have fragmentation and condensation of mitochondria, which can be prevented by antioxidant treatment [67]. Interestingly, mHtt seems to bind directly to the OMM in transgenic mice and forms large foci similar to Drp1 [19]. In addition, in HD neurons, one mechanism that activates Fis 1, and consequently fission, is the increase in mitochondrial free radicals [68]. Consequently, there is an increase in mitochondrial fragmentation, which in turn produces defective mitochondria that ultimately damage neurons. On the other hand, mitochondrial fusion, which occurs when the C-terminal part of Mfn1 mediates oligomerization between Mfn molecules of adjacent mitochondria, protects cells from the toxic effects of mtDNA and mitochondrial mHtt by allowing functional complementation of mtDNA, proteins and metabolites [68]. It was recently demonstrated that mHtt triggers mitochondrial fragmentation in neurons and fibroblasts of HD individuals in vitro and HD mice (YAC128) in vivo before the presence of neurological deficits and Htt aggregates. Song and colleagues


Modified mitochondrial dynamics in neurodegeneration

157

(2011) showed that mHtt abnormally interacts with the Drp1 in YAC128 mice and individuals which in turn stimulates its enzymatic activity. Moreover, mHtt mediated mitochondrial fragmentation, defects in anterograde and retrograde mitochondrial transport, and neuronal cell death, which were rescued by reducing Drp1 GTPase activity with the dominantnegative DRP1K38A mutant [69].

2. Clearance of cytoplasmic content through autophagy One of the mechanisms responsible for the maintenance of cellular homeostasis is the degradation of cellular contents, from small molecules up to whole organelles, through distinct pathways: ubiquitin-proteasome system (UPS) and endossomal-lysosomal system. The UPS pathway plays an essential role in the controlled degradation of most short- and long-lived intracellular proteins in eukaryotic cells and appears to reduce the levels of soluble misfold proteins, with an aberrant conformation, that exit from the protein-folding machinery of the ER. Three enzymes, E1 ubiquitin-activating enzyme, E2 ubiquitin-carrier enzyme and E3 ubiquitin ligase, act sequentially to conjugate ubiquitin to proteins, generally resulting in their degradation [70]. However, the narrow pore of the proteasome prevents the entrance of protein complexes or aggregates and organelles [71]. On the other hand, the endosomal-lysosomal system seems to play a role in clearing cells from protein aggregates and/or dysfunctional organelles, and has three different pathways: i) Cvt, cytosol to vacuole targeting; ii) Vid, vacuolar import and degradation; iii) autophagy. Autophagy, the most important degradation pathway, can be divided in three different types according to the mechanism involved in the vacuolar formation and delivery of material to lysosome: chaperone-mediated autophagy (CMA), microautophagy and macroautophagy. In CMA, there is the degradation of cytosolic proteins containing a pentapeptide motif sequence KFERQ (Lys-Phe-Glu-Arg-Gln). This sequence is recognized by the chaperone complex that is afterwards linked to receptors present in the lysosomal membrane, the LAMP-2A. Then, the protein is transported across the lysosomal membrane. The microautophagy is a process in which lysosome itself takes up cytosolic components including macromolecules and organelles by invagination of its membrane. However, this mechanism appears not to be subjected to metabolic regulation [72]. The macroautophagy, commonly named as autophagy, is a highly conserved process [70] that is characterized by the formation of vacuoles/vesicles that have a double (or multiple) membrane. This is referred to a cellular degradative pathway that involves the delivery of cytoplasmic cargos to the lysosome [73].


158

Tatiana R. Rosenstock & A. Cristina Rego

In eukaryotic cells, autophagy constitutes a degradative mechanism for removal and turnover of bulk cytoplasmic constituents via the endosomallysosomal system. Early studies revealed autophagy as an adaptive response of cells to nutrient deprivation, i.e. to ensure minimal housekeeping functions (nutrient recycling), providing amino acids which can be used by the liver for gluconeogenesis. More recently, it was recognized that the function of autophagy is much more complex and is involved in physiological processes, such as regulation of metabolism, morphogenesis, cellular differentiation, tissue remodeling, aging and cellular defense [74, for review]. It was also shown that activation of autophagy probably contributes to excitotoxicity, since the activation of N-methyl-D-aspartate (NMDA) and kainic acid (KA) receptors stimulated autophagy and lysosomal enzymes [75-76] (Figure 1). In yeast (the most “popular� model for genetic studies) autophagy were classified into non-selective and selective autophagy [77]. Both types present, as the morphological hallmark, the sequestration of cargo within double-membrane cytosolic vesicles. Non-selective autophagy is the bulk degradation of cytoplasmic components that allows cells to respond to various types of stress and to adapt to changes in nutrient conditions [78]. During selective types of autophagy, the membrane of the sequestering vesicle is closely opposed to the cargo, resulting in the exclusion of bulk cytoplasm. Selective autophagy includes the Cvt [79], pexophagy (peroxisomes) [80], reticulophagy (ER), ribophagy (ribosomes), crinophagy (granules), and xenophagy (pathogens) [81], nucleophagy (cytosol, cytoskeleton, nuclei), aggrephagy (protein aggregates) [82] and mitophagy (mitochondria).

Figure 1. Possible roles for autophagy in different cellular organisms. The importance of autophagy can be related to survival pathways (green) or mechanisms related to cell death and degeneration (red).


Modified mitochondrial dynamics in neurodegeneration

159

Autophagy starts with the formation of a “C” structure which extremities are elongated until it is completely closed. Recent findings in yeast and mammalian cells suggest that the ER, Golgi and/or plasma membrane can be the origin of this double membrane-bound vesicle [83-84]. In the past, it was believed that the membrane was from a structure named “phagophore” [85]. Phagophore is an autophagosome precursor also known as preautophagosomal structures-assembly sites (PAS). Following membrane closing and engulfment of cytoplasm or organelles inside the vesicle (the first step of autophagy in mammals) there is the formation of a structure named autophagosome. Then, the autophagosome fuses its outer membrane with the lysosome creating the autolysosome or autophagic vacuole. It is the inner membrane of the old autophagosome containing the cytoplasmic material (autophagic bodies) that will be degraded by the lysosomal enzymes [86] (Figure 2).

Figure 2. Schematic representation of the main steps regarding the formation of autophagosome and autolysosome. The first step is the formation of a “C” structure, that when closed, engulfs the cytosolic content including organelles, such as mitochondria. The induction of the phaphofore, the elongation of its membrane and the completely formation of autophagosome are related to Atg12, Atg5, Atg16 and Atg8 genes as well as with phosphatidylethanolamine (PE). The autolysosome formation occurs due to the fusion of autophagosome with the lysosome and involves the Atg8-PE complex. After this step, lysosomal enzymes such as cathepsins are responsible for the degradation of the cytosolic engulfed content.

2.1. Molecular mechanisms of autophagy Different steps of autophagy require the participation of cytoskeletal proteins, and thus become an integral part of this mechanism. Intermediate filaments (as vimentin and cytokeratin) are needed for the sequestration of


160

Tatiana R. Rosenstock & A. Cristina Rego

cytoplasmic material and/or organelles, and the microtubular system is needed for the fusion of lysosomes with the late autophagosome [85]. One of these proteins is the microtubule-associated protein-1 light chain 3 (LC3), the mammalian orthologue for the Atg8 in yeast (see ATG gene section below; 3.1.1.). LC3 remains in the membrane of the vesicle until the end of the process. All steps including the final degradation of the sequestered cytoplasmic material in autolysosomes are ATP dependent [85]. Some of these mechanisms will be discussed below.

2.1.1. ATG genes in autophagy Several AuTophaGy-related (ATG) genes that regulate yeast autophagy have been identified along the years, and many of these genes have mammalian orthologues. To date, studies in yeast allowed the isolation of 33 ATG genes. Fifteen of these genes are essential for both autophagy and selective autophagy and are categorized as part of the core autophagic machinery [87]. ATG genes are mainly responsible for the autophagosome formation and control of the fusion between autophagosomes and lysosomes. Two unique ubiquitin-like conjugation systems, Atg8-phosphatidylethanolamine (Atg8-PE) and Atg12Atg5, are involved in the biogenesis of autophagic vesicles [88]. These conjugation systems are widely conserved in various eukaryotes and have an essential role in autophagy (Figure 2) [89]. During autophagosome formation, the elongation of the phagophore involves the Atg12-Atg5 system, in which Atg12 is conjugated to Atg5. Two additional proteins are required to form the conjugate; one is Atg7, which is a homolog of the E1 ubiquitin activating enzyme Uba1, [90] the other is Atg10, which functions like an E2 ubiquitin conjugating enzyme [91]. As in the ubiquitin system, the E1-like Atg7 binds to Atg12 to form an intermediate complex. Subsequently, activated Atg12 (after ATP hydrolysis) is transferred to the E2-like Atg10. Finally, Atg12 is covalently bound to Atg5 and this conjugate then forms a complex with Atg16L. This complex drives the expansion and/or curvature of the membrane envelope and finally dissociates from the vesicle just before, or immediately after, completion (Figure 2). The function of the Atg12 system is closely linked to another ubiquitinlike system involving LC3, (the mammalian ortholog of yeast Atg8) which is the only known mammalian protein that specifically associates with the autophagosome membrane [92]. In wild types cells, LC3 can be detected in two different forms: LC3-I and LC3-II. LC3-I is located in the cytoplasm. After autophagy induction, LC3-I is conjugated with PE, resulting in LC3-II. LC3-II is a membrane bound protein that remains attached to autophagosome [92]; thus, the relative amount of LC3-II reflects the level of autophagosome.


Modified mitochondrial dynamics in neurodegeneration

161

As defined in yeast, in the first step of Atg8 conjugation, the cysteine protease Atg4 proteolytically removes a C-terminal arginine of Atg8, exposing a glycine that is then accessible to the E1-like Atg7, the same enzyme used in the Atg12-Atg5 conjugation system. Atg7 activates Atg8, which is then transferred to another E2-like enzyme, Atg3, and eventually conjugated to PE through an amide bond. Atg8 conjugated to PE behaves like a membrane protein. Unlike the Atg12-Atg5 conjugation, modification of PE with Atg8 is a reversible event, in which Atg4 can again cleave Atg8 after the glycine residue to remove it from the lipid [93]. Atg8-PE is detected on both the forming intermediate vesicle and the completed autophagosome [94]. It is transported to the lysosome/vacuole and is degraded along with the cargo. Accordingly, Atg8-PE (LC3-II or LC3-PE) is the best candidate for a structural component of the autophagosome. However, both the Atg12-Atg5Atg16 complex and the Atg8-PE conjugate localize at the PAS, the initial sequestering membrane structure (Figure 2) [95]. Atg5, which cooperates with LC3-II [96] is an essential autophagy gene involved in the early stages of autophagosome formation [97], and seems to be extremely important to the membrane targeting of LC3 [96]. Indeed, downregulation of Atg5 or expression of Atg5 (K130R) mutant, suppresses vacuole formation and cell death [98]. Atg5 was also found to interact with FADD, Fas-Associated protein with Death Domain, an adaptor molecule that bridges the Fas-receptor, and other death receptors, to caspase-8 to form the death-inducing signaling complex (DISC) during extrinsic apoptosis [98]. Moreover, Atg5 can be cleaved by calpains. Thus, Simon and coworkers [99] reported that in cells overexpressing Atg5, a calpain cleavage product of Atg5 translocates to mitochondria, triggering cell death involving cytochrome c release and partial antagonism of Bcl-2 and Bcl-XL, as well as caspase activation [100]. Thus, Atg5 may turn into a pro-apoptotic signal at two levels, first via death receptor adaptor molecule FADD, activating the extrinsic pathway, and second via translocation of a calpain-cleavage product of Atg5 to mitochondria, activating the intrinsic pathway. As mentioned before, most of the ATG genes are required for both autophagy and selective autophagy, but other genes have a role only in certain types of autophagy. Atg11, an adaptor protein for selective autophagy, is needed along with Atg19 (a receptor protein involved in the formation of the Cvt complex) to recruit the Cvt complex to the PAS [101]. Similarly, during pexophagy Atg30 localizes to peroxisomes. Atg11 binds Atg30 and recruits the peroxisomes to the PAS [80]. In addition, Atg11 is essential for mitophagy, a selective type of autophagy that engulfs mitochondria, suggesting that this organelle is selectively imported into the vacuole. The role of other Atg molecules in mitophagy is described in section 4.1.1.


162

Tatiana R. Rosenstock & A. Cristina Rego

Besides the ATG genes already mentioned, the core Atg proteins also includes Beclin 1 and Atg7. Beclin 1 is a mammalian ortholog of Atg6/Vps30, a component of the class III phosphatidylinositol 3-kinase (PI3K) complex in yeast that is essential for the initial formation of autophagosomes [102]. In addition, Beclin1 is localized in membranes from ER and mitochondria [103; 3 for review]. Atg7 is essential for autophagic vesicle elongation through an ubiquitin-like conjugation pathway [104]. A protective role of autophagy has been suggested in some neurodegenerative disorders, cancer and infectious diseases [89]. Indeed, conditional knockouts of Atg5 or Atg7 genes in the brains of mice resulted in a neurodegenerative phenotype caused by aberrant accumulation of ubiquitinated proteins [104-105]. Komatsu (2007) additionally showed that a specific ablation of Atg7 in Purkinje cells caused progressive dystrophy, degeneration of axon terminals and behavioral deficits in the affected mice [106].

2.1.2. Target of Rapamycin signaling pathways One of the molecules that play a role during autophagy is the protein Target of Rapamycin (TOR) that, in mammals is named mTOR [107-108]. mTOR is a Ser/Thr kinase conserved through evolution with 289 kDa, which is ubiquitously expressed and belongs to the PI3K-related kinase family. TOR is involved in most regulatory factors that control the response to changes in nutrient conditions and energy metabolism [109]. mTOR constitutes the central regulatory catalytic core of at least two functionally distinct multiprotein complexes, mTOR complex 1 (mTORC1) and mTORC2, which can be distinguished based on their composition and substrates. mTORC1 and mTORC2 were initially identified on the basis of their differential sensitivity to the inhibitory effects of rapamycin, mTORC1 being originally considered as rapamycin-sensitive and mTORC2 as rapamycin-insensitive [110]. Both protein complexes, mTORC1 and mTORC2, are activated in response to several stress conditions and growth factor signals and, therefore, regulate different cellular functions, including protein synthesis, cell growth and proliferation, ribosomal and mitochondrial biogenesis, cytoskeleton organization, innate and adaptive immune responses and autophagy. mTORC1 integrates upstream nutrient signals (e.g. amino acids), growth factors (such as insulin and insulin-like growth factors), energy signals (ATP) and various cellular stressors (such as hypoxia or DNA damage) to regulate protein synthesis (gene transcription and mRNA translation), mitochondria, lipid metabolism and autophagy. Signal integration mainly occurs at the level of the tuberous sclerosis 1 (TSC1)–TSC2 complex, which exerts an inhibitory


Modified mitochondrial dynamics in neurodegeneration

163

effect on mTORC1 signaling. Growth factors activate mTORC1 indirectly by suppressing the function of TSC1–TSC2; this suppression occurs through the phosphorylation of TSC2 by PI3K-dependent kinases, such as AKT (or protein kinase B), or by the mitogen-activated protein kinase (MAPK) pathway involving activation of extracellular signal-regulated kinase 1 and 2 (ERK1/2). By contrast, phosphorylation of TSC2 by AMP-activated protein kinase (AMPK) — a sensor of intracellular energy that is activated in response to low nutrient availability and ATP depletion — results in activation of TSC1–TSC2, thereby inhibiting mTORC1 signaling. mTORC1 activity can also be modulated by TSC1–TSC2 independently of TSC2 phosphorylation. For example, hypoxia can indirectly inhibit mTORC1 by inducing the expression of the pro-cell death protein RTP801 (also known as REDD1 and DDIT4), the protein products of which promote the assembly and subsequent activation of TSC1–TSC2. RTP801 is induced by stresses including DNA damage, oxidative stress, hypoxia, ER stress and energy depletion that have been raised as causes of neurodegeneration in Parkinson’s disease (PD). In response to amino acids, mTORC1 is recruited by resistanceassociated gene protein (RAG) GTPases to lysosomal membranes, where mTORC1 become activated and inhibit autophagy. Rapamycin disrupts the assembly of the mTORC1 protein complex, inhibiting mTORC1 signaling by binding to and activating its intracellular receptor FKBP12 (FK506-binding protein of 12 kDa). mTORC1 is also able to stimulate lipid synthesis and mitochondrial proliferation and function, all of which contribute to mTORC1-mediated cell growth and proliferation and promote the accumulation of energy stores [111, for review]. During periods of nutrient availability, activation of mTOR promotes anabolic cellular processes, whereas following starvation (amino acid restriction) inhibition of mTOR promotes autophagy. Starvation is the most universal stressor to induce autophagy. Although a number of pathways may be involved, AMPK is clearly an important element. As described before, AMPK exerts a negative regulatory effect on mTOR, thereby inducing autophagy [112]. In addition to the loss of an inhibitory signal, AMPK triggers sirtuin 1 (SIRT1)-dependent deacetylation of PGC-1α and forkhead box protein transcription factor 1 of the O class (FOXO1), culminating in the transcriptional modulation of mitochondrial biogenesis [113]. Thus autophagy and mitochondrial biogenesis are coordinately regulated. In neurons, the regulation of these processes has a key role in brain development and contributes to several functions in the adult normal brain, including synaptic plasticity, learning and memory [111, for review]. Constitutively, autophagy is essential to maintain basal neuronal cell homeostasis, and its genetic inactivation leads to the formation of ubiquitinated intracellular


164

Tatiana R. Rosenstock & A. Cristina Rego

inclusions and neuronal cell death in mutant mice (deficient for Atg5 or Atg7) [105]. In contrast to mTORC1, little is known about the upstream regulation of mTORC2. It was shown, however, that mTORC2 is insensitive to nutrients and energetic stress [114]. It has been also reported that the TSC1–TSC2 complex may be able to activate mTORC2, which is in contrast to its inhibitory effect on mTORC1 [115]. It seems that mTORC2 is activated in response to growth factors promoting AKT signaling thereby stimulating cell survival, proliferation and migration and regulating various metabolic processes [116-117]. These effects can be achieved directly, through AKTmediated phosphorylation of specific substrates (such as FOXO1 and FOXO3, the phosphorylation of which prevents their proapoptotic functions) [118], or indirectly, through AKT-dependent activation of mTORC1 [117]. mTORC2 is also able to activate other AGC kinases (a group of Ser/Thr protein kinases named after the protein kinase A, G, and C families, respectively PKA, PKG, PKC) that are involved in cell survival and cytoskeletal organization, such as serum- and glucocorticoid-regulated kinase (SGK) and PKC [116-117]. More recently, mTORC2 was shown to be activated by directly binding to ribosomes [119].

2.1.3. Bcl-2 family members Autophagic and apoptotic pathways are intimately interconnected. When apoptosis is impaired, the cell may respond by activating autophagy and viceversa. Proteins belonging to the Bcl-2 family are well known regulators of apoptosis. Bcl-2, Bcl-XL and MCL-1 suppress apoptosis, while Bax, Bak and the BH3-only proteins are pro-apoptotic factors. Nevertheless, the role of many apoptotic proteins in autophagy regulation emerged when Beclin 1, which is a BH3-only protein [120] that interacts with Bcl-2 and Bcl-XL [121], was identified as an essential autophagy protein [122]. Beclin 1 binding by Bcl-2 is reported to negatively regulate autophagy [123], and displacement of Bcl-2 by BH3-only proteins can stimulate autophagy [124-125]. Bcl-2, in particular the Bcl-2 fraction localized at the ER, inhibits Beclin 1 dependent autophagy, thus exerting a dual role of anti-apoptotic and anti-autophagic protein [126]. Given the importance of Bcl-2 family members in regulating mitochondrial integrity, it seems reasonable to hypothesize that Bcl-2 proteins might also govern selective mitophagy. In mammals, Beclin 1 is also regulated by AMBRA1 (activating molecule in beclin 1-regulated autophagy) in a positive manner, opposed to the role of Bcl-2 [127].


Modified mitochondrial dynamics in neurodegeneration

165

Moreover, Beclin 1 and Bcl-2 binding can not only regulate autophagy levels and cell survival [126], but this mechanism can also be modulated by nutrient status. For instance, starvation causes c-Jun N-terminal kinase 1 (JNK1) activation, which leads to phosphorylation of Bcl-2 and dissociation of Bcl-2 and Beclin 1 [128], and the starvation-induced dissociation is abrogated in cells lacking JNK1 or in which JNK1 is inhibited. Furthermore, BH3-only proteins such as Bnip3 (Bcl-2 and adenovirus E1B 19 kDainteracting protein 3) [129], Bad and the BH3 peptidomimetic domain [130] induce autophagy by competitively disrupting the interaction between Beclin 1 and Bcl-2 or Bcl-XL. Other BH3 only proteins, such as Bik [131], Noxa and Puma [132] have been shown to induce autophagic cell death. RNAi against Beclin 1 and Atg5, that cooperates with LC3-II, prevented cell death of Bax/Bak double knockout cells treated with the apoptogenic compounds staurosporine and etoposide [133]. These examples show that induction of autophagy (including its transcriptional level) may be linked to the execution of cell death by making use of the apoptotic machinery. This conclusion is in line with other observations showing that both the autophagic as well as mitochondrial compartment may be targeted by the same intrinsic pro-apoptotic signals [134]. The mechanism of autophagy induction seems to be similar among all BH3-only proteins or, alternatively, may involve different effectors. For example, the BH3-only protein Puma [132] is a central mediator of p53-dependent apoptosis and functions by activating Bax and OMM permeabilization. In response to mitochondrial perturbations Puma can also induce autophagy through Bax, leading to selective removal of mitochondria, and this does not seem to be due to the release of Beclin 1 from Bcl-2 [135]. Additionally, inhibition of autophagy diminishes Puma and Bax mediated apoptosis [135], suggesting a link between selective autophagic targeting of mitochondria and apoptosis. The physiological relevance of the induction of cross-talk between apoptosis and autophagy induction is best characterized by two BH3 only proteins, Bnip3 and Nix (or Bnip3L or Bnip3-like protein X). Bnip3 and Nix play a role in cellular responses to ischemia/reperfusion injury in the heart [136]. Increase in the Bnip3 levels are induced after stress such as hypoxia in a HIF-1-dependent manner [129]. Increased levels of Bnip3 can localize at the mitochondria causing increase in ROS levels, opening of the permeability transition pore (PTP) and loss of MMP [137], leading to cell death, most likely via both apoptosis and autophagy. Autophagy activation has also been observed as a consequence of PTP opening [138]. Although the regulation of Bnip3 at mitochondria is unclear, Bax and Bak appear to be downstream effectors of Bnip3-mediated mitochondrial dysfunction [139].


166

Tatiana R. Rosenstock & A. Cristina Rego

2.1.4. ROS and calcium Autophagy is also induced by oxidative stress. Indeed, Elazar and colleagues (2007) identified Atg4 as the component of the autophagy machinery that responds to ROS [140]. Using an in vitro assay, they demonstrated that hydrogen peroxide directly regulates HsAtg4A (mammalian homolog of Atg4 in yeast). In fact, there are two homologues in mammals, HsAtg4A and HsAtg4B, that differentiate from each other by its efficient to cleave the homologues of Atg 8: HsAtg4A cleaves mainly GATE-16, whereas HsAtg4B cleaves all three homologues (GATE-16, GABARAP and LC3), with the highest efficiency for LC3 [141]. Chen and co-workers also showed that mitochondrial ROS may induce autophagy [142]. Interestingly, the nuclear levels of the transcription factor nuclear factorerythroid 2 (NF-E2) related factor 2 (Nrf2), which translocates to the nucleus in response to oxidative stress activating the transcription of various detoxifying enzymes [1], were increased in autophagy-deficient liver [106]. Moreover, induction of detoxifying enzymes and Nrf2 nuclear translocation in autophagy-deficient liver were suppressed by loss of p62, suggesting that autophagy deficiency causes cell stress, with concomitant p62-dependent Nrf2 activation [106]. In addition to the increase in ROS and changes in ATP levels, autophagy can also be regulated by Ca2+ [107]. However, it is still controversial how an increase in Ca2+ in the cytosol actually modulates autophagy. Hoyer-Hansen and coworkers (2007) suggested that an increase in cytosolic Ca2+ activates CaMKK-β (Ca2+/calmodulin-dependent protein kinase-β) and, therefore, the AMPK, a negative-regulatory protein of mTOR, resulting in autophagy induction [143]. Furthermore, activation of NMDA receptors, largely associated with increase cytosolic Ca2+, stimulated autophagy and the lysosomal enzymes activity [75]. Additionally, L-type Ca2+ channel antagonists, as well as the K+ ATP channel openers, seem to prevent the influx of Ca2+ and decrease intracytosolic Ca2+ levels, leading to inhibition of Ca2+-dependent cysteine proteases, calpains, and induction of autophagy. This is consistent with an earlier finding showing that raised intracytosolic Ca2+ levels impaired autophagy [144]. Accordingly, autophagy was inhibited with agents that increase intracytosolic Ca2+ levels, such as thapsigargin (an ER Ca2+-ATPase inhibitor that promotes the release of Ca2+ from ER stores) and ionomycin (a Ca2+ ionophore that also releases Ca2+ from intracellular stores) [144]. Interestingly, thapsigargin blocks autophagic flux at two stages of the pathway. It increases LC3-II levels and vesicle numbers by impairing


Modified mitochondrial dynamics in neurodegeneration

167

autophagosome-lysosome fusion and also reduces autophagosome synthesis by activating calpains [145]. The release of Ca2+ from the ER leads to a great inhibition of autophagy. It was previously shown that autophagy can be induced by lowering intracellular inositol trisphosphate (IP3) or even inhibiting inositol synthesis [146]. Consistent with a role of IP3 in autophagy, pharmacological inhibition of the inositol trisphosphate receptors (IP3R) by xestospongin B also induces autophagy [147]. Xestospongin B may also induce autophagy by disrupting the IP3R-Beclin 1 complex, which can also be modulated by Bcl-2 levels [148]. Regulation of autophagy by intracellular IP3 levels is most likely dependent on the fact that IP3 is a signal for ER Ca2+ release, as elevated cytosolic IP3 binds the ER resident IP3Rs to mobilize ER Ca2+ stores and increase cytosolic Ca2+, which has autophagy inhibitory effects [145; 149], creating an elaborate mTOR-independent autophagy pathway. Moreover, IP3 levels had no effect on the autophagy-inducing property of mTOR inhibition by rapamycin, suggesting that these two pathways are independent of each other [150]. Therefore, agents that reduce inositol or IP3 levels may be possible therapeutic candidates under circumstances where induction of autophagy is a protective mechanism. These studies suggest complex roles for Ca2+ in autophagy.

2.1.5. p53 pathway The p53 protein is a tumor suppressor that functions within the cell to integrate stress signals and acts as a transcription factor exerting both nuclear and cytosolic functions [1, for review]. The transactivation and transcriptional repression functions of p53 are well documented, but p53 is also known to be involved in cell death. Indeed, upon various cell-death stimuli, p53 translocates to mitochondria and causes permeabilization of the OMM and consequent release of pro-apoptotic factors. On the other hand, transcription factors such as p53 translocate easily to the mitochondrial matrix, and possess binding sites in the mitochondrial genome, homologous to their binding sites in the nuclear genome. Besides, a role for these factors in mitochondrial transcription and energy metabolism in relation to apoptosis is now emerging. A small but significant fraction of the stress-induced p53 protein was detected in the mitochondria after DNA damage or hypoxia. p53 was previously described to negatively influence the expression of enzymes of oxidative phosphorylation (OXPHOS) and mitochondrial 16S rRNA in stress-related apoptotic states, in addition to its non-genomic actions. The translocation of p53 into mitochondria seems to be rapid and to precede the


168

Tatiana R. Rosenstock & A. Cristina Rego

effects on mitochondrial membrane permeabilization and release of cytochrome c. Moreover, p53 was also demonstrated to interact with Bcl-xL [151, for review]; alternative interaction with anti- or pro-apoptotic proteins may regulate its function at the mitochondrial surface [152]. At the same time, p53 regulates autophagy, by transcriptional activation of target genes and by nontranscriptional mechanisms. For example, DRAM (damageregulated autophagy modulator) is a p53 target genes which encodes a lysosomal protein. Interestingly, DRAM is essential for p53-mediated apoptosis [153]. Unexpectedly, Kroemer and co-workers (2008) found that autophagy may be stimulated in the absence of p53 [154], which could act as an endogenous repressor of autophagy. The cytosolic fraction of p53 is the one responsible for inhibiting autophagy. In glucose starvation conditions, ATP levels are drastically reduced in wild type cells, whereas in the absence of p53, ATP levels are maintained high. The resistance of p53-/- cells to metabolic stress is dependent on autophagy, since suppression of autophagy by depletion of AMPKalpha or Beclin 1 reduced the capacity of p53-/- cells to maintain ATP levels during starvation [154]. It is not clear which mechanism allows p53 to function either as autophagy regulator or apoptosis inducer, but the two processes may be coordinately regulated by cell death inducers.

2.2. Autophagy in HD Despite all the discussion around protein aggregation, autophagy has emerged as a pathway of extreme importance in neurodegenerative diseases and has a role in maintaining normal neural function by degrading aggregateprone proteins, even when neurons are not exposed to mutant misfold peptides [155]. Therefore, one of the main goals of research in neurodegenerative disorders has been to improve clearance of these accumulated proteins [156]. Indeed, evidences suggest that an up-regulation of autophagy may constitute a therapeutic intervention for clearing diseasecausing proteins, such as mHtt [157]. The extent of protein misfolding and aggregation is correlated with the length of polyQ tracts in a variety of HD models. Multiple N-terminal Htt fragments containing the polyQ tract are present in the brains of HD patients and mice, and various cleavage sites in the N-terminal region of Htt were identified [158]. However, smaller N-terminal fragments of mHtt seem to be more prone to misfolding and aggregation and appears to be more toxic than full-length mHtt, as demonstrated in cellular models of HD expressing different Htt fragments [159].


Modified mitochondrial dynamics in neurodegeneration

169

Several evidences corroborate to the important role of autophagy in the pathophysiology of HD, namely: (i) The presence of endosomal and/or lysosomal organelles and autophagic vacuole-like structures in the brains of patients with HD [160]; (ii) Biochemical and morphological markers of autophagy-like structures in different models overexpressing a variety of polyQ proteins, such as in cultured cells [161; 162; 163; 164], fly [165] and mouse models of HD [166]; (iii) Endosomal and/or lysosomal activity caused by expression of mHtt [167]; (iv) The translocation of Htt to the nucleus in response to ER stress is inhibited when Htt contains the polyQ expansion and, as a result, mHtt expressing cells have a perturbed ER and an increase in autophagic vesicles [168]; (v) Molecular determinants of autophagic vacuole formation appear to be recruited more easily to cytoplasmic than to nuclear aggregates in the presence of mHtt, which might help to explain why protein aggregates are more toxic when directed to the nucleus [163]; (vi) In lymphoblasts from patients with HD, the number of autophagic vacuoles correlates with the length of the polyQ expansion [169]; (vii) Blocking autophagy reduces cell viability and increases the number of cells bearing mHtt aggregates, whereas stimulating autophagy promotes Htt degradation [161]; indeed, it was shown that the induction of autophagy attenuates toxicity caused by mHtt in cell, fly and mouse models [162]; (viii) mTOR is sequestered into aggregates of mHtt with subsequent inhibition of its kinase activity [162] and induction of autophagy, which may promote Htt degradation and the clearance of aggregates [161-162]; (ix) Accumulation of mHtt can lead to mTOR independent autophagy; (x) Sequestration of Beclin 1 into neuronal intranuclear inclusions in patients with HD appears to reduce the autophagic clearance of mHtt [146]; notably, expression of Beclin 1 decreases, in an age-dependent fashion, in human brains [146], suggesting an increased susceptibility for the occurrence of neurodegenerative diseases. In cultured neurons from transgenic R6/2 mice, however, activation of the lysosome–autophagy system was only observed when the neurons were exposed to dopamine-mediated oxidative stress, suggesting that the combination of mHtt and oxidative stress induces autophagy [166]. Furthermore, the autophagic clearance of mHtt aggregates is likely to be a consequence of degrading the aggregate precursors (soluble and oligomeric species), rather than large aggregates, which are bigger than typical autophagosomes [89; 162]. In the case of HD, autophagy is the key pathway to degrade mHtt (both full-length and exon 1 forms) [146; 162], whereas wild-type Htt (full-length or exon 1) has a very low dependence on autophagy [170].


170

Tatiana R. Rosenstock & A. Cristina Rego

Mouse clonal striatal cells transiently transfected with truncated and fulllength human wild-type Htt or mHtt show the presence of both normal and mutant proteins in dispersed and perinuclear vacuoles [171]. Furthermore, Htt-labeled vacuoles display the ultrastructural features of early and late autophagosomes, and Htt-enriched cytoplasmic vacuoles appear to be more abundant in cells expressing mHtt [167]. Indeed, brains from HD patients and transgenic mice exhibit multiple vesicular structures similar to endosomes and lysosomes [160]. Moreover, increased number of autophagosomes have been found in lymphoblasts of HD patients, as compared to control lymphoblasts [169]. This increased presence of endocytic and autophagic compartments was attributed to enhanced endocytosis and autophagy. Autophagy in HD was also demonstrated by Zhang and coworkers (2009), who showed the conversion of LC3-I to LC3-II in the toxic model of HD, in rats stereotaxically injected with 3NP in the striatum [172]. They also demonstrated increased formation of autophagosomes and the expression of active lysosomal cathepsin B and D. These data are in agreement with Qin and co-workers (2003), who previously showed that autophagy upregulates cathepsins and enhances the clearance of Htt fragments, despite the fact that mHtt was relatively resistant to degradation by cathepsin D [161]. In addition, Tung and co-workers (2010) suggested that p62 directly binds to the evolutionarily conserved cargo receptor-binding domain of Atg8/LC3 and selectively mediates the clearance of mHtt [173]. Posttranslational modification of mHtt by acetylation at lysine 444 (K444) facilitates trafficking of mHtt into autophagosomes [157]. Therefore, clearance of the mutant protein by autophagy reverses the toxic effects of mHtt in primary striatal and cortical neurons and in a transgenic C. elegans model of HD. In contrast, mHtt that is rendered resistant to acetylation dramatically accumulates and leads to neurodegeneration [157]. During HD, Atg7 seems to be related to the autophagosome formation and regulation of clearance of phosphorylated Htt which implies that vesicles may mediate phosphorylated Htt autophagic degradation [174]. In addition, Metzger and co-workers (2010) identified the V471A polymorphism in the Atg7 gene as a new genetic modifier of HD [175]. This polymorphism seems to affect the age of onset in more than 900 European HD patients and represents about 1% of the variance in the age of onset that cannot be accounted for by the expanded CAG repeat in the HD gene. Although the influence of the V471A polymorphism on the variability of the age of onset is not very high, it leads to approximately a 4-year earlier onset of the first symptoms and thus has an effect of aggravating the disease. The association of the Atg7 V471A polymorphism with the HD age of onset was also


Modified mitochondrial dynamics in neurodegeneration

171

detected in the German and Italian patients separately, confirming that this was not a population-specific effect [175]. The intracellular accumulation of Htt with expanded polyQ is highly sensitive to the expression levels of Beclin 1. Shibata and colleagues (2006) showed an age-dependent decline of Beclin 1 expression in human brains that might provide a mechanism for the accumulation of expanded polyQ species [146]. Besides, they showed that Beclin 1 is recruited to Htt inclusions in the R6/2 mouse brain and in the striatal samples of human HD patients. Thus, sequestration of Beclin 1 in the vulnerable neuronal population of HD patients might further reduce Beclin 1 function and autophagic degradation of expanded polyQ-containing Htt, which suggest a potentially important role of Beclin 1 in both the initiation and progression of HD [146]. Moreover, the accumulation of mHtt in STHdhQ111/Q111 cells (immortalized striatal neurons derived from knock-in mice expressing full-length mHtt with 111 glutamines) induced by the partial inhibition of Beclin 1 expression, supports the hypothesis that the age-dependent decline of Beclin 1 expression is a contributing factor to the age-delayed onset of HD. To corroborate these data, Shibata and co-workers (2006) also investigated the importance of Beclin 1 in HD in the presence of siRNA to Beclin 1 [146]. It was shown that Beclin 1 knockdown induced an intracellular accumulation of mHtt, which was much milder in cells expressing wild-type Htt. This suggests at least two mutually nonexclusive possibilities regarding the catabolism of wild-type and mHtt: i) The increased accumulation of mHtt in Beclin 1 knockdown cells may indicate the exclusive dependence of mHtt degradation on the autophagy pathway, whereas the degradation of wild-type Htt may be carried out by multiple pathways, such as the UPS, and/or ii) The excessive accumulation of mHtt in Beclin 1 knockdown cells may be due, in part, to the oligomerization of mHtt which in turn further reduces its own degradation. Thus, it seems that Beclin 1-regulated autophagy pathway mediates a part of intracellular long lived protein turnover that is critical for the catabolism of mHtt [146]. Besides Beclin1, it was shown that p62 localize with Htt aggregates and can modulate Htt-induced cell death. Therefore, p62 may act as an adapter autophagy protein by linking toxic substrates to core autophagic machinery and facilitating the clearance of these substrates by the autophagic lysosomal pathway. The subcellular localization of Htt and many of its interactors also suggest a role of Htt in endocytosis. Recently it has been shown that Htt interacts, indirectly, with the early endosomal protein Rab5 [176]. Rab5, a member of the small GTPase family, is a key regulator of the early endocytic pathway in mammalian cells [177]. Interestingly, Rab5 acts at an early stage


172

Tatiana R. Rosenstock & A. Cristina Rego

of autophagosome formation in a macromolecular complex that contains Beclin 1 and Vps34 [176]. However, the pathway responsible for the clearance of mHtt and the precise mechanism behind macroautophagy malfunction in HD is still poorly understood. It is known, because of a functional genetic screen of HD, that 56 transcripts were up-regulated in the presence of pathogenic polyQ lenghts, and from these, 23 were required for mHtt clearance [164]. Interestingly, the pattern of genes revealed that activation of insulin receptor substrate 2 (IRS-2), a scaffolding protein that mediates the signaling cascades of growth factors such as insulin and insulin-like growth factor 1 (IGF-1) [178], leads to a macroautophagy-mediated clearance of the accumulated polyQ proteins. Clearance is present despite the activation of AKT, mTOR, and p70S6 kinase, but still requires proteins previously implicated in macroautophagy, such as Beclin1 and hVps34. This is surprising because activated mTOR inhibits the classic, starvation-induced macroautophagy [109]. Together, this study suggests that macroautophagy in the presence of accumulated proteins can also occur in a mTOR-independent manner and that this represents another important pathway through which growth factors such as insulin and IGF-1 may exert beneficial effects [164]. The observation of an alternate route of autophagy regulation via a pathway leading from IRS2 to Vps34/Beclin1 independent of class I PI3K activity is intriguing, and raises interesting questions. While the effect of IGF-1 on Htt polyQ protein clearance was seen in both HeLa and N2a neuroblastoma cell lines, the signaling pathways were only explored in HeLa cells, a model system for insulin/IGF1 action. Additionally, AKT/mTOR/S6K signaling in mHttexpressing cells was not compared to those not expressing the mutant form of the protein. Despite these data, the mechanism of insulin-mediated regulation of autophagy is less explored in neuronal cells; moreover, it is possible that decreased mTOR activation in mutant cells leads to ineffective role in preventing autophagy. Using cellular and mouse models of HD and cells from humans with HD, Martinez-Vicente and co-workers (2010) [179] identified a primary defect in the ability of autophagic vacuoles to recognize cytosolic cargo in HD cells. Autophagic vacuoles form at normal or even enhanced rates in HD cells and are adequately eliminated by lysosomes, but they fail to efficiently trap cytosolic cargo in their lumen. They proposed that inefficient engulfment of cytosolic components by autophagosomes is responsible for their slower turnover, functional decay and accumulation inside HD cells. Additionally, the dynamic interaction of Htt with the ER [180], an organelle genetically linked to the formation of autophagosome [95], the association of Htt with


Modified mitochondrial dynamics in neurodegeneration

173

late endosomes and autophagic vesicles [180] and the interaction of Htt with Rab5 (involved in autophagosome formation [176]), all indirectly support a relationship between Htt and the autophagic system. However, the possible function of Htt in macroautophagy and the step(s) affected by mHtt are still unclear.

3. Mitophagy The first report demonstrating the presence of mitochondria within an autophagosome in mammalian cells was made in 1957 in kidneys of newborn mice [181]. Engulfment of mitochondria by autophagy occurs through a selective process, termed mitophagy, which seems to play an important role for mitochondrial quality control and recycling [182-183]. Besides, mitophagy has been shown to have a role in cellular quality control [183-184] and in neurodegenerative diseases, including AD and PD [185-186], and therefore it has been the center of growing attention.

3.1. Molecular mechanisms of mitophagy Recent evidences from yeast to mammals suggest that autophagy is the primary mechanism to eliminate dysfunctional, aged or excessive mitochondria. Damaged mitochondria can induce apoptosis by releasing cytochrome c, which is correlated with aging and development of neurodegenerative diseases [187]. Although the molecular mechanisms of selective mitochondrial autophagy (mitophagy) are poorly understood, several stimuli support the occurrence of this process, including nutrient deprivation (starvation), hypoxia, mitochondrial damage (including modified mitochondrial turnover, biogenesis, transmembrane potential, fission/fusion) and/or apoptotic stimuli [188] (Figure 3).

3.1.1. ATG genes in mitophagy Besides the fundamental genes involved in autophagy (see previous section 3.1.1.), during mitophagy other genes take a part. ATG11, ATG20 and ATG24 are required for mitophagy [183-184], but also for both the Cvt pathway and pexophagy, but not for autophagy [189]. ATG7 is also an important gene for mitophagy. A study using conditional Atg7 knockout mice revealed that, when this gene was disrupted in the adult body, degradation of mitochondria in response to food deprivation was impaired in the liver [104]. Similarly, liver-specific Atg7 knockout and systematically mosaic Atg5-


174

Tatiana R. Rosenstock & A. Cristina Rego

Figure 3. Several cellular mechanisms seem to be related to the induction of mitophagy, such as starvation, mitochondrial damage, changes in the levels of proapoptotic proteins, and changes in mitochondrial membrane potential (MMP), mitochondrial biogenesis and turnover.

deficient mice displayed hepatocellular tumorigenesis accompanied by mitochondrial swelling and decreased respiration [190], revealing the importance of the core autophagy to mitochondrial turnover. Additionally, Atg32 has a central role in mitophagy. The Atg32 is a 59 kDa protein located in the OMM with its N- and C-terminal domains oriented towards the cytoplasm and the intermembrane space, respectively. Following the induction of mitophagy, Atg32, which works as a mitochondrial receptor, binds Atg11, an adaptor protein for selective types of autophagy, and then Atg32 is recruited to and imported into the vacuole along with mitochondria. Atg11 recruits mitochondria to the PAS where Atg32 can bind Atg8 to promote the generation of the phagophore, which encloses the mitochondria [102; 191]. Interestingly, Atg32 has a WXXI/L/V motif that is present in yeast Atg19 or in the mammalian protein p62/SQSTM1 (sequestosome-1), and which functions as an Atg8 or LC3 binding sequence, respectively [191]. Mammalian p62/sequestosome-1 protein binds to both LC3 and polyubiquitinated cargo proteins destined to undergo autophagy-mediated degradation. Atg32 binds Atg8 through this motif, and this binding is


Modified mitochondrial dynamics in neurodegeneration

175

required for efficient sequestration of mitochondria by the phagophore [192]. Atg32 confers selectivity for mitochondrial sequestration as a cargo and is necessary for recruitment of this organelle by the autophagy machinery [102]. Indeed, it was demonstrated that overexpression of Atg32 promotes the induction of mitophagy and that Atg32 mutant cells are defective in mitophagy, but competent in bulk autophagy and the Cvt pathway [102; 191]. Nevertheless, the mammalian homologues or counterparts of Atg32 and Atg11 have not been identified yet. From the genomic screen for yeast mutants, ATG33 was also identified as mitophagy-related genes [102; 191; 193] and is not required for other types of selective autophagy and nonselective autophagy. The Atg33 protein has 20 kDa and also localizes in the OMM. Although the deletion of ATG33 blocks mitophagy to half the level of the wild type when induced by starvation, it blocks mitophagy almost completely when induced at stationary phase [193]. This finding leads to the hypothesis that Atg33 is required to detect or present aged mitochondria for mitophagy when cells reached the stationary phase.

3.1.2. Decrease in MMP and changes in mtDNA The hypothesis that mitophagy contributes both to eliminating damaged mitochondria and reducing the amount of mitochondria to adjust its volume in accordance with cellular energy requirements is in agreement with the fact that interference with F1F0-ATPase biogenesis in a temperature sensitive mutant [194], or osmotic swelling of mitochondria caused by depletion of Mdm38, a mitochondrial K+/H+ exchanger, [184] induce mitophagy. Still, studies in pancreatic β-cells and COS7 cells showed that depolarized mitochondria are eventually autophagocytosed and once mitophagy is compromised, oxidized proteins accumulate, and cellular respiration and insulin secretion decrease [48; 183]. Yeast cells lacking core ATG genes have also shown a reduced mitochondrial activity and low MMP [182]. In mammalian cells, various lines of evidence suggest that mitophagy distinguishes damaged and intact mitochondria by sensing their membrane potential. Depolarized mitochondria are selectively surrounded by doublemembrane and sequestered to be degraded by hydrolytic enzymes [3]. On the other hand, mitochondrial depolarization caused by an uncoupler such as carbonyl cyanide m-chlorophenylhydrazone (CCCP) does not induce mitophagy in wild-type yeast [193; 195], although mitophagy can be induced by the same stimulus in mammalian cells [48; 196]. This finding suggests that induction of mitophagy in yeast requires not only mitochondrial depolarization but also an additional unidentified factor(s) or signaling.


176

Tatiana R. Rosenstock & A. Cristina Rego

A drastic depletion of mitochondria might be useful to eliminate mtDNA copies that contain enough mutations that interfere with organelle function. In fact, it was shown that elimination of mitochondria works as a purifying selection of functional mtDNA, decreasing the frequency of transmission of mutated genomes to the offspring [196]. While this has been clearly demonstrated in the oocyte, it is unknown whether such elimination might take place in somatic cells to maintain fitness of the mitochondrial genome. However, the removal of mitochondria usually needs to be compensated by mitochondrial biogenesis or it will become detrimental [197]. It is thus possible that increased biogenesis is a compensatory mechanism for defective mitochondrial function.

3.1.3. Mitochondrial dynamics In the mitochondrial life cycle, autophagy selectively targets to depolarized mitochondria that are generated via fission events. It is therefore conceivable that the number of depolarized mitochondria correlates with the fission efficiency, which in turn affects the degree of mitophagy. In agreement with this idea, expression of a dominant negative variant of the fission GTPase Drp1 in INS1 (rat insulinoma derived -cell line) cells resulted in the formation of elongated mitochondrial tubules and reduction of mitophagy [183]. But how do cells specifically prevent disordered mitochondria from fusion with others? A primary process is that dissipation of MMP causes proteolytic processing or degradation of the OPA1 long isoforms, leading to inhibition of mitochondrial fusion [27]. Another process involves ubiquitination of the fusion GTPases Mfn1 and Mfn2 by Parkin in depolarized mitochondria, being then degraded in a proteasome-dependent manner [198-199]. These MMP-linked inactivation mechanisms for the OPA1- and Mfn1 and Mfn2-mediated fusion pathways are likely to ensure effective mitophagy (Figure 3). Although half of the genes identified by screens with yeast mutants are involved with membrane trafficking pathways, which have an impact on autophagy-related pathways, the other half includes several genes encoding mitochondrial or cytosolic proteins. Some of these genes may be involved in the regulation of mitophagy. Such an example is DNM1 gene, which encodes a mitochondrial dynamin-related GTPase required for mitochondrial fission named Dnm1 [193]. The Dnm1D conditional knockout in yeast inhibits mitophagy induced by Mdm38 [184]. Thus, these data are in agreement with the literature that demonstrates that fragmentation of mitochondria is a prerequisite for mitophagy in mammalian cells [183].


Modified mitochondrial dynamics in neurodegeneration

177

3.1.4. PINK-Parkin, Bnip3 and Nix PINK1 is encoded by the PARK6 gene; it is a mitochondrial targeted Ser/Thr kinase that plays a physiological role in mitochondrial maintenance, suppressing mitochondrial oxidative stress, fission and autophagy [for review, 200]. On the other hand, Parkin, encoded by the PARK2 gene [201] is an E3 ubiquitin ligase important to protect cells, removing damaged mitochondria, and therefore is important to mitophagy. Both Parkin and PINK1 were identified as genes responsible for an early-onset PD and thus are associated with familial parkinsonism [202]. PINK1 is a 581-amino acid Ser/Thr kinase localized in the cytosol and in the mitochondria due to its N-terminal mitochondrial targeting sequence [202-203]; in fact, PINK1 is in the IMM with its kinase domain facing the intermembrane space/cytosol [203-205]. In coupled mitochondria, PINK1 is imported into the IMM via the general mitochondrial import machinery, TOM and TIM23 complexes [205]. Consistent with it, deletion of PINK1’s putative transmembrane domain results in targeting of its kinase domain to the matrix [203]. The primary sequence also includes a putative transmembrane domain important for orientation of the PINK1 domain [203], a conserved kinase domain homologous to calcium calmodulin kinases, and a C-terminal domain that regulates autophosphorylation activity [204]. PINK1 catalytic activity is necessary for its neuroprotective role, because a kinasedeficient K219M substitution in the ATP binding pocket of PINK1 abrogates its ability to protect neurons [206]. Although PINK1 mutations do not seem to impair mitochondrial targeting, PD-associated mutations differentially destabilize the protein, resulting in loss of neuroprotective activities [207-208]. Although overexpression of PINK1 is neuroprotective, less is known about neuronal responses to loss of PINK1 function. Dagda and colleagues (2009) [47] found that stable knockdown of PINK1 induced mitochondrial fragmentation and autophagy in SH-SY5Y cells, which was reversed by the reintroduction of an RNAi-resistant plasmid for PINK1. Overexpression of wild-type PINK1, but not its PD-associated mutants, protects against several toxic insults in neuronal cells [202; 209]. Mitochondrial targeting is necessary for some [207] but not all of the neuroprotective effects of PINK1 [206], implicating involvement of cytoplasmic targets that modulate mitochondrial pathobiology [203]. Moreover, stable or transient overexpression of wild-type PINK1 increased mitochondrial interconnectivity and suppressed toxin-induced mitophagy. Up to now, two models by which the PINK1/Parkin pathway influences mitochondrial integrity are proposed. The models postulate that the PINK1 directly or indirectly senses mitochondrial damage resulting from mtDNA mutations, toxins, oxidative stress, or other sources and communicates this


178

Tatiana R. Rosenstock & A. Cristina Rego

information to its substrates. Models 1 and 2 differ from one another in terms of the subcellular distribution of PINK1, the kinase domain facing the cytoplasm [203], and the possible substrates of PINK1. In model 1, a proteolytically processed, soluble form of PINK1 is exported from the intermembrane space of damaged mitochondria to phosphorylate its possible substrates in the cytoplasm. PINK1 phosphorylates Parkin localized in the cytoplasm, thereby inducing Parkin to translocate to mitochondria and ubiquitinate its mitochondrial target proteins [200; 210]. However, Parkin seems to translocate to mitochondria and exerts its E3 activity only when the mitochondrial membrane potential decreases, which means, when there are damaged mitochondria [210]. In model 2, PINK1 anchored to mitochondria with its kinase domain facing the cytoplasm phosphorylates its cytoplasmic substrates upon mitochondrial damage. PINK1 phosphorylates proteins residing on the OMM, which in turn elicits the recruitment of Parkin to ubiquitinate these phosphorylated mitochondrial proteins [200]. It should be emphasized that the PINK1 substrates in the two models are potentially interchangeable (i.e., soluble PINK1 in model 1 could proceed to phosphorylate mitochondrial proteins, and membrane localized PINK1 in model 2 could phosphorylate Parkin). In fact, it was shown that expression of PINK1 on the OMM is sufficient for Parkin recruitment and mitophagy [205]. Excellent candidate targets of PINK1/Parkin include the fission promoting factor Fis1 and the fusion-promoting factor Mfn, both of which reside at the OMM. Ubiquitination of Fis1 could serve to activate its fission-promoting function, which could involve the recruitment of Drp1 from the cytoplasm to initiate the fission event. Alternatively, ubiquitination of Mfn could lead to its inactivation to promote the segregation of terminally damaged mitochondria that are unable to re-fuse with the mitochondrial network, while simultaneously labeling them for degradation by mitophagy [200]. PINK1 levels on individual mitochondria are regulated by voltagedependent proteolysis to maintain low levels of PINK1 on healthy, polarized mitochondria. On damaged mitochondria that have lost their membrane potential, however, PINK1 cleavage is inhibited, leading to high PINK1 expression on the dysfunctional mitochondria [205]. PINK1 accumulation on mitochondria following depolarization is both necessary and sufficient for Parkin recruitment to mitochondria and, therefore, Parkin-induced mitophagy. Thus, Narenda and co-workers suggested that full-length mitochondrial PINK1 is the active form in the PINK1/Parkin pathway [205]. Disease-causing mutations in PINK1 and Parkin disrupt Parkin recruitment and Parkin-induced mitophagy at distinct steps [205]. Interestingly, overexpression of Parkin can partially compensate for PINK1 loss, but


Modified mitochondrial dynamics in neurodegeneration

179

PINK1 overexpression cannot compensate for Parkin loss, corroborating the previous findings that PINK1 functions upstream of Parkin in a common pathway. Additionally, mice null for either Parkin or PINK1 exhibit increased oxidative damage and decreased mitochondrial function in the striatium [211]. These findings suggest that Parkin and PINK1 may function in an evolutionarily conserved pathway critical for the maintenance of mitochondrial integrity and function [205]. Additionally, stable loss or knockdown of PINK1 in mammalian cellular models and mice leads to a number of mitochondria-related abnormalities. Mitochondria in these cells or tissues exhibit ETC dysfunction, diminished membrane potential, increased ROS production, mitochondrial fragmentation, and calcium deregulation, among other abnormalities [212-213]. Although some of these abnormalities may be a reversible consequence of others, for instance, mitochondrial fragmentation may be due to low membrane potential [212], ETC dysfunction and calcium deregulation [213]. For instance, complex I and the putative Na+/Ca2+ transporter seem to be dysfunctional in cultured cells following PINK1 knockdown [213], whereas complex I and II appear to be dysfunctional in the striatum of mice lacking PINK1 [211]. In addition to PINK1 and Parkin, other proteins have attracted attention regarding the autophagy-dependent degradation of depolarized mitochondria [48]. Indeed, proteins such as Bnip3 and Nix [212; 214] seem to participate not only in non selective autophagy, as already mentioned herein, but also in mitophagy in mammalian cells (Figure 3). It is known that Bnip3 induces mitochondrial translocation of Drp1, and Drp1-mediated mitochondrial fission is correlated with increased autophagy [215]. On the other hand, inhibition of Drp1 reduced Bnip3-mediated autophagy. Overexpression of Drp1K38E, a dominant negative of Drp1, or Mfn1 prevented mitochondrial fission and autophagy by Bnip3. Also, inhibition of mitochondrial fission or autophagy resulted in increased death of myocytes overexpressing Bnip3. Moreover, Bnip3 promoted translocation of the E3 ubiquitin ligase Parkin to mitochondria which was prevented in the presence of a Drp1 inhibitor. Interestingly, induction of autophagy by Bnip3 was reduced in Parkin deficient myocytes [215]. Regarding Nix, it is known to be required to autophagic degradation of reticulocytes mitochondria, since mitochondrial autophagy is defective in Nix−/− reticulocytes [214]. Nix-dependent collapse of the MMP appears to be an essential event for erythroid mitophagy and maturation [196]. Moreover, Nix is specifically required for mitophagy, since ribosomes were normally eliminated in the absence of Nix [196]. Erythroid maturation is also regulated by the Ser/Thr kinase Ulk1, the mammalian homolog of yeast Atg1p, which,


180

Tatiana R. Rosenstock & A. Cristina Rego

in complex with Atg13, is responsible for autophagosome formation regulated by mTOR activity. Recently, it was reported that Nix is a mitochondrial receptor that can directly connect to one of the autophagic machinery components, the Atg8 homologs LC3 and GABARAP (GABA receptor associated protein) [216].

3.1.5. Starvation Starvation plays a role in various aspects of cell physiology such as survival during starvation, intracellular clearance of dysfunctional or superfluous proteins and organelles, proper development and aging [73]. Studies in yeast elucidated that autophagy-dependent recycling is vital for maintenance of mitochondria during starvation [217]. Mitophagy can be induced by nitrogen starvation after pre-culturing yeast in a non-fermentable medium that induces the proliferation of mitochondria (e.g. where lactate or glycerol are the sole carbon source) [218-219]. Under nutrient-rich conditions, Bcl-2 interacts with AMBRA1 at the mitochondrial surface, and Beclin 1 at the ER to inhibit autophagy [123]. Upon starvation, AMBRA1 dissociates from Bcl-2, and associates with Beclin 1 at the ER and mitochondrial surface to stimulate autophagy [123]. As is evident from the pivotal role for programmed cell death (PCD), mitochondria may serve as integrated circuits to coordinate autophagy and apoptosis. Moreover, Kim and Lemasters (2010) [220] demonstrated that in hepatocytes, with complete growth medium, GFP-LC3 fluorescence was distributed diffusely in the cytosol and incorporated in mostly small patches, which likely represent PAS in proximity to mitochondria after nutrient deprivation plus glucagon to simulate fasting, PAS grew into green cups (phagophores) and then rings (autophagosomes) that enveloped individual mitochondria, a process that was blocked by 3-methyladenine [220]. Autophagic sequestration of mitochondria took place in 6.5 Âą 0.4 min and often occurred coordinately with mitochondrial fission. After ring formation and apparent sequestration, mitochondria depolarized in about 12 min [220]. In addition, after ring formation, LysoTracker Red uptake, a marker of acidification, occurred gradually, becoming fully evident after about 10 min. PicoGreen that labels mtDNA showed that mtDNA was also sequestered and degraded in autophagosomes. Overall, the results indicated that PAS serve as nucleation sites for mitophagy in hepatocytes during nutrient deprivation. In addition, after autophagosome formation, mitochondrial depolarization and vesicular acidification occur, and mitochondrial contents, including mtDNA, were degraded [220]. Importantly, starvation is also a trigger for mitochondria


Modified mitochondrial dynamics in neurodegeneration

181

elongation [221]. This seems to be related to increased levels of cAMP, protein kinase A (PKA) activation, and subsequent phosphorylation of Drp1, which is retained in the cytoplasm [221]. Elongation of mitochondria may have a role of sparing autophagic degradation and promoting energy production [221].

3.2. Mitophagy and neurodegenerative disorders While mitochondrial autophagy can occur as part of a nonselective upregulation of autophagy, selective degradation of damaged or unneeded mitochondria through mitophagy is a rapidly growing area in development, cancer and neurodegeneration. However, up to now, there is no technique specific enough to evaluate mitophagy in neurons. It is known that removal of aberrant mitochondria play a protective role in age-related neurodegenerative disorders, such as in PD. Parkin, the loss-offunction of which causes PD, is selectively recruited to dysfunctional mitochondria with low membrane potential in mammalian cells and causes their autophagy-mediated degradation [48], suggesting that PD may be at least in part associated with failure to eliminate dysfunctional mitochondria. It was also shown that in SH-SY5Y cell line, loss of PINK1 elevated superoxide production, induced mitochondrial fragmentation, MMP collapse, autophagy and mitophagy [47]. Furthermore, overexpression of Parkin enhanced the mitophagy response. Thus, PINK1 appears to maintain the mitochondrial networks which, together with Parkin-induced mitophagy, may serve to reduce toxicity associated with dysfunctional mitochondria in PD. Mitophagy also seems to play a role during AD. Moreira and coworkers [222] showed that there is increased mitochondrial sequestration in autophagosomes. Whether these mitochondria are degraded upon their autophagosomal sequestration or remain sequestered and accumulated within autophagosomal vesicles remains to be clarified. Hirai and coworkers [223] demonstrated a decreased mitochondria in vulnerable neurons, in a specific region of the brain during AD. Furthermore, the same authors demonstrated increased cytosolic accumulation of mitochondrial markers such as mtDNA and subunit I of COX [223], which is inconsistent with an efficient autophagic-lysosomal proteolytic degradation, suggesting a leak of sequestered material from autophagic vacuoles. In addition, it has been reported that Aβ induces lysosomal membrane permeabilization [224]; multiple-oligomeric aggregates of Aβ42, but not Aβ40, insert into lysosomal membrane in a pH-dependent manner, contributing to its instability [225]. Accordingly it was also demonstrated that the overexpression of Aβ42 in


182

Tatiana R. Rosenstock & A. Cristina Rego

Drosophila neurons induced an age-dependent impairment of neuronal autophagy due to a leakage of autolysosome, causing a cytosolic acidification and damage of several cellular constituents [226]. Deficient of Beclin 1 was previously described in AD patients [227]. Since there are indications that mitochondrial fission and selective fusion tag damaged mitochondria for mitophagy elimination [183], and that mitochondrial fission/fusion events in fibroblasts from sporadic AD patients [228] and M17 neuroblastoma cells overexpressing the Swedish variant of APP [75] is imbalanced, it is expected that mitophagic elimination of damaged mitochondria in AD brains is failing. Autophagy may represent an initial attempt of the HD cell to eliminate the mutant protein; however mitophagy features have not been identified during HD yet. Since mHtt interferes directly, and indirectly, with mitochondrial function and, therefore, the organelle has to be recycled to keep cellular homeostasis, mitophagy is expected to occur, and altered mitophagy activity may interfere with the disease.

Conclusions – converging mitochondrial abnormalities in HD Like in many neurodegenerative disorders, the most common feature of neuronal dysfunction in HD is altered mitochondrial function [1, for review; 230-231]. Mitochondrial changes in HD have been correlated with: i) Reduced activity of several components of oxidative phosphorylation, namely complexes II, III, and IV of the ETC in striatal neurons from latestage HD patients, as well as in HD transgenic mice and HD rodent models treated with 3NP [232]; ii) Low mitochondrial ATP and decreased mitochondrial ADP-uptake [e.g. 233]; iii) Impaired mitochondrial movement and mitochondrial dynamics [63-64]; iv) Calcium-induced mitochondrial permeability [234-235]; and v) Diminished glucose metabolism in striatum from HD patients [236-237]. Early changes in mitochondrial function during HD were previously reported to be due to a direct interaction of mHtt with the organelle. An association between the N-terminal portion of mHtt and OMM was found in brain and liver mitochondria from several HD models [234]. This interaction can lead to the pore formation and thus the release of pro-apoptotic factors (e.g. cytochrome c), affecting respiratory chain activity and increasing ROS generation [1, for review]. Moreover, mutant, but not wild type Htt, induces PTP opening in isolated mitochondria [238], and also facilitates this process in permeabilized cells [239]. In addition, it was demonstrated that mHtt can interfere with complex II activity [238; 240].


Modified mitochondrial dynamics in neurodegeneration

183

Mitochondrial function can further be affected by changes in transcription regulation [241]. Indeed, a link between mitochondrial dysfunction, transcription deregulation and HD was established more recently. mHtt may cause mtDNA defects, including mtDNA deletions in HD brains and peripheral tissues from HD patients [242]. Moreover, PGC-1α is also altered by mHtt. Cui et al. found that mHtt inhibits expression of PGC-1α by associating at the promoter and interfering with CREB/TAf4-mediated expression of PGC-1α [243]. In addition, mHtt suppress uncoupling protein-1 (UCP-1, the effector of adaptive thermogenesis) promoter activity, which is reversed by PGC-1α [244]. PGC-1α also binds to transcription factors such as nuclear respiration factors −1 and −2 (NRF-1, 2), which regulate nuclear-encoded mitochondrial genes such as cytochrome c, mitochondrial transcription factor A (Tfam) and components of the respiratory chain complexes I-V [5]. In addition, mHtt induces deficits in mitochondrial calcium buffering capacity [240] and inhibits histone acetyltransferases, an effect counteracted by histone deacetylase (HDAC) inhibitors [245]. Notably, HDAC inhibitors could also control mitochondrial Ca2+ handling and dysfunction [240]. Apart from these changes, mHtt causes mitochondrial fragmentation, namely through interaction with Drp1 [69], thus altering the critical balance between fission and fusion, and mitochondrial neuronal dynamics. Along with these changes, autophagy is also impaired [e.g. 179], possibly due to direct effects of expression of mHtt or mHtt-induced changes in apoptotic proteins, ROS generation or deregulation of Ca2+ homeostasis. Although the precise mechanisms are still undefined, decreased macroautopahgy of aggregates of mHtt and/or decreased mitophagy certainly contribute for the accumulation of dysfunctional mitochondria and exacerbate HD pathogenesis in post-mitotic cells. Considering that several mechanisms are triggered following expression of mHtt, HD therapeutics will largely benefit when the selective targets are identified and thus combined therapies can be applied directed against mechanisms involving mitochondrial dysfunction, such as altered mitochondrial fission/fusion, biogenesis, decrease ETC activity, increase production of ROS, loss of Ca2+ homeostasis and inhibition of autophagy processes.

Acknowledgements The authors were supported by grants from the Fundação para a Ciência e a Tecnologia (FCT), Portugal, project reference PTDC/SAUFCF/108056/2008. T. R. Rosenstock further acknowledges postdoctoral funding from FCT, Portugal.


184

Tatiana R. Rosenstock & A. Cristina Rego

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.

Rosenstock T.R., Duarte A.I., and Rego A.C. 2010, Curr Drug Targets 11:1. Knott A.B., and Bossy-Wetzel E. 2008, Ann NY Acad Sci 1147:283. Okamoto K., and Kondo-Okamoto N. 2011, Biochim Biophys Acta (http://dx.doi.org/10.1016/j.bbagen.2011.08.001). Wenz T., 2011, J Aging Res 2011:810619. Lin J., Handschin C., and Spiegelman B.M. 2005, Cell Metab 1:361. Kanki T., Klionsky D.J., and Okamoto K. 2011, Antioxid Redox Signal 14:1989. Narendra D.P., and Youle R.J. 2011, Antioxid Redox Signal 14:1929. Tanaka A. 2010, FEBS Letters 584:1386. Brunk U.T., and Terman A. 2002, Eur J Biochem 269:1996. Goffart S., von Kleist-Retzow J.C., and Wiesner R.J. 2004, Cardiovasc Res 64:198. Menzies R.A., and Gold P.H. 1971, J Biol Chem 246:2425. Pfeifer U. 1978, J Cell Biol 78:152. Gil J.M., and Rego A.C. 2008, Eur J Neurosci 28(10):2156. Palmer C.S., Osellame L.D., Stojanovski D., and Ryan M.T. 2011, Cell Sig 23:1534. Reddy P.H. 2007, Antioxid Redox Signal 9(10):1647. Knott A.B., Perkins G., Schwarzenbacher R., and Bossy-Wetzel E. 2008, Nat Rev Neurosci 9:505. Benard G., Bellance N., James D., Parrone P., Fernandez H., Letellier T. and Rossignol R. 2007, J Cell Sci 120:838. Detmer S.A., and Chan D.C. 2007, Nat Rev Mol Cell Biol 8(11):870. Smirnova E., Griparic L., Shurland D.L., van der Bliek A.M. 2001, Mol Biol Cel, 12:2245. Yoon H., Lim S., Heu S., Choi S., and Ryu S. 2003, FEMS Microbiol. Lett 226(2):391. Ingerman E., Perkins E.M., Marino M., Mears J.A., McCaffery J.M., Hinshaw J.E., Nunnari J. 2005, J Cell Biol 170(7):1021. Lee Y.J., Jeong S.Y., Karbowski M., Smith C.L. and Youle R.J. 2004, Mol Biol Cell 15:5001. Griparic L., van der Wel N.N., Orozco I.J., Peters P.J., and van der Bliek A.M. 2004, J. Biol. Chem., 279(18):18792. Ishihara N., Eura Y., and Mihara K. 2004, J Cell Sci 117(Pt 26):6535. Olichon A., Baricault L., Gas N., Guillou E., Valette A., Belenguer P., and Lenaers G. 2003, J Biol Chem 278(10):7743. Cipolat S., Martins de Brito O., Dal Zilio B., and Scorrano L. 2004, Proc Natl Acad Sci USA 101:15927. Song Z., Chen H., Fiket M., Alexander C., and Chan D.C. 2007, J Cell Biol 178:749. Amutha B., Gordon D.M., Gu Y., and Pain D. 2004, Biochem J 381(Pt 1):19. Arnoult D., Grodet A., Lee Y.J., Estaquier J., and Blackstone C. 2005, J Biol Chem 280(42):35742.


Modified mitochondrial dynamics in neurodegeneration

185

30. Bras M., Yuste V.J., Roué G., Barbier S., Sancho P., Virely C., Rubio M., Baudet S., Esquerda J.E., Merle-Béral H., Sarfati M., and Susin S.A. 2007, Mol. Cell Biol 27(20):7073. 31. Perkins G., Bossy-Wetzel E., and Ellisman M.H. 2009, Exp Neurol 218(2):183 32. Vogel F., Bornhövd C., Neupert W., and Reichert A.S. 2006, J Cell Biol 175(2):237. 33. Karbowski M., Lee Y.J., Gaume B., Jeong S.Y., Frank S., Nechushtan A., Santel A., Fuller M., Smith C.L., and Youle R.J. 2002, J Cell Biol 159:931. 34. Brooks C., and Dong Z. 2007, Cell Cycle 6(24):3043 . 35. Barsoum M.J., Yuan H., Gerencser A.A., Liot G., Kushnareva Y., Gräber S., Kovacs I., Lee W.D., Waggoner J., Cui J., White A.D., Bossy B., Martinou J.C., Youle R.J., Lipton S.A., Ellisman M.H., Perkins G.A., and Bossy-Wetzel E. 2006, EMBO J 25:3900. 36. Yuan H., Gerencser A.A., Liot G., Lipton S.A., Ellisman M., Perkins G.A., and Bossy-Wetzel E. 2007, Cell Death Differ 14:462. 37. Chen H., McCaffery J.M., and Chan D.C. 2007, Cell 130:548. 38. Alirol E., James D., Huber D., Alirol E., James D., Huber D., Marchetto A., Vergani L., Martinou J.C., and Scorrano L. 2006, Mol Biol Cell 17:4593. 39. Wasiak S., Zunino R., and McBride H.M. 2007, J Cell Biol 177:439. 40. Cassidy-Stone A., Chipuk J.E., Ingerman E., Song C., Yoo C., Kuwana T., Kurth M.J., Shaw J.T., Hinshaw J.E., Green D.R., and Nunnari J. 2008, Dev Cell 14(2):193. 41. Jahani-Asl A., Cheung E.C., Neuspiel M, MacLaurin J.G., Fortin A., Park D.S., McBride H.M., and Slack R.S. 2007, J. Biol Chem 282:23788. 42. Yu T., Robotham J.L., and Yoon Y. 2006, Proc Natl Acad Sci USA 103:2653. 43. McBride H.M., Neuspiel M., and Wasiak S. 2006, Curr Biol 16(14):R551. 44. Taguchi N., Ishihara N., Jofuku A., Oka T., and Mihara K. 2007, J Biol Chem 282(15):11521. 45. Alvira D., Ferrer I., Gutierrez-Cuesta J., Garcia-Castro B., Pallàs M., and Camins A. 2008, Parkinsonism Relat. Disord 14(4):309. 46. Grammas P., Samany P.G., and Thirumangalakudi L. 2006, J Alzheimers Dis 9(1):51. 47. Dagda R.K., Cherra S.J. 3rd, Kulich S.M., Tandon A., Park D., and Chu C.T. 2009, J Biol Chem 284:13843. 48. Narendra D., Tanaka A., Suen D.F., and Youle R.J. 2008, J Cell Biol 183:795. 49. Park J., Lee G., and Chung J. 2009, Biochem Biophys Res Commun 378(3):518. 50. Hollenbeck P.J. and Saxton W.M. 2005, J Cell Sci 118:5411. 51. Campello S., Lacalle R.A., Bettella M., Manes S., Scorrano L. and Viola A. 2006, J Exp Med 203:2879. 52. Verstreken P., Ly C.V., Venken K.J., Koh T.W., Zhou Y., and Bellen H.J. 2005, Neuron 47(3):365. 53. Guo X., Macleod G.T., Wellington A., Hu F., Panchumarthi S., Schoenfield M., Marin L., Charlton M.P., Atwood H.L., and Zinsmaier K.E. 2005, Neuron 47:379. 54. Misko A., Jiang S., Wegorzewska I., Milbrandt J. and Baloh R.H. 2010, J Neurosci 30:4232.


186

Tatiana R. Rosenstock & A. Cristina Rego

55. Chen H., and Chan D.C. 2009, Hum Mol Genet 18(R2):R169. 56. Zuchner S., Mersiyanova I.V., Muglia M., Bissar-Tadmouri N., Rochelle J., Dadali E.L., Zappia M., Nelis E., Patitucci A., Senderek J., Parman Y., Evgrafov O., Jonghe P.D., Takahashi Y.,Tsuji S., Pericak-Vance M.A., Quattrone A., Battaloglu E., Polyakov A.V., Timmerman V., Schröder J.M., and Vance J.M. 2004, Nat Genet 36:449. 57. Alexander C., Votruba M., Pesch U.E., Thiselton D.L., Mayer S., Moore A., Rodriguez M., Kellner U., Leo-Kottler B., Auburger G., Bhattacharya S.S., and Wissinger B. 2000, Nat Genet 26:211. 58. Waterham H.R., Koster J., van Roermund C.W., Mooyer P.A., Wanders R.J. and Leonard J.V. 2007, N Engl J Med 356:1736. 59. Parone P.A., Da Cruz S., Tondera D., Mattenberger Y., James D.I., Maechler P., Barja F. and Martinou J.C. 2008, PLoS ONE 3:e3257. 60. Nakagomi S., Barsoum M.J., Bossy-Wetzel E., Sütterlin C., Malhotra V., and Lipton S.A. 2008, Neurobiol Dis 29(2):221. 61. Gonatas N.K., Stieber A., and Gonatas J.O. 2006, J Neurol Sci 246(1-2):21. 62. Trushina E., Dyer R.B., Badger J.D. II, Ure D., Eide L., Tran D.D., Vrieze B.T., Legendre-Guillemin V., McPherson P.S., Mandavilli B.S., Van Houten B., Zeitlin S., McNiven M., Aebersold R., Hayden M., Parisi J.E., Seeberg E., Dragatsis I., Doyle K., Bender A., Chacko C., and McMurray C.T. 2004, Mol Cell Biol 24:8195. 63. Chang D.T., Rintoul G.L., Pandipati S., and Reynolds I.J. 2006, Neurobiol Dis 22:388. 64. Orr A.L., Li S., Wang C.E., Li H., Wang J., Rong J., Xu X., Mastroberardino P.G., Greenamyre J.T., and Li X.J. 2008, J Neurosci 28:2783. 65. Trushina E., Heldebrant M.P., Perez-Terzic C.M., Bortolon R., Kovtun I.V., Badger J.D. 2nd, Terzic A., Estévez A., Windebank A.J., Dyer R.B., Yao J., and McMurray C.T. 2003, Proc Natl Acad Sci USA 100(21):12171. 66. Wang H., Lim P.J., Karbowski M. and Monteiro M.J. 2009, Hum Mol Genet 18:737. 67. Liot G., Bossy B., Lubitz S., Kushnareva Y., Sejbuk N. and Bossy-Wetzel E. 2009, Cell Death Differ 16:899. 68. Reddy P.H., Stockburger E., Gillevet P., and Tagle D.A. 1997, Genomics 46(2):174. 69. Song W., Chen J., Petrilli A., Liot G., Klinglmayr E., Zhou Y., Poquiz P., Tjong J., Pouladi M.A., Hayden M.R., Masliah E., Ellisman M., Rouiller I., Schwarzenbacher R., Bossy B., Perkins G., and Bossy-Wetzel E. 2011, Nat Med 17(3):377. 70. Bursch W. and Ellinger A. 2005, Folia Neuropathol 43:297. 71. Klionsky D.J., and Ohsumi Y. 1999, Annu Rev Cell Dev Biol 15:1. 72. Wang C.W. and Klionsky D.J. 2004, Microautophagy. In: Klionsky, D.J. editor. Autophagy. Georgetown, Tx: Landes Bioscience, 107-114. 73. Mizushima N., Tsukamoto S., and Kuma A. 2008, 53(16 Suppl):2170. 74. Mammucari C., and Rizzuto R. 2010, Mech Ageing Dev 131(7-8):536.


Modified mitochondrial dynamics in neurodegeneration

187

75. Wang Y., Han R., Liang Z.Q., Wu J.C., Zhang X.D., Gu Z.L., and Qin Z.H. 2008, Autophagy 4(2):214. 76. Sadasivan S., Zhang Z., Larner S.F., Liu M.C., Zheng W., Kobeissy F.H., Hayes R.L, and Wang K.K. 2010, BMC Neurosci 18:11. 77. Cao Y., and Klionsky D.J. 2007, Cell Res 17:839. 78. Yorimitsu T., and Klionsky D.J. 2007, Trends Cell Biol 17:279. 79. Yorimitsu T., and Klionsky D.J. 2005, Mol Biol Cell 16:1593. 80. Farre J.C., Manjithaya R., Mathewson R.D., and Subramani S. 2008, Dev Cell 14:365. 81. Beau I., Esclatine A., and Codogno P. 2008, Trends Cell Biol 18:311. 82. Ravikumar B., Acevedo-Arozena A., Imarisio S., Berger Z., Vacher C., O’Kane C.J., Brown S.D., and Rubinsztein D.C. 2005, Nat Genet 37:771. 83. Weidberg H., Shvets E and Elazar Z. 2010, Annual Rev Biochem 80:125. 84. Tooze S.A., and Yoshimori T. 2010, Nat Cell Biol 12:831. 85. Codogno P., and Meijer A.J. 2004, Signaling pathways in mammalian autophagy. In: Autophagy, D. Klionsky (Ed.), Landes Bioscience, Georgetown Texas, USA and Eurekah.com, Austin Texas, USA, 26. 86. Fengsrud M., Lunde Sneve, M. and Overbue A. 2004, Structural aspects of mammalian autophagy. In: Autophagy, D. Klionsky (Ed.), Landes Bioscience, Georgetown, Texas, USA and Eureka.com, Austin, Texas, USA, 11. 87. Xie Z., and Klionsky D.J.2007, Nat Cell Biol 9:1102. 88. Ohsumi Y. 2001, Nat Rev Mol Cell Biol 2:211. 89. Rubinsztein D. C., Gestwicki J. E., Murphy L. O. and Klionsky D. J. 2007, Nat Rev Drug Discov 6:304. 90. Kim J., Dalton V.M., Eggerton K.P., Scott S.V., and Klionsky D.J. 1999, Mol Biol Cell 10:1337. 91. Shintani T., Mizushima N., Ogawa Y., Matsuura A., Noda T., and Ohsumi Y. 1999, EMBO J 18:5234. 92. Reggiori F., and Klionsky D.J. 2002, Eukaryot Cell 1:11. 93. Kirisako T., Ichimura Y., Okada H., Kabeya Y., Mizushima N., Yoshimori T., Ohsumi M., Takao T., Noda T., and Ohsumi Y. 2000, J Cell Biol 151:263. 94. Kirisako T., Baba M., Ishihara N., Miyazawa K., Ohsumi M., Yoshimori T., Noda T., Ohsumi Y. 1999, J Cell Biol 147:435. 95. Kim J., Huang W-P., Stromhaug P.E., and Klionsky D.J. 2002, J Biol Chem 277:763. 96. Mizushima N., Yamamoto A., Hatano M., Kobayashi Y., Kabeya Y., Suzuki K., Tokuhisa T., Ohsumi Y. and Yoshimori T. 2001, J Cell Biol 152:657. 97. Mizushima N., Noda T., Yoshimori T., Tanaka Y., Ishii T., George M.D., Klionsky D.J., Ohsumi M., and Ohsumi Y. 1998, Nature 395:395. 98. Pyo J.O., Jang M.H., Kwon Y.K., Lee H.J., Jun J.I., Woo H.N., Cho D.H., Choi B.Y., Lee H., Kim J.H., Mizushima N., Oshumi Y., Yong-Keun Jung Y.K. 2005, Biol Chem 280:20722. 99. Simon H.U. (2005) An autophagy gene product as a molecular switch between autophagy and apoptosis. 13th ECDO Euroconference on Apoptosis, Budapest, Hungary.


188

Tatiana R. Rosenstock & A. Cristina Rego

100. Yousefi S., Perozzo R., Schmid I., Ziemiecki A., Schaffner T., Scapozza L., Brunner T., and Simon H.U. 2006, Nat Cell Biol 8:1124. 101. Shintani T., Huang W.-P., Stromhaug P.E., and Klionsky D.J. 2002, Dev Cell 3:825. 102. Kanki T., Wang K., Cao Y., Baba M., and Klionsky D.J. 2009, Dev Cell 17:98. 103. He C., and Levine B., 2010, Curr Opin Cell Biol 22:140. 104. Komatsu M., Waguri S., Ueno T., Iwata J., Murata S., Tanida I., Ezaki J., Mizushima N., Ohsumi Y., Uchiyama Y., Kominami E., Tanaka K., and Chiba T. 2005, J Cell Biol 9:425. 105. Komatsu M., Waguri S., Chiba T., Murata S., Iwata J., Tanida I., Ueno T., Koike M., Uchiyama Y., Kominami E., and Tanaka K. 2006, Nature 441:880. 106. Komatsu M., Wang Q.J., Holstein G.R., Friedrich V.L. Jr, Iwata J., Kominami E., Chait B.T., Tanaka K., and Yue Z. 2007, Proc Natl Acad Sci USA 104:14489. 107. Yang Y.P., Liang Z.Q., Gu Z.L. and Qin Z.H. 2005, Acta Pharmacol Sin 26:1421. 108. Yorimitsu T. and Klionsky D.J. 2005, Cell Death Differ 2;1542. 109. Meijer A.J. and Codogno P. 2004, Int J Biochem Cell Biol 36:2445. 110. Sarbassov D.D, Ali S.M., Kim D.H., Guertin D.A., Latek R.R., ErdjumentBromage H., Tempst P., and Sabatini D.M. 2004, Curr Biol 14:1296. 111. Bové J., Martínez-Vicente M., and Vila M.. 2011, Nat Rev Neurosci 12(8):437. 112. Deshmukh AS, Treebak JT, Long YC, Viollet B, Wojtaszewski JF, Zierath JR. 2008, Mol Endocrinol 22:1105–1112. 113. Canto C., Jiang L.Q., Deshmukh A.S., Mataki C., Coste A., Lagouge M., Zierath J.R., and Auwerx J. 2010, Cell Metab 11:213. 114. Charest P.G. Shen Z., Lakoduk A., Sasaki A.T., Briggs S.P., and Firtel R.A. 2010, Dev Cell 18:737. 115. Huang J., Dibble C. C., Matsuzaki M. and Manning B. D. 2008, Mol Cell Biol 28:4104. 116. Facchinetti V., Ouyang W., Wei H., Soto N., Lazorchak A., Gould C., Lowry C., Newton A.C., Mao Y., Miao R.Q., Sessa W.C., Qin J., Zhang P., Su B., and Jacinto E. 2008, EMBO J 27:1932. 117. Sarbassov D.D., Guertin D.A., Ali S.M. and Sabatini D. M. 2005, Science 307:1098. 118. Guertin D.A., Thoreen C.C., Burds A.A., Kalaany N.Y., Moffat J., Brown M., Fitzgerald K.J., and Sabatini D.M. 2006, Dev Cell 11:859. 119. Zinzalla V., Stracka D., Oppliger W. and Hall M.N. 2011, Cell 144:757. 120. Oberstein A., Jeffrey P.D., and Shi Y. 2007, J Biol Chem 282:13123. 121. Sinha S., and Levine B. 2008, Oncogene 27(Suppl 1):S137. 122. Yue Z., Jin S., Yang C., Levine A.J., and Heintz N. 2003, Proc Natl Acad Sci USA 100:15077. 123. Strappazzon F., Vietri-Rudan M., Campello S., Nazio F., Florenzano F., Fimia G.M., Piacentini M., Levine B., and Cecconi F. 2011, EMBO J 30:1195. 124. Legros F., Lombes A., Frachon P., and Rojo M. 2002, Mol Biol Cell 13:4343. 125. Nakahira K., Haspel J.A., Rathinam V.A., Lee S.J., Dolinay T., Lam H.C., Englert J.A., Rabinovitch M., Cernadas M., Kim H.P., Fitzgerald K.A., Ryter S.W., and Choi A.M. 2011, Nat Immun 12:222.


Modified mitochondrial dynamics in neurodegeneration

189

126. Pattingre S., Tassa A., Qu X., Garuti R., Liang X.H., Mizushima N., Packer M., Schneider M.D., and Levine B. 2005, Cell 122:927. 127. Cecconi F., and Levine B. 2008, Dev Cell 15:344. 128. Wei Y., Pattingre S., Sinha S., Bassik M., and Levine B. 2008, Mol Cell 30:678. 129. Zhang H., Bosch-Marce M., Shimoda L.A., Tan Y.S., Baek J.H., Wesley J.B., Gonzalez F.J., and Semenza G.L. 2008, J Biol Chem 283:10892. 130. Maiuri M.C., Le Toumelin G., Criollo A., Rain J.C., Gautier F., Juin P., Tasdemir E., Pierron G., Troulinaki K., Tavernarakis N., Hickman J.A., Geneste O., and Kroemer G. 2007, Embo J 26:2527. 131. Rashmi R., Pillai S.G., Vijayalingam S., Ryerse J., and Chinnadurai G. 2008, Oncogene 27:1366. 132. Abedin M.J., Wang D., McDonnell M.A., Lehmann U., and Kelekar A. 2007, Cell Death Differ 14:500. 133. Shimizu S., Kanaseki T., Mizushima N., Mizuta T., Arakawa-Kobayashi S., Thompson C.B., and Tsujimoto Y. 2004, Nat Cell Biol 6:1221. 134. Gozuacik D., and Kimchi A., 2004, Oncogene 23:2891. 135. Yee K.S., Wilkinson S., James J., Ryan K.M., and Vousden K.H. 2009, Cell Death Differ 16:1135. 136. Hamacher-Brady A., Brady N.R., Logue S.E., Sayen M.R., Jinno M., Kirshenbaum L.A., Gottlieb R.A., and Gustafsson A.B. 2007, Cell Death Differ 14:146. 137. Chen G., Cizeau J., Vande Velde C., Park J.H., Bozek G., Bolton J., Shi L., Dubik D., and Greenberg A. 1999, J Biol Chem 274:7. 138. Rodriguez-Enriquez S., He L., and Lemasters J.J. 2004, Int J Biochem Cell Biol 36:2463. 139. Kubli D.A., Ycaza J.E., and Gustafsson A.B. 2007, Biochem J 405:407. 140. Scherz-Shouval R., Shvets E., Fass E., Shorer H., Gil L., and Elazar Z. 2007, EMBO J 26:1749. 141. Kabeya Y., Mizushima N., Yamamoto A., Oshitani-Okamoto S., Ohsumi Y., and Yoshimori T. 2004, J Cell Sci 117(Pt 13):2805. 142. Chen Y., Azad M.B., and Gibson S.B. 2009, Cell Death Differ 16:1040. 143. Høyer-Hansen M., Bastholm L., Szyniarowski P., Campanella M., Szabadkai G., Farkas T., Bianchi K., Fehrenbacher N., Elling F., Rizzuto R., Mathiasen I.S., and Jäättelä M. 2007, Mol Cell 25(2):193. 144. Berger Z., Ttofi E.K., Michel C.H., Pasco M., Tenant S., Rubinsztein D.C. and O’Kane C.J. 2005, Hum Mol Genet 14:3003. 145. Sarkar S. and Rubinsztein D.C. 2006, Autophagy 2:132. 146. Shibata M., Lu T., Furuya T., Degterev A., Mizushima N., Yoshimori T., MacDonald M., Yankner B. and Yuan J. 2006, J Biol Chem 281:14474. 147. Sarbassov D.D., Ali S.M. and Sabatini D.M. 2005, Curr Opin Cell Biol 17:596. 148. Bissler J.J., McCormack F.X., Young L.R., Elwing J.M., Chuck G., Leonard J.M., Schmithorst V.J., Laor T., Brody A.S., Bean J., Salisbury S., and Franz D.N. 2008, N Engl J Med 358:140. 149. Kouroku Y., Fujita E., Tanida I., Ueno T., Isoai A., Kumagai H., Ogawa S., Kaufman R.J., Kominami E. and Momoi T. 2007, Cell Death Differ 14:230.


190

Tatiana R. Rosenstock & A. Cristina Rego

150. Sarkar S., Floto R.A., Berger Z., Imarisio S., Cordenier A., Pasco M., Cook L.J. and Rubinsztein D.C. 2005, J Cell Biol 170:1101. 151. Psarra A.M., and Sekeris C.E. 2008, Biochim Biophys Acta 1783(1):1. 152. Speidel D. 2010, Trends Cell Biol 20(1):14. 153. Crighton D., Wilkinson S., O'Prey J., Syed N., Smith P., Harrison P.R., Gasco M., Garrone O., Crook T., and Ryan K.M. 2006, Cell 126:121. 154. Tasdemir E., Maiuri M.C., Galluzzi L., Vitale I., Djavaheri-Mergny M., D'Amelio M., Criollo A., Morselli E., Zhu C., Harper F., Nannmark U., Samara C., Pinton P., Vicencio J.M., Carnuccio R., Moll U.M., Madeo F., PaterliniBrechot P., Rizzuto R., Szabadkai G., Pierron G., Blomgren K., Tavernarakis N., Codogno P., Cecconi F., and Kroemer G. 2008, Nat Cell Biol 10:676. 155. Zu T., Duvick L.A., Kaytor M.D., Kaytor M.D., Berlinger M.S., Zoghbi H.Y., Clark H.B., and Orr H.T. 2004, J Neurosci 24(40):8853. 156. Krainc D. 2010, Arch Neurol 67(4):388. 157. Jeong H., Then F., Melia T.J. Jr, Mazzulli J.R., Cui L., Savas J.N., Voisine C., Paganetti P., Tanese N., Hart A.C., Yamamoto A., and Krainc D. 2009, Cell 137(1):60. 158. Li X.J., and Li S. 2011, Neurobiol Dis 43(1):4. 159. Gutekunst C.A., Li S.H., Yi H., Ferrante R.J., Li X.J., and Hersch S.M. 1998, J Neurosci 18:7674. 160. Sapp E., Schwarz C., Chase K., Bhide P.G., Young A.B., Penney J., Vonsattel J.P., Aronin N., and DiFiglia M. 1997, Ann Neurol 42:604. 161. Qin Z.H., Wang Y., Kegel K.B., Kazantsev A., Apostol B.L., Thompson L.M., Yoder J., Aronin N., and DiFiglia M. 2003, Hum Mol Genet 12(24):3231. 162. Ravikumar B., Vacher C., Berger Z., Davies J.E., Luo S., Oroz L.G., Scaravilli F., Easton D.F., Duden R, O’Kane C.J., and Rubinsztein D.C. 2004, Nat Genet 36:585. 163. Iwata A., Christianson J.C., Bucci M., Ellerby L.M., Nukina N., Forno L.S., and Kopito R.R. 2005, Proc Natl Acad Sci USA 102(37):13135. 164. Yamamoto A., Cremona M.L., and Rothman J.E. 2006, J Cell Biol 172(5):719. 165. Pandey U.B., Nie Z., Batlevi Y., McCray B.A., Ritson G.P., Nedelsky N.B., Schwartz S.L., DiProspero N.A., Knight M.A., Schuldiner O., Padmanabhan R., Hild M., Berry D.L., Garza D., Hubbert C.C., Yao T.P., Baehrecke E.H., and Taylor J.P. 2007, Nature 447(7146):859. 166. Petersén A., Hansson O., Puschban Z., Sapp E., Romero N., Castilho R.F., Sulzer D., Rice M., DiFiglia M., Przedborski S., and Brundin P. 2001, Eur J Neurosci 14:1425. 167. Kegel K.B., Kim M., Sapp E., Mcintyre C., Castaño J.G., Aronin N. and Difiglia M. 2000, J Neurosci 20:7268. 168. Atwal R.S., and Truant R. 2008, Autophagy 4(1):91. 169. Nagata E., Sw A., Ross C.A. and Snyder S.H. 2004, Neuroreport 15:1325–1328. 170. Ravikumar B., Duden R. and Rubinsztein D. C. 2002, Hum Mol Genet 11:1107-1117. 171. Kim M., Lee H.S., LaForet G., McIntyre C., Martin E.J., Chang P., Kim T.W., Williams M., Reddy P.H., Tagle D., Boyce F.M., Won L., Heller A., Aronin N., and DiFiglia M. 1999, J Neurosci 19:964.


Modified mitochondrial dynamics in neurodegeneration

191

172. Zhang X.D., Wang Y., Wang Y., Zhang X., Han R., Wu J.C., Liang Z.Q., Gu Z.L., Han F., Fukunaga K., and Qin Z.H. 2009, Autophagy 5(3):339. 173. Tung Y.T., Hsu W.M., Lee H., Huang W.P., and Liao Y.F. 2010, Cell Mol Neurobiol 30(5):795. 174. Steffan J.S. 2010, Cell Cycle 9(17):3401. 175. Metzger S., Saukko M., Van Che H., Tong L., Puder Y., Riess O., and Nguyen H.P. 2010, Hum Genet 128(4):453. 176. Ravikumar B., Imarisio S., Sarkar S., O'Kane C.J., and Rubinsztein D.C. 2008, J Cell Sci 121(Pt 10):1649. 177. Stenmark H., Parton R. G., Steele-Mortimer O., Lutcke A., Gruenberg J. and Zerial M. 1994, EMBO J 13:1287. 178. White J.K., Auerbach W., Duyao M.P., Vonsattel J.P., Gusella J.F., Joyner A.L., and MacDonald M.E. 1997, Nat Genet 17:404. 179. Martinez-Vicente M, Talloczy Z, Wong E, Tang G, Koga H, Kaushik S, de Vries R, Arias E, Harris S, Sulzer D, and Cuervo AM. 2010, Nat Neurosci 13(5):567-76. 180. Atwal R.S., Xia J., Pinchev D., Taylor J., Epand R.M., and Truant R. 2007, Hum Mol Genet 16:2600. 181. Clark S.L. Jr. 1957, J Biophys Biochem Cytol 3:349. 182. Zhang Y., Qi H., Taylor R., Xu W., Liu L.F., and Jin S. 2007, Autophagy 3:337. 183. Twig G., Elorza A., Molina A.J., Mohamed H., Wikstrom J.D., Walzer G., Stiles L., Haigh S.E., Katz S., Las, G., Alroy J., Wu M., Py B.F., Yuan J., Deeney J.T., Corkey B.E., and Shirihai O.S. 2008, Embo J 27;433. 184. Nowikovsky K., Reipert S., Devenish R.J., and Schweyen R.J. 2007, Cell Death Differ 14:1647. 185. Gao Z., Gammoh N., Wong P.M., Erdjument-Bromage H., Tempst P., and Jiang X. 2010, Autophagy 6:126. 186. Geisler S., Holmström K.M., Treis A., Skujat D., Weber S.S., Fiesel F.C., Kahle P.J., and Springer W. 2010, Autophagy 6(7):871. 187. Wallace, D.C. 2005, Annu Rev Genet 39:359. 188. Kim I., Rodriguez-Enriquez S., and Lemasters J. J. 2007, Arch Biochem Biophys 462:245. 189. Nice D.C., Sato T.K., Stromhaug P.E., Emr S.D., and Klionsky D.J. 2002, J Biol Chem 277:30198. 190. Takamura A., Komatsu M., Hara T., Sakamoto A., Kishi C., Waguri S., Eishi Y., Hino O., Tanaka K., and Mizushima N. 2011, Genes Dev 25:795. 191. Okamoto K., Kondo-Okamoto N., and Ohsumi Y. 2009, Dev Cell 17:87. 192. Okamoto K., Kondo-Okamoto N., and Ohsumi, Y. 2009, Autophagy 5:1203. 193. Kanki T., Wang K., Baba M., Bartholomew C.R., Lynch-Day M.A., Du Z., Geng J., Mao K., Yang Z., Yen W.L., and Klionsky D.J. 2009, Mol Biol Cell 20:4730. 194. Priault M., Salin B., Schaeffer J., Vallette F.M., di Rago J.P., and Martinou J.C. 2005, Cell Death Differ 12:1613. 195. Kiššová I., Deffieu M., Manon S., and Camougrand N. 2004, J Biol Chem 279:39068. 196. Sandoval H., Thiagarajan P., Dasgupta S.K., Schumacher A., Prchal J.T., Chen M., and Wang J. 2008, Nature 454:232.


192

Tatiana R. Rosenstock & A. Cristina Rego

197. Cherra S.J., and Chu C.T. 2008, Future Neurol 3:309. 198. Rakovic A., Grunewald A., Kottwitz J., Bruggemann N., Pramstaller P.P., Lohmann K., and Klein C., 2011, PLoS One 6:e16746. 199. Chan N.C., Salazar A.M., Pham A.H., Sweredoski M.J., Kolawa N.J., Graham R.L., Hess S., and Chan D.C., 2011, Hum Mol Genet 20:1726. 200. Whitworth A.J., and Pallanck L.J. 2009, J Bioenerg Biomembr 41(6):499. 201. Zhang Y., Gao J., Chung K.K., Huang H., Dawson V.L., and Dawson T.M. 2000, Proc Natl Acad Sci USA 97:13354. 202. Valente E.M., Abou-Sleiman P.M., Caputo V., Muqit M.M., Harvey K., Gispert S., Ali Z., Del Turco D., Bentivoglio A.R., Healy D.G., Albanese A., Nussbaum R., González-Maldonado R., Deller T., Salvi S., Cortelli P., Gilks W.P., Latchman D.S., Harvey R.J., Dallapiccola B., Auburger G., and Wood N.W. 2004, Science 304:1158. 203. Zhou C., Huang Y., Shao Y., May J., Prou D., Perier C., Dauer W., Schon E.A., and Przedborski S. 2008, Proc Natl Acad Sci USA 105(33):12022. 204. Silvestri L., Caputo V., Bellacchio E., Atorino L., Dallapiccola B., Valente E. M., and Casari G. 2005, Hum Mol Genet 14:3477. 205. Narendra D.P., Jin S.M., Tanaka A., Suen D.F., Gautier C.A., Shen J., Cookson M.R., and Youle R.J. 2010, PLoS Biol 8(1):e1000298. 206. Haque M. E., Thomas K. J., D’Souza C., Callaghan S., Kitada T., Slack R. S., Fraser P., Cookson M. R., Tandon A., and Park D. S. 2008, Proc Natl Acad Sci USA 105:1716. 207. Wang H. L., Chou A. H., Yeh T. H., Li A. H., Chen Y. L., Kuo Y. L., Tsai S. R., and Yu S. T. 2007, Neurobiol Dis 28:216. 208. Beilina A., Van Der Brug M., Ahmad R., Kesavapany S., Miller D.W., Petsko G.A., and Cookson M.R. 2005, Proc Natl Acad Sci USA 102:5703. 209. Petit A., Kawarai T., Paitel E., Sanjo N., Maj M., Scheid M., Chen F., Gu Y., Hasegawa H., Salehi-Rad S., Wang L., Rogaeva E., Fraser P., Robinson B., St George-Hyslop P., and Tandon A. 2005, J Biol Chem 280:34025. 210. Matsuda N., Sato S., Shiba K., Okatsu K., Saisho K., Gautier C.A., Sou Y.S., Saiki S., Kawajiri S., Sato F., Kimura M., Komatsu M., Hattori N., and Tanaka K. 2010, J Cell Biol 189(2):211. 211. Gautier C.A., Kitada T., and Shen J. 2008, Proc Natl Acad Sci USA 105:11364. 212. Sandebring A., Thomas K.J., Beilina A., van der Brug M., Cleland M.M., Ahmad R., Miller D.W., Zambrano I., Cowburn R.F., Behbahani H., Cedazo-Mínguez A., and Cookson M.R. 2009, PLoS One 4:e5701. 213. Gandhi S., Wood-Kaczmar A., Yao Z., Plun-Favreau H., Deas E, Klupsch K., Downward J., Latchman D.S., Tabrizi S.J., Wood N.W., Duchen M.R., and Abramov A.Y. 2009, Mol Cell 33:627. 214. Schweers R.L., Zhang J., Randall M.S., Loyd M.R., Li W., Dorsey F.C., and Kundu M. 2007, Proc Natl Acad Sci USA 104:19500. 215. Lee Y., Lee H.Y., Hanna R.A., and Gustafsson A.B. 2011, Am J Physiol Heart Circ Physiol 301(5):H1924-31. 216. Kanki T. 2010, Autophagy 6(3):433. 217. Suzuki S.W., Onodera J., and Ohsumi Y., 2011, PLoS One 6:e17412.


Modified mitochondrial dynamics in neurodegeneration

193

218. Tal R., Winter G., Ecker N., Klionsky D.J., and Abeliovich H. 2007, J Biol Chem 282:5617. 219. Kanki T., and Klionsky D.J. 2008, J Biol Chem 283:32386. 220. Kim I., and Lemasters J.J. 2011, Am J Physiol Cell Physiol 300(2):C308. 221. Gomes L.C., Di Benedetto G., and Scorrano L. 2011, Nat Cell Biol 13(5):589. 222. Moreira P.I., Siedlak S.L., Wang X., Santos M.S., Oliveira C.R., Tabaton M., Nunomura A., Szweda L.I., Aliev G., Smith M.A., Zhu X., and Perry G. 2007, Autophagy 3:614. 223. Hirai K., Aliev G., Nunomura A., Fujioka H., Russell R.L., Atwood C.S., Johnson A.B., Kress Y., Vinters H.V., Tabaton M., Shimohama S., Cash A.D., Siedlak S.L., Harris P.L., Jones P.K., Petersen R.B., Perry G. and Smith M.A. 2001, J Neurosci 21:3017. 224. Ditaranto K., Tekirian T.L., and Yang A.J. 2001, Neurobiol Dis 8:19. 225. Liu R.Q., Zhou Q.H., Ji S.R., Zhou Q., Feng D., Wu Y., and Sui S.F. 2010, J Biol Chem 285:19986. 226. Ling D., Song H.J., Garza D., Neufeld T.P., and Salvaterra P.M. 2009, PLoS One 4:e4201. 227. Pickford F., Masliah E., Britschgi M., Lucin K., Narasimhan R., Jaeger P.A., Small S., Spencer B., Rockenstein E., Levine B., and Wyss-Coray T. 2008, J Clin Invest 118:2190. 228. Wang X., Su B., Zheng L., Perry G., Smith M.A., and Zhu X. 2009, J Neurochem 109 (Suppl 1):153. 229. Wang X., Su B., Siedlak S.L., Moreira P.I., Fujioka H., Wang Y., Casadesus G., and Zhu X. 2008, Proc Natl Acad Sci USA 105:19318. 230. Rosenstock T.R., Carvalho A.C.P., Jurkiewicz A., Frussa-Filho R., and Smaili S.S. 2004, J Neurochem 88:1220. 231. Rosenstock T.R., Bertoncini C.R., Teles A.V., Hirata H., Fernandes M.J., and Smaili S.S. 2010, Eur J Neurosci 32(1):60. 232. Pandey M., Varghese M., Sindhu K.M., Sreetama S., Navneet A.K., Mohanakumar K.P., and Usha R. 2008, J Neurochem 104(2):420. 233. Seong I.S., Ivanova E., Lee J.M., Choo Y.S., Fossale E., Anderson M., Gusella J.F., Laramie J.M., Myers R.H., Lesort M., and MacDonald M.E. 2005, Hum Mol Genet 14(19):2871. 234. Panov A.V., Gutekunst C.A., Leavitt B.R., Hayden M.R., Burke J.R., Strittmatter W.J., and Greenamyre J.T. 2002, Nat Neurosci 5:731. 235. Panov A.V., Lund S., and Greenamyre J.T. 2005, Mol Cell Biochem 269(12):143. 236. Powers W.J., Videen T.O., Markham J., McGee-Minnich L., Antenor-Dorsey J.V., Hershey T., and Perlmutter J.S. 2007, Proc Natl Acad Sci USA 104:2945. 237. Berent S., Giordani B., Lehtinen S., Markel D., Penney J.B., Buchtel H.A., Starosta-Rubinstein S., Hichwa, R., and Young A.B. 1988, Ann Neurol 23:541. 238. Choo Y.S., Johnson G.V., MacDonald M., Detloff P.J., and Lesort M. 2004, Hum Mol Genet 13:1407. 239. Lim D., Fedrizzi L., Tartari M., Zuccato C., Cattaneo E., Brini M., and Carafoli E. 2008, J Biol Chem 283:5780.


194

Tatiana R. Rosenstock & A. Cristina Rego

240. Oliveira J.M., Chen S., Almeida S., Riley R., Gonรงalves J., Oliveira C.R., Hayden M.R., Nicholls D.G., Ellerby L.M., and Rego A.C. 2006, J Neurosci 26:11174. 241. Luthi-Carter R., Apostol B.L., Dunah A.W., DeJohn M.M., Farrell L.A., Bates G.P., Young A.B., Standaert D.G., Thompson L.M., and Cha J.H. 2003, Neurobiol Dis 14:624. 242. Banoei M.M., Houshmand M., Panahi M.S., Shariati P., Rostami M., Manshadi M.D., and Majidizadeh T. 2007, Cell Mol Neurobiol 27:867. 243. Cui L., Jeong H., Borovecki F., Parkhurst C.N., Tanese N., and Krainc D. 2006, Cell 127:59. 244. Weydt P., Pineda V.V., Torrence A.E., Libby R.T., Satterfield T.F., Lazarowski E.R., Gilbert M.L., Morton G.J., Bammler T.K., Strand A.D., Cui L., Beyer R.P., Easley C.N., Smith A.C., Krainc D., Luquet S., Sweet I.R., Schwartz M.W., and La Spada A.R. 2006, Cell Metab 4:349. 245. Steffan J.S., Kazantsev A., Spasic-Boskovic O., Greenwald M., Zhu Y.Z., Gohler H., Wanke, E.E., Bates G.P., Housman D.E., and Thompson L.M. 2000, Proc Natl Acad Sci USA 97:6763.


Research Signpost 37/661 (2), Fort P.O. Trivandrum-695 023 Kerala, India

Cellular Bioenergetics in Health and Diseases: New Perspectives in Mitochondrial Biology, 2012: 195-215 ISBN: 978-81-308-0487-3 Editors: Phing-How Lou and Natalia Petersen

6. Mitochondrial involvement in stemness and stem cell differentiation Ana誰s Wanet, Thierry Arnould and Patricia Renard

Laboratory of Biochemistry and Cell Biology (URBC), NARILIS (Namur Research Institute for Life Sciences), University of Namur (FUNDP), Belgium

Abstract. Mitochondria are known to exhibit different abundances, morphologies and functions in different cell types that adapt their number and activity in response to environmental and cellular cues. Interestingly, a number of recent studies have highlighted changes in mitochondrial content, architecture and function during the differentiation of stem cells, as well as during the reprogramming of somatic stem cells into induced pluripotent stem cells. Indeed, while pluripotent cells generally contain fewer mitochondria and rely mainly on glycolysis to meet their energy demand, differentiated cells display a more developed mitochondrial network and rely on oxidative phosphorylation for most of their energy production. Moreover, these mitochondrial changes would also contribute to the differentiation process itself. These observations strongly suggest the existence of an interplay between mitochondrial biogenesis and stem cell differentiation. In this chapter, we review the current knowledge about the involvement of mitochondria in the stemness and differentiation abilities of different stem cell types such as embryonic stem cells, induced pluripotent stem cells and somatic stem cells.

Introduction During the last few years, a growing interest has been devoted to the study of the mitochondrial morphology, dynamics and functions in stem cells. Correspondence/Reprint request: Dr. Ana誰s Wanet, Laboratory of Biochemistry and Cell Biology (URBC) NARILIS (Namur Research Institute for Life Sciences), University of Namur (FUNDP), Belgium E-mail: anais.wanet@fundp.ac.be


196

Anaïs Wanet et al.

Indeed, accumulating data have highlighted the different mitochondrial phenotypes in pluripotent and differentiated cells and suggested that mitochondrial biogenesis would contribute to and/or be regulated by cell differentiation. Indeed, while pluripotent cells display an « immature » mitochondrial network (characterized by mitochondria with poorly developed cristae and translucent matrices) and preferentially use anaerobic metabolism for their energy supply, differentiated cells tend to exhibit a more developed mitochondrial network and to rely mainly on oxidative phosphorylations to produce ATP [1]. Stem cells are defined by two key properties: self-renewal, i.e. the ability to proliferate without lineage commitment, and the capacity to differentiate into one or more specialized cell types [2, 3]. Although many stem cell types can be distinguished based on their pluripotency degree, stem cells can be gathered in three categories: embryonic stem cells (ESCs), somatic (or adult) stem cells (SSC) and induced pluripotent stem cells (iPSCs). ESCs, which arise from the inner cell mass of the early blastocyst, have the potential to generate any cell type derived from all three germ layers (endoderm, mesoderm and ectoderm) and therefore exhibit the highest degree of pluripotency [2, 3]. SSC, on the other hand, display reduced self-renewal and pluripotency compared to ESCs [2]. Among this category are included – among others – hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs). While ESCs and SSCs are natural stem cells, iPSCs are mature adult cells (such as fibroblasts) that have been artificially reprogrammed to an ESC-like state by overexpressing master « stemness » regulators such as Oct4, Nanog, Sox2, KLF-4 and/or c-Myc [2, 4, 5]. Studies performed on these three stem cell categories brought evidence of changes in mitochondrial morphology, dynamics and/or functions during stem cell differentiation and, interestingly, suggested an interplay between « stemness », stem cell differentiation and mitochondrial biogenesis.

1. The interplay between mitochondrial biogenesis and cell differentiation: The ESCs perspective 1.1. A mitochondrial «maturation» is observed during ESC differentiation During the last decade, several analyses of the mitochondrial morphology using electron microscopy revealed a particular mitochondrial phenotype in mouse and human ESCs, characterized by few mitochondria with poorly developed cristae [1, 6-8]. Intriguingly, St. John and colleagues reported that the mitochondria of a human ES cell line tend to localize perinuclearly [9], an observation which was verified in mouse and other human ESC lines [10-13]


Mitochondrial involvement in stemness and stem cell differentiation

197

but whose physiological significance is still obscure. Several studies demonstrated that both the mitochondrial mass and mitochondrial DNA (mtDNA) content increase during hESC differentiation, induced by the retrieval of the feeder layer, FGF-2 (or b-FGF) and serum replacement, or induced by embryoid bodies formation (depending on studies) [12, 14, 15]. These observations should nevertheless be moderated by the size of the cell types compared, as recent data indicate that when considered relative to the total cell protein content, ESCs (and iPSCs) and differentiated cells would actually display similar mitochondrial mass ratio [16]. More interestingly nevertheless, the mitochondria of differentiated cells display a larger morphology and more distinct cristae [9, 13, 14] and these changes in mitochondrial morphology are accompanied by an increase in ATP content and reactive oxygen species (ROS) levels [14] (Figure 1). Besides, in a specific model of cardiac differentiation, it was shown that mESC differentiation is accompanied by the restructuration of the glycolytic transcriptome and a shift from glycolysis to mitochondrial respiration [17], suggesting that the changes in mitochondrial morphology are accompanied by metabolic modifications during ESC differentiation. Supporting this notion, ESCs were shown to depend mainly on glycolysis for ATP production, and to produce minimal ATP amounts by OXPHOS in comparison with differentiated cells (fibroblasts) [16]. Moreover, ESCs induced to differentiate by retinoic-acid exposure display a gradual decrease in glycolysis, reinforcing the concept of a metabolic reprogramming during differentiation. Regarding the mitochondrial membrane potential of ESCs and differentiated cells, divergent data were obtained. On one hand, St John and colleagues observed a lower proportion of mitochondria with a high mitochondrial membrane potential in hESCs compared with differentiated cardiomyocytes, which could indicate a lower oxidative metabolism in ESCs [9]. On the other hand, Prigione et al. revealed a more elevated mitochondrial membrane potential in hESCs (and in iPSCs) than in differentiated cells, which is interpreted as the result of a reduced ATP consumption and a glycolysis-based energy metabolism [18]. The use of different ES cell lines, of various differentiation protocols, different culture conditions as well as the pluripotency state and differentiation potential of the ESCs studied might be partly involved in the discrepancies between these observations. Indeed, by analyzing mESCs sorted for low or high resting mitochondrial membrane potential, Schieke and colleagues demonstrated that mESCs with low mitochondrial membrane potential differentiate efficiently in cells of the mesodermal lineage but fail to efficiently form teratomas (which is an in vivo measure of their pluripotency), whereas mESCs with high mitochondrial


198

Ana誰s Wanet et al.

Figure 1. Embryonic stem cells and differentiated cells display different mitochondrial morphology and functions. While the mitochondria of ESC are characterized by a punctate, perinuclear arrangement, an electron-lucid matrix and poorly developed cristae, mitochondria of differentiated cells form more developed networks, have an electron-dense matrix and developed cristae. These morphological modifications are accompanied by a shift from a glycolytic-based metabolism in ESCs towards increased oxidative phosphorylation in differentiated cells. Conversely, an opposite change for most of these features is observed during the reprogramming of somatic cells.

membrane potential exhibit the opposite behavior [19]. Further studies are nevertheless required to determine (1) if variations in the mitochondrial membrane potential are observed during cell differentiation, and if these variations are linked to the commitment to a specific lineage or are a hallmark of cell differentiation; (2) what are the physiological reasons underlying the change in mitochondrial membane potential and (3), what are the molecular actors involved in mitochondrial membrane potential regulation and variations. In relation with this last question, it was demonstrated in a recent study that, while the mitochondrial membrane potential of differentiated cells is maintained by the electron transport chain, that of ESCs depends on glycolysis and the ATP hydrolase activity of the F1F0-ATPase. Interestingly, the maintenance of the mitochondrial membrane


Mitochondrial involvement in stemness and stem cell differentiation

199

potential through glycolytic ATP hydrolysis in pluripotent cells would be required for their viability and proliferation capacity [16]. Surprisingly, although both the mitochondrial mass and mtDNA copy number are increased during hESC differentiation, the protein levels of TFAM, PGC-1α and NRF-1, which are all three key regulators of mitochondrial biogenesis, are decreased [14]. In line with this observation, the mRNA levels of TFAM, PGC-1α and PGC-1β, Polγ and Polγ2, TFBM1 and TFBM2 and NRF1 (depending on studies) are reported to decrease during the spontaneous in vitro differentiation of hESCs into embryoid bodies, as well as the nuclear genes coding for the mitochondrial electron transport chain (ETC) complex subunits [12, 20, 21]. However, the transcript expression levels of ETC-coding genes and of several mitochondrial biogenesis regulators (TFAM, Polγ, PGC-1α and PGC-1β) are increased in fully differentiated teratomas, representing an in vivo model of differentiation. Although these latter results may be biased by the cancerous features of teratomas, these data underline the importance of considering the level of differentiation, which is less mature in embryoid bodies than in teratomas [21]. One can also expect that tissue- or cell type-directed differentiations of ESCs or iPSCs might provide a still different picture of the regulatory networks of mitochondrial biogenesis. Although this hypothesis needs to be investigated, the decreased expression of the mitochondrial biogenesis regulators observed in in vitro differentiating ESCs has been proposed to represent a nuclear response to the decreased amount of mtDNA in pluripotent cells [21] (one can refer to this book section devoted to the «mitochondrial retrograde communication»). If true, this hypothesis raises a still unresolved question: how is mitochondrial biogenesis prevented in pluripotent cells expressing high levels of these regulators? In order to correlate the kinetics of mitochondrial biogenesis and network formation with the loss of pluripotency and the differentiation process, Mandal and colleagues analyzed the changes in mitochondrial morphology and the expression of OCT4 and NANOG during the differentiation of HSF1 cells (a hESC line), induced by the retrieval of the feeder layer, of FGF-2 and serum replacement. They found that the formation of the mitochondrial network is initiated while the cells are still expressing the pluripotency markers, and that an extensive mitochondrial network has developed by the time OCT4 and NANOG expressions are lost. Accordingly, an increase in ATP and ROS levels is seen during the course of differentiation [10]. However, a study performed on differentiating mouse ESCs (mESCs) (using a differentiation protocol based on embryoid bodies formation) demonstrated that the expression of several pluripotent markers (Dppa5, developmental pluripotency associated gene; Pramel7, Prame-like 7 and Ndp5211 or


200

Anaïs Wanet et al.

Calcoco2) decreases before the increase in mtDNA copy number, suggesting that mtDNA would be extensively replicated after the commitment of mESCs to a specific cell fate [15]. Although the different data obtained may result from the different experimental settings (such as the pluripotency markers analyzed, the way differentiation is triggered and the methods used to evaluate mitochondrial biogenesis) and cell types, further studies seem necessary to determine what is the event, between the loss of pluripotency or mitochondrial biogenesis, that occurs first, and if this chronology is conserved among species, stem cell types and differentiation protocols.

1.2. Mitochondrial biogenesis not only correlates with ESC differentiation, but could also participate in it The use of different chemical molecules has provided further evidence about the involvement of mitochondrial biogenesis and function in stem cell differentiation. When mitochondrial function is uncoupled in mESCs and hESCs by carbonyl cyanide m-chlorophenylhydrazone (CCCP, a protonophore depolarizing the inner mitochondrial membrane, resulting in uncoupled oxidative phosphorylation) in mESCs and hESCs, the transcriptional programs necessary for the embryonic lineage differentiation, and especially HOX genes expression, are repressed, suggesting that attenuated mitochondrial function results in compromised differentiation. On the other hand, CCCP-treated mESCs and hESCs demonstrate enhanced glycolytic metabolism and increased expression of OCT4, NANOG and SOX2 compared with the untreated controls, suggesting that the pluripotency of mESCs and hESCs is maintained, or even enhanced upon CCCP uncoupling. It needs to be mentioned, nevertheless, that the proliferation of self-renewing mESCs and hESCs is slowed down upon CCCP treatment, which suggests that these cells may rely, at least partly, on mitochondrial functions for their proliferation [10]. Regarding hepatogenic differentiation, a robust increase in PPAR-β expression (the involvement of PPAR members in mitochondrial biogenesis, as well as that of other mitochondrial biogenesis inducers, is developed in a chapter of this book devoted to mitochondrial biogenesis) is observed during the late differentiation course of mESCs. Interestingly, a PPAR-β agonist significantly increases mitochondrial abundance and the number of albuminpositive cells differentiated from mESCs. On the opposite, both hepatogenic differentiation and mitochondrial biogenesis are hampered in the presence of a PPAR-β antagonist. PPAR-α and PGC-1α, on the other hand, display both transient increased expressions during the early phase of hepatocyte differentiation. However, although stimulating mitochondrial biogenesis, a


Mitochondrial involvement in stemness and stem cell differentiation

201

PPAR-α agonist does not increase hepatocyte differentiation. Together, these results suggest that PPAR-α-induced mitochondrial biogenesis would only be necessary for the early phase of mitochondrial biogenesis, while PPAR-β would play a more important role in the control of cellular energy metabolism during hepatocyte differentiation and maturation [22]. S-NitrosoN-AcetylPenicillamine (SNAP), a NO donor, also stimulates the mitochondrial biogenesis and enhances the hepatogenic differentiation of mESCs [23], as assessed by the measurement of urea and albumin secretion, cyp7a1 promoter activity and glucose and lactate metabolisms, which are increased during hepatocyte differentiation and maturation. These results further support the involvement of mitochondrial biogenesis in the hepatogenic differentiation of mESCs. A specific role for the complex III of the mitochondrial electron transport chain in ESC differentiation was suggested by several groups using antimycin A, a molecule blocking the electron flow in the complex III. Indeed, the treatment of mESCs with this inhibitor results in a blockade of heart cell differentiation, while the use of complex II and complex IV inhibitors (thenoyltrifluoroacetone and potassium cyanide) does not prevent cardiomyocyte differentiation. This suggests that the activity of complex III is necessary, while those of complex II and IV are dispensable, for heart cell differentiation. As antimycin A treatment inhibits spontaneous intracellular Ca++ oscillations and as a pulse of ionomycin (an ionophore used to raise the intracellular level of Ca++), given at an appropriate time, restores cardiomyocyte differentiation, mitochondrial complex III activity has been suggested to be required for the differentiation of cardiomyocytes through its involvement in Ca++ oscillations [24]. More recently, it appeared that complex III activity is not only required for cardiomyocyte differentiation, but also involved in stem cell pluripotency. Varum et al. showed that the treatment of hESCs with antimycin A results in an increased expression of NANOG (OCT4 mRNA levels remain unchanged), maintains the ESC ability to form teratomas exhibiting tissues of all three germ layers (which suggests that treated ESCs are still pluripotent), and results in the repression of genes associated with differentiation. Interestingly, they demonstrated that the ROS generation occuring at complex III upon antimycin A treatment is at least partially responsible for the upregulation of NANOG expression in these cells, implying a role of ROS in governing stemness and differentiation [25]. The involvement of ROS in stem cell differentiation is further supported in the model of cardiomyocyte differentiation of ESCs, although in an opposite fashion. Indeed, it was shown that ESCs cultured in physiological glucose concentration (5 mM) display an altered metabolism, decreased ROS levels and fail to generate cardiomyocytes whereas ESCs maintained in


202

Anaïs Wanet et al.

supraphysiological glucose concentration (25 mM) exhibit an opposite behavior. Besides, the outcome of ESCs cultured in low-glucose medium is reversed when the medium is supplemented with ascorbic acid, paradoxically acting as a pro-oxidant, or when an upstream p38 MAPK kinase (MKK6) is expressed, and those of ESCs cultured in high-glucose medium are reversed when cells are exposed to antioxidant treatment. These obervations suggest that supraphysiological levels of glucose are required for cardiomyocyte formation through ROS-dependent p38 activation [26]. Therefore, we may speculate that ROS regulate both stemness and differentiation, depending on their levels, the cell types and differentiation models studied.

1.3. Several mitochondrial and mitochondria-related proteins have been involved in the mitochondrial modifications occuring during ESC differentiation Beside the use of chemical compounds to increase or inhibit mitochondrial content or function, several authors analyzed the modifications in the expression of mitochondria-related proteins on ESC pluripotency and differentiation. These studies have pinpointed a possible involvement of Polγ, Gfer (growth factor erv1-like), Ptpmt1 (Pten-like phosphatidylinositol phosphate (PIP) phosphatase, mitochondrial 1) and UCP2 (uncoupling protein 2) in governing stemness and differentiation. Indeed, steady-state levels of Polγ, the DNA polymerase responsible for mtDNA replication (see this book chapter devoted to mitochondria biogenesis), are suggested to be necessary for the maintain of mESC pluripotency, as both up- and downregulation of Polγ mRNA are observed during the onset of differentiation of different mESC types, and as Polγ knockdown in mESCs results in reduced OCT4 protein levels and increased brachyury levels (a specific marker of the mesodermal lineage) [15]. The growth factor Gfer, a FAD-dependent sulfhydryl oxidase predominantly localized in the intermembrane space of the mitochondria, was demonstrated to maintain mitochondrial dynamics and pluripotency of mESCs through the modulation of Drp1 (dynamin-related protein 1) expression [27]. Drp1 is a dynamin-like GTPase capable of self-assembly into multimeric ring-like structures necessary to mitochondria fission [28]. The knockdown of Gfer in mESCs results in the reduced expression of NANOG, OCT4 and SSEA1 (stage specific embryonic antigen 1), three markers of mESC pluripotency, in diminished survival, in impaired embryoid body formation and in the loss of mitochondrial function through an increase in Drp1 levels, which results in enhanced mitochondrial fission and the loss of mitochondrial membrane potential. On the opposite, Gfer overexpression


Mitochondrial involvement in stemness and stem cell differentiation

203

is associated with an increased expression of NANOG and OCT4 and a decrease in Drp1 levels, which impedes mitochondrial fission. Interestingly, the effects of Gfer expression modulation on pluripotency gene expression depend on Drp1 expression, as the expression of a dominant negative mutant of Drp1 in Gfer-knockdown mESCs restores OCT4 and SSEA1 expression to a level similar to control cells. Gfer may therefore regulate ESCs pluripotency or stemness by regulating Drp1 expression and therefore controlling mitochondrial dynamics. It is also worth mentioning that Gfer knockdown in differentiated cells such as mouse embryonic fibroblasts does not affect cell survival and mitochondrial content, morphology and function, suggesting that Gfer would be dispensable for the survival and mitochondrial functions of differentiated cells [27]. Ptpmt1, which is specifically localized in the mitochondrial inner membrane, was also identified as specifically important for the maintain of ESC ability to differentiate. The disruption of the gene encoding Ptpmt1 in mice results in postimplantation lethality, impairs the proliferation of the inner cell mass cells of blastocyts, decreases the proliferation and differentiation of mESCs. Ptpmt1-depleted cells also display a decreased oxygen consumption associated with enhanced glycolysis. Besides, the depletion of Ptpmt1 in mESCs reduces fusion events, resulting in the fragmentation of the mitochondrial network, an effect that would be linked to the accumulation of PIP substrates such as PI(3,5)P2, as the perfusion of PI(3,5)P2 in WT mESCs induces mitochondrial fragmentation in a similar fashion than Ptpmt1 deficiency. Similarly than for Gfer, Ptpmt1 knockdown in differentiated cells does not disturb cell function, suggesting that Ptpmt1 is specifically important for stem cells [29]. In a recent report, UCP2 was identified as a central regulator of ESC and iPSC energy metabolism. As previously mentioned, a metabolic switch from glycolysis to oxidative phosphorylation was suggested to occur upon cell differentiation. Interestingly, UCP2, which is overexpressed in pluripotent cells when compared with differentiated cells or embryoid bodies, prevents mitochondrial glucose oxidation and favors glycolysis through a substrate shunting mechanism in pluripotent cells (UCP2 is proposed to block glucosederived pyruvate oxidation in mitochondria [30]). During cell differentiation, UCP2 expression is repressed (as demonstrated in the model of retinoic-acidinduced differentiation of ESCs), which enables the transition from glycolysis to glucose oxidation in mitochondria. Interestingly, the ectopic expression of UCP2 in pluripotent cells induced to differentiate by retinoicacid exposure, impairs the induction of developmental gene expression, suggesting that UCP2-mediated regulation of energy metabolism is required for the early differentiation of pluripotent cells. Although UCP2 repression


204

AnaĂŻs Wanet et al.

was demonstrated to contribute to the ROS accumulation seen in differentiating cells, UCP2 would regulate cell differentiation by a still unknown mechanism other than ROS (at least in the model studied), as the use of ROS scavengers does not reverse the repression of differentiation seen in pluripotent cells ectopically expressing UCP2 [16].

2. A ÂŤ rejuvenated Âť mitochondrial phenotype is observed in iPSCs, and is reversed during iPSC differentiation Because of their potential therapeutic applications in regenerative medicine, their advantages over ESCs in terms of immune rejection but also in terms of ethical issues, a lot of attention has been paid to iPSCs in the aim to evaluate to what extent these cells resemble ESCs. Several groups therefore analyzed the mitochondrial morphology and functions during iPSC reprogramming and differentiation. In different studies, hESCs and hiPSCs (human iPSCs) were shown to have comparable mitochondrial masses and genome copy numbers, both lower than those of fibroblast-like cells differentiated from these hESCs and hiPSCs and of control fibroblasts [12, 20]. Besides, even though a report mentioned that hiPSC mitochondria display an intermediate phenotype between hESCs and differentiated cells [13], another one found that hiPSCs exhibit fewer mitochondria, localized at the two sides of the nuclei, and with poorly developed cristae [12], which are characteristic features of ESC mitochondria as described in the previous section. During the differentiation of hESCs and hiPSCs, however, an increase in mtDNA copy number and in the mitochondrial content is observed, and mitochondria (re)acquire an elongated shape with developed cristae [12]. Interestingly, a recent report mentioned that the reprogramming of aged fibroblasts (derived from a 84year-old woman) into iPSCs is associated with similar changes in mitochondrial morphology compared to the reprogramming of young cells, despite the presence of karyotype aberrations in the aged reprogrammed cells. These observations suggest that the presence of chromosomal alterations in aged somatic cells does not prevent their reprogramming into iPSCs neither the associated changes in mitochondrial morphology [18]. Further supporting their similarities, both hESCs and hiPSCs are characterized by similar low ROS levels compared with adult human dermal fibroblasts, and both cell types produce higher ROS levels during differentiation [12, 20]. In addition, both hESCs and hiPSCs display lower ATP levels and higher lactate production compared with fibroblasts [12, 13], whereas ATP level increases and lactate production decreases during hESC and hiPSC differentiation


Mitochondrial involvement in stemness and stem cell differentiation

205

[12]. In agreement with these data, it was demonstrated that both hESCs and hiPSCs rely less on OXPHOS than fibroblasts [13]. Instead, pluripotent cells would rely on glycolysis to meet their energy demands. This hypothesis is strengthen by the observation that pluripotent cells express higher protein levels of hexokinase II compared with fibroblasts, and increased levels of phosphorylated PDH E1α [13] that inactivates the pyruvate dehydrogenase (PDH) complex and results in lower levels of substrates entering the TCA cycle [31]. Intriguingly, hESCs and hiPSCs were found to express components of the mitochondrial complexes II, III and V at higher levels than fibroblasts. Although this hypothesis needs to be further explored, Varum et al. suggested that the higher expression of c-Myc in hESCs and hiPSCs compared with fibroblasts may be responsible for this effect [13]. Indeed, c-Myc, which is essential for the maintenance of ESC self-renewal and used as a reprogramming factor during the generation of iPSCs [32], is reported to promote mitochondrial biogenesis and the expression of genes involved in mitochondrial structure and function [33]. As far as mitochondrial biogenesis regulators are concerned, differentiating hESCs and hiPSCs also exhibit similar changes in their expression. For instance, TFAM, Polγ, PGC-1, Polγ2, POLRmt, and TFBM1 mRNAs are all downregulated during the in vitro differentiation of both hESCs and hiPSCs into embryoid bodies [12, 20, 21]. As mentioned earlier, the higher expression of these mitochondrial biogenesis regulators in pluripotent cells may represent a nuclear response to decreased amounts of mtDNA [21]. Nevertheless, some differences were also noted for few genes, including for TFBM2 and MTERF, which exhibit different trends during the differentiation of hESCs and hiPSCs [20]. Altogether, these data strongly suggest that, although not identical, iPSCs share similar mitochondrial morphology and function with ESCs and both cell types exhibit similar changes in mitochondrial architecture and activity during their differentiation. These findings suggest that when fibroblasts, with a mature mitochondrial network, are reprogrammed into pluripotent stem cells, the mitochondrial phenotype is also reversed towards an « immature » phenotype, strongly suggesting that cell differentiation and mitochondria maturation are intimately intricated. This raises an important question : is it the cell differentiation process that governs mitochondria maturation, and/or can we consider that mitochondria maturation allows the cellular differentiation?

3. Mitochondrial biogenesis in somatic stem cell differentiation Although fewer reports were published about the involvement of mitochondria in SSC differentiation compared with ESCs and iPSCs, several


206

Anaïs Wanet et al.

studies nevertheless suggest a mitochondrial biogenesis and mitochondrial contribution during SSC differentiation. The following sections will only discuss the findings emerging from studies performed on MSCs and HSCs, but increasing evidence also point toward a possible involvement of mitochondrial biogenesis in the differentiation of other stem cell categories, such as neural stem cells [34] and cardiac mesangioblasts [35].

3.1. Emerging data point toward a role of mitochondria in MSC differentiation Although no change was observed in the mitochondrial mass, studies performed on hMSCs brought evidence of increased mtDNA copy number, enhanced expression of the protein subunits of the respiratory enzymes, increased oxygen consumption rate, and increased intracellular ATP content during the osteogenic differentiation process [36]. As mentioned in the previous section, Varum et al. reported a higher expression of the protein levels of complexes II, III and V in hESCs and iPSCs than in fibroblasts [13] – that might seem in contradiction with the expression changes observed by Chen et al. Nevertheless, Varum and coworkers did not analyze the abundance of these complexes upon hESC and iPSC differentiation – therefore, the diverging data obtained may, at least partly, result from the use of different cell lines to compare pluripotent and differentiated cells, and/or from the different biology of ESCs, iPSCs and MSCs. Besides, although only studied at the mRNA levels, the electron transport chain genes were shown to be downregulated during the in vitro differentiation of ESCs and iPSCs, while they tend to upregulate during in vivo ESC and iPSC differentiation (teratoma formation) [21]. As mentioned earlier, although these observations may be biased by the tumorigenic nature of teratomas, the level of differentiation could also account for the discrepancies between these observations and therefore, for the diverging results obtained by [36] and [13]. Human MSCs are also reported to be more dependent on glycolysis than differentiated osteoblasts [36], a finding that is in agreement with the data obtained in ESCs and iPSCs. However, unlike the expression patterns observed in differentiating ESCs and iPSCs, the mRNA levels of TFAM, PGC-1α and Polγ were found to increase during the differentiation of hMSCs [36]. According to Prigione and Adjaye, these different observations may be explained by different nuclear responses due to more or less abundant mtDNA in different cell types [21], or they may also be linked to the features of the differentiation model used. Indeed, the spontaneous in vitro differentiation of ES cells into embryoid bodies [21] leads to the emergence of cells committed into several cell types, but not fully differentiated, while


Mitochondrial involvement in stemness and stem cell differentiation

207

the population of cells undergoing a directed osteogenic differentiation process [36] is less heterogenous and closer to the terminal differentiation state. Importantly, a study performed on ATSC cells, an adult rhesus macaque stromal cell line isolated from adipose tissue and known to spontaneously differentiate when cultured over a period of 20 passages, demonstrated that the differentiation-related changes in mitochondrial properties may be used as indicators of stem cell differentiation competence [11]. Indeed, a lower proportion of cells of a later passage displays a perinuclear arrangement of mitochondria, and these cells have higher ATP content and differentiate more efficiently into adipocytes compared with cells from an earlier passage. Therefore, the perinuclear distribution of mitochondria and a low ATP content per cell are suggested to be indicators of stem cell differentiation competence, while a shift from this profile to the one observed at later passages may indicate that cells are differentiating or, possibly, becoming senescent [11]. Supporting the notion that stem cell mitochondria may be involved in their stemness status, it was found that the reprogramming of adult cardiomyocytes toward a progenitor-like state, which occurs during their partial fusion with hMSCs, depends on the transfer of stem cell mitochondria into cardiomyocytes [37]. Further studies are nevertheless needed to elucidate the role of stem cell mitochondria in cardiomyocyte reprogramming. Regarding ROS, both increased and decreased ROS levels, depending on the differentiation type studied, were found to be involved in MSC differentiation. On one hand, a decrease in ROS levels was suggested to be necessary for the osteogenic differentiation of human bone-marrow MSCs (BM-MSCs). Indeed, the expression of catalase and manganese-dependent superoxide dismutase (MnSOD, also known as SOD2) are increased in osteoblasts compared to undifferentiated MSCs, resulting in a decrease in intracellular ROS levels during the early phase of osteogenic differentiation. Importantly, the exogenous administration of H2O2, as well as the treatment of differentiating hMSCs with oligomycin, retard the osteogenic differentiation process, further supporting the importance of reduced levels of ROS (and, upstream of this, of induced catalase and MnSOD expression) for the osteogenic differentiation [36]. Given that the MnSOD was identified as a nucleoid complex component [38] and demonstrated to act as a mitochondrial fidelity protein for the PolÎł [39], one may wonder, while this has not been studied so far, whether the MnSOD could not be also involved (beside its role in decreasing ROS levels) in the mitochondrial biogenesis observed during cell differentiation. On the other hand, Tormos et al. demonstrated that the ROS produced by the mitochondrial complex III promote adipogenic


208

Anaテッs Wanet et al.

differentiation of human BM-MSCs by inducing the expression of PPAR-ホウ [40], the master regulator of adipogenesis [41]. Early in the adipogenic differentiation process (two days after the initiation of the adipogenic treatment), the oxygen consumption rate and ATP synthesis of BM-MSCs are increased, as well as the intracellular concentration of H2O2. These ROS are required for adipocyte differentiation, as adipocyte differentiation is hampered in the presence of mitochondrial-targeted antioxidants, while the addition of exogenous H2O2 rescues the differentiation process [40]. Similarly, the treatment of a MSC line with the antioxidant N-acetyl-lcysteine also blocks their differentiation into adipocytes [42]. It needs to be mentioned, nevertheless, that the role played by ROS during the adipogenic differentiation process is likely complex, as both differentiation-promoting and differentiation-inhibiting effects were observed depending on studies [43, 44]. Whether these discrepancies are related to the use of different cell lines and cell types, to a particular timing and/or to different differentiation protocols remains to be determined. From a molecular point of view, during the adipogenic differentiation of MSCs, the mitochondrial complex III was demonstrated to be involved in the production of superoxide ions, which are converted to H2O2, that initiate the PPAR-ホウ窶電ependent transcriptional machinery driving adipocyte differentiation. Besides, it was shown that mTORC1 (mammalian target of rapamycin complex 1) is involved in the increased ROS levels during the initiation of adipocyte differentiation, and that it would also drive adipocyte differentiation by acting on unidentified effectors other than ROS. Therefore, the increase in mitochondrial metabolism observed during differentiation would not only be necessary to meet the energy demands of the differentiation process but would also be required, through ROS production, for promoting the differentiation process [40].

3.2. Mitochondrial features are also modified during HSC differentiation As seen for other pluripotent cell types, HSCs are characterized by a poor mitochondrial content, spread in bipolar perinuclear clusters, a poor oxygen consumption, a decreased expression of OXPHOS complex subunits per mitochondondria and a weak OXPHOS activity [45]. Moreover, as observed in ESCs, iPSCs and other SSCs, a mitochondrial biogenesis occurs during the loss of pluripotency/differentiation of HSCs. CD34 is a specific surface marker of hematopoeitic stem/progenitor cells whose expression is lost upon HSC differentiation. Interestingly, Piccoli et al. observed an inverse


Mitochondrial involvement in stemness and stem cell differentiation

209

correlation between CD34 expression and mitochondrial content that might indicate a shift toward a more efficient bioenergetic metabolism during the onset of commitment [45]. However, in another study, devoted to long-term repopulating HSCs, an increase in mitochondrial mass and mitochondrial membrane potential was found in cells upregulating CD34 [46]. While in apparent paradox with Piccoli and coworkers’ findings, these discrepancies may be explained by the fact that CD34, while being a HSC marker, is poorly expressed in long-term repopulating HSCs [46]. Therefore, a mitochondrial biogenesis is observed when HSCs lose their long-term repopulating ability, which is in agreement with the more general observation of a mitochondrial biogenesis during cell commitment and loss of pluripotency. In agreement with this correlation between mitochondrial biogenesis and loss of pluripotency, it was observed that the deletion of the autophagy gene Atg7 in the hematopoietic system results in an accumulation of mitochondria with higher membrane potential, in an accumulation of ROS and DNA damages as well as in the reduction of the number of progenitors of multiple lineages [47]. These observations suggest, although not yet confirmed for other types of stem cells, a role of mitophagy in the maintenance and regulation of mitochondrial content and ROS production on the one hand, and in the maintenance of HSC quality on the other hand. The role of ROS in regulating HSC maintenance is supported by other findings. Indeed, the conditional deletion of Tsc1 (tuberous sclerosis complex), a negative regulator of mTORC1, results in mTOR signaling pathway hyperactivation, drives a shift of HSC state from quiescence to rapid cycling and results in decreased repopulation capability of HSCs. Interestingly, Tsc1-deficient HSCs have increased mitochondrial mass, mitochondrial DNA copy number and ROS levels. ROS could thus be involved in regulating HSC quiescence, as the use of the antioxidant N-acetylcysteine rescues the self-renewal ability of HSCs [48]. Beside mTORC1 signaling, another pathway, mediated by Lkb1 (liver kinase B1 or serine-threonine kinase 11), has been involved in regulating HSC quiescence and proliferation possibly through an effect on mitochondrial functions. Indeed, Lkb1-deficient HSCs display decreased repopulation abilities, are prone to exhaustion, downregulate PGC-1ι and have reduced mitochondrial DNA copy number, mitochondrial potential and ATP levels [49]. These results, along with those of Chen et al. [48], suggest the need of maintaining mitochondrial content and function in a certain range to ensure HSC maintenance. Lkb1 loss has also been demonstrated to induce apoptosis of HSCs and bone marrow cells, a process which is preceeded by an autophagic survival response [50]. Interestingly, Lkb1 has been proposed to be required for the maintenance of energy homeostasis in bone marrow


210

Anaïs Wanet et al.

cells, as its inactivation results, as previously mentioned, in a decrease in mitochondrial membrane potential and ATP levels but also in a decrease in basal mitochondrial oxygen consumption and total mitochondrial oxidative capacity, despite an increased glucose uptake [50]. These effects of Lkb1 on the cell cycle regulation, cell survival, mitochondrial function and energy homeostasis, which are independent of mTOR signaling, depend both on AMPK-dependent and on AMPK-independent mechanisms [51]. It needs to be stressed however, as mentioned by Gurumurthy and colleagues, that it is still unknown if these mitochondrial function defects are direct or secondary consequences of Lkb1 inactivation, as the cell death program is rapidly induced in Lkb1-deficient cells [50].

4. From development to disease: The mitochondria phenotype of cancer stem cells As described in the previous sections, changes in mitochondrial abundance, morphology and functions are observed during the differentiation of different stem cells types, ranging from embryonic stem cells to induced pluripotent stem cell and somatic stem cells. Interestingly, the mitochondrial phenotype observed in pluripotent cells would not only be a feature of normal stem cells, but may also be used as an indicator of cancer stem cells (CSCs). Indeed, lung cancer stem cells (LCSCs) isolated from the A549 lung cancer cell line are characterized by mitochondria with a perinuclear arrangement, a low mtDNA content, consume less oxygen and have reduced ATP and ROS levels compared with non-LCSCs [52], suggesting that mitochondrial and energy metabolism features could be used to determine the « stemness » of both normal and cancer stem cells. Interestingly, an induction of mtDNA abundance and mitochondrial mass is observed during the early differentiation of LCSCs, reinforcing their similarity with normal stem cells. Given the importance of CSCs in tumor progression and recurrence, it would be of particular interest to study the mitochondrial properties of other CSC types, to determine if the « immature » mitochondrial phenotype is a hallmark of all CSCs.

5. Questions and future prospects Considering the huge therapeutic potential offered by stem and progenitor cells, as well as the necessity to get deeper insights into the biology of CSCs in order to counteract cancer progression and recurrence, the field of mitochondrial maturation during cell differentiation appears very


Mitochondrial involvement in stemness and stem cell differentiation

211

promising. Indeed, given the mitochondrial involvement in stemness and differentiation described in the previous sections, one can ask whether manipulating mitochondrial content and/or function, or mitochondrial-related signaling pathways, couldn’t be used for the more efficient generation of iPSCs or, on the opposite, for the more efficient differentiation of pluripotent cells, which could be of particular interest for regenerative therapies. One should bear in mind, however, several important facts and unresolved questions before considering such a perspective. First of all, although sharing the characteristic stemness properties, i.e., self-renewal and pluripotency, ESCs and iPSCs are not equivalent with respect to proteomes and phosphoproteomes, transcriptomes and epigenetic marks [53-56]. For instance, reprogrammed cells have been shown to retain an « epigenetic memory » of their original tissue, and to exhibit a unique gene expression signature independent of their origin or the method by which they were generated [53-54]. Besides, it was shown that the reprogramming of somatic cells into iPSCs is accompanied by alterations not only in the nuclear genome [54], but also in the mitochondrial genome [57]. Therefore, although these alterations do not prevent the reprogramming process, the occurrence of pathogenic mutations (both in the nuclear and mitochondrial genomes) in reprogrammed cells should be an important parameter to monitor in the perspective of their use in cell-based therapy. Secondly, the understanding of the role played by mitochondria during stem cell differentiation is still in its early stages and requires more in depth studies, focussing on an enlarged panel of stem cell types and differentiation processes. It is generally accepted that mitochondria are phenotypically different in stem cells than in differentiated cells, especially in terms of metabolic activity, the stem cells relying essentially on a glycolytic phenotype while differentiated cells are more oxidative. However, when trying to depict more precisely the mitochondrial phenotype of stem cells versus differentiated cells, and the regulatory pathways leading to mitochondria “maturation” along differentiation, attention must be paid to critical parameters: the type of stem cells, the spontaneous or directed (specific) differentiation, as well as the level differentiation. To illustrate this, it is instructive to examine two works: one dedicated to the osteogenic differentiation of human bone marrow-derived MSCs [36], and the second one to the spontaneous differentiation of ESCs and iPSCs [21]. For instance, an unexpected downregulation of critical mitochondria biogenesis regulators including PGC-1α, Polγ and TFAM has been observed upon in vitro differentiation (embryoid bodies formation) of hESCs and hiPSCs, while these regulators tend to be upregulated when the same cells are differentiated


212

Anaïs Wanet et al.

in vivo (teratoma formation) [21]. In line with this latter observation, the same mitochondrial biogenesis regulators are clearly upregulated when hMSCs undergo a directed differentiation towards osteoblasts [36]. On the other hand, the different mitochondrial biogenesis markers do not necessarily evolve in parallel. Although mtDNA content has been shown to increase with cell differentiation in all the examined studies (and to the best of our knowledge), this is not the case with some other mitochondrial markers like nuclear-encoded subunits of the ETC. The expression of several proteins of the ETC increase in hMSCs after osteogenic differentiation [36], while their mRNA level is decreased in hESCs spontaneously differentiated into embryoid bodies (the authors attributed this latter data to a high c-Myc activity in ESCs and iPSCs, c-Myc being involved in the transcriptional regulation of the genes coding for OXPHOS subunits) [21]. Whether this contradiction is due to discrepancies between the transcript and protein levels, which happens quite often, or to biological differences between the two different models studied remains to be solved. However, if it is confirmed that in ESCs the protein level of the nuclear-encoded ETC subunits is downregulated upon differentiation, along with an increase in mtDNA, then the observed metabolic oxidative switch might be due to the critical role of the 13 mitochondria-encoded ETC subunits in the assembly of respiratory supercomplexes (see this book chapter devoted to mitochondria biogenesis). Eventually, beside the specificities linked to cell types and differentiation types and efficiencies, the general increase in mitochondrial biogenesis and/or function occuring during the differentiation of different pluripotent cell types leaves many open questions. For example, what are the mechanisms restricting mitochondrial biogenesis and favoring a glycolytic metabolism in stem cells? What are the signals controlling mitochondrial biogenesis upon stem cell differentiation? How can an activated mitochondrial biogenesis favor various differentiation processes, as illustrated in the different previous sections? What are the molecular players linking the mitochondrial biogenesis and the differentiation processes? Here are so many questions whose answers should help to better understand the biology of stem cells and, undoubtedly, the biology of the mitochondrion itself.

Acknowledgements A. Wanet is recipient of the doctoral fellowship from the Fonds National pour la Recherche Scientifique (FNRS, Belgium). This work was supported


Mitochondrial involvement in stemness and stem cell differentiation

213

by the Association Belge contre les Maladies neuro‐Musculaires (ABMM, Belgium). The authors also thank Michel Savels for his contribution to the figure layout.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

17. 18. 19. 20. 21.

Lonergan T., Bavister B., Brenner C. 2007, Mitochondrion 7: 289-96. Rehman J. 2010, J Mol Med (Berl) 88: 981-6. Zhang H., Wang Z.Z. 2008, J Cell Biochem 103: 709-18. Mohyeldin A., Garzon-Muvdi T., Quinones-Hinojosa A. 2010, Cell Stem Cell 7: 150-61. Wang Y., Mah N., Prigione A., Wolfrum K., Andrade-Navarro M.A., Adjaye J. 2010, Stem Cell Rev 6: 282-96. Sathananthan H., Pera M., Trounson A. 2002, Reprod Biomed Online 4: 56-61. Baharvand H., Matthaei K.I. 2003, Reprod Biomed Online 7: 330-5. Oh S.K., Kim H.S., Ahn H.J., Seol H.W., Kim Y.Y., Park Y.B., Yoon C.J., Kim D.W., Kim S.H., Moon S.Y. 2005, Stem Cells 23: 211-9. St John J.C., Ramalho-Santos J., Gray H.L., Petrosko P., Rawe V.Y., Navara C.S., Simerly C.R., Schatten G.P. 2005, Cloning Stem Cells 7: 141-53. Mandal S., Lindgren A.G., Srivastava A.S., Clark A.T., Banerjee U. 2011, Stem Cells 29: 486-95. Lonergan T., Brenner C., Bavister B. 2006, J Cell Physiol 208: 149-53. Prigione A., Fauler B., Lurz R., Lehrach H., Adjaye J. 2010, Stem Cells 28: 721-33. Varum S., Rodrigues A.S., Moura M.B., Momcilovic O., Easley C.A.t., Ramalho-Santos J., Van Houten B., Schatten G. 2011, PLoS One 6: e20914. Cho Y.M., Kwon S., Pak Y.K., Seol H.W., Choi Y.M., Park do J., Park K.S., Lee H.K. 2006, Biochem Biophys Res Commun 348: 1472-8. Facucho-Oliveira J.M., Alderson J., Spikings E.C., Egginton S., St John J.C. 2007, J Cell Sci 120: 4025-34. Zhang J, Khvorostov I, Hong JS, Oktay Y, Vergnes L, Nuebel E, Wahjudi PN, Setoguchi K, Wang G, Do A, Jung HJ, McCaffery JM, Kurland IJ, Reue K, Lee WN, Koehler CM, Teitell MA. UCP2 regulates energy metabolism and differentiation potential of human pluripotent stem cells. EMBO J. 2011 Nov 15;30(24):4860-73. doi: 10.1038/emboj.2011.401. Chung S., Arrell D.K., Faustino R.S., Terzic A., Dzeja P.P. 2010, J Mol Cell Cardiol 48: 725-34. Prigione A., Hossini A.M., Lichtner B., Serin A., Fauler B., Megges M., Lurz R., Lehrach H., Makrantonaki E., Zouboulis C.C., Adjaye J. 2011, PLoS One 6: e27352. Schieke S.M., Ma M., Cao L., McCoy J.P., Jr., Liu C., Hensel N.F., Barrett A.J., Boehm M., Finkel T. 2008, J Biol Chem 283: 28506-12. Armstrong L., Tilgner K., Saretzki G., Atkinson S.P., Stojkovic M., Moreno R., Przyborski S., Lako M. 2010, Stem Cells 28: 661-73. Prigione A., Adjaye J. 2010, Int J Dev Biol 54: 1729-41.


214

Ana誰s Wanet et al.

22. Zhu D.Y., Wu J.Y., Li H., Yan J.P., Guo M.Y., Wo Y.B., Lou Y.J. 2010, J Cell Biochem 109: 498-508. 23. Sharma N.S., Wallenstein E.J., Novik E., Maguire T., Schloss R., Yarmush M.L. 2009, Tissue Eng Part C Methods 15: 297-306. 24. Spitkovsky D., Sasse P., Kolossov E., Bottinger C., Fleischmann B.K., Hescheler J., Wiesner R.J. 2004, FASEB J 18: 1300-2. 25. Varum S., Momcilovic O., Castro C., Ben-Yehudah A., Ramalho-Santos J., Navara C.S. 2009, Stem Cell Res 3: 142-56. 26. Crespo F.L., Sobrado V.R., Gomez L., Cervera A.M., McCreath K.J. 2010, Stem Cells 28: 1132-42. 27. Todd L.R., Damin M.N., Gomathinayagam R., Horn S.R., Means A.R., Sankar U. 2010, Mol Biol Cell 21: 1225-36. 28. Otera H., Mihara K. 2011, J Biochem 149: 241-51. 29. Shen J., Liu X., Yu W.M., Liu J., Groot Nibbelink M., Guo C., Finkel T., Qu C.K. 2011, Mol Cell Biol. 30. Bouillaud F. 2009, Biochim Biophys Acta 1787: 377-83. 31. Holness M.J., Sugden M.C. 2003, Biochem Soc Trans 31: 1143-51. 32. Takahashi K., Tanabe K., Ohnuki M., Narita M., Ichisaka T., Tomoda K., Yamanaka S. 2007, Cell 131: 861-72. 33. Li F., Wang Y., Zeller K.I., Potter J.J., Wonsey D.R., O'Donnell K.A., Kim J.W., Yustein J.T., Lee L.A., Dang C.V. 2005, Mol Cell Biol 25: 6225-34. 34. Wang W., Osenbroch P., Skinnes R., Esbensen Y., Bjoras M., Eide L. 2010, Stem Cells 28: 2195-204. 35. San Martin N., Cervera A.M., Cordova C., Covarello D., McCreath K.J., Galvez B.G. 2011, Stem Cells 29: 1064-74. 36. Chen C.T., Shih Y.R., Kuo T.K., Lee O.K., Wei Y.H. 2008, Stem Cells 26: 960-8. 37. Acquistapace A., Bru T., Lesault P.F., Figeac F., Coudert A.E., le Coz O., Christov C., Baudin X., Auber F., Yiou R., Dubois-Rande J.L., Rodriguez A.M. 2011, Stem Cells 29: 812-24. 38. Kienhofer J., Haussler D.J., Ruckelshausen F., Muessig E., Weber K., Pimentel D., Ullrich V., Burkle A., Bachschmid M.M. 2009, FASEB J 23: 2034-44. 39. Bakthavatchalu V, Dey S, Xu Y, Noel T, Jungsuwadee P, Holley AK, Dhar SK, Batinic-Haberle I, St Clair DK. Manganese superoxide dismutase is a mitochondrial fidelity protein that protects Pol? against UV-induced inactivation. Oncogene. 2012 Apr 26;31(17):2129-39. doi: 10.1038/onc.2011.407. Epub 2011 Sep 12. 40. Tormos K.V., Anso E., Hamanaka R.B., Eisenbart J., Joseph J., Kalyanaraman B., Chandel N.S. 2011, Cell Metab 14: 537-44. 41. Cristancho A.G., Lazar M.A. 2011, Nat Rev Mol Cell Biol 12: 722-34. 42. Kanda Y., Hinata T., Kang S.W., Watanabe Y. 2011, Life Sci 89: 250-8. 43. Carriere A., Carmona M.C., Fernandez Y., Rigoulet M., Wenger R.H., Penicaud L., Casteilla L. 2004, J Biol Chem 279: 40462-9. 44. Calzadilla P., Sapochnik D., Cosentino S., Diz V., Dicelio L., Calvo J.C., Guerra L.N. 2011, Int J Mol Sci 12: 6936-51.


Mitochondrial involvement in stemness and stem cell differentiation

215

45. Piccoli C., Ria R., Scrima R., Cela O., D'Aprile A., Boffoli D., Falzetti F., Tabilio A., Capitanio N. 2005, J Biol Chem 280: 26467-76. 46. Mantel C., Messina-Graham S., Broxmeyer H.E. 2010, Cell Cycle 9: 2008-17. 47. Mortensen M., Soilleux E.J., Djordjevic G., Tripp R., Lutteropp M., SadighiAkha E., Stranks A.J., Glanville J., Knight S., Jacobsen S.E., Kranc K.R., Simon A.K. 2011, J Exp Med 208: 455-67. 48. Chen C., Liu Y., Liu R., Ikenoue T., Guan K.L., Zheng P. 2008, J Exp Med 205: 2397-408. 49. Gan B., Hu J., Jiang S., Liu Y., Sahin E., Zhuang L., Fletcher-Sananikone E., Colla S., Wang Y.A., Chin L., Depinho R.A. 2010, Nature 468: 701-4. 50. Gurumurthy S., Xie S.Z., Alagesan B., Kim J., Yusuf R.Z., Saez B., Tzatsos A., Ozsolak F., Milos P., Ferrari F., Park P.J., Shirihai O.S., Scadden D.T., Bardeesy N. 2010, Nature 468: 659-63. 51. Nakada D., Saunders T.L., Morrison S.J. 2010, Nature 468: 653-8. 52. Ye X.Q., Li Q., Wang G.H., Sun F.F., Huang G.J., Bian X.W., Yu S.C., Qian G.S. 2011, Int J Cancer 129: 820-31. 53. Sullivan G.J., Bai Y., Fletcher J., Wilmut I. 2010, Mol Hum Reprod 16: 880-5 54. Puri MC, Nagy A. Concise review: Embryonic stem cells versus induced pluripotent stem cells: the game is on. Stem Cells. 2012 Jan;30(1):10-4. doi: 10.1002/stem.788. 55. Kim SY, Kim MJ, Jung H, Kim WK, Kwon SO, Son MJ, Jang IS, Choi JS, Park SG, Park BC, Han YM, Lee SC, Cho YS, Bae KH. Comparative Proteomic Analysis of Human Somatic Cells, Induced Pluripotent Stem Cells, and Embryonic Stem Cells. Stem Cells Dev. 2011 Aug 29. [Epub ahead of print]. 56. Phanstiel D.H., Brumbaugh J., Wenger C.D., Tian S., Probasco M.D., Bailey D.J., Swaney D.L., Tervo M.A., Bolin J.M., Ruotti V., Stewart R., Thomson J.A., Coon J.J. 2011, Nat Methods 8: 821-7. 57. Prigione A., Lichtner B., Kuhl H., Struys E.A., Wamelink M., Lehrach H., Ralser M., Timmermann B., Adjaye J. 2011, Stem Cells 29: 1338-48.



Issuu converts static files into: digital portfolios, online yearbooks, online catalogs, digital photo albums and more. Sign up and create your flipbook.