International Microbiology

Official journal of the Spanish Society for Microbiology Founded in 1998 by Lynn Margulis & Ricardo Guerrero

RESEARCH REVIEW

Padilla-Vaca F, Anaya-Velázquez F, Franco B Synthetic biology: Novel approaches for microbiology

Espinosa-Asuar L, Escalante AE, GascaPineda J, Jazmin B, Lorena P, Eguiarte LE, Valeria S Aquatic bacterial assemblage structure in Pozas Azules, Cuatro Ciénegas Basin, Mexico: Deterministic vs. stochastic processes

105

117

127

Armas-Freire PI, Trueba G, ProañoBolaños C, Levy K, Zhang L, Marrs CF, Cevallos W, Eisenberg JNS Unexpected distribution of the fluoroquinolone-resistance gene qnrB in Escherichia coli from human and poultry origins in Ecuador

85

Toledo A, López S, Aulicino M, de Remes Lenicov AM, Balatti P Antagonistic of activity of entomopathogenic fungi by Bacillus spp. associated with the integument of cicadellids and delphacids

91

Križanović S, Butorac A, Mrvčić J, Krpan M, Cindrić M, Bačun-Družina V, Stanzer D Characterization of S-adenosyl–L– methionine (SAM) accumulating strain of yeast Scheffersomyces stipitis

Martínez-Gamboa A, Silva C, FernándezMora M, Wiesner M, Ponce de León A, Calva E IS200 and multilocus sequence typing for Salmonella enterica serovar Typhi strains from Indonesia

99

Cappello S, Calogero R, Santisi S, Genovese M, Denaro R, Genovese L, Giuliano L, Mancini G, Yakimov MM Bioremediation of oil polluted marine sediments: A bio-engineering treatment

INTERNATIONAL MICROBIOLOGY www.im.microbios.org

2015 pp 71-134

RESEARCH ARTICLES

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Volume 18 Number 2

Volume 18 · Number 2 · June 2015

International Microbiology

INTERNATIONAL MICROBIOLOGY

Volume 18 · Number 2 · June 2015 · ISSN 1139-6709 · e-ISSN 1618-1905

18(2) 2015

INDEXED IN

Agricultural and Environmental Biotechnology Abstracts; ASFA/Aquatic Sciences & Fisheries Abstracts; BIOSIS; CAB Abstracts; Chemical Abstracts; SCOPUS; Current Contents®/Agriculture, Biology & Environmental Sciences®; EBSCO; EMBASE/Elsevier Bibliographic Databases; Food Science and Technology Abstracts; ICYT/CINDOC; IBECS/BNCS; ISI Alerting Services®; MEDLINE®/Index Medicus®; Latindex; MedBioWorldTM; SciELO-Spain; Science Citation Index Expanded®/SciSearch®

June 2015

Official journal of the Spanish Society for Microbiology

Publication Board

Editorial Board

Coeditors-in-Chief José Berenguer (Madrid), Autonomous University of Madrid Ricardo Guerrero (Barcelona), University of Barcelona

Juan Aguirre, Prince Edward Island University, Canada Ricardo Amils, Autonomous University of Madrid, Madrid, Spain Miguel A. Asensio, University of Extremadura, Caceres, Spain Shimshon Belkin, The Hebrew University of Jerusalem, Jerusalem, Israel Albert Bordons, University Rovira i Virgili, Tarragona, Spain Albert Bosch, University of Barcelona, Barcelona, Spain Javier del Campo, University of British Columbia, Vancouver, Canada Victoriano Campos, Pontificial Catholic University of Valparaíso, Chile Josep Casadesús, University of Sevilla, Sevilla, Spain Rita R. Colwell, Univ. of Maryland & Johns Hopkins Univ., Baltimore, MD, USA Katerina Demnerova, Inst. of Chem. Technology, Prague, Czech Republic Esteban Domingo, CBM, CSIC-UAM, Cantoblanco, Spain Mariano Esteban, Natl. Center for Biotechnol., CSIC, Cantoblanco, Spain Mariano Gacto, University of Murcia, Murcia, Spain Juncal Garmendia, Institute of Agrobiotechnology, Pamplona, Spain Olga Genilloud, Medina Foundation, Granada, Spain Steven D. Goodwin, University of Massachusetts, Amherst, MA, USA Juan C. Gutiérrez, Complutense University of Madrid, Madrid, Spain Johannes F. Imhoff, University of Kiel, Kiel, Germany Juan Imperial, Technical University of Madrid, Madrid, Spain John L. Ingraham, University of California, Davis, CA, USA Juan Iriberri, University of the Basque Country, Bilbao, Spain Roberto Kolter, Harvard Medical School, Boston, MA, USA Germán Larriba, University of Extremadura, Badajoz, Spain Rubén López, Center for Biological Research, CSIC, Madrid, Spain Bernard M. MacKey, University of Reading, Reading, UK Michael T. Madigan, Southern Illinois University, Carbondale, IL, USA Beatriz S. Méndez, University of Buenos Aires, Buenos Aires, Argentina Diego A. Moreno, Technical University of Madrid, Madrid, Spain Ignacio Moriyón, University of Navarra, Pamplona, Spain Juan A. Ordóñez, Complutense University of Madrid, Madrid, Spain José M. Peinado, Complutense University of Madrid, Madrid, Spain Antonio G. Pisabarro, Public University of Navarra, Pamplona, Spain Carmina Rodríguez, Complutense University of Madrid, Madrid, Spain Fernando Rojo, Natl. Center for Biotechnology, CSIC, Cantoblanco, Spain Manuel de la Rosa, Virgen de las Nieves Hospital, Granada, Spain Carmen Ruiz Roldán, University of Murcia, Murcia, Spain Claudio Scazzocchio, Imperial College, London, UK James A. Shapiro, University of Chicago, Chicago, IL, USA John Stolz, Duquesne University, Pittsburgh, PA, USA James Strick, Franklin & Marshall College, Lancaster, PA, USA Gary A. Toranzos, University of Puerto Rico, San Juan, Puerto Rico Antonio Torres, University of Sevilla, Sevilla, Spain José A. Vázquez-Boland, University of Edinburgh, Edinburgh, UK Antonio Ventosa, University of Sevilla, Sevilla, Spain Tomás G. Villa, Univ. of Santiago de Compostela, Santiago de C., Spain Miquel Viñas, University of Barcelona, Barcelona, Spain Dolors Xairó, Biomat, S.A., Grifols Group, Parets del Vallès, Spain

Associate Editors Mercedes Berlanga, University of Barcelona Mercè Piqueras, Catalan Association for Science Communication Wendy Ran, International Microbiology Secretary General Jordi Mas-Castellà, International Microbiology Managing Coordinator Carmen Chica, International Microbiology Specialized Editors Josefa Antón, University of Alicante Susana Campoy, Autonomous University of Barcelona Ramón Díaz, CIB-CSIC, Madrid Josep Guarro, University Rovira i Virgili Enrique Herrero, University of Lleida Emili Montesinos, University of Girona José R. Penadés, Inst. of Mountain Livestock-CSIC, Castellon Jordi Vila, University of Barcelona Digital Media Coordinator Rubén Duro, International Microbiology Webmaster Jordi Urmeneta, University of Barcelona

Addresses Editorial Office International Microbiology C/ Poblet, 15 08028 Barcelona, Spain Tel. & Fax +34-933341079 E-mail: int.microbiol@microbios.org www.im.microbios.org Spanish Society for Microbiology C/ Rodríguez San Pedro, 2 #210 28015 Madrid, Spain Tel. +34-915613381. Fax +34-915613299 E-mail: sem@microbiologia.org www.semicrobiologia.org Institute for Catalan Studies C/ Carme, 47 08001 Barcelona, Spain Tel. +34-932701620. Fax +34-932701180 E-mail: int.microbiol@microbios.org © 2015 Spanish Society for Microbiology, Madrid, & Institute for Catalan Studies, Barcelona. Printed in Spain ISSN (print): 1139-6709 e-ISSN (electronic): 1618-1095 D.L.: B.23341-2004

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International Microbiology, the official journal of the SEM, is a peer-reviewed, open access journal whose aim is to advance and disseminate information in the fields of basic and applied microbiology among scientists around the world. The journal publishes research articles and complements (short papers dealing with microbiological subjects of broad interest such as editorials, perspectives, book reviews, etc.). A feature that distinguishes it from many other microbiology journals is a broadening of the term “microbiology” to include eukaryotic microorganisms (protists, yeasts, molds), as well as the publication of articles related to the history and sociology of microbiology.

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CONTENTS International Microbiology (2015) 18:71-134 ISSN (print): 1139-6709. e-ISSN: 1618-1095 www.im.microbios.org

Volume 18, Number 2, June 2015

RESEARCH REVIEW

Padilla-Vaca F, Anaya-Velázquez F, Franco B Synthetic biology: Novel approaches for microbiology

RESEARCH ARTICLES

71

Armas-Freire PI, Trueba G, Proaño-Bolaños C, Levy K, Zhang L, Marrs CF, Cevallos W, Eisenberg JNS Unexpected distribution of the fluoroquinolone-resistance gene qnrB in Escherichia coli from human and poultry origins in Ecuador

85

Toledo A, López S, Aulicino M, de Remes Lenicov AM, Balatti P Antagonistic of activity of entomopathogenic fungi by Bacillus spp. associated with the integument of cicadellids and delphacids

91

Martínez-Gamboa A, Silva C, Fernández-Mora M, Wiesner M, Ponce de León A, Calva E IS200 and multilocus sequence typing for Salmonella enterica serovar Typhi strains from Indonesia

99

Espinosa-Asuar L, Escalante AE, Gasca-Pineda J, Jazmin B, Lorena P, Eguiarte LE, Valeria S Aquatic bacterial assemblage structure in Pozas Azules, Cuatro Ciénegas Basin, Mexico: Deterministic vs. stochastic processes

105

Križanović S, Butorac A, Mrvčić J, Krpan M, Cindrić M, Bačun-Družina V, Stanzer D Characterization of S-adenosyl–l–methionine (SAM) accumulating strain of yeast Scheffersomyces stipitis

117

Cappello S, Calogero R, Santisi S, Genovese M, Denaro R, Genovese L, Giuliano L, Mancini G, Yakimov MM Bioremediation of oil polluted marine sediments: A bio-engineering treatment

127

Journal Citations Reports 5-year Impact Factor of International Microbiology is 2,10.

The journal is covered in several leading abstracting and indexing databases, including the following ones: Agricultural & Environmental Bio­­technology Abstracts; ASFA/Aquatic Sciences & Fisheries Abstracts; BIOSIS; CAB Abstracts; Chemical Abstracts; SCOPUS; Current Contents/Agriculture, Biology & Environmental Sciences; EBSCO; EMBASE/Elsevier Bibliographic Databases; Food Science & Technology Abstracts; ICYT/CINDOC; IBECS/ BNCS; ISI Alerting Services; MEDLINE/Index Medicus; Latindex; MedBioWorld; PubMed; SciELO-Spain; Science Citation Index Expanded; SciSearch.

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Front cover legends Upper left. Electron micrograph showing morphology of bacteriophages that infect Rhizobium etli. They were obtained from rhizosphere soil of bean plants from agricultural lands in Mexico using an enrichment method. Micrograph by Víctor González, Evolutive Genomics, Center of Genomic Sciences, UNAM, Cuernavaca, Morelos, México. (Magnification, 200,000×)

Center. The Cuatro Ciénegas Basin (CCB) is part of the Chihuahuan Desert (Coahuila, Mexico) and has been considered a protected wetland since 1994 (according to Mexican federal law). Despite the extreme oligotrophy, this place harbors high microbial diversity in different environments, including soil, microbial mats, and water. The aquatic system known as Pozas Azules is characterized by many small, isolated circular ponds of different diameters (ranging from 6 to 50 m) with maximum depths of 10 m. (Photo by David Jaramillo). [See article by Espinosa-Asuar et al., p. 105, this issue].

Upper right. Darkfield micrograph of the cyanobacterium Nostoc sp., isolated from a freshwater pond. Note the differentiated cells known as heterocysts that fix atmospheric nitrogen. Photo by Rubén Duro, Center for Microbiological Research (CIM), Barcelona. (Magnification, 1000×)

Lower right. Photonic micrograph of spores of the fungus Alternaria. This fungus is a major aeroallergen in many parts of the world. Sensitivity to Alternaria has been increasingly recognized as a risk factor for the development and persistence of asthma. It is most common as an outdoor mold, as it thrives on various types of plants–including the black rot commonly seen on tomato fruit. by Rubén Duro, CIM. (Magnification, 1000×)

Lower left. Darkfield microsgraph of the unicellular cilliated Paramecium sp. Paramecia are widespread in freshwater, brackish and marine environments and are often very abundant in stagnant basins and ponds. Some species of Paramecium form mutualistic relationships with other organisms. Paramecium bursaria and P. chlorelligerum harbor endosymbiotic green algae, from which they obtain nutrients and protection from predators. Photo by Rubén Duro, CIM. (Magnification, 1000×)

Back cover: Pioneers in Microbiology Pedro Gutiérrez Igaravídez (1871–1935), Puerto Rico Pedro Gutiérrez Igaravídez (1871–1935), born in San Juan, was a Puerto Rican physician who left his successful private medical practice to collaborate with Bailey K. Ashford (1873–1934) to fight endemic tropical diseases in Puerto Rico, mainly uncinariasis. (Ashford had gone to Puerto Rico in 1898 to treat wounded American soldiers in the Spanish American War and after its end he remained in Puerto Rico to fight the disease that affected mostly the agricultural laborers or jíbaros.)

There is some controversy information about where he studied medicine, either Barcelona or Seville, but it seems sure that he went to Spain to study and that he worked for Jaime Ferran at the Laboratorio Microbiológico Municipal of Barcelona from 1895 to 1897. Before establishing as a private physician in his country, in Bayamón, in 1899, he did postgraduate studies in the USA, in Philadelphia. That very year, Ashford had discovered that the anemia that many rural Puerto Ricans suffered was not due to malnutrition but to a parasite—Necator americanus, a hookworm—that entered the body mainly through the feet. In 1904, following Ashford’s suggestion, the Government created the first Puerto Rico Anemia Commission to study the disease and provide treatment and prevention. Gutiérrez Igaravídez, who had also studied anemia, joined the Commission as one of its directors,

along with Ashford and Walter W. King. A second Commission followed in 1905-1906 with a main station in Aibonito and substations in each district of the island. In 1908, the Commission was converted into the Anemia Dispensary Service, with forty-two dispensaries in opeartion in the island, and Gutiérrez Igarávidez was appointed its director. This project made it possible to reduce by 90% the number of death caused by uncinariasis. As Puerto Ricans suffered also from tropical diseases other than anemia, the Anemia Dispensary Service was reorganized and resulted into the creation of the Transmittable and Tropical Disease Service. However, in the country there was not any institution devoted to researching tropical diseases, and in 1912 the Institute of Tropical Medicine was founded. Research on diseases such as malaria, yellow fever, tuberculosis, syphilis, typhoid fever, and bronchopneumonia was carried there.

Gutiérrez Igaravídez, Ashford and José Jenaro Soler were the members of the first provisional Board of the Institute. Gutiérrez Igaravidez was commissioned to visit several European schools of tropical medicine in Paris, London and Liverpool. In 1913, the Institute was renamed as Institute of Tropical Medicine and Hygiene, and was the precursor of the School of Medicine of the University of Puerto Rico. In 1918, the Government provided funds to build new labo­ ra­tories and the various members of the Institute specialized in the study of different diseases. Gutiérrez Igaravídez continued the study of uncinariasis, on which he produced several works including Uncinariasis in Porto Rico: A Medical and Economic Problem, which went beyond the medical aspects of the disease. He died in Puerto Rico in 1935.

Front cover and back cover design by MBerlanga & RGuerrero

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RESEARCH REVIEW International Microbiology (2015) 18:71-84 doi:10.2436/20.1501.01.236. ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

Synthetic biology: Novel approaches for microbiology Felipe Padilla-Vaca, Fernando Anaya-Velázquez, Bernardo Franco* Department of Biology, Division of Natural and Exact Sciences, University of Guanajuato, Guanajuato, Mexico Received 21 April 2015 · Accepted 9 June 2015

Summary. In the past twenty years, molecular genetics has created powerful tools for genetic manipulation of living organisms. Whole genome sequencing has provided necessary information to assess knowledge on gene function and protein networks. In addition, new tools permit to modify organisms to perform desired tasks. Gene function analysis is speed up by novel approaches that couple both high throughput data generation and mining. Synthetic biology is an emerging field that uses tools for generating novel gene networks, whole genome synthesis and engineering. New applications in biotechnological, pharmaceutical and biomedical research are envisioned for synthetic biology. In recent years these new strategies have opened up the possibilities to study gene and genome editing, creation of novel tools for functional studies in virus, parasites and pathogenic bacteria. There is also the possibility to re-design organisms to generate vaccine subunits or produce new pharmaceuticals to combat multi-drug resistant pathogens. In this review we provide our opinion on the applicability of synthetic biology strategies for functional studies of pathogenic organisms and some applications such as genome editing and gene network studies to further comprehend virulence factors and determinants in pathogenic organisms. We also discuss what we consider important ethical issues for this field of molecular biology, especially for potential misuse of the new technologies. [Int Microbiol 2015; 18(2):71-84] Keywords: synthetic biology · genetic engineering · genomics · pathogenesis · bioethics · artificial cells · astrobiology

“Defining” life What is life? This fundamental question has intrigued scientists for centuries and a strong definition is needed in order to understand life and all its manifestations. What are the universal constituents that make a living organism alive? This question is also complicated to give a compelling answer since not

Corresponding author: B. Franco Departamento de Biología División de Ciencias Naturales y Exactas Universidad de Guanajuato Noria Alta, s/n Guanajuato, Gto., 36050, México Tel. +52-4737320006, ext. 8154 E-mail: bfranco@ugto.mx *

all the components, dynamics and interactions inside a living organism are well understood and the relationship among them are also difficult to assess [6]. At first, life has been defined as an entity that is capable of passing on genetic information and is subjected to diverse environmental selective pressures that ensure diversity, but a more appropriate definition considers the following: “Life (a living individual) is a self-sustaining object belonging to a set of elements capable of undergoing Darwinian evolution” [10].

In the case of pathogenic organisms, this definition also involves the host and the selective pressures that affect both organisms. With the “omic” approach we are one step closer to answer this fundamental biological, philosophical question since we can tackle the limitation of the population diversity found in all living organisms and also gain knowledge on the constituents and the interactions they undergo. However, or-

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Fig. 1. Time line for the development of synthetic biology.

ganisms—even those with small genomes—are too complex to characterize and understand all the biological processes they carry out under different conditions. New technologies are bringing us closer to achieve the goal of understanding how living organisms work and evolve, which in turn may also lead us in the right path to fight pathogens. As presented in Fig. 1 some major technological advances and their applications may help basic and applied research to clarify the most important aspects of pathogenicity and bring about new strategies to fight pathogens. One important step towards understanding pathogenesis is to understand the genes and factors required from both pathogen and host.

“Reading” DNA The discovery of DNA structure has been a major advance to understand the molecular basis of heredity [13]. With that discovery, molecular genetics was born and made it possible to study both gene function and the molecular basis of development and disease, which ultimately led to modern biotechnology and genetic engineering. Understanding DNA structure allowed researchers to assess sequencing methods or the ability to “read” DNA that was important to uncover the diversity of life, evolution and taxonomy. Several DNA technologies including cloning, the polymerase chain reaction (PCR), se-

quencing, next-generation sequencing (NGS) and more recently synthetic biology have broadened our capabilities to study organisms [11,45].

The tools of genomic biology are the most effective analytical tools to determine the gene structure and content of any given organism. Unfortunately, they tells us nothing about gene function and protein networks. However, this is the most fundamental basis for assessing knowledge on gene function and, without it, synthetic biology loses its most fundamental tool.

Up to June 2015, 58,150 organisms have been sequenced that comprise 1,037 Archaea, 44,576 Bacteria and 8,181 Eukarya. In addition, GenBank contains data from 300,000 formally described species in the form of expressed sequence tag (EST), genome survey sequence (GSS) and whole-genome shotgun sequence (WGS) [7]. All these data are the basis for for the study of all model organisms. Having its genome sequenced, research on a given organism can be boosted to find out all gene functions and to develop new tools for manipulating it. This information can open up discovery of gene networks that provide us with comprehensive data for mining essential genes for particular biological functions.

The exponential growth of genome projects is explained in the reduction of costs (the first human genome project costs were around 2,700 millon US dollars, while currently it costs less than 1,000 US dollars) (Fig. 1) [11]. NGS equipment can

SYNTHETIC BIOLOGY

be afforded even by medium and small-sized laboratories, where data are required for antibacterial molecule research, vaccine development, diagnostics and epidemiology research [33].

The first genomes that were sequenced were confined to large consortia and a considerable amount of resources to achieve the full genome sequence and annotation was required. The first organism fully sequenced and annotated was Haemophilus influenza, a highly relevant human pathogen [22]. The genomes of Escherichia coli and Saccharomyces cerevisiae, which are the most extensively studied organisms, were both published in 1997 (Fig. 1). Refinement of sequencing and analytical tools to generate faster and more accurate sequences have been developed since then. With NGS technologies, whole genome data can be quickly generated and applied to different biological questions such as total RNA sequencing, which gives more information than microarray data on abundance, half-life and processing of RNA, genome sequencing and enriched pools of environmentally adquired DNA samples [39].

As it is discussed latter, microbial diversity has been uncover using these powerful techniques and more questions than answers have arisen from all the data generated. Genomics has been modified extensively, from Sanger sequencing methods to micro-reactor sequencing and single cell genomics, and it has become of great importance to study pathogenic organisms. Even though most of the information that we can gather on microbial populations come from classical microbiological studies, now it is possible to determine at broad scale the microbial populations present in a sample and their dynamics. One such example is the city-scale metagenomics survey undertaken by Afshinnekoo and colleagues by which several pathogen distributions, antibiotic resistance and large scale new organism identification was assessed and correlated to environmental, geographical distribution and even human associated genomic data [2]. Full genome sequencing and annotation also speeds up the research for identifying essential and virulence-related genes present in a given population or sample (see [24] for some review examples that illustrate this matter).

Uncovering microbes through genomics Evidence on genomic dynamics and evolution provides part of the answer to life’s diversity. Once an organism genome is fully sequenced, knowledge and research on the biological function of each gene is speed up and therefore more knowl-

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edge is gained from functional studies and genome data mining. Since the establishment of public genome databases and standard criteria for genomic data storage and mining, the amount of genomic, metagenomic and pangenomic data has increased rapidly [21]. The standards set for genomic data created by the Genomic Standards Consortium considered the following: standards for new genomic data, methods for storing and sharing the data generated and harmonization of the information so that the scientific community can easily access it [21]. The value of genomic data can be exemplified with the need to achieve rapid comparison between isolates (both by standard microbiological methods or whole sample sequencing), especially during epidemic conditions. Data accession is uniformed using sequence identifiers, and databases are linked together in order to keep information available, protected, curated and up to date [7].

The use of whole genome sequencing have opened up the field of evolutionary and population genomics, which allows to characterize population structure and dynamics, and to know what factors affect the population in certain environments [18]. In pathogenic organisms, this is of outmost importance. Recent efforts have shed light into the factors that modulate pathogenicity. Epidemiology can be studied in much depth since data from whole genome sequencing can inform the pattern of pathogenicity displayed by an outbreak, resistance to antibiotics, virulence and persistence factors.

Also, information about mobile genetic elements, horizontal gene transfer and adaptation features can be uncovered. Such information can be obtained not only as a general or representative sample, but also at the “local” level, such as in the event of a recurrent or emergency outbreak, contaminated sources or even at the level of hospital related infections [15]. Other aspects of microbial life have been revealed by genomic biology.

We are facing the “dark matter” of microbial life. This considers all those organisms and even divisions that have eluded isolation and characterization under laboratory conditions. McLean and collaborators showed that a “mini-metagenome” could be generated from rare events. The authors sequenced whole genomes from single cells isolated by flow cytometry (again, converging many techniques for new purposes) from a sink in a hospital restroom [46]. This type of work leads research in a different direction: single cell genomics. In this particular study, tackling the heterogeneity of samples (sequence itself or G+C content) as well as contaminants is cumbersome. But with the implementation of novel computational toolsfaster and more accurate sequencing methods could be developed. Once the genome of an organ-

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ism has been fully sequenced, the next step is to analyze it in order to obtain useful information. The study of pathogenesis and host-pathogen interactions has been favored by genomics.

In the case of virulence related genes it can be complicated to be certain of their specific role. Even defining virulence is not easy. It is often defined as the capability to damage or harm the host when invaded by a pathogen, but this definition does not consider the role of other factors that are important for survival within the host. Genomics provides the challenge when there is a dramatic increase in host damage or spreading or when pathogens shift to infect new host species (such as SARS or Ebola virus), resulting in new and devastating outbreaks and epidemics. The use of high throughput sequencing methods, can allow to define natural reservoirs of deadly pathogens, ecological interactions and perhaps to understand several of their pathogenesis mechanisms and how disease evolves. Sequencing environmental samples, such as pan enome sequencing projects, have revealed the true microbial diversity that is making evident also the real landscape of pathogenâ&#x20AC;&#x201C;epidemiologyâ&#x20AC;&#x201C;persistence. Hand in hand, this also may lead to important health-related discoveries for antibiotic resistance and pathogenicity statistics near real-time.

Microbiomes and health Disease is also related to our own microorganisms. Recently new sequencing techniques have revealed that humans hold an immense universe inside, which modulates many biological functions. Research efforts concentrate to achieve the power of all the living organisms inside the body to promote immune boost, vaccine delivery systems and keeping a safe environment for beneficial microorganisms. We now have knowledge on some effects of the microbiota present in vertebrates; one such example is the effect on circadian clock disturbances as reported by Thaiss and collaborators [59]. But how farther can we go? From environmental genome sequencing we learned that emerging pathogens are lurking that can potentially infect humans. One example is the enigmatic chlorovirus ATCV that usually infects algae but also can cause changes in cognitive functions in humans and mice [69].

The human microbiome plays a major role in health and disease and its actual composition is variable for each individual. Gut microbes have been proposed to regulate behavior and social skills needed for the bacteria to colonize other individuals. The Human Microbiome Project has two impor-

PADILLA-VACA ET AL.

tant goals; study the composition of healthy individuals and to understand the effect of changes during disease. Also, this project bypassed the limitation of cultivating and characterizing the microbial communities in samples from patients or experimental animals. NGS have provided a glimpse on the composition profile of normal intestinal microbiome and opened the possibility of treating certain disorders in patients.

The Human Microbiome Project is a good example of how modern science should thrive, as a collaborative and multidisciplinary effort. In a future, with the available data on the hostâ&#x20AC;&#x2122;s microbial DNA and transcriptome, we will be able to predict the metabolic capacity of the bacteria present in the host and measure its impact on health besides the host lifestyle and environmental stress [62].

With novel bioinformatic and statistical tools, identifying dysbioses (changes in the normal microbiota content) becomes easier with the possibility of developing new treatments for diseases such as chronic inflammatory bowel disease instead of using fecal transplant on patients [67]. Analyses that identify perturbations in molecules related to disease can be used to interpret their physiological role, interaction with truly pathogenic organisms or integrate sequence data with whole-community relationships.

New technologies are not problem-free and require troubleshooting. One good example is the amount of false positive organisms present in samples and library preparation, for example in ancient pathogen identification (which is a problem in any given sample), can give false diagnostics and population content biases. NGS requires stringent cleaning measurements and laboratory reagent preparations in order to avoid cross contamination and misleading results, especially with rare samples [53].

Technology and human wit have designed novel approaches to answer complex biological questions and problems, for instance, how many organisms are present in dust and airborne microbial communities [71]. This particular question poses a major problem of low biological sample content and avoids growth to eliminate biased conclusions.

Integrating engineering for designing and applying specialized apparatus for sample collection, enrichment and analysis, and novel computer algorithms to assemble and analyze data, provides an image of microbes present in air samples.

All this major technical advances are needed only to read DNA properly and then make some sense out of all that information. There are remaining questions about environmental samples, such as how many bacteria are metabolically active, capable of division and the cycles between population densities.

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Fig. 2. Basic and applied research areas that synthetic biology has important influence.

Understanding the genome “Data does not equal information; information does not equal knowledge; and, most importantly of all, knowledge does not equal wisdom. We have oceans of data, rivers of information, small puddles of knowledge, and the odd drop of wisdom.”

Henry Nix made this statement in his keynote address (A National Geographic Information System–An achievable objective?) to the Australasian Urban and Regional Information Systems Association in 1990) [32]. In fact, gathering terabytes of data does not mean givung them some sense or fully understand them. Figures 2 and 3 show some applications that data so far gathered can have in different fields. High-throughput analyses that have been carried out so far for genome, transcriptome and proteome assays, have provided more data that we can handle and make sense of it. Most of the research

in ‘omics’ is now done by molecular biologists, biochemists, mathematicians, physicists and computer experts, and the main reason for pluridisciplinarity is to find useful information and search for patterns in all that data.

Genomic data tell us which and how many genes are present in any organism’s genome, but cannot tell neither the function of all them nor the roles of protein networks, which requires specific experimental data. Moreover, gene structure and G+C contents are quite different between bacteria and eukaryotic organisms, so the algorithms for analysis must take into account different approaches for mining data, for example for generating comprehensive data for regulatory networks or genome analysis and annotation. With massive genome data, it is also possible to know some of the evolutionary relationships between organisms and the influence of the environment on genome structure, regulation and diversity (Fig. 2). In pathogenic organisms this is of outmost impor-

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tance, since the lifestyle of each organism modulates the genomic landscape and protein effector machineries that give rise to the phenotypes of pathogenic organisms (Figs. 2 and 3). In addition, interactions between the microbiome and host cells can remodel the genomic landscape in pathogens. Even with the most powerful computers and algorithms available, genetic sequence only tells half the story behind the physiology of the organism and the possible roles of the protein networks that it can use at a given point of its life cycle,. Most useful data are represented by well-known genes, but it is not possible to make sense of unknown open reading frames.

Two examples lead us to reformulate life and the dynamics that render diversity. These two examples can further increase our knowledge on gene and protein networks. The first one was developed by Suzuki and collaborators [58]. Using a novel approach of genome assembly [27] the authors generated multiple deletions (clusters from 5 to 24 genes) on the genome of Mycoplasma mycoides synthetic genome JCVI-syn 1.0 and marked each with a reporter protein (green fluorescent protein, GFP) on each insertion. Endeavors like this can only be achieved with full genome sequences. In this particular case, the complete nucleotide sequence (580,070 base pairs) of Mycoplasma genitalium [23], the smallest known genome of any free-living organism, was used to start building up large-scale molecules. The technique of genome transplantation was used to replace the full genome of a recipient cell and changing one bacterial species into another and to generate a new strain controlled by a chemically synthetized genome [27,40]. In this genome, 470 predicted coding regions identified include genes required for DNA replication, transcription and translation, DNA repair, cellular transport, and energy metabolism.

Using the mating machinery of yeast, several deletions were generated at eight unique deletable regions of the genome, eliminating in a single step 91 genes and approximately 10% of the original synthetic JCVI-syn 1.0 genome. With a second round of deletion and selection, the new strain contained a deletion on seven of the eight targeted regions, representing 84 genes. This approach also considers the use of Tn insertions to characterize the content and targeting of several genes and determine their essentiality. The engineered genomes were analysed to look for growth defects, but most strains retained doubling times similar to the original strain, except for JCVI-syn1.0 ∆L strain and the multiple deletions strain JCVI-syn1.0 ∆1–6 ∆A ∆B ∆C ∆D ∆E ∆I ∆L ∆N, which had growth defects. Under the conditions tested, genetic interactions of 91 genes were found. Despite predicted effects of the deletions on sugar metabolism, cell envelope and DNA metabolism, growth rates were unaffected by gene deletions in

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seven strains. All the data generated gave rise to the possibility of generating deletion strains or minimal genomes using rapid strategies and designing cells with the desired characteristics.

The data also pointed out to the minimal set of genes required for life. Essential genes are important for biotechnology applications. In fact, the limiting steps for most metabolic reactions can also be essential for cell physiology, and those genes, which are not present in the host’s genome, can also render specific targets for new drugs against pathogenic organisms.

Essential genes for Mycoplasma had been previously assessed by Glass and collaborators [30]. They carried out an extensive mutagenesis study in Mycoplasma genitalium to determine the number of essential genes required for lifeand was the milestone for generating the first chemically synthesized bacterial genome [30,34]. Both studies showed that there is a minimal set of genes to make a completely functional bacterial cell and that in vitro synthesis of a genome from scratch can give rise to a functional genome with the desired characteristics. In our opinion, this approach is complementary to systems biology and all the ‘omics’. Instead of handling extensive amounts of data, it creates and puts to the test whole engineered genomes. The ultimate goal is to simplify the created organisms with the minimal set of genes, given that, for M. genitalium, from 265 to 350 of the 480 protein-coding genes are essential under laboratory growth conditions, including about 100 genes of unknown function.

Now we can start characterizing at a large scale the world of genes with unknown function, something relevant and difficult to approach. But even with the minimal set of genes, understanding the function is still out of reach, especially when mutants of genes without evident function also lack any phenotype. These studies also made clear that even if “reading” DNA has been already achieved, understanding how to “write” DNA and make long functional “sentences” is still at an early stage of development.

Other studies in organisms such as in yeast with the same aim indicated that 12% of its genome was essential and that almost 70% of the genomic disruptions gave no new phenotype [31]. Now the question arises for other organisms with more complex genomes. Estimates on Bacillus subtilis indicate that only 9% of its genome is essential, rendering a nonessential 562-kb genetic material from its total genome [36].

With the birth of synthetic biology, microbes can be assembled for bits and render a desired phenotype or even behavior, thus providing a better understanding of how a cell works [20]. Other studies conducted in Escherichia coli have revealed that, from the total 4288 genes it has, 303 genes were unable to be deleted from those 37 of unknown function [70].

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Fig. 3. General overview of possible applications of synthetic biology for microbial and other organisms. Our understanding of life from the single cell to the population and multicellular levels.

This study used a novel technique for interrupting single genes using PCR products containing a selective marker that had been used also in other organisms [14].

Research with microorganisms has the advantage of having molecular tools for generating diverse mutants and genetic engineering, a task that is more complicated on higher organisms. In humans, knockouts are off-limits, but we can learn from naturally occurring diseases. One recent example of gene mutation and deletion in humans that renders inactive genes can shed light on possible future research on the general population for dispensable genes. Recently Sulem et al. identified deletions of up to 1000 autosomal genes from the genomes of normal human individuals. Thus, at least 1000 genes can be eliminated without observing defects. As defined in the research paper, these are “healthy knockouts” [57].

From this concept, a novel field has arisen that looks for genes that, when they are missing, can confer a benefit, such as resistance to certain diseases, including AIDS. The effect of a disease-related gene can be mimicked by blocking the normal gene’s protein the absence of that gene is known not to be a

trheat for the host. Regarding health-associated organisms, scientists are trying to understand the role of diverse gene networks that are involved in virulence and pathogenesis. Using cumulative data and with the help of systems biology (i.e., an holistic approach to biology) the fundamental cellular processes in organisms can be simulated [60] (Fig. 2).

All the data collected thus far from the minimal gene set or essential genes in diverse organisms can lead to a better understanding of cell basic functions and in a near future a whole picture of what is required to generate a fully functional living organism or at least make more accurate predictions on protein networks and the interactions within an organism.

From the biotechnological point of view, this may be the future for all pharmaceutical advances and pipeline production of different molecules, which must be expanded from E. coli, Mycoplasma and yeast due to ecological niche restrictions or environmental restrains. Synthetic biology can broaden our knowledge on organism’s basic functions and the regulation of life processes using a bottom-up approach of engineering (Figs. 2 and 3).

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“Writing” DNA: engineering biological systems Our ability to ‘read’ DNA is far better than that to ‘write’ it.

Although the development of software and databases have made this task easier, most of the biological processes are not yet well understood. The first modest attempt to generate a synthetic DNA fragment led to one of the most fundamental advances in molecular genetics, breaking the genetic code [1].

One impediment was related to costs and fidelity with DNA in vitro synthesis. The history of DNA synthesis has been brilliantly reviewed in [52], so we will focuse on its applications.

This technology, which allowed sequencing and PCR, nowadays is used also to produce synthetic genes in a cost-effective manner or large-scale genome synthesis. There are major advances in large DNA synthesis with important improvements in fidelity, length and yield. The transition from column-based synthesis to array synthesis, allowed companies to offer more cost-effective, higher yields and fidelity on the desired sequence, also making less expensive to generate full open reading frames (ORF) and with the desired codon usage for the proper host [36].

Higher fidelity synthesis either by PCR or de novo DNA synthesis is a major achievement for microbial and pathogen research. Both techniques allow to tackle the restrictions of construct design (which for certain applications can be troublesome) and assembly, gene expression and regulation, codon usage bypasses, transgene and vector creation, mutagenesis, protein engineering, and reporter protein adaptation for a particular host, creating new sequences with the desired codon usage for specific hosts among others [38]. One excellent example is the synthesis of nearly 44 Mb for knocking out with iRNA most human and mouse genes [12]. For protein studies, antimicrobial peptides or proteins and vaccine subunit preparation and purification, codon usage has proved to be a major hindrance to obtain high yields of recombinant proteins in many hosts, in particular for E. coli, which is a cost-effective host [5].

The motivation to review this subject was especially to encourage other researchers to explore synthetic biology as a powerful approach to complex problems. It is of particular interest to our laboratories to improve existing tools for understanding the fundamental basis of pathogenesis in the parasitic protist Entamoeba histolytica. There are tools for manipulating this organism, but there are also limitations to generate mutants and versatile plasmid vectors or reporter genes. Synthetic biology, however, can help to overcome these problems. We are also interested in the improvement of genetically encoded bio-

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sensors to couple metabolite, metals, nanoparticles, toxins and other environmental or biomedical relevant molecules to several outputs and easier to detect [48].

But, what does synthetic biology do? Using a broad definition, synthetic biology is in the quest to simplify our understanding of biological entities (viral, bacterial and eukaryal organisms) by constructing biochemical or genetic pathways both in vivo and in silico and building up computational models to simulate the behavior of those pathways with the ultimate goal of testing them in the real world [65] (Fig. 3). Synthetic biology also attempts to generate genetically recoded organisms or biological entities by using a design process more systematic and predictable and by analyzing models that use all the data available for that particular engineered process, robustness in the output of the designed organism, scalable to different niches or conditions, and ideally, more efficient than the wild type counterpart [65]. These criteria cover basically the necessities for applied genetic engineering with the easy approach of designing modules or parts to do specific tasks, which in a complete organism is still unpredictable at a large scale due to problems of interacting protein networks or enzyme cascades that are self-regulated or that interact with other pathways.

A major motivation for massive genome engineering is the applicability of modified microorganism that generates a profit or can contribute to generate new biologically based processes [66]. With the birth of synthetic biology, microbes can be assembled for bits and render a desired phenotype or even behavior, thus providing a better understanding of how a cell works. This approach, which is complementary to systems biology and all the ‘omics’, instead of handling extensive amounts of data, creates and puts to the test whole organisms with truly engineered parts and not using the traditional random mutagenesis or directed evolution and selection approach [25]. Now we can build biological parts and genetic modules that are not found in nature or that are poorly characterized due to difficulties on growing the desired microorganism, with the purpose of generating fully functional genetic circuits that can modulate the behavior of cells and respond to specific stimuli.

Synthetic biology shares many similarities with genetic engineering principles such as minimality to avoid complex interactions and futile cycles, modularity to improve interactions and sustain specificity, and controllability of a complete system [16] (Figs. 1 and 2). Controllability of any genetic circuit is important to achieve the desired results, but also to prevent modified microorganisms from escaping from the lab (see "Potential misuse of the new technologies" Section). When working with pathogens difficult to handle and transport, an

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alternative option might be inactivation and sequencing at the deployment site and revitalization in a laboratory for further scientific assessment [72] (see Section on â&#x20AC;&#x153;Potential misuse of the new technologiesâ&#x20AC;?).

The toolbox Based on engineering principles and specialized syntax, BioBricks can overcome the troubles found when designing large molecules composed of different modules comprising regulatory, coding and terminator sequences to generate circuits or recombinant plasmid vectors [51]. Using wild type or designed promoters, coding regions, terminators and reporter genes, we can integrate them to characterize cryptic genes or gene clusters, create new reporter plasmids and proteins, simplifying maintaining reading frame with the coding regions and the assembly itself. This particular strategy is an assembly method based on type II restriction enzymes, in which building blocks are assembled directionally by adding standardized flanking and complementary sequences that specify the orientation. The use of a reference syntax and access numbers, BioBrick parts sequence and properties are deposited in a specialized database [the Registry of Standars Biological Parts, http://parts.igem.org/Main_Page] available to the synthetic biology community and can speed up the research world-wide. Using traditional restriction and ligation reactions, assembly and joining together all the elements needed is easier when using in-house software to generate regulatory circuits with natural or artificially upgraded regulatory sequences according to the needs or aims.

Using modifications of the restriction enzymes for each BioBrick, the combinatorial capacity is sufficient for even constructing fragments of over 20 kb (such as the secondary metabolite actinorhodin gene cluster from Streptomyces coelicolor) [43]. However, this strategy is time consuming and troublesome for joining multiple genes or DNA fragments using different joining sequences and finding the proper endonucleases to achieve such constructs. Moreover, mutations can arise at the overlapping Section. Protocols using modified versions of BioBrick methods can produce faster results using PCR-fusion techniques were each BioBrick part contains overlapping ends and can be used to fuse up to four individual modules. However, there are still some limitations regarding fidelity during synthesis and assembly success, especially when designing the overlapping regions [55]. Improvements on this technique led to the Golden Gate assembly method where subcloning of up to 9 different modules (as undigested plasmids) can be cloned

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into a recipient vector with the usage of type II restriction enzymes and it is done in one step in a single tube with a 90% efficiency, rendering several clones that can be analyzed to verify sequence and orientation of the fragments [19]. This strategy is useful to shuffle modules of different molecules to generate combinatorial versions of it and improve its characteristics. It is especially useful to generate proteins with a desired activity or even for the production of vaccines in which epitopes can be screened for higher reactivity against anti-serum from patients. The generation of bigger constructs faces technical problems, including the lack of restriction enzymes to be used in a highly specific manner. Due to such problems, other techniques have been developed for both in vitro and in vivo assembly. The most prominent in vitro methods are PCR-based with overlapping 15 or more bases and require in vitro recombination such as In-Fusion, SLIC and Gibson, which are more efficient for generating kilobase-sized fragments which led to the assembly of a fully functional synthetic bacterial genome and more recently the complete and engineered chromosome III of yeast synthesized stepwise [4,28,54]. With the possibility of synthesizing larger DNA molecules in vitro, the next logical step was to start the assembly of genes and genomes. Now the assembly of viral, bacterial and yeast chromosomes is possible.

Before attempting the synthesis and assembly of large DNA molecules and facing the ethical implications of generating a fully functional organism, Smith and colleagues first assembled the fX174 genome under strict ethical evaluation (see also "Potential misuse of the new technologies" Section) [56].

A serious limitation is the formation of multimer assembled molecules, for which a method has been recently proposed to avoid using linear fragment assembly and ligation, followed by in vivo cyclization after transforming recipient E. coli cells [32]. Methods used for generating synthetic chromosome III of yeast involved 750-bp modules and all the techniques previously described. In this experimnent, undergraduate students from the Build-A-Genome class at Johns Hopkins University were involved in generating the starting building blocks as part of a class project [4]. The cases of Mycoplasma and chromosome III from yeast synthetic molecules, in which the cells remained viable and functional, set the foundations for more aggressive engineering for gene function and protein network studies, as well for full organism engineering. The accurate assembly of large DNA fragments is still challenging and requires refinement in order to generate fully functioning genes.

Gene clusters must be done in vivo as described for Mycoplasma, and yeast synthetic genome and chromosome III using overlapping fragments or pools of oligonucleotides, require yeast recombination machinery [26,32].

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Comprehension on essential genes is one step further into knowing new molecular targets for new drugs against pathogens (Fig. 3). To achieve this, extensive knowledge on each particular genome is necessary. With the discovery of enzymes capable of cleaving DNA at specific sequences, now this can be performed.

TALEN (transcription activator-like effectors) or CRISPR (clustered regularly interspaced short palindromic repeats)/ Cas both can be used nowadays to edit specific targets in any genome by designing either the zinc finger specificity in TALEN zinc finger nucleases ZFNs) or the RNA guide molecule that directs enzyme specificity [8,29]. TALEN technology relays on ZFNs that are fairly specific to triplet sequences and can cleave specific regions on any given genome and can be engineered (in combination with the techniques described so far) to target cleavage to a desired sequence [29]. The CRISPR/Cas system in bacteria is a prokaryotic adaptive defense barrier against foreign DNA and is the most basic form of adaptive defense mechanisms against foreign DNA inside the genome that can be active as a phage or inactive [29]. One spectacular feature is that this system learns to recognize the self from the non-self DNA and it can adapt to new molecules invading the hostâ&#x20AC;&#x2122;s genome. This novel system has been adapted in numerous ways to visualize (by only binding to target sequence), edit and control genes and gene expression in several organisms [29].

One of the limitations of this enzymatic system is that the isolated and characterized CRISPR/Cas enzymes have high molecular weight, making large constructs which are limited to transfection on eukaryotic cells. More recently, a smaller version of Cas9 has been isolated with the same genome editing properties and can be used more extensively than the previously isolated enzymes [50]. With all the current tools available we can envision that new tools can be generated. As already mentioned, many microorganisms have different G+C contents, codon usage and different cellular properties that can make certain studies more difficult. In such cases, synthetic biology offers techniques than can overcome such complications.

Plasmids for different purposes can be easily generated and adapted to each application, like expression plasmids designed with the regulatory elements necessary to render proper expression using limited information (for organisms which genome sequence is still underway or just partial sequences are available), codon usage bypass for gene function studies and reporter protein applications. Other examples are plasmids for the generation of genetic circuits with specific functions that can share light on gene function or protein network

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interactions, and codon usage expansion and incorporation of artificial amino acids for structural studies, or when protein expression is limited in alternate hosts due to specific posttranslational modifications that are under study, such as glycosylation. In our laboratories we do research to discover novel virulence factors and determinants in parasitic protists.

We now explore the generation of novel tools using approaches such as the one reported by Wegner and colleagues, who described the generation of a fully functional vector for Plasmodium falciparum by using the Gibson assembly method [64]. This kind of studies motivated us to expand our comfort zone and move into synthetic biology.

Applications for microbiology With the increasing antibiotic resistance in pathogens, the quest for new molecules that can attend the needs of patients requires novel approaches (Figs. 2 and 3). Nichols and colleagues have developed a high-throughput platform, the iChip, to grow and isolate bacteria [42]. This device allows to grow bacteria directly on soil avoiding the problems of growing microorganisms in the laboratory. Using this technology, a new antibiotic was isolated (teixobactin) without observing resistant mutants of the bacteria tested [42]. This technique offers the possibility of identifying and characterizing new antibiotics as well as new organisms for NGS. Coupling both strategies, synthetic cells can produce the desired secondary metabolites and open a new field for developing novel pharmaceuticals. A good example is reviewed by Nikel et al., who point out the applications that can be generated using synthetic biology applied to pseudomonads also taking the advantage of using the ecological niche of each organism isolated and characterized. If synthetic biology can be used to adapt organisms to grow in the lab, in its corresponding ecological niche and control them, the applications are endless for understanding the biology of of microorganisms, in particular molecular, cellular and environmental-ecological characteristics [47]. Molecular tools can be used also to study gene function and gene relationships. In the case of pathogenic protists, there are certain limitations regarding genetic manipulation. We have tools for transfecting protists and a limited set of vectors, but this is now not a limiting factor.

King et al. have described several novel genes involved in phagocytosis in Entamoeba histolytica that were discovered by using a genome-wide overexpression screening or the use of CRISPR/Cas system to generate single or multiple mutants in Trypanosoma cruzi or Toxoplasma gondii [37,49].

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Finally, epidemics and the role of emerging diseases with host shifts that can render more virulent strains (such as Ebola or SARS) are a growing concerned worldwide. In the previous sections we have provided some examples of the power of NGS to address major epidemiologic problems including the role of pathogen distribution even at city scale. One limitation for vaccine production is the expression of proteins for vaccine research and mass production. This powerful technique can solve this problem by rapidly synthesizing several epitopes from fully sequenced virus and testing for their effectiveness as vaccines. One example is the use of hemagglutinin and neuraminidase of influenza virus, optimized for their expression in MDCK cells. With such studies the response to epidemics can be improved and even optimized for local outbreaks with a particular mutant strain using sequence data from patients during an outbreak [17]. This kind of technology can also be used at large scale for other purposes, for example to produce full-length viral particles or several antigenic proteins from pathogens with genomic differential G+C content or that are difficult to grow in the laboratory. This strategy can be applied also in veterinary medicine. This kind of technology can speed up the trajectory from development, production and commercialization of both biopharmaceuticals and traditional antibiotics; the end molecules are the same, just the process of generation changes. Synthetic biology is useful not only to improve the yields of natural products of producing strains but also for chemical structure diversification to generate new active analogues.

As above mentioned, the microbiome plays a major role in health and disease. New sequencing techniques have revealed that humans are a holobiont, and their microorganisms can modulate many biological functions. Some recent research efforts have been made to achieve the power of all living organisms inside the body to promote immune boost, vaccine delivery systems, diagnostics, biosensors for different diseases and keeping a stable environment or promote the growth of beneficial organisms [68]. Genome editing strategies may have also therapeutic applications. One challenging task is to safely remove viral particles from any given genome, which under these terms seems science fiction. However, the genome editing capabilities of the CRISPR/Cas system theoretically might reduce the total viral genomes integrated to the genome in any integrating viral infection.

Using NGS on several infected cells we can design the specificity of either TALEN nucleases or Cas9 enzymes to edit and delete viral sequences, in the same manner that bacteria do it. This was achieved in human cells infected with HIV [42]. The application of the CRISPR/Cas technology to

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reduce the total viral pool in cells infected with HIV is still at an early stage, but the some results so far obtained are promising.

Potential misuse of synthetic biology With all the technology at hand and the resources to explore new boundaries, and despite of the oncoming benefits for science, and biotechnology in particular, many citizens including scientific community are wondering how dangerous this emerging field of biology can be. Definitely the understanding of virus, bacteria and protists that affect humans can increase with technologies such as the described above. However, how ethical is to synthesize a fully functional and potentially dangerous organism in order to study it?

With the announcement of the synthetic Mycoplasma genome, during a press conference, Dr. Hamilton Smith was asked about the potential application of this technology as a bioweapon. His answer was: “We could make the small pox genome”. At that moment, Dr. Venter tried to soften his colleague’s statement by saying that DNA is not infective by itself, and Dr. Smith insisted that “But you [Dr. Venter] and I [Dr. Smith] have discussed ways to get around that”. Dr. Smith finally said “I probably shouldn’t have said that, huh?”

[63]. The only limits to this technology are set by imagination. We can sinthesize many things, but science is strictly regulated and requires highly trained personnel to achieve the desired goals. For example, when Smith and colleagues attempte to create the synthetic fX174 phage [56], it took almost a year to set up a bioethics committee to review the proposal and deliberate that “[researchers are] taking a reasonable scientific approach to an important biological question” [9]. The biological question at hand can lead to important findings regarding the basis for life and perhaps one day also to understand what is necessary for life to flourish elsewhere.

The tools generated with the fX174 lead Venter’s group to a 10-year quest to achieve a fully functional chemically synthesized genome [27]. The “bottom-up” approach to generate genomes can lead to several unethical applications as envisioned by many since databases contain the genomic sequence of all kinds of organisms including pathogenic and potentially turned into weapons, but can this be real?

A major concern is the introduction of synthetic organisms that may be harmful to others or to the ecological niche. Synthetic organisms should be contained to laboratory or controlled conditions. Mandell et al. [44] reported the generation of two different modified strains of E. coli so that they exhib-

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ited metabolic dependence on non-standard amino acids and demonstrated that they were impaired of bypassing this biocontrol by horizontal gene transfer or mutagenesis [44].

This kind of research brings us closer to generate fully synthetic organisms that could be used on controlled environments and to rewire organisms to synthesize biomolecules with the desired function without the obvious dangers of releasing an uncontrolled organism into the environment. The synthetic biology community is aware of those dangers and works to provide more safety features to genetically modified organisms. It is true that infective viral particles, bacteria or other pathogens could be synthezided as bioweapons. However, the technical challenge is too great to consider it a constant treat. Synthetic biology techniques are more beneficial to understand the pathogenesis process than to do harm. One excellent example is the ability to obtain and characterize viruses that are not cultivable or can share light on the structure and infectiveness of virus no longer present and only their genomic sequence is available and renders it fully functional [61].

Concluding remarks Synthetic biology offers powerful, elegant techniquea to reduce genome sizes, generate new molecules or tools and expand our knowledge on the essential genes of bacteria and yeast and maybe in a near future for any given organism.

Technology based on synthetic biology can provide not only tools, but extensive new knowledge of gene function and in particular of essential genes. In our opinion, the fields that can take the most advantage from this technology are: biotechnology, medicine, cellular and molecular biology, and astrobiology. In the case of astrobiology (Fig. 3), we envision that, once the basic rules for life are understood here on Earth, life in other conditions can be tested on varying conditions and even at extreme environments such as the International Space Station. Synthetic organisms can be a powerful tool to study the requirements for alien conditions and hypotheses can be tested based using this experimental approach.

Genome engineering can lead to several and highly significant biotechnological advances. In other areas we expect that knowledge on protein machinery assembly and interactions can one day answer the most fundamental aspects of life and in particular what is required to generate functional gene and protein networks (Fig. 2). Thereof we can approach not only microbes in general, but pathogens or hosts, providing basis to advance our understanding on the molecular and cellular basis for disease.

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All these achievements require computational and molecular biology skills that our youth should develop and get involved as with the yeast chromosome III synthesis. With standardized methods of assembly and the design of particular modules that can be interchanged, the International Genetically Engineered Machine (iGEM) Foundation has established an international competition to propel youth into the field of synthetic biology [http://igem.org/Main_Page]. Soon genetic circuits and synthetic organisms will be part of basic molecular genetics courses at colleges and high schools.

Can this new technology be a potential hazard? As with any new technology, it is possible to be used for warfare and biological or organism-based terrorism, but the technical and scientific infrastructure needed renders this possibility to the minimum as for many other technologies. For that reason it is important to emphasize that scientists must avoid the modification of microorganisms for bioweapons production [3]. We envision more benefits than treats to synthetic biology and we encourage the scientific community dedicated to study microorganisms and pathogens to turn their eyesight to this new and exciting new field of molecular biology. It can expand the knowledge on microorganisms, neither harmful for the environment nor for living beings. Studies on pathogenicity mechanisms, for instance, will benefit from the responsible manipulation and ethical use of modified microorganisms.

Acknowledgements. Authors acknowledge support for the research conducted in our labs from CONACyT (CB-2012-01 182671), Promep-SEP (Grant number F-PROMEP-38/Rev-03 SEP-23-005) and University of Guanajuato (Grant number FO-DAI-05).

Competing interests. None declared.

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RESEARCH ARTICLE International Microbiology (2015) 18:85-90 doi:10.2436/20.1501.01.237. ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

Unexpected distribution of the fluoroquinoloneresistance gene qnrB in Escherichia coli isolates from different human and poultry origins in Ecuador Paulina I. Armas-Freire,1 Gabriel Trueba,1* Carolina Proaño-Bolaños,1 Karen Levy,2 Lixin Zhang,3,4 Carl F. Marrs,3 William Cevallos,5 Joseph N.S. Eisenberg3 1 Institute of Microbiology, Biological and Environmental Sciences College, University San Francisco de Quito, Quito, Ecuador. 2 Department of Environmental Health, Emory University, Atlanta, USA. 3Department of Epidemiology, University of Michigan, Ann Arbor, USA. 4Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA. 5Institute of Biomedicine, Central University of Ecuador, Quito, Ecuador

Received 15 March 2015 · Accepted 3 June 2015

Summary. Fluoroquinolone resistance can be conferred through chromosomal mutations or by the acquisition of plasmids carrying genes such as the quinolone resistance gene (qnr). In this study, 3,309 strains of commensal Escherichia coli were isolated in Ecuador from: (i) humans and chickens in a rural northern coastal area (n = 2368, 71.5%) and (ii) chickens from an industrial poultry operation (n = 827, 25%). In addition, 114 fluoroquinolone-resistant strains from patients with urinary tract infections who were treated at three urban hospitals in Quito, Ecuador were analyzed. All of the isolates were subjected to antibiotic susceptibility screening. Fluoroquinolone-resistant isolates (FRIs) were then screened for the presence of qnrB genes.

A significantly higher phenotypic resistance to fluoroquinolones was determined in E. coli strains from chickens in both the rural area (22%) and the industrial operation (10%) than in strains isolated from humans in the rural communities (3%).

However, the rates of qnrB genes in E. coli isolates from healthy humans in the rural communities (11 of 35 isolates, 31%) was higher than in chickens from either the industrial operations (3 of 81 isolates, 6%) or the rural communities (7 of 251 isolates, 2.8%). The occurrence of qnrB genes in human FRIs obtained from urban hospitals was low (1 of 114 isolates, 0.9%). These results suggested that the qnrB gene is more widely distributed in rural settings, where antibiotic usage is low, than in urban hospitals and industrial poultry operations. The role of qnrB in clinical resistance to fluoroquinolones is thus far unknown. [Int Microbiol 2015; 18(2):85-90] Keywords: Escherichia coli · gene qnrB · quinolone resistance · urban hospitals · industral poultry operations

Introduction Every year in the USA alone, more than two million people are infected with antibiotic-resistant bacteria, resulting in

Corresponding author: G. Trueba Instituto de Microbiología Universidad San Francisco de Quito Vía Interoceánica y Diego de Robles Quito, Ecuador E-mail: gtrueba@usfq.edu.ec *

more than 23,000 deaths [3]. The antibiotic resistance of pathogens has been linked to both the medical and the agricultural usage of antibiotics [6,7,15,16]. Resistance to fluoroquinolones poses a particularly challenging health problem because these broad-spectrum antibiotics are used to treat serious bacterial infections, especially those acquired in hospitals [2,3,5]. Both the mutations in chromosomal genes [2] and the presence of conjugative or non-conjugative plasmids carrying the quinolone resistance gene qnr or other genes [10,21,27] have been implicated in fluoroquinolone resistance. Plasmidmediated quinolone resistance in human pathogens has been

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associated with food-producing animals in some studies [8,28] but not in others [20].

Among the qnr genes, qnrB is widely distributed in South American countries [17,18,20]. In Ecuador, where there is no restriction on the use of fluoroquinolones in animal feed, the prevalence of fluoroquinolone resistance in community-acquired Escherichia coli isolates from the human urinary tract is 41% [22]. In the present study, we assessed fluoroquinolone resistance and the presence of qnrB genes in E. coli isolates obtained in Ecuador from fecal samples collected from chickens and humans in a rural, low antibiotic use setting and from two higher antibiotic use settings. Specifically, we compared fluoroquinolone-resistant isolates (FRIs) from (i) healthy humans living in rural communities, (ii) chickens (broiler and free-range) raised in rural communities, (iii) humans treated at urban hospitals, and (iv) chickens from an industrial poultry operation.

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Isolates from chickens in an industrial operation. The 827 E. coli isolates from an industrial poultry operation (located on the Ecuadorian coast, ~300 km from the study region) were obtained from broiler chickens sampled between March and November 2010. These animals had been kept in coops and received oxytetracycline (10 mg/l) in their drinking water. These isolates were labeled IndCHK. Isolates from humans in urban hospitals. The 114 clinical FRIs were isolated from patients with E. coli urinary tract infections who were seen at three hospitals in urban Quito from May to July 2010. Of these isolates, 61 were from Hospital Vozandes (kindly provided by Jeannette Zurita), 42 were from Hospital Carlos Andrade Marín (kindly provided by Isabel Narváez), and 11 were from the Institute of Microbiology at the Universidad San Francisco de Quito. These clinical human isolates were labeled E. coli Quito Hospital.

Table 1. Nucleotide sequences of qnrB genes from fluoroquinolone resistance isolates (FRIs) of Escherichia coli from humans and chickens inhabiting rural communities located on the northern coast of Ecuador, from chickens from an industrial operation, and from humans treated at an urban hospital in Quito Amplicon

Materials and methods Samples and bacterial isolates. Escherichia coli was isolated from 1,167 human fecal samples and 1,201 chicken cloacal swabs (from 1,134 chickens) cultured on MacConkey agar. Five lactose-fermenting colonies were selected from each sample and tested for glucuronidase activity on Chromocult agar. Glucuronidase-positive colonies were subjected to antibiotic susceptibility testing. One FRI was selected from each sample. In these FRIs, an inhibition zone ≤ 20 mm was produced in response to discs containing 5 µg of ciprofloxacin. Kirby-Bauer antibiotic susceptibility testing was carried out in accordance with the guidelines of the Clinical and Laboratory Standards Institute [3b].

Isolates from chickens in rural communities. The 1,201 E. coli isolates were obtained between January and March 2009 from chickens raised in small-scale poultry farming operations in a rural community in northwestern Ecuador. The majority of these isolates (955; 80%) were from broiler chickens that had been purchased from a local distributor and fed with commercial poultry feed. Of these 955 isolates, 831 were from 30 chickens sampled weekly for 6 weeks at three farms in one community and 124 were from 25 chickens sampled once cross-sectionally at another farm in the same community. An additional 246 (20%) isolates were obtained from household varietals (other breeds of chickens also purchased from a local distributor).

Isolates from all chickens in the rural communities were labeled RemCHK. In addition, one isolate (Rem6) was obtained from water drawn from a well in the same community as the chickens.

Isolates from humans in rural communities. The 1,167 commensal E. coli isolates were obtained from healthy humans (controls) residing in 24 communities in northwestern Ecuador who participated in a casecontrol study of diarrheal diseases between February 2009 and February 2010. Details about the region, study design, and sampling strategy were described previously [4]. The human isolates were labeled RemHUM. All interactions with human subjects were approved by the University of Michigan’s Institutional Review Board and the Universidad San Francisco de Quito’s Bioethics Committee.

GenBank accession number

RemHUM1

JN714812

RemHUM2

JN714813

RemHUM3

JN714814

RemHUM4

JN714815

RemHUM5

JN714816

RemCHK6

JN714817

RemCHK7

JN714818

RemCHK8

JN714819

RemCHK9

JN714820

RemCHK10

JN714821

RemCHK11

JN714822

RemCHK12

JN714823

RemCHK13

JN714824

RemCHK14

JN714825

RemCHK15

JN714826

RemCHK17

JN714828

RemCHK18

JN714829

RemCHK19

JN714830

RemCHK20

JN714831

RemCHK21

JN714832

RemCHK22

JN714833

RemCHK23

JN714834

IndCHK24

JN714835

IndCHK25

JN714836

IndCHK26

JN714837

Quito Hospital

JN714838

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Int Microbiol

DISTRIBUTION OF QNRB GENE IN E. COLI

Fig. 1. Maximum likelihood analysis of qnrB genes in Escherichia coli isolates. RemHUM: bacterial sequences obtained from humans in Ecuadorian rural communities. RemCHK: sequences from chickens in rural communities.

E. coli Quito Hospital: qnrB from hospital isolates in Quito, Ecuador. IndCHK: sequences from isolates from a poultry industrial operation. The remaining sequences were obtained from GenBank: E.coli strain F84, accession number KM094204.1; E. coli strain F257 accession number KM094205.1; E. coli strain ECH8, accession number KP268825.1; Haemophilus parasuis strain SC056, accession number HQ117877.1. Numbers are bootstrap values obtained after 500 pseudoreplicates. Asterisk indicates sequences analyzed in the present work.

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Table 2. Fluoroquinolone resistance isolates (FRIs) of Escherichia coli with and without the qnrB gene. Five E. coli isolates were analyzed from each fecal sample obtained from either humans or chickens inhabiting rural communities located on the northern coast of Ecuador Source of the isolate

Number of isolates

FRIs (%)

FRIs with qnrB (%)

Human isolates from a rural area

1,167

35 (2.9%)

11(31.4%)

Human isolates from urban hospitals

NDa

114

1(0.88%)

Isolates from broiler chickens in a rural area

955

214 (22.4%)

4(1.87%)

Household varietals from a rural area

246

37 (15.4%)

3 (8.1%)

Isolates from broiler chickens from an industrial poultry operation

827

81 (9.8%)b

3 (6.0%)b

P-value

P < 0.0001c

ND = Not determined.

Out of 81 isolates, only 50 were analyzed for qnrB genes. c P-value obtained from a comparison of fluoroquinolone resistant human isolates from the rural community and from the hospital in Quito. a b

Polymerase chain reaction amplification and DNA sequence analysis. Bacterial DNA from a single FRI colony was extracted from the cells using a boiling technique [25]. A PCR for qnrB genes was carried out following the method described in [8] and using the following primers to amplify internal fragments of the target gene: qnrB F 5′-GGMATHGAAATTCGCCACTG-3′ and qnrB R 5′-TTTGCYGYYCGCCAGTCGAA-3′. The PCR conditions were as follows: 95°C for 5 min, 35 cycles of 94°C for 30 s, 56°C for 40 s and 72°C for 1 min, and a final incubation at 72°C for 10 min. Amplicons were sequenced by the University of Michigan DNA Sequencing Core. The qnrB gene sequences described in this report were deposited under the accession numbers JN714812 to JN714838 (Table 1). A subset of these amplicons (IndCHK24, IndCHK25, IndCHK26. and E. coli Quito Hospital) were sequenced a second time at Functional Biosciences, Inc. (Madison, WI) to rule out errors. They were used in the phylogenetic analysis, performed with the Mega5.1 program (Fig. 1). Two qnrB sequences from the FRIs of broiler chicken came from the same animal, from two colonies collected 2 weeks apart (RemCHKb7 and RemCHKb9).

Presence of qnrB gene in fluoroquinolone-resistant isolates. The percentage of human FRIs carrying qnrB genes differed subtantially, depending on the origin of the isolates: 31.4% (11 of 35 isolates) of the human FRIs from rural communities carried the qnrB gene vs. 0.88% (1 of 114 isolates) of those from hospitals in Quito (P < 0.0001) (Table 2). The differences among the chicken isolates were smaller: 1.87% (4 of 214 isolates) of the FRIs from broiler chickens in rural communities 8.1% (3 of 37 isolates) of those from household varietals, and 6% (3 of 50 isolates analized) of those from broiler chickens raised in industrial operations (P = 0.4). The differences between FRIs from broiler chickens and household varietals in the rural community were not significant (P = 0.069).

Statistical analysis. A c2 test was used to analyze the differences in fluoroquinolone resistance and qnrB gene frequency between sample types.

Phylogenetic analysis of qnrB genes. The nucleotide sequences of all the amplicons showed high similarities to previously described qnrB genes (Fig. 1).

Results and Discussion Rates of resistance. The rates of fluoroquinolone resistance were significantly higher (P < 0.001) in isolates from chickens in rural communities (broilers: 214 of 961 isolates; household varietals: 37 of 246 isolates) than in isolates from chickens raised in industrial operations (81of 827 isolates) or in isolates from humans living in rural communities (35 of 1,167 isolates) (Table 2).

Conclusions. Based on the published literature [1] and the high prevalence of fluoroquinolone resistance previously reported in Ecuador [22], a higher frequency of qnrB genes was expected from pathogenic FRIs from hospitals in Quito, where antibiotic use is high, than in rural settings, where antibiotic use is low. However, a high proportion of commensal E. coli isolated from humans in rural communities carried qnrB genes. Conversely, despite the high prevalence of FRIs obtained from chickens raised in rural communities, the proportion carrying qnrB genes was low. FRI from urban hospitals in Quito, where antibiotic use is high, had the lowest proportion

DISTRIBUTION OF QNRB GENE IN E. COLI

of qnrB gene carriage. Although qnr genes cause low-level resistance to quinolones [9], they are thought to be important in the development of the highly resistant phenotype [14].

The lack of association of the qnrB gene with clinical fluoroquinolone reistance in this study was therefore unexpected.

Other studies have found that qnrB genes are widely distributed in commensal E. coli isolated from healthy humans, including children, living in urban settings in Peru and Bolivia [17,18], from humans in remote Peruvian Amazon communities [19], and from domestic and farm animals in Germany [23]. In our study, healthy humans from rural, but not urban areas of Ecuador had high rates of qnrB carriage. We also found that although chickens had high rates of fluoroquinolone resistance, the rates of qnrB carriage in the FRIs from these animals were low.

Although our findings suggest that qnrB genes are not linked to fluoroquinolone clinical resistance, they have been associated with the development of high resistance to quinolones [14,24]. Moreover, qnrB genes are one of the most common plasmid-mediated quinolone resistance genes [13]. The presence of qnrB genes in isolates from domestic animals is also a matter of concern, as these genes have been detected in food pathogens such as Salmonella [11]. The main limitation of this study was the amplified segment of DNA, which did not allow us to determine the diversity of the qnr genes. These and other studies on the role of qnr genes in fluoroquinolone resistance are needed.

Acknowledgements. The authors thank the Ecologia, Desarrollo, Salud, y Sociedad EcoDSS field team, and especially Nadia Lopez, for their invaluable contribution in collecting the data. This study was supported by grant numbers R01-AI050038 and K01-AI103544 from the National Institute of Allergy and Infectious Diseases NIAID; grant number 0811934 from the Ecology of Infectious Diseases program; the Fogarty International Center (FIC) of the USA National Institutes of Health (NIH); and the National Science Foundation (NSF).

Competing interests. None declared.

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PLAS-0006-2013 11. Karczmarczyk M, Martins M, McCusker M, Mattar S, Amaral L, Leonard N, Aarestrup FM, Fanning S (2010) Characterization of antimicrobial resistance in Salmonella enterica food and animal isolates from Colombia: identification of a qnrB19-mediated quinolone resistance marker in two novel serovars. FEMS Microbiol Lett 313:10-19 12. Kim HB, Park CH, Kim CJ, Kim EC, Jacoby GA, Hooper DC (2009) Prevalence of plasmid-mediated quinolone resistance determinants over a 9-year period. Antimicrob Agents Chemother 53:639-645 13. Ma J, Zeng Z, Chen Z, Xu X, Wang X, Deng Y, Lü D, Huang L, Zhang Y, Liu J, Wang M (2009) High prevalence of plasmid-mediated quinolone resistance determinants qnr, aac(6_)-Ib-cr, and qepA among ceftiofur- resistant Enterobacteriaceae isolates from companion and foodproducing animals. Antimicrob Agents Chemother 53:519-524 14. Martínez-Martínez L, Pascual A, Jacoby GA (1998) Quinolone resistance from a transferable plasmid. Lancet 351:797-799 15. Mathew AG, Cissell R. Liamthong S (2007) Antibiotic resistance in bacteria associated with food animals: A United States perspective of livestock production. Foodborne Pathog Dis 4:115-133 16. McGowan JE (1983) Antimicrobial resistance in hospital organisms and its relation to antibiotic use. Rev Infect Dis 5:1033-1048 17. Pallecchi L, Riccobono E, Mantella A, Bartalesi F, Sennati S, Gamboa H, Gotuzzo E, Bartoloni A, Rossolini GM ( 2009) High prevalence of qnr genes in commensal enterobacteria from healthy children in Peru and Bolivia. Antimicrob Agents Chemother 53:2632-2635 18. Pallecchi L, Riccobono E, Mantella A, Fernandez C, Bartalesi F, Rodriguez H, Gotuzzo E, Bartoloni A, Rossolini GM (2011) Small qnrB-harbouring ColE-like plasmids widespread in commensal enterobacteria from a remote Amazonas population not exposed to antibiotics. Antimicrob Chemother 66:1176-1178 19. Pallecchi L, Riccobono E, Sennati S, Mantella A, Bartalesi F, Trigoso C, Gotuzzo E, Bartoloni A, Rossolini GM (2010) Characterization of small

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ColE-like plasmids mediating widespread dissemination of the qnrB19 gene in commensal enterobacteria. Antimicrob Agents Chemother 54:678-682 20. Riccobono E, Pallecchi L, Mantella A, Bartalesi F, Chavez I, Trigoso C, Villagran AL, Bartoloni A, Rossolini GM (2012) Carriage of antibioticresistant Escherichia coli among healthy children and home-raised chickens: a household study in a resource-limited setting. Microb Drug Resist 18:83-87 21. Robicsek A, Jacoby GA, Hooper DC (2006) The worldwide emergence of plasmid-mediated quinolone resistance. Lancet Infect Dis 6:629-640 22. Salles MJ, Zurita J, MejĂ­a C, Villegas MV (2013) Resistant Gram-negative infections in the outpatient setting in Latin America. Epidemiol Infect 141:2459-2472 23. Schink AK, Kadlec K, Schwarz S (2012) Detection of qnr genes among Escherichia coli isolates of animal origin and complete sequence of the conjugative qnrB19-carrying plasmid pQNR2078. J Antimicrob Chemother 67:1099-1102 24. Strahilevitz J, Jacoby GA, Hooper DC, Robicsek A (2009) Plasmid-mediated quinolone resistance: a multifaceted threat. Clin Microbiol Rev 22:664-689

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25. Vieira N, Bates SJ, Solberg OD, Ponce K, Howsmon R, Cevallos W, Trueba G, Riley L, Eisenberg JN (2007) High prevalence of enteroinvasive Escherichia coli isolated in a remote region of northern coastal Ecuador. Am J Trop Med Hyg 76:528-533 26. Wang M, Guo Q, Xu X, Wang X, Ye X, Wu S, Hooper DC, Wang M (2009) New plasmid-mediated quinolone resistance gene, qnrC1, found in a clinical isolate of Proteus mirabilis. Antimicrob Agents Chemother 53:1892-1897 27. Wu JJ, Ko WC, Tsai SH, Yan JJ (2007) Prevalence of plasmid-mediated quinolone resistance determinants QnrA, QnrB, and QnrS among clinical isolates of Enterobacter cloacae in a Taiwanese hospital. Antimicrob Agents Chemother 51:1223-1227 28. Yue L, Jiang HX, Liao XP, Liu JH, Li SJ, Chen XY, Chen CX, LĂź DH, Liu YH (2008) Prevalence of plasmid-mediated quinolone resistance qnr genes in poultry and swine clinical isolates of Escherichia coli. Vet Microbiol 132:414-420

RESEARCH ARTICLE International Microbiology (2015) 18:91-97 doi:10.2436/20.1501.01.238 ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

Antagonism of entomopathogenic fungi

by Bacillus spp. associated with the integument of cicadellids and delphacids Andrea Toledo,1* Silvina López,2 Mónica Aulicino,3 Ana María de Remes Lenicov,4 Pedro Balatti1 1 Plant Pathology Research Center, Faculty of Agricultural Sciences and Forestry, National University of La Plata, La Plata, Buenos Aires, Argentina. 2Institute of Plant Physiology, Faculty of Agricultural Sciences and Forestry, National University of La Plata- CONICET, La Plata, Buenos Aires, Argentina. 3Institute of Phytotechnology Santa Catalina, Faculty of Agricultural Sciences and Forestry, National University of La Plata, Lavallol, Buenos Aires, Argentina. 4 Entomology Division, Faculty of Natural Sciences and Museum, National University of La Plata, La Plata, Buenos Aires, Argentina

Received 11 March 2015 · Accepted 12 June 2015

Summary. Entomopathogenic fungi are potential tools to biocontrol cicadellids and delphacids, two groups of insects that cause extensive damage to agricultural crops. However, bacteria living on the host cuticle may inhibit fungal growth. In the present work, following the molecular characterization of 10 strains of Bacillus isolated from the integument of cicadellids and delphacids, we selected isolates of the fungi Beauveria bassiana and Metarhizium anisopliae that are resistant to the antimicrobials secreted by these bacterial strains. The antagonistic activity of the 10 bacterial isolates belonging to the genus Bacillus (i.e., B. amyloliquefaciens, B. pumilus, and B. subtilis) against 41 isolates of Bea. bassiana and 20 isolates of M. anisopliae was investigated in vitro on tryptic soy agar using the central disk test. With this approach, isolates of Bea. bassiana and M. aniso­ pliae resistant to antagonistic bacteria were identified that can be further developed as biological control agents. [Int Microbiol 2015; 18(2):91-97] Keywords: Bacillus spp. · antagonism · entomopathogenic fungi · Cicadellidae · Delphacidae

Introduction Cicadellids and delphacids (Hemiptera: Auchenorrhyncha) include a large number of species, many of which cause extensive damage to agricultural crops. These insects are Corresponding author: A. Toledo Centro de Investigaciones de Fitopatología Facultad de Ciencias Agrarias y Forestales Universidad Nacional de La Plata La Plata, Buenos Aires, Argentina.

Tel. +54-2214236758. Fax +54-2214252346 E-mail: andytoledo75@yahoo.com.ar *

widely distributed and can be found anywhere between the southern United States and temperate areas of Argentina [29,48]. They not only cause mechanical damage to crop plants during feeding and oviposition, but are also vectors of phloem-associated plant pathogens, mainly viruses and bacterial phytoplasmas [21].

Within the cicadellids, Dalbulus maidis (DeLong & Wolcott, 1923) is the main vector of maize pathogens on the American continent, mostly in tropical areas of South and Central America but also in those of the Caribbean. In tropical America, D. maidis is a vector of Maize Rayado Fino Virus (MRFV), Corn Stunt Spiroplasma (CSS), and Maize

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Bushy Stunt Mycoplasma (MBSM). Corn stunt is the most important disease of maize in USA, Mexico, and South and Central America. It was first identified in Argentina in the early 1990s [4]. Among the delphacids, Delphacodes kuscheli Fennah 1955 is the main vector of Mal de Río Cuarto virus, an important endemic disease in the central region of Argentina [22] that has had a considerable impact along the country’s corn belt [24].

Among the many different strategies developed to control corn diseases, the use of maize genotypes tolerant to infection has gained the most attention [23,46]. However, biological control agents, including fungi that parasitize these insects, offer an interesting alternative [10,12,15]. Entomopathogenic fungi were the first organisms considered as control agents at the end of the 19th century. Since then, their value in insect control has been widely demonstrated, mainly within Integrated Pest Management programs [7,14]. Generally, the application of entomopathogenic fungi requires high specificity and the absence of resistance in the target organisms. As long as no secondary pest outbreaks occur, long-term control is feasible.

Moreover, the use of entomopathogenic fungal strains is frequently compatible with that of other biological control agents, certain fungicides, and many other types of pesticides.

A further advantage is that no pre-harvest interval is required [5,6,42,50].

The commercialization of entomopathogenic fungi is usually restricted to those species that are amenable to mass production in vitro on economical substrates. Among the commercial products developed to date are several that are based on species within the Hypocreales, such as Beauveria bassiana (Bals.-Criv.) Vuill., Bea. brongniartii (Sacc.) Petch, Isaria fumosorosea Wize, Lecanicillium spp. (Cordycipitaceae), Metarhizium anisopliae (Metchn.) Sorokin, and Nomuraea rileyi (Farl.) Samson (Clavicipitaceae) [7,49]. In the control of cicadellids and delphacids, entomopathogenic fungi have considerable potential because they invade their hosts through the integument [38]. However, fungal invasion of the host occasionally fails, not only due to the presence of antimicrobial substances associated with the insect cuticle, such as phenol groups, quinones, aldehydes, poisonous alkaloids, short-chain fatty acids, and cationic peptides [8,11,17,30,34], but also because of the presence of other fungi and bacteria on the insect surface that, by producing antimicrobial substances, inhibit germination of the conidia of entomopathogenic fungi [9,18,29,45].

According to Steinhaus [33], the bacterial populations found on the external surface of the insects are predominantly

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gram-positive, aerobic, spore-forming bacilli. Toledo et al.

[40] recently isolated different Bacillus species, including B. subtilis, B. pumilus, and B. amyloliquefaciens, from the integument of D. maidis and D. kuscheli. The bacteria were found to be antagonistic to entomopathogenic Bea. bassiana, inhibiting germination as well as growth of conidia. Indeed, the ability of Bacillus to produce antibiotic-like compounds, antifungal compounds, and/or bacteriocins, such as surfactin, bacylisin, fengycin, bacyllomicin, subtilin and iturin, has led to the use of these bacteria throughout the world to control phytopathogens [2,3,16,20,36].

The development of novel formulations of biocides for use in the sustainable management of maize agroecosystems requires an understanding of the interactions between entomopathogenic fungi and the microbial populations living on the cuticle of insects. Thus, in the present work we characterized 10 strains of Bacillus by means of molecular techniques and then selected isolates of Bea. bassiana and M. anisopliae that were resistant to the antimicrobial compounds secreted by these bacteria.

Materials and methods Bacterial strains. All bacterial strains used in this study were isolated from the integument of D. maidis and D. kuscheli [40]. Genomic DNA was extracted from these strains using the Wizard Genomic DNA purification kit (Promega). The 16S rDNA of strains Dm-B3, Dm-B4, Dm-B10, Dm-B17, Dm-B22, Dm-B23, Dm-B47, Dm-B55, Dm-B59, and Dk-B25 was amplified in a thermocycler (Minicycler, MJ Research) and sequenced according to Sanger et al. [27]. The sequences were deposited in the GenBank database of the National Center for Biotechnology Information (NCBI). From an analysis of the sequences using the Basic Local Alignment Search Tool (BLAST), 10 sequences were obtained and then aligned with those of reference strains B. amyloliquefaciens, B. pumilus, B. megaterium, B. cereus, and B. thuringiensis by means of the multiple sequence alignment program Clustal W. A UPGMA phylogenetic tree was constructed using Molecular Evolutionary Genetics Analysis version 5 (MEGA5) [35].

Fungal isolates. Forty-one isolates of Bea. bassiana and 20 isolates of M. anisopliae were used in this study. Fungal isolates were obtained from their insect hosts, which belonged to the orders Hemiptera, Coleoptera, and Dermaptera, and from soil samples collected from sorghum and corn crops.

All of the isolates were obtained in Buenos Aires, Corrientes, and Tucumán provinces of northern Argentina. They were stored in the Mycological Collections of Centro de Estudios Parasitológicos y de Vectores (CEPAVE, La Plata, Buenos Aires, Argentina), in the Agricultural Research Service, Collection of Entomopathogenic Fungi (ARSEF, Ithaca, New York, USA), and in the collection of the Centro de Investigaciones de Fitopatología (CIDEFI, La Plata, Buenos Aires, Argentina). The isolates were characterized according to both their morphology [39] and their virulence against cicadellids and delphacids [38].

BACILLUS SPP. AGAINST ENTOMOPATHOGENIC FUNGI

Inhibition of fungal growth by bacteria. The antagonistic activity of 10 Bacillus strains against 41 isolates of Bea. bassiana and 20 isolates of M. anisopliae was tested using the central disk test [26]. Fungal isolates were cultured on malt extract agar (MEA 2%) at 25°C in the dark for 7 days. A 7-mm mycelium disk was cut and transferred to the center of a tryptic soy agar (TSA; Britania) plate and cultured at 30°C for 48 h. Three such disks were transferred to each TSA plate and placed at equidistant points from the central disk. Each treatment consisted of six replicates and one control (plates containing only a central disk of the fungus). The plates were incubated at 30ºC in darkness. Mycelial growth was estimated based on the radial increase in colony size, which was measured between two orthogonal diameters drawn 10 days after the incubation. Antagonism was estimated based on the percentage of mycelial growth inhibition (MGI), which was calculated as suggested by Michereff et al. [19].

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Results and Discussion Bacterial isolates. The 16S rDNA sequences confirmed that all of the strains belonged to the genus Bacillus and suggested that strain Dm-B3 was Bacillus amyloliquefaciens (Gen Bank accession number: HQ339952), strains DmB22, Dm-B23, and Dk-B25 were B. pumilus (KC460218, KC460219, and KC460215, respectively), and that strains Dm-B4, Dm-B17, Dm-B47, and Dm-B55 were B. subtilis (HQ111352, KC460217, HQ111353, and HQ111354, respect­ ively). However, strains Dm-B10 and Dm-B59 (KC460216 and KC460220), initially identified by Toledo et al. [40] by means of biochemical reactions as B. megaterium, had a

Int Microbiol

Statistical analysis. The effects of treatments were determined by the factorial analysis of variance (ANOVA). The mean values were separated using Tukey’s honestly significant difference (HSD) test (P < 0.05) [31].

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Fig. 1. Dendrogram showing the identity and relationship of the major antagonistic bacteria isolated from the cuticular surfaces of Delphacodes kuscheli and Dalbulus maidis. Numbers on the branches represent bootstrap values obtained from 1000 replicates. The bar indicates 0.02 substitutions per site. Species names are followed, in parentheses, by the National Center for Biotechnology Information (NCBI) GenBank database accession numbers.

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16S rDNA sequence 99% homologous to the full sequence of B. subtilis and B. amyloliquefaciens. Therefore, pending additional molecular data, both strains were reclassified as Bacillus sp. (Dm-B10 and Dm-B59, respectively).

The bacterial strains were grouped into four clusters (Fig. 1). The first one comprised six strains and two reference sequences of B. amyloliquefaciens. It was supported by a bootstrap value of 100%. Within the cluster, there were four representatives of B. subtilis (Dm-B17, Dm-B4, Dm-B47, and Dm-B55) and two of Bacillus sp. (Dm-B59 and Dm-B10).

The isolates of B. subtilis were clustered in separate groups; thus, Dm-B4, Dm-B17, and Dm-B47 formed a cluster (30% bootstrap) that was clearly distinct from that formed by strain Dm-B55 (B. subtilis). The second cluster was also supported by a bootstrap value of 100% and was made up of three isolates of B. pumilus (Dm-B22, Dk-B25, and Dm-B23) and the reference sequences of B. pumilus (BY-1) and Bacillus sp.

(SAP751.2). The third cluster contained a single isolate, DmB3, identified as B. amyloliquefaciens. It merits further study to confirm its identity and to identify the nucleotides that render it distinct—including, perhaps, phenotypically—from the other isolates of the same species. Nonetheless, all of the studied strains belong to a monophyletic cluster comprising closely related organisms with strong similarity at the 16S rDNA sequence level and clustering separately from other Bacillus species, among them B. cereus, B. thuringiensis, and B. megaterium.

Inhibition of fungal growth by bacteria. The MGI of M. anisopliae was dependent on the bacterial strain (F = 9.5; df = 9, 1171; P < 0.0001) and on the targeted fungal isolate (F = 35.8; df = 19, 1171; P < 0.0001). The 10 bacterial strains differed in their antifungal activity (P < 0.05) and could be separated into four homogeneous groups. Bacillus subtilis Dm-B47 (68.4%), B. amyloliquefaciens Dm-B3 (64.4%), and B. pumilus Dk-B25 (64.3%) differed significantly from the other strains and showed the greatest antagonism against M. anisopliae, whereas B. subtilis Dm-B17 (52.8%) and Bacillus sp. Dm-B10 (52.6%), were the least antagonistic against the fungus. Similar results were obtained with Bea. bassiana.

Both the bacterial strains (F = 10.8; df = 9, 2350; P < 0.0001) and the fungal isolates (F = 91.2; df = 40, 2350; P < 0.0001) had a significant effect on MGI. In this case, the bacterial strains could be separated according to their antagonistic activity into five statistically different groups (P < 0.05).

Bacillus pumilus Dm-B22 (64.6%), Dm-B23 (62.8%), and Dk-B25 (63.2%) differed significantly from other strains and

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Table 1. Mycelial growth inhibition (MGI) of the entomopathogenic fungi Beauveria bassiana and Metarhizium anisopliae by Bacillus strains isolated from the cuticular surfaces of cicadellids and delphacids MGI (%)* Bacterial strain

M. anisopliae

Bea. bassiana

Dm-B10

52.6 ± 1.7 a

61.4 ± 0.8 cde

Dm-B17

52.8 ± 1.7 a

58.3 ± 0.8 abc

Dm-B55

56.2 ± 1.7 ab

57.5 ± 0.8 ab

Dm-B23

57.7 ± 1.7 abc

62.8 ± 0.8 de

Dm-B22

58.3 ± 1.7 abc

64.6 ± 0.8 e

Dm-B59

60.7 ± 1.7 bc

57.9 ± 0.8 abc

Dm-B4

63.3 ± 1.7 bcd

57.3 ± 0.8 a

Dk-B25

64.3 ± 1.7 cd

63.2 ± 0.8 de

Dm-B3

64.4 ± 1.7 cd

61.3 ± 0.8 cde

Dm-B47

68.4 ± 1.7 d

60.9 ± 0.8 bcd

*Mean ± standard error. Values with the same letters are not significantly different according to Tukey’s HSD test (P < 0.05).

were the most antagonistic strains when tested against Bea. bassiana, whereas B. subtilis Dm-B55 (57.5%) and Dm-B4 (57.3%) were the least antagonistic (Table 1). Some bacterial strains differed in their behavior against the two fungal species. For example, B. subtilis Dm-B4 was one of the least antagonistic strains against Bea. bassiana but it was one of the most antagonistic ones against M. anisopliae isolates.

The most antagonistic bacterial strains in our study belonged to the B. subtilis group, which includes B. amy­ loliquefaciens, B. licheniformis, B. pumilus and other close relatives of B. subtilis. In previous reports, a number of species of bacteria belonging to the B. subtilis group, such as B. pumilus, B. licheniformis, B subtilis, B. atrophaeus, and B. amyloliquefaciens, were shown to secrete inhibitors of bacterial and fungal growth. These compounds are thought to play a crucial role in competition or microbial interactions [2,3,16,36,47].

Fungal susceptibility to antagonistic bacteria seems to be a variable trait. In this study, the susceptibility of Bea. bassiana was much more variable than that of M. anisopliae. Thus, Bea. bassiana isolates 099 (5.9%) and 111 (12.4%) were the least inhibited, and isolates Bb075 (78.1%) and Bb189 (76.5%) the most inhibited ones (Table 2). By contrast, representatives of M. anisopliae exhibited less variability in terms of their susceptibility to bacteria. For these species, bacterial inhibition was strongest for isolates Ma120 (77.9%), Ma35

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Table 2. Isolates of Beauveria bassiana and Metarhizium anisopliae and the inhibition (expressed as the percent of the control) of mycelial growth (MGI) by Bacillus strains Isolate

MGI (%)*

Isolate

MGI (%)

Isolate

MGI (%)

Ma079

31.8 ± 2.4 a

Bb099

5.9 ± 3.6 a

Bb147

69.1 ± 1.6 jklmnop

Ma003

36.9 ± 2.4 ab

Bb111

12.4 ± 1.8 a

Bb112

69.1 ± 1.6 jklmnop

Ma34

44.1 ± 2.4 bc

Bb001

31.8 ± 1.6 b

Bb137

69.6 ± 1.6 jklmnopq

Ma31

44.3 ± 2.4 bc

Bb061

33.3 ± 1.6 b

Bb081

69.9 ± 1.6 jklmnopq

Ma33

45.6 ± 2.4 bc

Bb074

33.3 ± 1.6 b

Bb175

70.1 ± 1.6 jklmnopq

Ma076

51.0 ± 2.4 cd

Bb072

33.3 ± 1.6 b

Bb148

70.5 ± 1.6 jklmnopq

Ma36

53.6 ± 2.4 cd

Bb092

44.1 ± 1.6 c

Bb114

71.0 ± 1.6 klmnopq

Ma37

53.7 ± 2.4 cd

Bb249

46.7 ± 1.6 cd

Bb145

71.1 ± 1.6 klmnopq

Ma30

55.5 ± 2.4 cde

Bb140

48.4 ± 1.6 cde

Bb143

71.1 ± 1.6 klmnopq

Ma078

58.9 ± 2.4 def

Bb117

53.9 ± 1.6 def

Bb146

71.5 ± 1.6 lmnopq

Ma178

61.1 ± 2.4 defg

Bb136

57.1 ± 1.6 efg

Bb176

72.2 ± 1.6 lmnopq

Ma095

67.3 ± 2.4 efgh

Bb149

57.4 ± 1.6 fg

Bb142

72.9 ± 1.6 mnopq

Ma39

68.0 ± 2.4 fgh

Bb080

58.3 ± 1.6 fgh

Bb54

73.2 ± 1.6 mnopq

Ma086

71.6 ± 2.4 gh

Bb083

59.9 ± 1.6 fghi

Bb153

73.6 ± 1.6 mnopq

Ma32

73.5 ± 2.4 h

Bb118

62.2 ± 1.6 fghij

Bb069

73.7 ± 1.6 mnopq

Ma29

74.2 ± 2.4 h

Bb119

62.6 ± 1.6 fghijk

Bb116

75.1 ± 1.6 nopq

Ma160

74.9 ± 2.4 h

Bb077

63.7 ± 1.6 ghijkl

Bb150

75.3 ± 1.6 opq

Ma38

76.3 ± 2.4 h

Bb113

66.2 ± 1.6 hijklm

Bb141

75.5 ± 1.6 pq

Ma35

76.7 ± 2.4 h

Bb138

66.5 ± 1.6 hijklmn

Bb189

76.5 ± 1.6 pq

Ma120

77.9 ± 2.4 h

Bb002

66.6 ± 1.6 hijklmno

Bb075

78.1 ± 1.6 q

Bb151

66,7 ± 1.6 ijklmnop

*Mean ± standard error. Values with the same letters are not significantly different according to Tukey’s HSD test (P < 0.05).

(76.7%), Ma38 (76.3%), and Ma160 (74.9%) and weakest for isolates Ma003 (36.9%) and Ma079 (31.8%) (Table 2).

Therefore, in this study we identified two isolates of Bea. bassiana (Bb099 and Bb111) and two of M. anisopliae (Ma003 and Ma079) as the most resistant to antagonism by the ten Bacillus strains tested. Figure 2 shows the results of the disk tests for the most and the lest inhibited fungal species.

The differences in the responses of the fungal isolates to bacterial attack might be due to their different abilities to detoxify bacterial growth inhibitors, for example, by producing secondary metabolites with antibacterial activity.

Diverse toxic metabolites have been described in several fungal biological control agents, including species of Beauveria, Metarhizium, and Isaria [43]. Some of these metabolites have antibiotic, fungicidal, or insecticidal pro­ perties [13,43]. Recently, Sahab [28] characterized a crude

ethyl acetate extract of Bea. bassiana with antibacterial and antifungal activities. The antibacterial activity was effective at any of the concentrations tested when used against different strains of gram-positive and gram-negative bacteria.

Several studies have shown that the insect cuticle is an ecological niche for microbes, where fungi and bacteria co-exist and interact [9,18,29,45]. Among the mechanisms proposed for the biocontrol activity of Bacillus spp., competition, the induction of systemic resistance, and antibiotic production appear to be the most important one [1,32,37].

A better understanding of fungal-bacterial interactions may lead to the development of potent formulations of Bea. bassiana and M. anisopliae for their use in insect control.

Further studies on the diversity of microorganisms that colonize the insect cuticle, their role, and their impact in nature are needed in order to develop biological control agents

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Fig. 2. Antifungal activity of Bacillus strains against Metarhizium anisopliae and Beauveria bassiana (Bb) on tryptic soy agar plates after 10 days at 30°C in the dark. From left to right, fungal isolates more and less inhibited by bacteria. (A-D) M. anisopliae (Ma); (E-H) Bea. bassiana (Bb). (A) Ma35 control.

(B). Ma35 in the presence of B. subtilis DM-B17. (C). Ma079 control. (D). Ma079 in the presence of B. subtilis Dm-B4. (E) Bb075 control. (F) Bb075 in the presence of B. subtilis Dm-B55. (G) Bb099 control. (H) Bb099 in the presence of B. subtilis Dm-B4. Scale bar = 2.5 cm.

that are effective against insect pests such as cicadellids and delphacids.

Acknowledgements. We thank Dr. Nigel Hywel-Jones, and Lic. Arnaldo Maciá (Research Assistant, CICBA) for their critical reviews of the manuscript. We also thank two anonymous reviewers and the Editor of International Microbiology for helpful comments on the manuscript. This work was supported by CONICET (PIP 11220090100162) and ANPCyT (PICT 2007-00143).

Competing interests. None declared.

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Application and evaluation of pathogens for control of insects and other invertebrate pests. Springer, Dordrecht, Germany, pp 223-248

RESEARCH ARTICLE International Microbiology (2015) 18:99-104 doi:10.2436/20.1501.01.239. ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

IS200 and multilocus sequence typing for the identification of Salmonella enterica serovar Typhi strains from Indonesia Areli Martínez-Gamboa,1 Claudia Silva,2* Marcos Fernández-Mora,2 Magdalena Wiesner,2 Alfredo Ponce de León,1 Edmundo Calva2 National Institute of Medical Sciences and Nutrition Salvador Zubirán, Mexico City, Mexico. 2Institute of Biotechnology, Department of Molecular Microbiology, UNAM, Cuernavaca, Morelos, Mexico

1

Received 7 April 2015 · Accepted 11 June 2015

Summary. In this work, IS200 and multi-locus sequence typing (MLST) were used to analyze 19 strains previously serotyped as Salmonella enterica serovar Typhi and isolated in Indonesia (16 strains), Mexico (2 strains), and Switzerland (1 strain). Most of the strains showed the most common Typhi sequence types, ST1 and ST2, and a new Typhi genotype (ST1856) was described.

However, one isolate from Mexico and another from Indonesia were of the ST365 and ST426 sequence types, indicating that they belonged to serovars Weltevreden and Aberdeen, respectively. These results were supported by the amplification of IS200 fragments, which rapidly distinguish Typhi from other serovars. Our results demonstrate the utility of IS200 and MLST in the classification of Salmonella strains into serovars. These methods provide information on the clonal relatedness of strains isolated worldwide. [Int Microbiol 2015; 18(2):99-104] Keywords: Salmonella Typhi · bacterial molecular typing · multilocus sequence typing (MLST) · clonal complex · insertion sequence IS200

Introduction Typhoid fever, a systemic febrile illness in humans, is caused

by Salmonella enterica subspecies enterica serovar Typhi (Typhi). The disease is transmitted by the fecal-oral route, mainly via contaminated food and water. It is a global health problem, especially in the developing world, with more than 27 million cases determined each year, resulting in an estimated 217,000 deaths [6].

Corresponding author: C. Silva Instituto de Biotecnología- UNAM Av. Universidad 2001, Col. Chamilpa CP 62210, Cuernavaca, Morelos, México Tel. 52-7773291627. Fax: 52-7773138673 E-mail: csilvamex1@yahoo.com *

The classification of Salmonella has been traditionally based on serotyping, which depends on agglutination reactions using anti-sera specific for epitopes of the lipopolysaccharide antigens (O antigens) and one of two alternative flagellar antigens (phases 1 and 2 of the H antigen) [12]. Although the designation of serovars is widely used in epidemiology, serotyping has the disadvantage that, because it depends on numerous antibodies obtained by the immunization of rabbits, its implementation is expensive and laborious, such that it is performed by only a few reference laboratories.

Moreover, serotyping does not necessarily reflect evolutionary relationships [15,17]. In fact, previous studies have shown that strains with the same serovar can be distantly related at the chromosomal level whereas strains of different serovars may be closely related [3,20].

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Several molecular methods have been proposed as alternatives to serotyping in the classification of Salmonella [22].

For example, variations in the nucleotide sequences of multiple gene regions with cellular housekeeping functions have been used to determine relationships among bacterial strains [15,18]. This technique, referred to as multilocus sequence typing (MLST), consists of the amplification and sequencing of defined, internal, 400- to 500-bp fragments of selected housekeeping genes. The different gene sequences are assigned as alleles; for each strain, the combination of alleles for each gene locus defines the allelic profile, or sequence type

(ST). MLST is increasingly being used as a typing method for many different pathogenic organisms [17].

In 2002, Kidgell and colleagues designed an MLST scheme for Typhi strains [13] that makes use of the partial sequences of seven housekeeping loci (aroC, dnaN, hemD, hisD, purE, sucA, and thrA) and thus reveals the possible variation within 3,336 bp. This scheme has been applied in the publicly available on-line MLST database for Salmonella enterica [http://mlst.warwick.ac.uk/mlst/dbs/Senterica]. It is another of the advantages of MLST: comparisons of the allelic profiles of any strains reported worldwide.

Table 1. Description of the sources of the studied strains and the MLST results H antigen

aroC

dnaN

hemD

hisD

purE

sucA

thrA

ST

Complex

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

USNAMRU-2

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

Jakarta

USNAMRU-2

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

97457

Jakarta

USNAMRU-2

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

98531

Jakarta

USNAMRU-2

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

98864

Jakarta

USNAMRU-2

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

99282

Jakarta

USNAMRU-2

1989

Typhi

d

1

1

1

1

1

1

5

ST1

Cx13

99319

Jakarta

USNAMRU-2

1989

Typhi

d

1

1

1

1

1

1

5

ST1

Cx13

99155

Jakarta

USNAMRU-2

1989

Typhi

j

1

1

2

1

1

1

5

ST2

Cx13

2218

Yogyakarta

USNAMRU-2

1989

Typhi

d

1

1

1

1

1

1

5

ST1

Cx13

2219

Yogyakarta

USNAMRU-2

1989

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

2233

Yogyakarta

USNAMRU-2

1989

Typhi

d

1

1

1

1

1

1

5

ST1

Cx13

3042

Yogyakarta

USNAMRU-2

1989

Typhi

d

1

1

340

1

1

1

5

ST1856

Cx13

TY404

Indonesia

IP

1981

Typhi

d:Z66

1

1

2

1

1

1

5

ST2

Cx13

MM160

Switzerland

UZ

1995

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

IMSS1

Mexico

IMSS

1980

Typhi

d

1

1

2

1

1

1

5

ST2

Cx13

MK28

Mexico

INCMNSZ

1987

Weltevreden

130

97

25

125

84

9

101

ST365

Cx 205

11

Jakarta

USNAMRU-2

1989

Aberdeen

46

124

112

12

36

19

18

ST426

Cx165

Strain

Country

Laboratory

Year

Serovar

2

Jakarta

USNAMRU-2

1989

12

Jakarta

USNAMRU-2

13

Jakarta

84405

USNMRU-2, United States Naval Medical Research Unit-2; IP, Institute Pasteur; UZ, Universität Zürich; IMSS, Instituto Mexicano del Seguro Social; INCMNSZ, Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”. The new hemD allele and ST reported in this study are highlighted in boldface.

IS200 AND MLST IN S. ENTERICA SEROVAR TYPHI

In a previous study [4], we determined that Typhi strains possess a copy of the IS200 insertion element between the gyrA and rcsC genes. By PCR amplification, this region renders a band of ~1.5 kb for Typhi strains and of ~0.8 kb for other Salmonella strains. Accordingly, the method can be used in the rapid differentiation of Typhi from other serovars, and therefore perhaps also for taxonomic and epidemiologic purposes [4].

In the present work, 19 strains previously serotyped as Typhi were analyzed using the IS200 PCR typing method and the Salmonella enterica MLST scheme in order to confirm their serovar assignments and detect genetic diversity. Most of the strains displayed the more common genotypes in Typhi, ST1 and ST2, but a new genotype, ST1856,was also discovered. Two of the strains originally serotyped as Typhi instead corresponded to the serovars Weltevreden and Aberdeen (ST101 and ST426, respectively). The IS200 typing data and the MLST results were in good agreement. Our results demonstrated the utility of IS200 and MLST as molecular tools to distinguish among the serovars of Salmonella strains and to establish the genetic relationships of these isolates with those previously reported worldwide.

Materials and methods Salmonella strains. Nineteen strains isolated from blood cultures of human infections and identified serologically as Typhi were included in this study (Table 1). Fifteen strains were isolated from Jakarta and Yogyakarta in Indonesia, at the United States Naval Medical Research Unit-2 (USNAMRU-2) laboratory, and donated by Dr. Gary K. Schoolnik (Stanford University (Stanford, CA, USA). Strain Ty404 was also from Indonesia and was described by Guinee and colleagues in their report of H antigen type Z66 [9].

Strain MM160 was from the collection of Dr. Martin Altwegg (Universität Zürich, Switzerland). The remaining two strains were from Mexico: strain MK28, provided by Dr. Juan Sierra-Madero (Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán” (Mexico City), and strain IMSS1, provided by Dr. Armando Isibasi (Instituto Mexicano del Seguro Social (Mexico City). All of the strains were generously donated to the laboratory of one of us (EC). Strains IMSS-1 and MM160 were included in a previous study that identified a distinctive feature of the IS200 element that allows the rapid identification of Typhi strains [4].

DNA extraction and PCR amplifications. The lyophilized strains were resuspended in 5 ml of LB broth. Genomic DNA was extracted using standard laboratory protocols [19]. PCR amplifications were performed using Taq DNA polymerase (Invitrogen, Brazil). The products were purified with a purification kit from Qiagen (Valencia, CA, USA) and submitted for sequencing at Macrogen (Seoul, South Korea). The strains were analyzed using the seven loci of the S. enterica MLST scheme (aroC, dnaN, hemD, hisD, purE, sucA, and thrA) [13]. The PCR primers and conditions were those reported on the MLST web site [http://mlst.warwick.ac.uk/mlst/dbs/Senterica]. The sequences at each locus were submitted to the MLST database for allele assignment, and the combination of alleles was assigned to a ST.

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The strains were also tested for amplification of the IS200 sequence characteristic of Typhi [4], using specific primers (IS200A5 5′- GGTGCGTA­ CCCGAGTGTC-3′ and IS200B3 5′-CTGCCAATCAGGAAAACGCG-3′) and the same conditions as for the PCR used in MLST. The IS200 products were visualized on 1% agarose gels to determine the sizes of the amplification products, based on a comparison with the O’GeneRuler 1 kb Plus DNA ladder (Thermo Scientific) as a molecular ruler.

Sequence analyses. Multiple alignments were performed for the sequences of each MLST locus using the BioEdit program [10]. The sequences were edited to fit the size reported on the MLST web site and were submitted for the assignment of alleles and STs [http://mlst.warwick.ac.uk/mlst/dbs/ Senterica].

The genetic relationships among the multilocus genotypes (STs) of our strains and those reported as Typhi in the database were determined by a clonal complex analysis carried out using the eBURST software [8]. This program divides the MLST data in groups of related strains to identify clonal complexes, predicting the founder genotype (ancestral) for each of the latter.

The predicted founder genotype is the ST with the highest number of singlelocus variants (SLV) in the group. The visual representation of the descent patterns among the strains shows the diversification of the clones within the clonal complex [8].

Results and Discussion Combined IS200 amplification and MLST to discriminate Typhi strains. A collection of 19 strains from Indonesia, Mexico, and Switzerland that had been previously serotyped as Typhi were analyzed in parallel using IS200 PCR amplification and MLST to confirm their serovar assignments and to detect genetic diversity. Amplification of the IS200 region showed that most of the strains contained the 1.5-kb band characteristic of Typhi strains; however, bands of 2.8 kb and 0.5 kb were detected in strains MK28 and 11, respectively (Fig. 1). In 1997, Calva et al. [4] showed that a 0.5-kb band was displayed by most non-Typhi serovars, and that a 2.8-kb band was characteristic of Weltevreden strains [4]. Therefore, our results suggested that strain MK28 was a Weltevreden strain.

The MLST analysis supported the IS200 typing results.

The nucleotide sequences of the genes amplified in the 19 strains (aroC, dnaN, hemD, hisD, purE, sucA, and thrA) corresponded to alleles already described in the S. enterica MLST database, with the exception of a new allele, hemD340 (Table 1). The allelic combination for 12 strains was that of ST2, and that of 4 strains ST1. Both STs have been described for other Typhi strains and are the most abundant (93%) in the database for Typhi. Strain 3042, carrying the new allele hemD340, was assigned the new sequence type, ST1856, which is a SLV of both ST1 and ST2 (Table 1). This is shown in Fig. 2, where in ST1 a T replaces a C at position 129 in the alignment of the partial sequence of the hemD1 allele, with

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Fig. 1. Agarose gel showing the amplification products of the IS200 region for the 19 Salmonella strains. Lanes: 1, 2; 2, 12; 3, 13; 4, 84405; 5, 97457; 6, 98531; 7, 98864; 8, 99282; 9, 99319; 10, 99155; 11, 2218; 12, 2219; 13, 2233; 14, 3042; 15, TY404; 16, MM160; 17, IMSS1; 18, MK28; 19, 11; and 20, molecular ruler.

strains (ST1, ST2, and ST1856) belonged to clonal complex Cx13; whereas strain MK28, with ST365, grouped with clonal complex Cx205 of the Weltevreden strains and strain 11, with ST426, grouped with clonal complex Cx165 of the Aberdeen strains (Table 1). These results suggest that the initial serotyping of strains MK28 and 11 was incorrect and demonstrate the advantages of combining IS200 and MLST approaches to distinguish Typhi strains.

Low-variability MLST clonal complex of Typhi strains. Seventeen of the Typhi strains were submitted to the Salmonella enterica MLST database: They are the first strains from Mexico, Indonesia, and Switzerland entered in the database. In addition, we contributed a new Typhi sequence type, ST1856. Thus, the Salmonella enterica MLST

Int Microbiol

respect to the sequence of ST2 (hemD2 allele). In the hemD340 allele from ST1856, a T replaces a C at position 54.

Both substitutions are synonymous and do not alter the protein product. After hisD, which has four alleles, the hemD locus, with three alleles (including hemD340), is the second most variable gene for Typhi in the MLST database.

For strains MK28 and 11, the sequences of the seven loci differed completely from those of Typhi strains, although they corresponded to alleles already reported in the database.

In accordance with their allelic combinations, strain MK28 was ST365, which is characteristic of strains of the Weltevreden serovar, and strain 11 was ST426, characteristic of Aberdeen serovars.

According to the classification of Salmonella strains reported worldwide and entered in the database [1], all Typhi

Fig. 2. Multiple alignment of the three hemD alleles found in Typhi. The region displaying the three nucleotide substitutions that distinguish hemD1, hemD2, and hemD340 is shown. The dots indicate that the sequence is the same as that in the first row.

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[1,17]. However, the MLST data lack sufficient resolution to distinguish endemic clones among geographic regions. For example, ST1 and ST2 include most of the Typhi isolates reported worldwide, which does not allow the discernment of a geographic pattern of dissemination. Instead, evaluation of the genetic structure of Typhi populations requires other subtyping tools, such as PFGE [14,16], SNPs [2,18], the analysis of tandem repeated regions (variable number of tandem repeats, VNTR) [21], or clustered regularly interspaced short palindromic repeats (CRISPR) regions [7].

Acknowledgements. This study was partially supported by grants CONACyT/Mexico (No. 179946) and DGAPA/UNAM (No. IN-201513) to Edmundo Calva. We are grateful to the researchers who kindly donated the strains. Thanks to Francisco Javier Santana Estrada, and Lucía Perezgasga Ciscomani for technical support; Eugenio López, Santiago Becerra, Paul Gaytán, and Jorge Yáñez from the Unidad de Síntesis y Secuenciación of the Instituto de Biotecnología, UNAM; and the valuable comments of an anonymous reviewer, which substantially improved the content of the manuscript.

Fig. 3. eBURST clonal complex analysis of the 124 Typhi strains available in the MLST database [http://mlst.warwick.ac.uk/mlst/dbs/Senterica]. ST2 was established as the founder genotype, from which the other STs diversify. The other STs are single-locus variants of ST2. The size of the circles is proportional to the number of strains (in parentheses).

Competing interests. None declared.

database now lists 124 Typhi strains distributed in nine STs (ST1, ST2, ST3, ST8, ST890, ST892, ST911, ST1856, and ST1919). A clonal complex analysis was performed with eBURST to determine the genetic relationships of the worldwide Typhi strains. The results showed that there is little genetic variation in the Typhi population and that ST2 was the founder genotype from which the remaining STs were derived as SLVs (Fig. 3). A low variability of Typhi strains was reported in previous studies using several molecular techniques [11,13,18,20] . However, greater genetic diversity within the Typhi population of Indonesia was determined using either pulsed-field gel electrophoresis (PFGE) or denaturing highperformance liquid chromatography and DNA sequencing of gene fragments to identify rare single nucleotide polymorphisms (SNPs) that define specific haplotypes, some of them related to specific geographic regions in Indonesia [2,16].

Our study demonstrates the utility of IS200 amplification combined with MLST in the assignment of Typhi serovars.

IS200 amplification enables rapid screening to support, or not, a Typhi affiliation. MLST sequence analysis can then be conducted for further characterization of the strains of interest. As whole-genome sequencing becomes cheaper, it will be possible to use the data to extract the MLST of the strains and integrate the genomic sequences into the MLST framework

1. Achtman M, Wain J, Weill FX, Nair S, Zhou ZM, Sangal V, Krauland MG, Hale JL, Harbottle H, Uesbeck A, Dougan G, Harrison LH, Brisse S, Group SEMS (2012) Multilocus sequence typing as a replacement for serotyping in Salmonella enterica. PLoS Pathogens 8 2. Baker S, Holt K, van de Vosse E, Roumagnac P, Whitehead S, King E, Ewels P, Keniry A, Weill FX, Lightfoot D, van Dissel JT, Sanderson KE, Farrar J, Achtman M, Deloukas P, Dougan G (2008) High-throughput genotyping of Salmonella enterica serovar Typhi allowing geographical assignment of haplotypes and pathotypes within an urban District of Jakarta, Indonesia. J Clin Microbiol 46:1741-1746 3. Beltran P, Musser JM, Helmuth R, Farmer JJ 3rd, Frerichs WM, Wachsmuth IK, Ferris K, McWhorter AC, Wells JG, Cravioto A, et al. (1988) Toward a population genetic analysis of Salmonella: genetic diversity and relationships among strains of serotypes S. choleraesuis, S. derby, S. dublin, S. enteritidis, S. heidelberg, S. infantis, S. newport, and S. typhimurium. Proc Natl Acad Sci USA 85:7753-7757 4. Calva E, Ordonez LG, Fernandez-Mora M, Santana FJ, Bobadilla M, Puente JL (1997) Distinctive IS200 insertion between gyrA and rcsC genes in Salmonella typhi. J Clin Microbiol 35:3048-3053 5. Cooper JE, Feil EJ (2004) Multilocus sequence typing–What is resolved?

Trends Microbiol 12:373-377 6. Crump JA, Luby SP, Mintz ED (2004) The global burden of typhoid fever. Bull World Health Organ 82:346-353 7. Fabre L, Le Hello S, Roux C, Issenhuth-Jeanjean S, Weill FX (2014) CRISPR is an optimal target for the design of specific PCR assays for Salmonella enterica serotypes Typhi and Paratyphi A. PLoS Negl Trop Dis 8:e2671 8. Feil EJ, Li BC, Aanensen DM, Hanage WP, Spratt BG (2004) eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data. J Bacteriol 186:1518-1530

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9. Guinee PA, Jansen WH, Maas HM, Le Minor L, Beaud R (1981) An unusual H antigen (Z66) in strains of Salmonella typhi. Ann Microbiol 132:331-334 10. Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser 41:95-98 11. Holt KE, Parkhill J, Mazzoni CJ, Roumagnac P, Weill FX, Goodhead I, Rance R, Baker S, Maskell DJ, Wain J, Dolecek C, Achtman M, Dougan G (2008) High-through put sequencing provides insights into genome variation and evolution in Salmonella Typhi. Nat Genet 40:987-993 12. Kauffmann F (1966) The bacteriology of Enterobacteriacea. Williams & Wilkins, Baltimore, MD, USA 13. Kidgell C, Reichard U, Wain J, Linz B, Torpdahl M, Dougan G, Achtman M (2002) Salmonella typhi, the causative agent of typhoid fever, is approximately 50,000 years old. Infect Genet Evol 2:39-45 14. Kubota K, Barrett TJ, Ackers ML, Brachman PS, Mintz ED (2005) Analysis of Salmonella enterica serotype Typhi pulsed-field gel electrophoresis patterns associated with international travel. J Clin Microbiol 43:1205-1209 15. Maiden MC, Bygraves JA, Feil E, Morelli G, Russell JE, Urwin R, Zhang Q, Zhou J, Zurth K, Caugant DA, Feavers IM, Achtman M, Spratt BG (1998) Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms.

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16. Moehario LH (2009) The molecular epidemiology of Salmonella Typhi across Indonesia reveals bacterial migration. J Infect Dev Ctries 3:579-584 17. Perez-Losada M, Cabezas P, Castro-Nallar E, Crandall KA (2013) Pathogen typing in the genomics era: MLST and the future of molecular epidemiology. Infect Genet Evol 16:38-53 18. Roumagnac P, Weill FX, Dolecek C, Baker S, Brisse S, Chinh NT, Le TA, Acosta CJ, Farrar J, Dougan G, Achtman M (2006) Evolutionary history of Salmonella Typhi. Science 314:1301-1304 19. Sambrook J, Russell DW (2001) Molecular cloning. A laboratory manual. 3rd ed. Vol. 1.Cold Spri ng Harbor Laboratory Press, NY, USA 20. Selander RK, Beltran P, Smith NH, Helmuth R, Rubin FA, Kopecko DJ, Ferris K, Tall BD, Cravioto A, Musser JM (1990) Evolutionary genetic relationships of clones of Salmonella serovars that cause human typhoid and other enteric fevers. Infect Immun 58:2262-2275.

21. Tien YY, Ushijima H, Mizuguchi M, Liang SY, Chiou CS (2012) Use of multilocus variable-number tandem repeat analysis in molecular subtyping of Salmonella enterica serovar Typhi isolates. J Med Microbiol 61:223-232 22. Torpdahl M, Skov MN, Sandvang D, Baggesen DL (2005) Genotypic characterization of Salmonella by multilocus sequence typing, pulsedfield gel electrophoresis and amplified fragment length polymorphism. J Microbiol Methods 63:173-184.

RESEARCH ARTICLE International Microbiology (2015) 18:105-115 doi:10.2436/20.1501.01.240. ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

Aquatic bacterial assemblage structure in Pozas Azules, Cuatro Cienegas Basin, Mexico: Deterministic vs. stochastic processes Laura Espinosa-Asuar,1 Ana Elena Escalante,2 Jaime Gasca-Pineda ,1 Jazmín Blaz ,1 Lorena Peña,1 Luis E. Eguiarte,1 Valeria Souza1* Department of Evolutionary Ecology, Autonomous University of Mexico, Mexico City, Mexico. 2 National Laboratory of Sustainability Sciences, Institute of Ecology, Autonomous University of Mexico, Mexico City, Mexico

1

Received 29 April 2015 · Accepted 4 June 2015

Summary. The aim of this study was to determine the contributions of stochastic vs. deterministic processes in the distribution of microbial diversity in four ponds (Pozas Azules) within a temporally stable aquatic system in the Cuatro Cienegas Basin, State of Coahuila, Mexico. A sampling strategy for sites that were geographically delimited and had low environmental variation was applied to avoid obscuring distance effects. Aquatic bacterial diversity was characterized following a cultureindependent approach (16S sequencing of clone libraries). The results showed a correlation between bacterial beta diversity (1-Sorensen) and geographic distance (distance decay of similarity), which indicated the influence of stochastic processes related to dispersion in the assembly of the ponds’ bacterial communities. Our findings are the first to show the influence of dispersal limitation in the prokaryotic diversity distribution of Cuatro Cienegas Basin. [Int Microbiol 2015; 18(2):105-115] Keywords: bacterial assemblage structure · bacterial diversity · biogeography · inland waters· Cuatro Cienegas, Mexico

Introduction The causes of the observed distribution of microbial diversity have been the subject of investigation in microbial ecology for almost two decades. To date, two main groups of causative factors are under consideration: (i) those in which dispersal limitation, neutral assembly, and mass effects, also known as

Corresponding author: V. Souza Departamento de Ecología Evolutiva Universidad Nacional Autónoma de México Mexico City, Mexico Tel. + 52-5556229006. Fax +52-5556228995.

E-mail: souza@servidor.unam.mx *

stochastic processes, are preponderant and (ii) those in which local adaptation to environmental conditions, also known as deterministic processes, plays the larger role.

Biogeography is the study of organisms in space and time. It provides an understanding of the underlying causes of their distribution and ultimately of the drivers that give rise to biodiversity [28]. Traditionally, biogeographical studies have been conducted on macroorganisms, but in the past two decades the focus has expanded to include microorganisms, which have been investigated through culture-independent approaches. These studies have advanced the current understanding of the mechanisms that generate and maintain microbial diversity [15,29].

However, they have also generated debate regarding the processes that lead to the assembly of microbial communities, as both stochastic and deterministic models have been proposed.

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To investigate the biogeographical patterns in microorganisms, Martiny and colleagues [28] introduced a framework with which to provide evidence of the potential causes of microbial diversity. Four scenarios explaining microbial distribution were proposed: (i) a random scenario, as the null hypothesis, in which microorganisms are arbitrarily distributed over space; (ii) a deterministic hypothesis, in which the observed distribution reflects differences in the contemporary environment; (iii) the existence of dispersal limits for bacteria, in which the dispersal history is reflected in the beta diversity (dissimilarity in composition) coupled with geographic distances; and (iv) an association between stochastic and deterministic processes. To properly test these four hypotheses, sampling design is critical. Thus, samples must be obtained from several locations that are geographically delimited and from systems with low environmental variation, to avoid obscuring distance effects [1].

The Cuatro Ciénegas Basin (CCB) is part of the Chihuahuan Desert (State of Coahuila, Mexico) (Fig. 1) and has been a protected wetland since 1994 (APFF, according to Mexican Federal Law). Despite its extreme oligotrophy [42], the CCB harbors a high microbial diversity in different environments, including soil [26,27], microbial mats [2,35], and water [33,34,42]. Studies conducted in the CCB investigated the association between the distribution of microbial diversity and both spatial and environmental variables. Geographical associations were not detected at either the community [12,26,27] or the population [4,39,40] level. However, sampling in those studies was performed in contrasting environmental systems, which probably interfered with the ability to detect statistically relevant geographic associations [1].

Thus, in the present study we designed a sampling scheme in non-contrasting and equivalent environments that allowed us to distinguish the influence of stochastic vs. deterministic processes in the observed distribution of microbial diversity.

Specifically, we used a culture-independent approach to study the microbial diversity in an aquatic system, the Pozas Azules This system consists of many small, isolated, circular ponds of different diameters (6–50 m) and with maximum depths of 10 m. Locally, they are referred to as “pozas” (= wells or pits) (Fig. 1B). Both the temperature and the chemical properties of these ponds were previously shown to be stable over time [20,35]. Thus, the spatial distribution of the Pozas Azules is ideal for biogeographic exploration, as the ponds are essentially aquatic islands in a sea of land [9]. Because many of the reported spatial scales that have shown a historically contingent assembly (geographical association) in bacterial communities have been small (~1 km) [15], we selected four nearby

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ponds (maximum distance of ~1.8 km) with equivalent temperatures and low variations in pH and conductivity.

In accordance with the four scenarios proposed by Martiny et al. [28], we present evidence supporting an association between biogeographic patterns and the geographic distance of bacterial assemblages in Pozas Azules. This implies that bacterial dispersion at the CCB is limited, even at a scale of < 2 km. Thus, our study supports the third hypothesis: that community-structure patterns reflect stochastic processes related to dispersion.

Material and methods Study site and sampling. The four ponds of the Pozas Azules aquatic system were sampled in February, 2007. Pozas Azules is located in the southwestern flank of the San Marcos Sierra, within the CCB (Fig. 1A), and has been part of a private preserve ranch since 2000 (Pronatura A.C. and the Nature Conservancy). A recent general description of the hydrology of the CCB referred to the Pozas Azules as a karstic system, rich in CaHCO3. Its waters are a mixture of mountain recharge and carbonate aquifer groundwater [45].

A preliminary exploration of the ponds by our group showed that conductivity ranged from 1900 to 4500 mS cm–2.

Our study was carried out in four GPS-referenced neighboring ponds (A–C, F). These ponds were visually similar (clear blue water; Fig. 1B), not visibly connected (maximum separation of 1.8 km; Fig. 1C), and had a diameter of 10–30 m. The pH and conductivity of the selected ponds ranged from 7.5 to 7.8 and from 1900 to 2800 mS cm–2, respectively. In a preliminary analysis using terminal restriction fragment length polymorphism (T-RFLP), different samples taken from the same pond at the same time were very similar (data not shown). The relative homogeneity of the ponds in this area is due to the presence of a springtime stream that, together with the wind, mixes the waters and thus reduces spatial structuring—as suggested by for other aquatic environments [24]; the process is aided by the small size of the ponds.

Based on these preliminary data, we concluded that a composite sample of each pond would adequately represent the bacterial community. Five sites, four near the edge (equidistant) and one at the center of the pond, were sampled in each of the four ponds. All samples consisted of 2.5 l of water collected 50 cm below the surface. The five samples from each pond were transferred into a single clean and sterile container (composite sample) and preserved at 4°C until processed a few hours later. The composite samples were then filtered and divided into subsamples for DNA (three 4-l subsamples) and nutrient (three 100-ml subsamples) analyses. Water samples for the DNA analysis were filtered first through a 10-µm Durapore filter and then through a 0.22-µm Durapore filter (Millipore, USA) using a field vacuum pump. The filters were placed into sterile 2-ml Eppendorf tubes, which were preserved in liquid nitrogen and transferred to the laboratory, where they were stored at −80°C until DNA extraction. Water samples for the nutrient analysis were filtered through a 0.22-µm nitrocellulose filter; the filtrate was preserved at 4°C until processing.

Physicochemical and nutrient analyses. Total dissolved solids (TDS), pH, salinity, conductivity (Cond), temperature, and dissolved oxygen were measured in the field at each of the five sampling points using a Hydrolab YSI600QS (YSI, Yellow Springs, OH, USA). Total carbon (TC) and inorganic carbon (IC), total nitrogen (TN) and dissolved inorganic nitrogen (DIN or NH4+), nitrite (NO2–), and total phosphorus (TP) and inorganic phospho-

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Fig. 1. The Pozas Azules system. (A) Map of MĂŠxico displaying the location of the Cuatro CiĂŠnegas Basin (CCB) and the Pozas Azules system (squares) (modified from Rebollar et al. 2012). (B) Aerial view of pond C and the nearest ponds. The circular form of the pools can be visibly appreciated. Clear blue ponds associated with low conductivity values, such as pond C, were selected for this study (photograph used with permission of APFFCC). (C) Spatial distribution of the sampling sites at Pozas Azules (ponds A, B, C, and F); dashed lines show the distance between the sites in kilometers (Google Earth image). (D) Macroscopical aspect of one Poza azul.

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rous (PO3) were determined in the laboratory following standard procedures [35], using triplicate 100-ml subsamples from the composite samples. Dissolved organic carbon (DOC), phosphorus (DOP), and nitrogen (DON) concentrations were determined as the difference between the respective total values and the values of the inorganic components.

DNA isolation and sequencing of 16S rRNA clone libraries. Genomic DNA was extracted from the filtered samples (three replicates per pond) using an UltraClean water DNA kit (MoBio, Carlsbad, CA, USA) that included a final phenol-chloroform purification procedure. Each of the three DNA replicates per pond were independently subjected to PCR amplification targeting the 16S rRNA gene fragments of the bacterial domain using the universal primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTACGACTT-3′) [22]. The three PCR products of each pond were mixed prior to the cloning reaction. The cloning reaction, plasmid extraction, sequencing, and PCR conditions were performed as previously reported [33]. One clone library was generated per pond (n = 80±10).

Partial sequences of the 16S rRNA gene were obtained using the primer 27F. Sequence analysis. Sequences were trimmed and manually checked using BioEdit version 7.0.9.0 [14] . Sequences with an average length of 700 bp were aligned with the SILVA database and the NAST aligner [8] using Mothur v.1.31.2 [41] , they were then manually checked. Chimeric sequences were identified using Bellerophon software [17], UCHIME v4.2.40 [11], and DECIPHER [46] and were discarded from the dataset. Unique operational taxonomic units (OTUs) were defined with a threshold of 97% sequence similarity and were identified using Mothur v.1.31.2 [41].

Diversity and statistical analyses. Each OTU was phylogenetically identified using the classifier tool implemented on the Ribosomal Database Project (RDP) website [44]. Once unique sequences (the OTUs) were determined, their phylogenetic identities were confirmed using a neighborjoining analysis (500 bootstrap replicates) in MEGA v.5 [43] and a maximum-likelihood analysis (1000 bootstrap replicates) using a TIM3+I+G model as determined by the JModel test [7] in PhyML v3.0 [13]. After the total number of unique OTUs was defined, a matrix that included all of the OTUs, their abundance, and the sampling sites was constructed and then used in the diversity, ordination, and correlation analyses described below.

Diversity analyses. To evaluate sampling efforts and to compare diversity among sampling sites, we determined coverage values, constructed rarefaction curves, and estimated alpha diversity per pond with the Chao 1, Shannon, and Simpson diversity indices for all samples. Coverage values were determined using the Good estimator, following the equation C = (1 - n ⁄N) × 100, where C is the percentage of coverage of the library, n is the number of singletons, and N is the total number of clones analyzed [37]. Beta diversity was calculated using the Sorensen binary index (using dissimilarity 1-Sorensen). Rarefaction curves and diversity indices were calculated using Mothur v.1.31.2 [41].

Ordination analyses. Multivariate and ordination approaches were used to visualize patterns in the distributions of environmental variables and to evaluate their correspondence with the OTU distributions [38]. A principal component analysis (PCA) was performed to visualize the environmental variance of the sampling sites. The rda function was carried out using the Vegan package [32] of R, version 3.0.1 (2013-05-16) [36]. In addition, a canonical correspondence analysis (CCA) was performed to model species’ responses to environmental variables [13] using OTU abundance and standardized environmental data. The CCA function was implemented in the Ade4 package of R [10].

To standardize the environmental variables, raw data were transformed into z-scores using the decostand function (standardized method) implement-

ed in R (Vegan package) [32]. Non-redundant and explanatory environmental variables (except the geographic location) were identified using three statistical criteria for the standardized environmental variables: the PCA loadings (> 0.70), a paired correlation between variables (Pearson), and the colldiag index (>50; the table is available upon request). Ecological relevance was considered as well. The first component of the PCA accounted for 69.2% of the total variation and mainly involved DOC, Sal, IC, TDS, and Cond. Of these five variables, correlations between DOC, Sal, and IC were determined (data available upon request); therefore, these three variables were reduced and only IC was used as the representative explanatory variable. Likewise, TDS was discarded because it correlated with Cond, since both are derived measures. Consequently, from the first PCA component only IC and Cond were selected. The remaining four variables were retained either to explore nutrient variables (NO2–, TP) or because of their ecological relevance in determining aquatic bacterial communities (pH and TC) [25]. All of these calculations were performed using R packages as follows: The correlation between the variables was explored using the cor function of the Stats R package; graphics (order.single function) were created with Gclus [19] using the panelutils.r script. Condition indexes and variance decomposition proportions (colldiag function) were generated in Perturb v.2.05 [16].

Correlation analyses. To statistically determine the relative importance of geographic and contemporary environmental conditions, correlation analyses were conducted using OTU incidence data and environmental and geographical variables. These analyses were performed with functions implemented in the Vegan package [32] in R, unless otherwise specified. Three kinds of data matrices were constructed: a biological matrix composed of the 1-Sorensen index (estimated with the vegdist function), an environmental matrix using selected environmental variables (standardized z-scores) and constructed by calculating the Euclidian distance (dist function, Stats R package [36], and a spatial matrix that contained distances between ponds as calculated from the coordinate data.

The effects of geographic distance vs. environmental dissimilarity on the composition of the assemblages (beta-diversity) were tested in pairwise Mantel tests among the three matrices using the Mantel function (Pearson’s correlation and 9999 permutations). Environmental variables that best correlated with community dissimilarities (biological matrix) were identified using the BIOENV function [Pearson correlation and 1-Sorensen (Bray binary) arguments].

Nucleotide sequence accession numbers. The sequences were deposited in GenBank under the accession numbers KJ998817 to KJ999144.

Results OTU composition: moderate turnover and phylum dominance. The diversity estimated at the OTU and phylum levels was distributed differently among the sampling sites. At 97% sequence similarity, 48 OTUs, distributed across 10 phyla, were identified (Fig. 2A). Of these, 17 OTUs were shared (i.e., they occurred in more than one pond; Fig. 2B).

Few OTUs were present in abundance (>15 sequences per OTU) in the four ponds but many OTUs were rare (one or two sequences each; Fig. 2B). OTUs that were abundant in one pond were not exclusive and were generally found in the other three ponds, but at lower numbers (Fig 2B). At the phylum

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Fig. 2. Taxonomic description of the Pozas Azules bacterial communities. (A) Neighbor-joining tree of representative 16S rDNA gene sequences. The representative 48 OTUs of the sequences are defined at a 0.03 distance cutoff. The numbers after the OTU names correspond to sequence abundance (ML phylogeny gave very similar topologies; data not shown). (B) OTU abundance bars; each refers to a different pond. The F_49 OTU abundance bar is out of scale. (C) Relative abundance (in percent) of bacterial phyla from the clone library data of the four ponds. Each bar refers to a different pond. The number of clones obtained from each site (n) is indicated.

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level, differences between ponds were determined (Fig. 2C).

Thus, the most abundant phyla were actinobacteria in pond A, betaproteobacteria in pond B, cyanobacteria in pond C, and verrucomicrobia in pond F. To describe the compositional differences between the ponds, a pairwise dissimilarity index (1-Sorensen, which ranges from 0, i.e., the identical composition of the samples, to 1, no similitude among the samples) based on OTU composition was calculated, followed by the average of all of the paired comparisons (0.548 ± 0.0973).

The latter indicated moderate turnover between ponds.

Despite the fact that the rarefaction curves did not reach an asymptote (Fig. 3A), the coverage value for each gene library exceeded 80%. The diversity comparisons among sampling sites was compared based on different indices; rarefaction curves were then used to compare species richness in samples of different sizes. For the majority of indices (Chao 1, Shannon, and Simpson; Fig. 3B) and rarefaction curves (Fig. 3A), ponds A and B were the most diverse whereas pond F was the least diverse. Given the small number of sequences per sample, only the most abundant taxa per sample were recovered, which in the determination of biodiversity distribution patterns was previously shown to be robust [49,50] and thus valid despite the limited sampling depth.

Low environmental variation across aquatic ponds. Environmental variables among ponds did not show

Fig. 3. Diversity-related data of bacterial communities in Pozas Azules CCB. (A) Rarefaction curves showing the 95% confidence interval of 16S rDNA gene sequences for each pond. The number of OTUs detected (at 97% cutoff) vs. the number of sequences analyzed per pond is shown. (B) The diversity data obtained with 16S rRNA sequences for each pond (OTUs at 97% cutoff).

major differences. For example, conductivity and pH ranged from 1940 to 2883.2 µS cm–2 and from 7.53 to 7.81, respectively (Table 1). Due to the extreme oligotrophy of these sites, the concentrations of some nutrients were below the limit of detection (Table 1). As shown in the PCA including all environmental variables, environmental variation was present across ponds (Fig. 4). The first component of the PCA explained 69.2% of the variation, which divided the ponds into two groups, pond F and ponds A–C, based on differences in Cond, Sal, TDS, IC, and DOC (Table 1).

Stochastic processes influencing the bacterial assemblages of Pozas Azules. The association between OTU composition and environmental variables was explored with a CCA using OTU abundance and environmental parameter data for each pond. The 15 original environmental variables (Table 1) were reduced to six non-redundant explanatory variables (except geographic location) based on the three statistical criteria described in the Methods section. The final environmental variables used in the analyses were TC, IC, TP, Cond, NO2–, and pH. Many OTUs were detected in only one of the four ponds. Although this may have been due to the low sequencing coverage, the abundances of these OTUs permitted their association with specific environmental variables (CCA data are available upon request).

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Table 1. Environmental data obtained for each pond Pond A

B

C

F

Latitude

N 26 49´24.4´´

N 26 49´31.8´´

N 26 49´34.7´´

N 26 48´37.08´´

Longitude

W 102 00´53.2´´

W 102 01´5´´

W 102 01´22.8´´

W 102 00´47.64´´

7.76 (0)

7.53 (0)

7.712 (0.09)

7.81 (0.10)

Cond (µS cm )

2501.6 (8.73)

2529 (3.67)

2883.2 (3.63)

1940 (0)

Sal (mg/l)

8.70 (0.53)

8.51 (1.24)

7.66 (0.82)

3.18 (0)

TDS (ppt)

1.3 (0)

1.3 (0)

1.5 (0)

1 (0)

TC (mg/l)

120.265(0.54)

109.22 (14.03)

110.41 (19.36)

102.28 (3.84)

IC (mg/l)

120.265 (0.54)

117.51 (7.13)

111.28 (18.12)

41.87 (1.38)

DOC (mg/l)

0

0

0

60.4 (3.54)

TP (mg/l)

0.83 (0.090)

0.89 (0.018)

0.87 (0.051)

0. 92 (0.025)

IP (PO3) (mg/l)

0

0

0

0

DOP (mg/l)

0.83 (0.090)

0.89 (0.018)

0.87 (0.051)

0. 92 (0.025)

TN (mg/l)

0

0

0

0

DIN (NH4+) (mg/l)

0

0

0

0

DON (mg/l)

0

0

0

0

NO2 (mg/l)

0

0

0.0026 (0.0015)

0

pH –2

−

Nutrient and other environmental data except for coordinates are the mean values. Standard deviations are in parentheses. (See Material and M methods for abbreviations.)

The relative importance of geographic (stochastic) and contemporary environmental (deterministic) conditions in the distribution of microbial diversity was determined in correlational analyses using environmental and geographical variables and by using the OTU incidence data. Environmental and geographic distance matrices were independently tested for their correlation with the biological distance matrix (differences among sites were represented by the dissimilarity index 1-Sorensen), using Mantel tests. There was no significant association between the biological distance matrix and the environmental matrix (P = 0.1671, r = 0.5876), whereas the biological distance matrix correlated significantly with geographic distance (P = 0.04117, r = 0.7979). To further evaluate the environmental variables that best correlated with community differences (i.e., the biological matrix), a BIOENV test was performed that identified TC, NO2–, pH, and Cond as the best-fitted variables; however, a Mantel correlation for these variables was not statistically significant (P = 0.0862, r = 0.1635).

Overall, our results provide evidence of a statistical correlation between bacterial composition and geographic distance.

Discussion Diversity distribution studies have been used to investigate the influence of geographic and environmental factors in biogeographic patterns. For many years, the paucity of biogeographical studies of microbial diversity hindered an understanding of the ecological and evolutionary processes shaping diversity at the microbial scale. However, technological advances developed in the past two decades have allowed explorations of microbial diversity in natural environments and the testing of hypotheses regarding the factors determining its distribution.

To distinguish deterministic from stochastic dynamics, sampling design is critical. Specific environments should be chosen from locations that are geographically delimited and have low environmental variation, to avoid obscuring distance effects [1]. In this study, we adopted a culture-independent approach with a sampling strategy that minimized environmental variation, by looking at bacterial diversity in four ponds within the Pozas Azules aquatic system in the CCB, Mexico. Our findings show the influence of stochastic pro-

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cesses in the distribution of prokaryotic diversity in the CCB. OTU composition: moderate turnover and dominance by a particular phylum. The phylogenetic analysis of microbial diversity showed one numerically dominant phylum in each pond that could nonetheless be found in all of the other ponds, albeit in lower numbers. These results provide further evidence of the complexity of bacterial community assembly mechanisms, in which “no two, naturally assembled bacterial communities appear to be the same” [6]. The average beta diversity between ponds indicated that nearly 50% of the OTUs were shared among them. The sharing of these taxa (many of them also abundant) is likely due to the fact that they are widely dispersed and likely to be generalist bacteria [23]. In fact, the same phyla have been reported in aquatic systems of the CCB valley [12,33,34] and in other freshwater systems [48]. Specifically, Actinobacteria are widely distributed in freshwater lakes, because of their dispersal capacity and ability to form spores [30]. Another potential generalist group shared among the studied ponds was Cyanobacteria, of which Chlorococcales [33] is the most abundant order in freshwater lakes [30]. Alphaproteobacteria (Rhodobacteriaceae) are common taxa of water bodies (e.g., OTU A_2) [5] and Betaproteobacteria predominated in the mesocosm experiments of Langenheder and Székely [23]. The most abundant verrucomicrobial OTU in our study (OTU F_49) was that of Luteolibacter, a widely distributed, non-motile bacterium [47].

In contrast to the dominant and shared phyla, other groups, such as Bacteroidetes, although shared, were not abundant, despite being registered as ubiquitous in aquatic environments [31]. We also found OTUs that were exclusive to one sampling site, even though most of them are highly abundant worldwide (e.g., the Hyphomicrobiaceae OTU A2_21) [18]. Although this discrepancy may be a consequence of the low sequencing coverage, whether shared or not these potentially generalist taxa occurred in different abundances across the four ponds. Langenheder and Székely [23] suggested that generalist groups usually represent neutrally assembled taxa.

Since it has been reported that patterns of abundance and diversity of bacterial communities are not substantially different whether or not rare taxa are included [49,50], the interpretation and discussion of the data and the observed patterns presented here are valid, even with the limited sampling.

Local factors in the Pozas Azules system: low environmental variation. There were no major variations in the environmental conditions of the sampled ponds.

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The similarity of the environmental variables at the study site was essential to statistically distinguish the influence of stochastic processes with respect to diversity distribution [1].

The 15 measured environmental variables were reduced to six non-redundant explanatory variables. Of these, the four variables selected by the BIOENV test (TC, Cond, NO2−, and pH), while not statistically correlated with community dissimilarities among ponds, were previously reported as important elements shaping the bacterial communities of lakes [25]. However, given our sampling strategy, in which the sites were selected based on the minimal environmental variation between them, a statistical correlation of these variables with assemblage dissimilarity was not expected. Despite the low environmental variability, one pond (F) deviated from the other three (Fig. 4). Thus, the use of finer measurements at smaller spatial scales may allow the detection of environmental variation, which can in turn explain otherwise confounding observations, such as differences in the abundance of some phyla between ponds.

Stochastic processes influence the Pozas Azules bacterial assemblages. Our sampling strategy allowed us to independently evaluate the correlation between community dissimilarity among the ponds and geographic distance or environmental variables.

With respect to the environmental variables, the Mantel correlation test was not statistically significant, and in the CCA analysis most OTUs were not associated with any specific environmental condition. These non-conclusive correlations and associations were not surprising, given that the ponds were selected based on their minimal environmental variability. In terms of geographic effects on community composition, our results showed an association between aquatic bacterial composition and the geographic distances among the ponds.

Several studies have described the bacterial diversity in the CCB [2,4,12,26,27,33–35,39,40,42], but ours is the first to analyze environmentally similar sites to specifically determine the contribution of stochastic processes (dispersal limitation and neutral assembly) vs. deterministic processes (physicochemical conditions) to the observed distribution of microbial diversity. Two previous studies examined aspects of the microbial diversity from Pozas Azules [33,34], but their focus was the effect of environmental changes on the composition and diversity of bacterial communities, as determined in a long-term mesocosm experiment. The samples used in the studies of Pajares and colleagues were collected 2 years later than our samples [33,34]. One of the ponds ana-

Fig. 4. Biplot generated from a principal component analysis (PCA) of the standardized environmental variables. The four ponds (A, B, C, and F) are shown. Vectors show scaled environmental variables. The first component of the PCA analysis accounted for 69.2% of the total variation, mainly involving DOC, Sal, IC, and Cond. (See Materials and methods for abbreviations.)

lyzed here was also included in their studies. It should be noted that ~50% of this study’s OTUs were detected in the bacterial communities reported by those authors. This coincidence in OTU composition suggests the existence of a common pool of successful bacterial taxa whose presence is constant over time. If these taxa consist of generalists this would explain the neutrally assembled dynamics observed in this study [23]. Our results also suggest that the community dynamics in these ponds are associated with life-history traits— as reported by Nemergut et al. [29]—which are dependent on dispersion capacity, and with other bacterial ecological attributes (generalist or specialist). Thus, rare and slow-growing taxa would have no geographic association in aquatic communities [23], as was the case for the slow-growing phylum Planctomycetes in our samples (data not shown) [21].

The geographical pattern found in this study is consistent with the third hypothesis of Martiny et al., which states that there are limits to the dispersal of bacteria and that this dispersal history is reflected in the correlation between beta-diversity and geographic distances.

Conclusions and perspectives. Our results provide evidence of the primary importance of stochastic processes in determining the composition of aquatic bacterial communities in the Pozas Azules system. By using a sampling design that minimized the effect of environmental variability, we were

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able to show a correlation between beta diversity (1-Sorensen) and geographic distance. However, this evidence is suggestive, not conclusive, given the limited sample number. A larger number of samples and greater sequence coverage through a high-throughput sequencing approach should be part of future explorations of the CCB’s aquatic bacterial communities.

Many of the OTUs detected in Pozas Azules were similar to generalist taxa, which are usually neutrally assembled. More advanced investigations into the causes of the diversity distribution of prokaryotes should include controls for environmental variation (for example, using both contrasting and similar environmental sampling sites), to test the relative influence of stochastic and deterministic processes, and he study of specialist or rare taxa within the total communities (using specific primers or PhyloChip-designed approaches).

Finally, the inclusion of life-history aspects, such as relative abundance, dispersion capacities, and ecological range (generalist vs. specialist), will lead to a better understanding of the processes underlying biogeographical patterns.

Acknowledgements. We are grateful to Felipe García Oliva (CIEco, Biogeoquímica de Suelos) for nutrient analysis of the samples. We thank Juan Carlos Ramírez Gloria (PRONATURA), Eria Rebollar, Morena Avitia, Esmeralda López-Lozano, René Cerritos, Enrique Scheinvar, and Germán Bonilla for field assistance, and especially PRONATURA Noreste for access to the Pozas Azules ranch. We also thank Juan Carlos Ibarra Flores from Área de Protección de Flora y Fauna Cuatro Ciénegas (APFFCC) for letting us use their aerial photographs of Pozas Azules. We are grateful to Eria Rebollar for

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reviewing the manuscript and to Laura Figueroa and Santiago Ramírez Barahona for their collaboration in the realization of the figures. Thanks also go to Dr. Erika Aguirre for technical support and language corrections to the manuscript. The project was supported by grants 0237A1 Secretaría de Educación Pública-Consejo Nacional de Ciencia y Tecnología (SEP-CONACyT) and the World Wildlife Fund (WWF)-Alianza Carlos Slim. This paper is part of the doctoral dissertation of the first author, who thanks the Posgrado en Ciencias Biomédicas (Universidad Nacional Autónoma de México) and CONACyT grant no. 113997, for financial support.

Competing interests. None declared.

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RESEARCH ARTICLE International Microbiology (2015) 18:117-125 doi:10.2436/20.1501.01.241. ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

Characterization of a S-adenosyl-l-methionine (SAM)accumulating strain of Scheffersomyces stipitis Stela Križanović,1 Ana Butorac,2 Jasna Mrvčić,1 Maja Krpan,1 Mario Cindrić,3 Višnja Bačun-Družina.2 Damir Stanzer1* Laboratory for Fermentation and Yeast Technology, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia. 2Laboratory for Biology and Microbial Genetics, Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia. 3Laboratory for System Biomedicine and Centre for Proteomics and Mass Spectrometry, Ruder Bošković Institute, Zagreb, Croatia

1

Received 3 May 2015 ·Accepted 9 June 2015

Summary. S-adenosyl-l-methionine (SAM) is an important molecule in the cellular metabolism of mammals. In this study, we examined several of the physiological characteristics of a SAM-accumulating strain of the yeast Scheffersomyces stipitis (M12), including SAM production, ergosterol content, and ethanol tolerance. S. stipitis M12 accumulated up to 52.48 mg SAM/g dry cell weight. Proteome analyses showed that the disruption of C-24 methylation in ergosterol biosynthesis, a step mediated by C-24 sterol methyltransferase (Erg6p), results in greater SAM accumulation by S. stipitis M12 compared to the wild-type strain. A comparative proteome-wide analysis identified 25 proteins that were differentially expressed by S. stipitis M12. These proteins are involved in ribosome biogenesis, translation, the stress response, ubiquitin-dependent catabolic processes, the cell cycle, ethanol tolerance, posttranslational modification, peroxisomal membrane stability, epigenetic regulation, the actin cytoskeleton and cell morphology, iron and copper homeostasis, cell signaling, and energy metabolism.

[Int Microbiol 2015; 18(2):117-125] Keywords: Scheffersomyces stipitis · S-adenosyl- l-methionine (SAM) · SAM accumulating yeast · C-24 sterol methyltransferase (Erg6p)

Introduction S-adenosyl-l-methionine (SAM, also known as AdoMet) is a catalytic and synthetic cofactor in enzymatic reactions and participates in many biological processes [21], including as a methyl group donor for methyltransferase reactions in the biosynthesis of nucleic acids, proteins, phospholipids, and Corresponding author: D. Stanzer Faculty of Food Technology and Biotechnology University of Zagreb Pierottijeva 6 10000 Zagreb, Croatia Tel. + 385-14605286. Fax +385-14605072 E-mail: dstanzer@pbf.hr *

sterols [20]. SAM has found wide application in medicine as a chemotherapeutic agent in the treatment of a broad range of diseases, including depression, liver disease, Lesch-Nyhan disease, Alzheimer´s disease, and diarrhea [26]. These promising therapeutic results have increased the demand for SAM. The yeasts Saccharomyces cerevisiae, Saccharomyces sake, Pichia pastoris, Saccharomyces uvarum, Kluyveromyces lactis, Kluyveromyces marxianus, and Candida species have been studied for their SAM-producing ability [5,16,24]. Scheffersomyces stipitis, formerly known as Pichia stipitis [14], is a Crabtree-negative yeast able to grow on different carbon, nitrogen, sulfur, and phosphorus sources [3]. This growth flexibility makes it a viable economic candidate for the industrial production of a variety of value-added products, such as SAM.

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Ergosterol biosynthesis is one of many metabolic pathways in which SAM is required [20]. Ergosterol, the major sterol in yeast [8], participates in numerous structural and signaling functions, including membrane permeability, membrane fluidity, the activity and distribution of integral proteins, and cell cycle control [7]. Most of the more than 20 proteins that contribute to ergosterol biosynthesis are essential; only five proteins, involved in the final steps of the pathway, are nonessential [8]. Among the latter is C-24 sterol methyltransferase (Erg6p), which catalyzes a late step of ergosterol biosynthesis [27] in which SAM acts as the methyl group donor.

In previous studies, strains of S. cerevisiae and Candida sp. defective in ergosterol biosynthesis were isolated and characterized [7,16,18,24]. In the present work, we characterized a SAM-accumulating strain of S. stipitis (M12) with respect to SAM production, ergosterol content, and ethanol tolerance, and also analyzed its proteomic profile.

Materials and methods Strains and cultivation media. Wild-type Scheffersomyces stipitis BS 5776 was obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ). A SAM-accumulating strain of S. stipitis, M12, was obtained by UV irradiating strain BS 5776 at a dose of 160 J/m2 and subsequent isolation on YPD medium (1% yeast extract, 2% peptone, 2% glucose) containing 15 μg nystatin/ml [13]. YPD medium was used for yeast cultivation and YPDE medium [YPD with 0.5–5% (v/v) ethanol] for ethanol-tolerance testing [11]. The additive effect of d,l-methionine on SAM production was determined by cultivating the yeast on modified O-medium (5% d-glucose, 1% peptone, 0.6% (NH4)2SO4, 0.5% yeast extract, 0.4% KH2PO4, 0.2% K2HPO4, and 0.05% MgSO4· 7 H2O, pH 6) containing 0.6% d,l-methionine [24]. The cells were cultivated in Erlenmeyer flasks placed on a shaker (200 rpm) at 30˚C for 48 h. Stationary-phase cells of S. stipitis strain M12 and of S. stipitis wild-type were harvested by centrifugation at 4000 ×g for 10 min, washed twice with sterilized distilled water, and then analyzed as described below. Growth was measured gravimetrically and expressed as grams of cell dry weight (CDW) per liter.

Determination of SAM and ergosterol in yeast cells. SAM production was quantified using the HPLC method of Valko et al. [29]. The

amount of SAM in the extracted supernatant was analyzed on a ChromSep HPLC SS column (250 × 4.6 mm) using an IonoSpher 5C guard column with the Varian Prostar 230 pumping system and a UV lamp at 260 nm. Yeast sterols were extracted and the ergosterol content was measured following the procedure described by Arthington-Skaggs et al. [1].

Isolation of lipid particles and microsomal fractions. Lipid particles and microsomal protein fractions were isolated as described by Mo et al. [17]. The protein content in the isolated fractions was measured using a Bradford assay.

SDS-PAGE and protein identification by MALDI TOF/TOF mass spectrometry. The microsomal protein fraction was separated by 1-D SDS-PAGE (10% polyacrylamide gel) and then visualized by Coomassie blue staining [15]. Protein bands with a molecular mass of 35–50 kDa were excised from the gel and subjected to tryptic in-gel digestion as described by Shevchenko et al. [23]. After digestion, the extracted peptides were purified on C4 ZipTip columns and evaporated to dryness in a SpeedVac. The dried samples were dissolved in 5 µl of α-cyano-4-hydroxycinnamic acid (5 mg/ml CHCA matrix dissolved in water/MeCN mixture, 1:1 v/v) and spotted onto a metal matrix-assisted laser desorption/ionization (MALDI) plate. Mass spectrometry (MS) was performed using a MALDI time of flight (TOF)/TOF 4800 Plus tandem mass spectrometer (Applied Biosystems) equipped with a 200-Hz, 355-nm Nd: YAG laser. Acquisitions were performed in positive ion reflectron mode. Mass spectra were obtained by averaging 1800 laser shots covering a mass range m/z 800–4000. Internal calibration of the mass range was performed with tryptic autolysis fragments. Sterol methyltransferase was digested in silico to generate a list of peptide ions suitable for further MS/MS analyses (maximum of two trypsin miscleavages). MS/MS analysis was completed under a 1-kV collision energy in positive ion mode with air used as the collision gas. Protein identification and database searching were performed

by GPS Explorer Software v3.6 (Applied Biosystems). The results of the combined ion searches using MS and MS/MS data were matched against the NCBInr using the MASCOT search engine [19]. The parameters were two missed trypsin cleavages and oxidation on methionine within a mass tolerance of 21 ppm. Protein scores > 71 were considered as significant (P < 0.05).

DNA isolation and ERG6 gene sequencing. Chromosomal DNA was prepared using the protocol for yeast Saccharomyces cerevisiae [2].

Polymerase chain reactions (PCRs) were carried out using Hot Start Taq DNA polymerase. The ERG6 gene was sequenced with specific primers (Sigma Aldrich). Both the primer sequences and the annealing temperatures are shown in Table 1. PCR amplification was performed with an Eppendorf Mastercycler EP. PCR-amplified DNA was analyzed by gel electrophoresis (40 min at 8.3 V/cm) on a 1% agarose gel in TBE buffer. The amplified products were purified from the gel using the QIAquick gel extraction kit. Sequencing was carried out on an ABI PRISM 3100-Avant genetic analyzer (Applied

Table 1. Sequences and conditions of oligonucleotide primers used in PCR analysis Scheffersomyces stipitis gene name (gene product)

GenBank accession no.

Primer

Nucleotide sequence (5′ to 3′)

Nucleotide coordinates*

Conditions

ERG6 (Erg6p, C-24 sterol methyltransferase)

gi126257970

ERG6-1F

GTTCCACCGGGCTTCAAAAC

2481588 to 2481607

ERG6-1R

AGCCTGGACACCAAAAGTCT

2482401 to 2482382

ERG6-2F

AGGTCATGTCGAACAGTTCAGT

2480818 to 2480839

ERG6-2R

GTTTTGAAGCCCGGTGGAAC

2481607 to 2481588

1× at 95°C for 4 min 1× at 59°C for 30s 35× at 72°C for 50s 1× at 72°C for 7min

ERG6-3F

AGCCAGAGACCTTTGATGCC

2480596 to 2480615

ERG6-3R

TGCTTAGAGCCCTTTGGAGC

2481052 to 2481033

*Nucleotide coordinates refer to the corresponding gene sequence in the GenBank database.

SAM-ACCUMULATION IN S. STIPITIS

Biosystems) using the same primers as in the genomic DNA amplification.

DNA was verified in both the sense and antisense directions. The sequence was submitted to GenBank with the accession number KR01466.

Sample preparation for comparative proteomic analysis.

The cells were disrupted and lysed with 0.5 mm glass beads at 4°C, followed by centrifugation at 3000 ×g for 10 min at 4°C. The isolated cytosolic proteins were subjected to tryptic digestion (final trypsin concentration of 20 µg/ ml) for 18 h at 37°C.

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collected at constant collision energy of 4 eV. In elevated-energy MS mode, the collision energy was ramped from 20 to 45 eV during each 1-s data collection cycle. Each sample was analyzed in triplicate runs. The mass accuracy of the raw data was corrected using leucine enkephalin (1 ng/μl, 0.4 µl/min flow rate, 556.2771 Da [M + H]+ ), which was infused into a mass spectrometer as a lock mass during sample analysis. The raw data were processed with a ProteinLynx Global Server (PLGS; version 3.0.1; Waters). The UniProt S. stipitis database (release 2015_01, January 2015, 5570 entries) was used for database searches with the following parameters: peptide tolerance 10 ppm, fragment tolerance 0.015 Da, trypsin-missed cleavages 2, and methionine oxidation.

Statistical analysis. The data are presented as the means ± SD of three independent experiments.

Results SAM production. SAM is formed in yeast cells from d,lmethionine and ATP [5]. The effect on the SAM content of S. stipitis strain M12 grown in medium containing 0.6% d,lmethionine was therefore determined (Fig. 1A). In medium

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Ultra performance liquid chromatography (UPLC)–MSE in the analysis of cytosolic proteins. Peptide samples were analyzed by nano-UPLC-MSE using an Acquity UPLC column BEH130 C18 (100 µm × 100 mm; Waters, Milford) and a 60-min gradient of 0.1–99% solvent B (solvent A: 99.9% H2O, 0.1% formic acid; solvent B: 95% acetonitrile, 0.1% formic acid in H2O) on a Waters nanoAcquity UPLC system (flow rate 1 µl/ min) coupled to an ESI-qTOF SYNAPT G2-Si (resolution mode of operation) mass spectrometer (Waters). A pre-column 2G-V/M 5 µm Symmetry C18 trap (180 µm × 20 mm), with a flow rate of 15 µl/min, was used to desalt the samples prior to their separation. LC-MS data were collected in alternating low-energy and elevated-energy (MSE) modes of acquisition. The variables were as follows: positive ion mode, desolvation nitrogen flow 0.6 bar at 150°C, capillary voltage of 3.5 kV, and a cone voltage of 40 V. The spectral acquisition time in each mode was 1 s. In low-energy MS mode, data were

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Fig. 1. (A) SAM and (B) ergosterol contents of Scheffersomyces stipitis strain M12 and S. stipitis wild-type. (C) Effect of ethanol on the growth of S. stipitis M12 strain and S. stipitis wild-type in YPD (without ethanol) and YPDE (with 0.5–5% ethanol) media. The values are the means ± SD of three independent measurements.

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Ergosterol yield. SAM accumulates in yeast strains defective in ergosterol biosynthesis [24]. We therefore measured the ergosterol content in the wild-type and SAM accumulating strain of S. stipitis (Fig. 1B). The ergosterol content was two-fold lower in strain M12 (1.1 mg/g CDW) than in the wild-type (2.2 mg/g CDW) (Fig. 1B).

The effect of ethanol on Scheffersomyces����� stipitis growth. Interruption of the ergosterol biosynthesis pathway in yeast can result in physiological changes in the membrane that affect ethanol tolerance [11]. An analysis of the effect of ethanol on the growth of S. stipitis wild-type and strain M12 was carried out using medium without ethanol (YPD) and media containing various amounts of ethanol [YPDE; 0.5–5 % ethanol] (Fig. 1C). The cell yield in YPD medium was 3.00 g CDW/l for strain M12 and 3.78 g CDW/l for the wild-type. In YPDE medium the total cell yield of the wild-type decreased significantly when the yeast was cultured in medium containing 3% ethanol (1.45 g CDW/l); however, for strain M12 the decrease in total cell yield was already significant in medium containing 1.5% ethanol (1.77 g CDW/l). In YPDE medium containing > 3% ethanol, the growth of M12 was nearly the same as that of the wild-type.

Scheffersomyces stipitis Erg6p protein analysis.

To the best of our knowledge, the expression of Erg6p in S. stipitis has not been reported. Using the NCBI database (www.ncbi.nlm.nih.gov), we detected a protein similar to S. cerevisiae S288c Erg6p. The accession number of the S. cerevisiae Erg6p protein in the NCBI database is 6323635 [4]. Using the BLASTp algorithm [www.ncbi.nlm.nih.gov], we matched S. cerevisiae Erg6p against the S. stipitis CBS 6054 annotated genome and found an analog protein, denoted as a “predicted protein,” with 95% similarity to S. cerevisiae S288c Erg6p. The analog protein (accession number 126275196) contains 377 amino acids and has a molecular mass of 43,323 Da. The S. cerevisiae protein has 383 amino acids anda molecular mass of 43,431 Da. To confirm the cellular expression of Erg6p in S. stipitis, the microsomal fraction of the yeast cells was isolated and separated by one-dimensional (1-DE) SDS-PAGE (Fig. 2). Protein bands with a

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without d,l-methionine, the SAM content of strain M12 (24.7 mg/g CDW) was similar to that of the wild-type strain (23.95 mg/g CDW). The addition of d,l-methionine to the medium increased the content of SAM in both strains, but SAM accumulation in the M12 strain (52.48 mg/g CDW) was twofold higher than in the wild-type strain (26.37 mg/g CDW).

Fig. 2. SDS-PAGE of the microsomal fraction of proteins extracted from Scheffersomyces stipitis M12 strain and S. stipitis wild-type. Proteins with a molecular mass of 35–50 kDa were excised from the gel and analyzed by mass spectrometry. Erg6p was identified in protein bands excised from S. stipitis wild-type (marked with an arrow and black border) but was not detected within a range of 35–50 kDa in strain M12.

molecular mass of 35–50 kDa were subjected to tryptic in-gel digestion. The isolated proteins were identified by a MALDITOF/TOF MS/MS and a database search (Fig. 3). The results confirmed the expression of the “predicted” protein and that it belongs to the Erg6p family. The microsomal proteins from S. stipitis M12 strain and the wild-type were isolated and then separated by 1-DE SDS-PAGE (Fig. 2). Erg6p was positively confirmed by mass spectrometry in the wild-type (Fig. 3) but was not detected in S. stipitis M12 strain, neither at the same excision spot nor in the mass range of 35–50 kDa. These results suggested that Erg6p is not expressed by strain M12.

Comparative proteomic analysis of Scheffersomyces stipitis strain M12 and the wild-type.

The cytosolic protein fraction from the wild-type and strain M12 strain was subjected to a comparative proteomic analysis

by UPLC-MSE. A comparison of the proteomic profiles of the wild-type and strain M12 showed a difference of 25 proteins (Table 2). Proteins overexpressed in strain M12 relative to the wild-type were involved in ribosome biogenesis, translation, the stress response, and ubiquitin-dependent catabolic processes, whereas proteins down-regulated in strain M12 compared to the wild-type were involved in the cell cycle, translation, the stress response, ethanol tolerance, posttranslational modification, protein-protein interactions, the catalytic removal of an amino group, peroxisomal membrane stability,

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Fig. 3. (A) MALDI-TOF mass spectra of tryptic peptides extracted from the gel (43 kDa band). (B) MS/MS spectrum of the peptide ion m/z 1285.5933 obtained in CID mode. The protein identified against the MASCOT search engine is Erg6p. Red peaks indicate matched peaks.

metal ion transport, epigenetic regulation, the membrane targeting of proteins, the actin cytoskeleton and cell morphology, translational elongation, iron and copper homeostasis, cell signaling, and energy metabolism.

Based on these differentially expressed proteins, we mapped the protein-protein interactions using the STRING web-tool and the UniProt S. stipitis database (Fig. 4). Two separate groups of proteins were thus identified: interacting proteins and non-interacting proteins. The interacting proteins were separated into two networks. The first consisted of ten proteins, nine belonging to S. stipitis wild-type and one belonging to S. stipitis strain M12. All of these proteins are involved in protein synthesis and processing. In this network, ribosomal protein L16b/L23e (PICST_89371), 60s ribosomal protein L13 (RPL13), and ribosomal protein S7A (RPS7A) interacted with 7 proteins (out of 10); a predicted protein (PICST_76243) interacted with 6 proteins; the 40s ribosomal protein (PICST_85487), elongation factor 1-alpha (TEF1), and ribosomal protein L37 (PICST_76246) interacted with 5 proteins; signal recognition particle subunit (SEC65) interacted with 3 proteins; and two predicted protein (PICST_39616 and PICST_39616) interacted with 1 protein each.The second

network consisted of only two proteins, one belonging to S. stipitis M12 strain and the other to S. stipitis wild-type. These proteins are associated with mitochondria.

Discussion Several strains and species of yeasts have been screened for SAM production [5,16,24]. To increase SAM accumulation in S. stipitis CBS 5776, we produced a SAM-accumulating strain (M12) with interrupted ergosterol biosynthesis [13]. In the biosynthesis of ergosterol, C-24 sterol methyltransferase (Erg6p) mediates the transfer of a methyl group from SAM to zymosterol, forming fecosterol. This step is metabolically expensive for the cell, requiring 12â&#x20AC;&#x201C;14 ATP equivalents [27].

Blocking this reaction could enhance SAM accumulation in two ways, by preventing the yeast cells from spending the accumulated SAM or by costing them less ATP in the biosynthesis of ergosterol; this ATP could then be used in SAM biosynthesis [24]. Together with ATP, d,l-methionine is required for SAM biosynthesis [5]. We showed that the addition of d,lmethionine to the medium stimulated SAM production by

Predicted protein

40S ribosomal protein

Predicted protein

Predicted protein

Ribosomal protein L14b/L23e

Heat shock protein of the HSP70 family (SSB1)

Suppressor of lethality protein

Predicted protein

Ankyrin repeat protein

Maintenance of mitochondrial DNA

Ribosomal protein S7A

Hypothetical protein

40kDa farnesylated protein associated with peroxisomes

Signal recognition particle subunit

Antioxidant and copper/iron homeostasis protein

60S ribosomal protein L13

Ribosomal protein L37

Predicted protein

Frataxin homolog, mitochondrial

A3LZU1

A3LPX4

A3LWU4

A3LT69

A3LZ15

A3LVA1

A3LNK8

A3M0L8

A3GFL1

A3LP14

A3LPL6

A3LZ32

A3LV98

A3LPB2

A3LX38

A3LNK5

A3LPG9

A3LYQ3

Protein name

A3LUM4

Accession no.a

FRD1

PICST_76243

18.23

10.81

9.86

22.80

80.06

31.27

35.84

70.90

21.26

12.02

19.98

10.97

40.70

66.40

14.47

18.90

19.57

15.92

15.03

MWb (kDa)

4.56

3.66

12.19

11.08

5.36

6.78

4.00

4.62

10.25

5.52

4.18

4.97

4.65

5.06

10.65

4.32

5.98

9.94

5.04

pIc

65.45

88.78

30.68

26.73

48.64

30.07

29.60

9.72

13.82

19.26

36.81

9.37

14.79

7.34

14.59

29.87

17.18

19.44

13.43

Coverage (%)

1.10

4.72

2.03

3.54

3.77

3.10

5.62

6.58

2.22

3.39

6.02

4.47

4.32

7.25

7.76

6.23

6.79

2.83

5.67

Precursor Mass Error (ppm)

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

+

+

+

+

Expression

continued on next pag.

Iron-sulfur cluster assembly, regulate sensitivity to oxidative stress

Translational elongation, regulate the activity of the 60S subunit, regulate the pattern of protein expression

Translation, protein which binds to RNA (RNA binding), metal (Zn) binding

Translation, rRNA maturation

Metal ion transport, protection against oxygen radical toxicity and in the delivery of copper to Fet3p

SRP-dependent cotranslational protein targeting to membrane

Required for proper localization and stability of peroxisomal membrane proteins, tolerance to ethanol

Unknown

Translation

Catalysis of the removal of an amino group from substrate

Mediate innumerable protein-protein interaction

Posttranslational modefication of protein

Cell cycle, DNA processing, tolerance to ethanol

Response to stress

Translation

Ubiquitin-dependent protein catabolic process

Response to stress

Translation

Mitochondrial precursor, ribosome biogenesis

Function

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PICST_76246

RPL13

ATX1

SEC65

PEX19

PICST_29989

RPS7A

MMD1

ANK2

PICST_56381

SHP1

HSP70.1

PICST_89371

PICST_32591

PICST_67049

PICST_85487

PICST_31817

Gene

Table 2. Overview of the proteins that are differentially expressed between S. stipitis M12 strain and S. stipitis wild type

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KRIŽANOVIĆ ET AL.

Nucleic acid binding – 4.02 57.24 9.40 30.14 UniProt accession number.

Database molecular weight (MW). c Isoelectric point. b

a

Predicted protein A3GHE8

PICST_39616

Translational elongation,regulation of actin cytoskeleton and cell morphology, play central role in regulation of cell signaling – 6.75 26.63 9.45 49.90 Elongation factor 1-alpha A3LQC6

TEF1

Protein of unknown function involved in energy metabolism under respiratory conditions, involve in iron homeostasis – 5.58 46.50 5.39 23.47 Respiratory growth induced protein 1 A3LTT0

PICST_83546 (RGI1)

Modulator of gene transcription,control the organization of the actin cytoskeleton in a flexible manner – 4.35 27.55 4.52 65.85 Actin binding protein A3GGK5

PICST_80641

Epigenetic regulation – 6.06 62.58 4.14 17.24 Histone H2A.Z-specific chaperone CHZ1 A3LXX5

CHZ1

Metal detoxification, responsible for high-level copper ion resistance – 4.20 57.33 4.68 7.65 Hypothetical protein

PICST_86552

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A3GG84

Accession no.a

Table 2 (cont'd)

Protein name

Gene

MWb (kDa)

pIc

Coverage (%)

Precursor Mass Error (ppm)

Expression

Function

SAM-ACCUMULATION IN S. STIPITIS

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strain M12 (Fig. 1A). Transmethylation is an important for preserving yeast cell membrane integrity and for conferring ethanol tolerance [11]. The elimination of this step interrupts ergosterol biosynthesis, resulting in SAM accumulation (Fig. 1A) and a decreased ergosterol level (Fig. 1B). Strain M12, with a lower ergosterol content, was hypersensitive to ethanol (Fig. 1C).

In exploring the molecular mechanism underlying SAM accumulation in strain M12 we found that the mutant lacked detectable Erg6p expression (Figs. 2 and 3). Previous studies of C. albicans and S. cerevisiae showed that the ERG6 gene is nonessential for cell viability but critical for the production of ergosterol [16,18,24] and for ethanol tolerance [11,24]. SAMaccumulating strain M12 does not have a mutation in its ERG6 gene, which suggests that the absence of Erg6p expression is due to defective transcription or translation. In S. cerevisiae, ERG6 expression is regulated at the level of transcription, through the Mot3 protein or by sterol regulatory element binding proteins such as UPC/ECM22 [9, 28].

The physiological characteristics of strain M12 were examined using UPLC–MSE. In terms of protein expression, the greatest differences between strain M12 and the wild-type were in proteins involved in protein synthesis and processing and in proteins involved in the stress response (Table 2). In strain M12, four ribosomal proteins involved in translation were underexpressed (ribosomal protein L14b/L23e, ribosomal protein S7A, 60S ribosomal protein L13, ribosomal protein L37) and only one (40S ribosomal protein) was overexpressed compared to the wild-type. The underexpression of several ribosomal proteins may have facilitated isolation of SAM-accumulating strain M12 on medium containing the polyene drug nystatin [13,31]. Ribosome biogenesis is a critical factor in yeast metabolism under ethanol stress [12]. The reduced ethanol tolerance of strain M12 (Fig. 1C) may be related to the underexpression of many ribosomal proteins and to two different non-ribosomal proteins (lethality suppressor and 40kDa farnesylated protein) (Table 2). Proteome analysis showed that respiratory growth reduced protein 1 was not expressed by strain M12. This finding is in agreement with previous studies showing that this protein is not expressed by yeast strains resistant to polyene drugs [���������������������� 6��������������������� ] and by those sensitive to ethanol [12]. The changes in lipid metabolism by strain M12 may be related to the underexpression of the specific chaperone Chz1p (Table 2), which interacts with Htz1p, an expression modulator of many oleate-responsive genes involved in lipid metabolism [�������������������������������� 30������������������������������ ]. The synthesis of lipid components such as sterols or phospholipids is dependent on the transfer of a methyl group from SAM [20]. Thus, our study

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Fig. 4. Protein–protein interaction map of proteins differentially expressed in Scheffersomyces stipitis strain M12 and wild-type S. stipitis as determined using the STRING web-tool and the UniProt S. stipitis database. The proteins are described in Table 2.

supports an association between ergosterol biosynthesis, ethanol sensitivity, and SAM accumulation.

We also integrated the differentially expressed proteins into a map of protein–protein interactions (Fig. 4). The elucidation of protein-protein interaction networks analysis can shed light on changes in cellular functions, the co-expression and co-regulation of proteins, and phenotypic behavior [10].

The changes in SAM-accumulating S. stipitis strain M12 strain (Fig. 4) may therefore be due to the altered expression of proteins involved in protein synthesis and processing and those associated with mitochondria [12,31]. Three proteins (ribosomal protein L16b/L23e, 60S ribosomal protein L13, and ribosomal protein S7A) involved in translation were identified as the main components of the network of proteins involved in protein synthesis and processing in wild-type S. stipitis (Fig. 4), based on their large number of interactions [�������������������������������������������������������������� 25������������������������������������������������������������ ]. In strain M12, these proteins are replaced by a 40S ribosomal protein and one predicted protein (PICST_32591), which could explain the changes in the translation of ERG6 by this strain. However, protein production in yeast cells is

mostly limited by the availability of free ribosomes [22], but in strain M12 strain the predicted protein (PICST_31817), involved in ribosome biogenesis, was overexpressed. A genomic evaluation of an ethanol-tolerant strain of S. cerevisie also showed changes in protein synthesis and energy metabolism [12].

In summary, the disruption of C-24 methylation in ergosterol biosynthesis in S. stipitis strain M12 resulted in the higher accumulation of SAM, a decrease in ergosterol content, and an increased sensitivity to ethanol compared to the wild-type.

A proteomic analysis to investigate the changes in the physiological characteristics of S. stipitis strain M12 showed that this mutant has an altered proteomic profile compared to the wild-type. Further development of this SAM-accumulating strain could lead to new and innovative therapeutic and commercial applications.

Acknowledgements. This work was funded in part by grants from the Ministry of Science, Education and Sports of the Republic of Croatia (00580580477-0374; 058–0583444–3466; 058-0583444–3483).

Competing interests. None declared.

SAM-ACCUMULATION IN S. STIPITIS

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RESEARCH ARTICLE International Microbiology (2015) 18:127-134 doi:10.2436/20.1501.01.242. ISSN (print): 1139-6709. e-ISSN: 1618-1095

www.im.microbios.org

Bioremediation of oil polluted marine sediments: A bio-engineering treatment Simone Cappello,1,* Rosario Calogero,1,2 Santina Santisi,1,3 Maria Genovese,1 Renata Denaro,1 Lucrezia Genovese,1 Laura Giuliano,1 Giuseppe Mancini,4 Michail M. Yakimov1 1 Institute for Coastal Marine Environment (IAMC)-CNR of Messina, Italy. 2Ph.D. School in Applied Biology and Experimental Medicine, University of Messina, Italy. 3Ph.D School in Biology and Cellular Biotechnology, University of Messina, Italy. 4Department of Industrial Engineering, University of Catania, Italy

Received 26 April 2015 · Accepted 15 June 2015

Summary. The fate of hydrocarbon pollutants and the development of oil-degrading indigenous marine bacteria in contaminated sediments are strongly influenced by abiotic factors such as temperature, low oxygen levels, and nutrient availability. In this work, the effects of different biodegradation processes (bioremediation) on oil-polluted anoxic sediments were analyzed.

In particular, as a potential bioremediation strategy for polluted sediments, we applied a prototype of the “Modular Slurry System” (MSS), allowing containment of the sediments and their physical-chemical treatment (by air insufflations, temperature regulation, and the use of a slow-release fertilizer). Untreated polluted sediments served as the blank in a non-controlled experiment. During the experimental period (30 days), bacterial density and biochemical oxygen demand were measured and functional genes were identified by screening. Quantitative measurements of pollutants and an eco-toxicological analysis (mortality of Corophium orientale) were carried out at the beginning and end of the experiments. The results demonstrated the high biodegradative capability achieved with the proposed technology and its strong reduction of pollutant concentrations and thus toxicity. [Int Microbiol 2015; 18(2):127-134] Keywords: bioremediation · biostimulation · chronically polluted sediments · oil-degrading bacteria · Corophium orientale (Crustacea, Amphipoda)

Introduction The fate of hydrocarbon pollutants in marine environment is largely determined by abiotic factors (e.g., temperature, pH, dissolved oxygen and nutrient concentration), which influence the processes of oil weathering and degradation [25]. At an early stage, light fractions of oil are naturally removed; mostly by evaporation, then by photo-oxidation and geoCorresponding author: S. Cappello Institute for Coastal Marine Environment (IAMC)-CNR Sp. San Raineri, 86 98121 Messina, Italy Tel. +39-906015421. Fax +39-90669003 E-mail: simone.cappello@iamc.cnr.it *

chemical reactions��������������������������������������� . Heavy fractions are dispersed or dissolved by waves but a portion settles at the sea bottom, where local chemical-physical conditions tend to limit biodegradation. In many aquatic systems the bottom layer of the water column contains a higher concentration of pollutants than the upper layer, mainly because of the settling process and the lower rate of biodegradation. Consequently, sediments become a sink for pollutants that may later travel back up through the water column by re-suspension, causing a deterioration of water quality.

Among the many environmental challenges, the management and remediation of contaminated marine sediments is perhaps the most difficult one, but it must be dealt wit, because of the significant threat to the quality of aquatic and

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terrestrial ecosystems. An appreciation of the relationships between contaminated marine sediments and ecosystem quality has driven the search for technologies aimed at sediment remediation [3,24].

Sediment management techniques can be classified as ex situ and in situ. In the former, the contaminant is removed from the environment and treated elsewhere. In situ technology, however, has several advantages over ex situ approaches because it does not require the removal and transport of the contaminants and therefore minimizes their potential risks outside the contaminated environment. In situ remediation processes used in the field can be physical (including capping or solidification/stabilization, containment with barriers) or biological (including bioaugmentation and biostimulation).

Of these, biological techniques have become more important over the last decade, mainly because of their low environmental impact, generally lower costs, and their ability to degrade organic contaminants and thus restore the sediments [30].

Although the positive results achieved using bioremediation strategies have been reported [1,27], their success depends on the presence of specialized hydrocarbon-degrading microbial consortia and suitable environmental conditions. In marine sediments, the low temperature, unavailability of principal nutrients (e.g., nitrogen, phosphorus, iron), and the low oxygen concentration are limiting factors for the development of a microbial population capable of biodegradation [5]. These limitations have hindered the application of bioremediation methods (bio-stimulation/bio-augmentation) in marine anoxic environments. Instead, the use of aerobic bioremediation methods in the recovery of polluted sediments has relied on historical observations that microorganisms use oxygen-incorporating enzymes to initiate their attack on hydrocarbons [27].

Given these difficulties, the development of an in situ confined moving system would offer a promising technical solution.

In the present study, a modular system aimed at optimizing the biodegradation process was implemented and tested.

The system was designed to operate directly in the field but retains the advantages of controlled methods that do not impact the surrounding environment. The effects of the addition of air and/or slow-released fertilizer, temperature regulation, and the addition of oil sorbents on the efficiency of the system were evaluated. The abundance of the indigenous microbial communities ������������������������������������������������� in both the untreated (control) and treated sediments was monitored for 30 days after the contamination event. We also measured microbial ������������������������������������ activity (biochemical oxygen demand, BOD), screened for functional genes, analyzed petroleum hydrocarbon content, ��������������������������� and carried out ����������� eco-toxicological bioassays using the organism Corophium orientale.

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Materials and methods Experimental mesocosms. Five different experiments were carried out in rectangular tanks of 144-liter capacity (120 cm long, 30 cm deep, 40 cm wide). The tanks were supplied with a continuous flow (l liter/min) of seawater drawn from Messina harbor (38º11′42.58″ N, 15º34′25.19″ E) and filtered through a 200-μm nylon mesh to remove large metazoans and detritus. Sandy sediments were also collected from the harbor and artificially polluted with 11 g of crude oil (Kashagan Fresh Oil, ENI Technology, Italy)/kg to simulate an oil spill. Prior to its use in sediment contamination, the oil was weatherized in a 2-day incubation at 30 ± 1ºC. The temperature inside the mesocosms was maintained at about 17 ± 1ºC throughout the experimental period.

Sediment characterization. The main physical-chemical variables (pH, temperature, redox) were measured using a multiparameter probe (Waterproof CyberScan PCD 650, Eutech Instruments, the Netherlands). Sediment mineralogy was analyzed by X-ray powder diffraction (PW 14 1373, Philips, Holland). Sediment water content was calculated as the difference between the wet and dry weights of the sediment and expressed as a percentage. Grain size was determined by the sieving technique. Total organic matter was measured as previously described [11]. The concentrations of NO3, NO2+, NH4+, and PO43– were estimated using a QuAAtr AutoAnalyzer (Seal). After their collection and artificial contamination, the sediments (26 kg) were left in stagnant water for 10 months (T300) to immerse them in anoxic conditions.

Biodegradation system. The system designed to produce biostimulating conditions consisted of cylinders 30 cm long and 15 cm in diameter. Each cylinder had a glass cap with four holes for the introduction of: (i) the aeration system; (ii) the thermostat; (iii) the oil sorbent; and (iv) the slow-release fertilizer. The cap could be removed for seawater and sediment sampling. The aeration system consisted of five tubes (28 cm long, 4 mm diameter) externally connected to an air pump (Turbolence 2000 AirPump, Tetra). The cylinders were filled with 3 l of natural seawater and 1.5 kg of polluted sediments and placed inside the experimental mesocosms to promote biostimulation (Fig. 1).

Experimental conditions. Experiments. The five different experimental conditions evaluated in this study are described in Table 1. The control (SED) experiments consisted of polluted sediment without air, fertilizer, sorbent, and/or temperature regulation. The four other experiments examined the effect of the continuous addition of air (A), slow-release fertilizer (F), and both air and fertilizer as well as either the oil-sorbent (SED+A+F+S) or a temperature control (SED+A+F+T). All experiments were performed in duplicate. Slow-release fertilizer. At the beginning (T0) of the experiment, 25 mg of a slow-release fertilizer (Miracle-Gro NPK 18:9:11; ScottsMiracleGrow, Marysville, OH, USA) was added to the systems SED+A+F, SED+A+F+S, and SED+A+F+T. Oil sorbent. Five g of oil-absorbent material (X-Oils,Hellmann-tech, Lehrte, Gemany) was added to the SED+F+S mesocosms. The sorbent was confined within the cylinder throughout the experiment. Temperature. In the SED+F+T experiment, the temperature within the biostimulation cylinder was controlled and maintained at 28 ± 1°C , throughout the experiment. This was higher than the temperature of the surrounding environment within the mesocosm (17 ± 1°C). Sampling strategy and variables tested. Microbial dynamics were monitored by using a sterile corer to collect sediment samples from each bioremediation system at regular intervals (0, 5, 10, 15, 20, 25 and 30 days). Total DAPI (4���������������������������������� ′��������������������������������� ,6-diamidino-2-phenylindole; Sigma-Aldrich, Milan, Italy) counts, MPN (most probable number), BOD, and TERHC (total extracted and resolved hydrocarbons and their derivates) composition were determined. The expression of functional genes was screened as described below. Ecotoxicological tests (mortality of Corophium orientale) were carried out at the beginning (T0) and end (T30) of the experiments.

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Fig. 1. Diagram of the system used in this study (A), and engineering and hydraulic schematization of the system and mesocosm used in this study (B, C).

Dispersion methods, bacterial counting (DAPI), and MPN counts. Prior to dispersion of the samples, they were incubated for at least 15 min with Tween 80 (final concentration, 1 mg/l). An ultrasonic cleaner bath (Branson 1200 Ultrasonic Cleaner, Branson, USA) and the protocol described by ������������������������������������������������������������� Kuwae and Hosokawe (1999) were used to achieve bacterial dispersion from the sediments (10 min). After centrifugation (8 min, 8000 ×g) and collection of the water-Tween 80 phase, the samples were prepared as previously reported [5,35]. DAPI counts were obtained using formaldehyde (2% final concentration) fixed cells. The results are expressed as the number of cells/ml. At the same time, the hydrocarbon-degrading bacteria were enumerated using a miniaturized MPN method [4] with slight modifications [5].

Screening of functional genes. The presence/absence of functional genes involved in the degradation of hydrocarbons was examined to study the metabolic and functional efficiency of the microbial population that developed during the experiments. RNA (from 10 g of experimental sediments) was extracted from the cells using ����������������������������������� the MasterPure complete DNA&RNA purification kit (Epicenter, Biotechnologies, Madison, WI, USA) according to the manufacturer’s protocol.���������������������������������������� The rRNA-crDNA heteroduplex was synthe-

sized by reverse transcription as previously indicated [5]. PCR assays were done using specific primers created for the metabolic genes of the main members of hydrocarbonoclastic bacteria; specifically, for the alkane hydroxylase of Alcanivorax sp. (alkB1-R, 5′-GCTTAGGAACAACGGTTCAGG-3′; alkB1-F; 5′-AATTGGCCTATATCTCGTA-3′) [9] and Thalassolituus sp. (alkB-ThlR342, 5′-GGGCCATACAGACAAGCAA-3′; alkBThlF125, 5′-GA­ ­C­G­TC­GCC­ACACCTGCC-3′) [21] and for the aromatic ring-hydroxylating dioxygenase of Cycloclasticus sp. (PhnC-R, 5′-GCCCAATACCTTGGTTAC CG-3′, PhnaC-F, 5′-CCCATCAGGCAACCCGACAG-3′) [16]. The crDNA products were used as the templates in a standard PCR. Total DNA from strains Alcanivorax sp., Thalassolituus sp., and Cycloclasticus sp. was extracted as described above and served as the positive controls in the PCR amplifications. The PCRs were performed by using Qiagen Taq Polymerase (QIAGEN, Valencia, CA, USA) according to the manufacturer’s protocol.

Hydrocarbon determination and BOD measurement. The com­position of TERHC was analyzed by high-resolution gas chromatography-mass spectrometry (GC-MS) at the beginning (T0) and end (T30) of the experiments and quantified according to previously described protocols [10].

Table 1. Set-up of experimentations carried out during the study Sediment

Air

Temperature

Oil-sorbent

Fertilizer

Experiment 1



–

–

–

–

SED

Experiment 2





–

–

–

SED+A

Experiment 3





–

–



SED+A+F

Experiment 4





–





SED+A+F+S



–



SED+A+F+T

Experiment 5





*SED, marine sediment; A, air addition; F, fertilizer addition; S, sorbent addition and T, temperature regulation.

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TERHC were extracted from sediment samples (1 kg) using the 3550C EPA (US Environmental Protection Agency) procedure [28]. High-resolution GCMS was carried out using a TurboMSAutoSystemXL GC (Perkin-Elmer, Foster City, CA, USA) as previously indicated [5]. BOD was measured using a BOD sensor (VELP Scientifica, Milan, Italy) according to the manufacturer’s instructions.

Corophium orientale bioassay. Corophium orientale was obtained from CIBM (Livorno, Italy) and used according to a previously described procedure [26]. Juveniles and young adults that passed through a 1000-μm sieve and were retained on a 710-μm mesh were selected for the ecotoxicology experiments, which were carried out in 2.5-liter glass flasks containing approximately 2 cm of sediments and 1000 ml of filtered seawater. The seawater was aerated and kept at a constant temperature (16 ± 2°C). The flasks were illuminated for 12 h daily using a lamp containing six light tubes (36W, 120 cm). Each flask was inoculated with 100 randomly selected amphipods.

No food was added to either the test or the control chambers. After 10 days, the surviving organisms were counted. Missing amphipods were assumed to have died. The sensitivity of the populations was estimated as the fraction of dead organisms compared to the initial number of added amphipods. All the experiments were performed twice.

Results Sediment composition. According to the geochemical analysis, the sediment used in the study had a low concentration of water (<15%) and was dominated by the silt-clay fraction (i.e., <60 µm, ~95%),���������������������������������� mainly constituted by quartz, albite, and silicates. Further analysis showed that the sediment contained low concentrations of organic matter (total organic matter, ~34 mg/g) and heavy metals (As, Cd, Mn, Hg, Pg, Cu, Zn, and Fe, all <10 mg/kg).

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Total bacterial counts (DAPI) and MPN. Figures 2 and 3 show the total bacterial counts (DAPI) and the MPN, respectively, for all of five experimental conditions.

Screening of functional genes. At the beginning of the experiments, genes involved in biodegradation processes were not expressed, as determined by amplification with primers specific for functional genes. This lack of expression persisted throughout the experimental period under the control condition (SED). After five days (T5), genes encoding the alkane mono-oxygenase of Alcanivorax sp. were amplified in the SED+A+F+T experiment. In the remaining experiments (SED+A, SED+A+F, SED+A+F+S and SED+A+F+T), positive amplification results were obtained only for the expression of genes related to Alcanivorax genus and only on days 15 and 20, close to the end of the 30day experimental period. No positive amplification signal corresponding to the alkane hydroxylase of Thalassolituus sp. and the di-oxygenease of Cycloclasticus sp. was detected in any of the experiments.

Hydrocarbons composition and biochemical oxygen demand. The GC-MS analysis of hydrocarbon degradation showed that more than half of the TERHCs were degraded in the SED+A system after 30 days of incubation. In both the SED+A+F and SED+A+F+S mesocosms, 56% of the TERHCs were degraded. The best degradation results (76%) were obtained in the SED+A+F+T mesocosm, where the in-

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Fig. 2. Bacterial abundance determined by DAPI staining. Concentration (log cell/ml) of the cells observed during experimentations performed during this work. SED (empty circles), SED+A (filled circles), SED+A+F (filled squares), SED+A+F+S (filled diamonds) and SED+A+F+T (empty squares).

Fig. 3. Most Probable Number (MPN, log MPN/ml) during biostimulation experimentations. SED (empty circles), SED+A (filled circles), SED+A+F (filled squares), SED+A+F+S (filled diamonds) and SED+A+F+T (empty squares).

cubation temperature was higher. Minimum and maximum degradation of n-alkanes was achieved in the SED (5%) and SED+A+F+T (68%) experiments, respectively. In the SED+A, SED+A+F, and SED+A+F+S mesocosms, n-alkane (C10–C32) degradation was 47, 50, and 43%, respectively (Fig. 4). The BOD values measured at the beginning (T0) and end (T30) of the experimental period are reported in Fig. 5.

Assays on Corophium orientale. Based on the rates of Corophium orientale mortality, toxicity was highest in the SED sample, both at time zero and after 30 days of incubation. At the end of the experiments, toxicity in the SED+A, SED+A+F, and SED+A+F+S systems resulted in a mortality close to 50%. Toxicity in the sediments sampled in the SED+A+F+T and control mesocosms was very low, as at the end of the experiments 70% and 90% of the organisms were still alive.

Discussion Recent efforts in environmental biotechnology have focused on the use of biological treatments for the restoration of polluted areas, with the best results obtained using bioremediation [1,27]. This emerging and promising technology can be used to clean up oil pollution in coastal and open seas, including areas that are chronically polluted (e.g., harbors, shipyards) [8,14,25]. However, hydrocarbon pollutants (crude oil) discharged in deep marine environments (sediments) are difficult to degrade [22,23]. Indeed, in natural polluted sedi-

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ments, the rates of biodegradation are very low even after a long period of incubation [22,23].

In this study, we tested a methodology for the in situ recovery of anoxic marine sediments and evaluated the results of its laboratory-scale application. The proposed approach makes use of a simple device in which the limiting factors for biodegradation are controlled so as to enhance the development and selection of a bacterial microflora able to effectively degrade hydrocarbons. The proposed system was designed according to the two needs highlighted by Nikolopoulou and Kalogerakis [25]: (i) the ������������������������������� development of low-cost oxygenation systems for aerobic bioremediation of contaminated anoxic sediments, and (ii) the development of novel bio-stimulant methods that are nontoxic to the marine environment.

Our results demonstrated the importance of air injection in the development of bacterial biomass; indeed, the DAPI and MPN counts and the BOD values obtained in the experiments with air insufflation were higher than those in the control (untreated sediments). However, the introduction of nutrients and especially temperature regulation were also crucial in achieving high levels of biodegradation in the shortest possible time.

Studies have shown that bacterial degradation proceeds slowly at low temperature and that only certain bacterial genera can degrade aliphatic oil fractions [7,21]. In our system, the temperature increment may have favored the development of mesophilic bacteria, which are effective biodegraders. In the SED+A+F+T mesocosm, ~70% of the total oil and linear hydrocarbons were degraded. This level of degradation can be explained by an increase in microbial abundance/activity, as

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Fig. 4. Relative percentage of crude oil (dark grey bars) and n-alkanes (C10-C32; white grey bars) present in sediment at the beginning of experiment (T0) and after 30 days of biostimulation experiment with addition of air (SED+A), slow-release fertilizer (SED+A+F), sorbent (SED+A+F+S) and temperature (SED+A+F+T).

evidenced by the increase in DAPI and MPN counts and by the increased BOD values.

These results are in accordance with those obtained by the screening of functional genes. In experiments carried out with air insufflations, nutrient addition, and temperature regulation, bacteria belonging to the Alcanivorax genus were present as early as day 5 of the experiment; under the other conditions, these bacteria were detected after day 15. The lack of expression of functional genes related to the other bacterial genera (e.g., Thalassolituus and/or Cycloclasticus) may have been due to the characteristics of the oil used in this study [21].

Although the experiments were conducted over 30 days, substantial differences in the abundance of the microbial population were detected only during the first 15 days. This suggests that specialization of the degrading population occurred within the first few hours, after the limiting factors characteristic of natural systems had been removed by the addition of an oleophilic slow-release fertilizer (Miracle-Gro NPK 18:9:11). However, the dissolution of these oleophilic nutrients depended on the mechanical-physical characteristics of the system, and the non-uniform dissolution resulted in their less than optimal availability to the microbial population. The addition of dissolved nutrients in the form of salts that are introduced into the experimental systems at defined concentrations may better promote the development of oil-degrading

bacteria and of hydrocarbon-tolerant bacteria. Kasai et al. [16] examined the effects of slow-release fertilizers on oil biodegradation. They found that the addition of fertilizers promoted the degradation mainly of certain components of crude oil; thus, > 90% of n-alkanes (C15â&#x20AC;&#x201C;C30) and > 60% of (alkyl)naphthalenes were degraded within 30 days whereas degradation of three-ring aromatics (phenanthrene, anthracene, fluorene and their alkyl substituted derivatives) was only 30%â&#x20AC;&#x201C;40%.

By contrast, in field experiments carried out on the sand and cobblestone beaches of Japan after the Nakhodka oil spill [18,19], alkanes were degraded to a lesser extent than naphthalene or fluorene and to the same extent as dibenzothiophene and phenanthrene.

Nutrient amendment to the hydrocarbon-contaminated sediments enhanced the biodegradation activity of the natural microbial community. Highly specialized marine hydrocarbonoclastic bacteria represent only a minor fraction of this community but play an important role in the biodegradation of petroleum hydrocarbons that accidentally enter the marine environment [33]. Our results were confirmed by the variation in BOD, which was higher in the microcosm containing the oleophilic slow-release fertilizer.

The addition of absorbent material (X-Oils, Hellmanntech) to the system was crucial in the early stages of the experiment, when air blown into the system caused the release of the oil present in the sediment into the water, as also shown

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Fig. 5. Dynamic of oxygen consumption (biochemical oxygen demand, BOD, mg/l day kg ) at the beginning (T0) and end (30 days) of the experiments.

in similr studies [12,13]. The reduction of contaminant petroleum in the sediment was accompanied by a reduction of the toxicity of the sediments. The sensitivity of C. orientale, an endemic Mediterranean amphipods species, to pollutants contaminating the water column (due to dredging activities) has been well demonstrated [2,26]. In fact, C. orientale is used as an indicator species in standardized ������������������������������������� eco-toxicological bioassays of harbor sediments [26]. We found that, after a reduction of the oil in the sediments, the toxicity to C. orientale was ~30%, which was much lower than that at the beginning of the experiment and in the untreated system (~ 95%; SED, T0 and SED, T30). Toxicity in the other mesocosms was also reduced (SED+A, 50%; SED+A+F, 55% and SED+A+F+S, 45%).

This study shows that, by stimulating the native microbial population, capping combined with in situ aeration is a feasible approach to the efficient recovery of petroleum-contaminated anaerobic marine sediments.

Acknowledgements. This work was supported by grants from the National Counsel of Research (CNR) of Italy and by: Italian Project PRIN2010-2011 “System Biology”; National Operative Project PON R&C 2007-2013 “STI-TAM”; National Operative Project PON R&C 2007-2013 “SEA-PORT”; PNRA2013 “STRANgE”; and European Project KILL-SPILL (KILL-SPILL-FP7-KBBE-2013.3.4-01).

Competing interests. None declared.

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27. Rocchetti L, Beolchini F, Ciani M, Dell’Anno A (2011) Improvement of bioremediation performance for the degradation of petroleum hydrocarbons in contaminated sediments. Appl Environ Soil Sci, doi:10.1155/2011/319657 28. Rocchetti L, Beolchini F, Hallberg KB, Johnson B, Dell’Anno A (2012) Effects of prokaryotic diversity changes on hydrocarbon degradation rates and metal partitioning during bioremediation of contaminated anoxic marine sediments. Mar Poll Bull 64:1688-1698 29. Rodríguez-Blanco A, Vetion G, Escande M-L, Delille D, Ghiglione J-F (2010) Gallaecimonas pentaromativorans gen. nov., sp. nov., a bacterium carrying 16S rRNA gene heterogeneity and able to degrade high-molecular mass polycyclic aromatic hydrocarbons. Int J Syst Evol Microb 60:504-509 30. Rulkens WH, Bruning H (2005) Cleanup technologies for dredged fine sediments: review and future challenges. In: Olfenbuttel RF, White PJ (eds) Remediation of contaminated sediments: Finding achievable risk reduction solutions, C6-01 31. Schwieger F, Tebbe CC (1998) A new approach to utilize PCR-singlestrand-conformation polymorphism for 16S rRNA gene-based microbial community analysis. Appl Environ Microbiol 64:4870-4876 32. Yakimov MM, Cappello S, Crisafi E, Tursi A, Corselli C, Scarfì S, Giuliano L (2006) Phylogenetic survey of metabolically active microbial communities associated with the deep-sea coral Lophelia pertusa from the Apulian Plateau, Central Mediterran Sea. Deep Sea Res, part I:62-75 33. Yakimov MM, Denaro R, Genovese M, Cappello S, D’Auria G, Chernikova TN, Timmis KT, Golyshin PN, Giuliano L (2005) Natural microbial diversity in superficial sediments of Milazzo Harbour (Sicily) and community successions during microcosm enrichment with various hydrocarbons. Environ Microbiol 7:1426-1441 34. Yakimov MM, Gentile G, Bruni V, Cappello S, D’Auria G, Golyshin PN, Giuliano L (2004) Crude oil-induced structural shift of coastal bacterial communities of Rod Bay (Terra Nova Bay, Ross Sea, Antarctica) and characterization of cultured cold-adapted hydrocarbonoclastic bacteria.

FEMS Microbiol Ecol 49:419-432 35. Zampino D, Zaccone R, La Ferla R (2004) Determination of living and active bacterioplankton: a comparison of methods. Chem Ecol 20: 411442 36. Zhao B, Wang H, Li R, Mao X (2010) Talassospira xinhensis sp. nov., a polycyclic aromatic hydrocarbon-degrading marine bacterium. Int J System Bacteriol 60:1125-1129

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Coeditors-in-Chief José Berenguer (Madrid), Autonomous University of Madrid Ricardo Guerrero (Barcelona), University of Barcelona

Juan Aguirre, Prince Edward Island University, Canada Ricardo Amils, Autonomous University of Madrid, Madrid, Spain Miguel A. Asensio, University of Extremadura, Caceres, Spain Shimshon Belkin, The Hebrew University of Jerusalem, Jerusalem, Israel Albert Bordons, University Rovira i Virgili, Tarragona, Spain Albert Bosch, University of Barcelona, Barcelona, Spain Javier del Campo, University of British Columbia, Vancouver, Canada Victoriano Campos, Pontificial Catholic University of Valparaíso, Chile Josep Casadesús, University of Sevilla, Sevilla, Spain Rita R. Colwell, Univ. of Maryland & Johns Hopkins Univ., Baltimore, MD, USA Katerina Demnerova, Inst. of Chem. Technology, Prague, Czech Republic Esteban Domingo, CBM, CSIC-UAM, Cantoblanco, Spain Mariano Esteban, Natl. Center for Biotechnol., CSIC, Cantoblanco, Spain Mariano Gacto, University of Murcia, Murcia, Spain Juncal Garmendia, Institute of Agrobiotechnology, Pamplona, Spain Olga Genilloud, Medina Foundation, Granada, Spain Steven D. Goodwin, University of Massachusetts, Amherst, MA, USA Juan C. Gutiérrez, Complutense University of Madrid, Madrid, Spain Johannes F. Imhoff, University of Kiel, Kiel, Germany Juan Imperial, Technical University of Madrid, Madrid, Spain John L. Ingraham, University of California, Davis, CA, USA Juan Iriberri, University of the Basque Country, Bilbao, Spain Roberto Kolter, Harvard Medical School, Boston, MA, USA Germán Larriba, University of Extremadura, Badajoz, Spain Rubén López, Center for Biological Research, CSIC, Madrid, Spain Bernard M. MacKey, University of Reading, Reading, UK Michael T. Madigan, Southern Illinois University, Carbondale, IL, USA Beatriz S. Méndez, University of Buenos Aires, Buenos Aires, Argentina Diego A. Moreno, Technical University of Madrid, Madrid, Spain Ignacio Moriyón, University of Navarra, Pamplona, Spain Juan A. Ordóñez, Complutense University of Madrid, Madrid, Spain José M. Peinado, Complutense University of Madrid, Madrid, Spain Antonio G. Pisabarro, Public University of Navarra, Pamplona, Spain Carmina Rodríguez, Complutense University of Madrid, Madrid, Spain Fernando Rojo, Natl. Center for Biotechnology, CSIC, Cantoblanco, Spain Manuel de la Rosa, Virgen de las Nieves Hospital, Granada, Spain Carmen Ruiz Roldán, University of Murcia, Murcia, Spain Claudio Scazzocchio, Imperial College, London, UK James A. Shapiro, University of Chicago, Chicago, IL, USA John Stolz, Duquesne University, Pittsburgh, PA, USA James Strick, Franklin & Marshall College, Lancaster, PA, USA Gary A. Toranzos, University of Puerto Rico, San Juan, Puerto Rico Antonio Torres, University of Sevilla, Sevilla, Spain José A. Vázquez-Boland, University of Edinburgh, Edinburgh, UK Antonio Ventosa, University of Sevilla, Sevilla, Spain Tomás G. Villa, Univ. of Santiago de Compostela, Santiago de C., Spain Miquel Viñas, University of Barcelona, Barcelona, Spain Dolors Xairó, Biomat, S.A., Grifols Group, Parets del Vallès, Spain

Associate Editors Mercedes Berlanga, University of Barcelona Mercè Piqueras, Catalan Association for Science Communication Wendy Ran, International Microbiology Secretary General Jordi Mas-Castellà, International Microbiology Managing Coordinator Carmen Chica, International Microbiology Specialized Editors Josefa Antón, University of Alicante Susana Campoy, Autonomous University of Barcelona Ramón Díaz, CIB-CSIC, Madrid Josep Guarro, University Rovira i Virgili Enrique Herrero, University of Lleida Emili Montesinos, University of Girona José R. Penadés, Inst. of Mountain Livestock-CSIC, Castellon Jordi Vila, University of Barcelona Digital Media Coordinator Rubén Duro, International Microbiology Webmaster Jordi Urmeneta, University of Barcelona

Addresses Editorial Office International Microbiology C/ Poblet, 15 08028 Barcelona, Spain Tel. & Fax +34-933341079 E-mail: int.microbiol@microbios.org www.im.microbios.org Spanish Society for Microbiology C/ Rodríguez San Pedro, 2 #210 28015 Madrid, Spain Tel. +34-915613381. Fax +34-915613299 E-mail: sem@microbiologia.org www.semicrobiologia.org Institute for Catalan Studies C/ Carme, 47 08001 Barcelona, Spain Tel. +34-932701620. Fax +34-932701180 E-mail: int.microbiol@microbios.org © 2015 Spanish Society for Microbiology, Madrid, & Institute for Catalan Studies, Barcelona. Printed in Spain ISSN (print): 1139-6709 e-ISSN (electronic): 1618-1095 D.L.: B.23341-2004

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International Microbiology, the official journal of the SEM, is a peer-reviewed, open access journal whose aim is to advance and disseminate information in the fields of basic and applied microbiology among scientists around the world. The journal publishes research articles and complements (short papers dealing with microbiological subjects of broad interest such as editorials, perspectives, book reviews, etc.). A feature that distinguishes it from many other microbiology journals is a broadening of the term “microbiology” to include eukaryotic microorganisms (protists, yeasts, molds), as well as the publication of articles related to the history and sociology of microbiology.

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The Spanish Society for Microbiology (SEM) is a scientific society founded in 1946 at the Jaime Ferrán Institute of the Spanish National Research Council (CSIC), in Madrid. Its main objectives are to foster basic and applied microbiology, promote international relations, bring together the many professionals working in this science, and contribute to the dissemination of science in general and microbiology in particular, among society. It is an interdisciplinary society, with about 1800 members working in different fields of microbiology.

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Official journal of the Spanish Society for Microbiology Founded in 1998 by Lynn Margulis & Ricardo Guerrero

RESEARCH REVIEW

Padilla-Vaca F, Anaya-Velázquez F, Franco B Synthetic biology: Novel approaches for microbiology

Espinosa-Asuar L, Escalante AE, GascaPineda J, Jazmin B, Lorena P, Eguiarte LE, Valeria S Aquatic bacterial assemblage structure in Pozas Azules, Cuatro Ciénegas Basin, Mexico: Deterministic vs. stochastic processes

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Armas-Freire PI, Trueba G, ProañoBolaños C, Levy K, Zhang L, Marrs CF, Cevallos W, Eisenberg JNS Unexpected distribution of the fluoroquinolone-resistance gene qnrB in Escherichia coli from human and poultry origins in Ecuador

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Toledo A, López S, Aulicino M, de Remes Lenicov AM, Balatti P Antagonistic of activity of entomopathogenic fungi by Bacillus spp. associated with the integument of cicadellids and delphacids

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Križanović S, Butorac A, Mrvčić J, Krpan M, Cindrić M, Bačun-Družina V, Stanzer D Characterization of S-adenosyl–L– methionine (SAM) accumulating strain of yeast Scheffersomyces stipitis

Martínez-Gamboa A, Silva C, FernándezMora M, Wiesner M, Ponce de León A, Calva E IS200 and multilocus sequence typing for Salmonella enterica serovar Typhi strains from Indonesia

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Cappello S, Calogero R, Santisi S, Genovese M, Denaro R, Genovese L, Giuliano L, Mancini G, Yakimov MM Bioremediation of oil polluted marine sediments: A bio-engineering treatment

INTERNATIONAL MICROBIOLOGY www.im.microbios.org

2015 pp 71-134

RESEARCH ARTICLES

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Volume 18 Number 2

Volume 18 · Number 2 · June 2015

International Microbiology

INTERNATIONAL MICROBIOLOGY

Volume 18 · Number 2 · June 2015 · ISSN 1139-6709 · e-ISSN 1618-1905

18(2) 2015

INDEXED IN

Agricultural and Environmental Biotechnology Abstracts; ASFA/Aquatic Sciences & Fisheries Abstracts; BIOSIS; CAB Abstracts; Chemical Abstracts; SCOPUS; Current Contents®/Agriculture, Biology & Environmental Sciences®; EBSCO; EMBASE/Elsevier Bibliographic Databases; Food Science and Technology Abstracts; ICYT/CINDOC; IBECS/BNCS; ISI Alerting Services®; MEDLINE®/Index Medicus®; Latindex; MedBioWorldTM; SciELO-Spain; Science Citation Index Expanded®/SciSearch®

June 2015

Official journal of the Spanish Society for Microbiology