SMU Journal Of
RESEARCH ISSUE I | 2015
ABOUT the
JOURNAL SMU Honors Research Association, in conjuction with the university’s research community, has come together to highlight the novel works that are found from the students on our very own campus - students from all interests, backgrounds, and majors. Our organization’s initial goal was to foster a research community at SMU by creating discussion and offering opportunities to those interested or already involved in research. Now, as we move into the second anniversary of our organization, we want to begin showcasing the accomplishments of our student body. With this, we welcome you to our first ever SMU Journal of Research.
Special thanks to the SMU Department of Research, Office of Engaged Learning, Dr. Kehoe, Dr. Vik, Mohammad-Hopkins Foundation, and everyone who has helped us tremendously along the way.
Ide
a r s e E a ndless
Founders of SMU Honors Research Association
We started this organization with our own passions and interests at mind. We both began our journey as BRITE Research Scholars here at SMU, which ultimately pushed us into the world of research. Both of us grew in love with Chemistry and its application, with much due credit to our advisors, Dr. Tsarevsky (Zoya’s advisor) and Dr. Zoltowski (Hilary’s advisor). However, at SMU, we often noticed that the spotlight was pointed to athletics, business, or engineering. This often left the truly ingenious researching happening in all fields - in labs and places in every building on campus - in the shadows, unnoticed by students, and underfunded by administration. Our college careers were molded by our research experience, and we wanted every student to be aware of the research opportunities afforded to them at SMU. For those already involved in research, we wanted to build a community. With this in mind, we set off. SMU Honors Research Association started with an information session in May 2013. Expecting upwards to (maybe) twenty people, we were extremely shocked that our informational drew in close to a hundred! Fast-foward to today and we are celebrating our second anniversary with this journal and a chartered organization our senior year. As we pass on this organization and its initiatives to future students, we hope our passion remains embodied in this foundation and our legacy continue to help future students. Research is just a funny way of saying “to search for solutions, create innovation, and answer universal questions”. Immerse yourself in it! Cheers,
Zoya Mohammad & Hilary Hopkins Founders/Co-Presidents, SMU HRA
ZOYA Zoya Mohammad is a senior here at Southern Methodist University and will be graduating May 2015. She is a double major in Biology and Chemistry, with a minor in Photography. Her research involved controlled polymer manipulation for both biological and commercial application. She will be attending medical school this Fall at The University of Texas at Houston as a MD/MPH candidate.
Hilary Hilary Hopkins is a senior here at Southern Methodist University and will be graduating May 2015. She is majoring in Biochemistry, with a minor in English. Her research involved the study of the circadian rythm under biochemical means. She will be attending medical school this Fall at The University of Texas at San Antonio as a MD/MPH candidate.
2014-2015
SMU Honors Research Association
OFFICER BOARD & RESEARCH COUNCIL MAR McCREARY
Mar is the Head of Research Council. She oversees the tasks of the council and works to create the research database.
(Not Pictured)
NEHA RAO
Neha is the Secretary of HRA. She collects meeting minutes and oversees advertising. She is a environmental engineering major.
(Not Pictured)
My name is Daehee Kim and I am studying Mechanical Engineering with a Pre-Medicine Specialization. I was the Treasurer for SMU Honor Research Association for the 2014-2015 year.
Hi! I’m Purnima, currently a freshman majoring in Biochemistry and Economics. I served as the Physics Chair for HRA this year and am thrilled to continue my association with the organization for the years to come!
My name is Brooke Davis. I am the Psychology/Statistics Chair for the Honors Research Association. I am majoring in Psychology, double minoring in Biomedical Anthropology and Statistics, as well as on the Pre-Veterinary track.
Kenny is a first-year pre-medical student majoring in music and English. He has enjoyed working to promote research in the arts and humanities through his position as Liberal Arts Research Co-Chair.
Ben is a Freshman Math and Chemistry double major who is currently the Chemistry chair of HRA. He is also very interested in computational chemistry and biomedical research.
My name is Patrick Arraj. I am a first-year biochemistry major on the pre-med track and I am interested in minoring in Psychology. I am one of the 2014-2015 Liberal Arts Research Chairs in the Honors Research Association and look forward to exposing more SMU students to research opportunities!
Sasha is the Biology Chair of the Honors Research Association. She is a double major in Biology and Chemistry. She will continue as the 2015-2016 Co-President of HRA.
Arya is the Math and Computer Science Chair of the Honors Research Council. Aside from his role as a chair, he is heavily involved in the works of this journal and the organization. He will continue as the 2015-2016 Co-President of HRA.
Arya McCARTHY (Not Pictured)
2015-2016 Officers Co-Presidents - Sasha Mohammad & Arya McCarthy Vice President - Daehee Kim Treasurer - Hamza Malik Secretary - Ben Chi Applications for Research Council chair positions go out in the Fall.
Want to publish or collaborate with this Journal? Need to contact someone who is mentioned in this journal? Want to publish on the SMU Department of Research blog? Want to learn more about Honors Research Association? Want to apply to be on the Research Council? Want to become a member?
Email smuhonorsresearch@gmail.com We are an interdisciplinary organization that supports people of all majors! Ideas are not limited to science, math, and engineering. We are a community of students involved in research or are interested in undergraduate research. Further, SMU has many sources to fund your ideas to allow them to fruitition. We can help you connect with the right people.
Characterizing Aging Pathways in a Rapid-Aging Peroxiredoxin Mutant Kyle Nakatsuka
Olena Odnokoz
Sveta Radyuk
William Orr
Characterizing Aging Pathways in a Rapid-Aging Peroxiredoxin Mutant Kyle Nakatsuka knakatsuka@smu.edu
Olena Odnokoz oodnokoz@smu.edu
Sveta Radyuk snradyuk@smu.edu
William Orr borr@smu.edu Abstract
Recent developments implicate redox stress as a key regulator of aging via effects in signaling pathways of molecules with altered redox state. This research investigates the mechanisms of the redox stress theory of aging in a Drosophila melanogaster model of rapid aging in which two perodiredoxin genes, dPrx3 and dPrx5, are knocked down. Using ageacquired, temperature-sensitive paralysis experiments, a novel biomarker of aging adapted from Reenan and Rogina [6], we show that both the double mutants and the wild type controls undergo little to no change in temperature sensativity until a marked increase at 50% of age (10 days). These data coincide with our data from lifespan and other biomarkers of aging in double mutants. Since shifts in redox state can affect pathway signaling through modification of signaling molecules, we assayed transcriptome-wide gene expression using RNAseq to identify any changes in signaling pathways in the double mutants. Functional clustering of genes that are significantly altered in dPrx3/dPrx5 double mutants compared to wild type controls shows that the double mutants have altered expression in biological functions relating to immune response, triacylglycerol metabolism, carbohydrate metabolism, heavy metal processing, and heat shock response. Among genes that fall into these clusters, we identified nine candidate genes for further investigation by qPCR. The results of these experiments may identify the key mechanistic pathways that describe how redox state regulates the aging process.
1
Introduction
As the average human lifespan has increased steadily over the last century, our population has witnessed a corresponding increase in rates of age-related disease, driving strong interest in the fundamental process underlying aging. Aging entails an accumulation of damage to cells, tissues, and organs, accompanied by increased frailty and a decline in physiological functions. Research into the cell signaling pathways associated with aging in several model organisms has revealed
a complex process that intersects with multiple other systems including immunity, circadian rhythms, cancer, and redox state. Multidisciplinary approaches to understanding the aging process—organismal biology, cell biology, genetics, and biochemistry—promise a better understanding of the aging process, which in turn could eventually help to extend lifespan and health in humans [3]. One promising approach to investigating the aging process is to study organisms with altered rates of aging. Genetic mutations that result in rapid or slowed aging allow an investigator to study the role of discrete processes (e.g. the mutated gene/pathway) in aging. This approach has been used in multiple organisms including D. melanogaster to elucidate the mechanisms of both targeted and previously unknown mechanisms [1]. The premise of studying rapid or delayed-aging mutants is that these organisms, easily identified by their aberrant lifespan, can reveal difficult-to-predict mechanisms that control the rate of aging in “normal”, wild-type organisms. The mutation offers a replicable experimental “treatment” that can be compared with wild-type controls identify phenotypic differences driven by the mutation. Studying the nature of those phenotypic differences can give valuable insights into the mechanisms by which mutated gene or pathway affect the rate of aging. We utilize this approach to investigate an interesting rapid-aging Drosophila melanogaster genetic mutant discovered in our lab. In these flies, the knockdown of two mitochondrial peroxiredoxins 3 and 5 (dPrx3, dPrx5) by RNAi and gene knockout, respectively, results in a dramatic reduction in lifespan (Figure 1). These peroxiredoxins regulate mitochondrial and cellular redox state through catalytic removal of hydrogen and organic peroxides, and seem to play a major role in the redox stress theory of aging [7]. The popular redox stress theory of aging suggests that reactive oxygen species generated in the mitochondria during cellular respiration drive aging by causing oxidative damage and disrupting redox signaling. Peroxiredoxins stem the disruptive effect of reactive oxygen species, but downregulating them in our double mutants may be accelerating the aging process through some yet unknown mechanisms [5, 7]. Based on this information, this research attempts to better understand the mechanisms by which these peroxiredoxin double mutations lead to the observed short-lifespan phenotype. Here, we present two approaches to understanding the mechanisms of aging in the peroxiredoxin double mutant. First, we provide a physiological measure of age-associated neurodegeneration in the double mutants through a temperature-sensitive paralysis assay adapted from Reenan and Rogina [6]. Second, we look at genome-wide changes in expression in the double mutants to identify the functional pathways that are altered in double mutants. This genome-wide approach, enabled by RNAseq and the Database for Annotation, Visualization and Integrated Discovery (DAVID) allow us to identify the genes and pathways responsible for rapid aging in the double mutants. In this way, we hope to better understand exactly how peroxiredoxins may be linked to aging, which will offer us a better understanding of the redox signaling theory of aging.
Figure 1: Time to recovery from temperature-sensitive paralysis increases across an organisms lifetime in all three strains tested. The experimental dPrx3/dPrx5 double mutants (red) demonstrate a dramatic increase at 10 days, around 45% of lifespan, while a background-matched wt/da,dPrx5 control and yellow white control flies show a gradual increase across their lifespan. Error bars represent 95% confidence intervals (df = 10).
2
Results
We characterized the aging process across the lifespan of our short-lived peroxiredoxin double mutants on an organismal level by a temperature sensitive paralysis assay and on a molecular level by RNAseq.
2.1
Temperature-Sensitive Paralysis
As organisms age, they become more susceptible to paralysis as a result of declining voltage-gated sodium channel transmission. We adapted an organismal biomarker of neurodegeneration from Reenan and Rogina [6] to measure this change in voltage-gated sodium channel transmission over time. While Reenan and Rogina’s assay measures temperature-sensitive paralysis by counting the number of flies paralyzed after 30 seconds at elevated temperature, we adapted the assay to measure the time it took for flies to recover from paralysis after 30 seconds at an elevated temperature. Using time to recovery instead of number of flies puts our variable of interest on a more meaningful interval scale, rather than the categorical scale of the original method. Using this new adapted method, we determined that double mutant flies demonstrated little change in temperature-sensitive paralysis from days 1–9, but experienced a dramatic increase at 10 days, or around 50% of their maximal lifespan. This pattern is somewhat similar to the background-matched control (wt/da,dPrx5) and yellow white flies, which demonstrated gradual increase across three time points (Figure 1). Low sample sizes and non-matching time points
prevented statistical tests of significance, but ongoing tests on a larger scale seem to confirm the data so far. These data demonstrate that aging across the lifespan of the double mutants is comparable to wild type controls.
2.2
Genome-wide RNA Expression Assay
The key challenge in identifying mechanisms of accelerated aging in dPrx3/dPrx5 double mutants is that we cannot easily predict the effects of peroxiredoxin underexpression because dPrx3 and dPrx5 reducing activity includes such a broad set of targets. To determine how the peroxiredoxin mutation affects gene expression across all ≈14,000 genes in the Drosophila melanogaster genome, we conducted an RNA sequencing (RNAseq) assay to measure transcriptome-wide changes in mRNA (Figure 1). Two biological replicates of dPrx3/dPrx5 double mutants and backgroundmatched wt/dPrx5 controls were submitted for RNAseq library preparation (Illumina TruSeq mRNA-seq) and sequencing by the University of Rochester Genomics Resource Center (URGRC). The URGRC performed differential expression analysis by cuffdiff2 [8] with an FDR cutoff of 0.05 (95% confidence interval). The differential expression analysis yielded 193 genes with significantly altered expression relative to controls in either of the two biological replicates. Among those 193 significantly altered genes, 90 were upregulated, and 103 were downregulated. To determine the functional outcome of these shifts in expression, the 193 genes were submitted to the Database for Annotation, Visualization, and Integrated Discovery (DAVID) functional clustering web tool [4]. Functional clustering yielded 41 genes in the immune response pathway, 9 genes in triacylglycerol metabolism, 12 genes in carbohydrate metabolism, 8 genes in heavy metal processing, and 21 genes in heat shock response. To determine which of those 193 genes are involved in aging, the genes were compared to a dataset of 40d aged Canton S flies from Girardot et al. [2]. The comparison yielded 24 genes specifically involved in aging (Figure 2).
3
Discussion
This research characterizes of the aging process in a rapid aging dPrx3 and dPrx5 double mutant and offers insight into the signaling mechanisms behind that rapid aging process. The results support the hypothesis that dPrx3 and dPrx5 are important regulators of aging. Using temperature-sensitive paralysis as a biomarker of neurodegeneration, we demonstrate that at the organismal level, double mutants age in a pattern comparable to the wild type controls. However, the delayed increase in temperature-sensitive paralysis shows that D. melanogaster are able to cope with the loss of peroxiredoxins in early stages, which suggests that their cells may in fact be able to maintain redox balance in spite of lost peroxiredoxin 3 and 5 function until they reach a certain critical
Figure 2: Comparison of genes significantly altered in dPrx3/dPrx5 double mutants (relative to wt/da,dPrx5 control flies) and genes significantly altered in aged (40 day old) Canton S flies (relative to 3 day old flies).
point at which redox balance is lost. Unpublished data on other biomarkers of aging suggest a similar result, and our earlier data on GSH:GSSG ratios across the double mutant lifespan seem to support this hypothesis [5], but require more precise day-by-day testing to confirm. Transcriptome-wide expression data gives a more precise look at exactly what changes are occurring at the cellular level, identifying 193 genes significantly altered in our double mutants. Large numbers of these genes were involved in a few important functional pathways, suggesting that the peroxiredoxin mutations are indeed driving meaningful changes in cell signaling, consistent with the redox theory of aging. However, more precise gene signaling analysis is required to determine the exact way in which alterations in these pathways drive aging. Notably, the fact that only 24 of these genes coincide with genes in an aging fly dataset suggest that the peroxiredoxin mutation may be having most of its effect on pathways independent of aging. Future work will focus on continuing to characterize the signaling networks of the genes altered in our peroxiredoxin mutants. Understanding the role of peroxiredoxin 3 and 5 will involve separate analyses of both aging and agingindependent effects. In either case, we will gain a better understanding of precisely how redox signaling drives aging. These findings will carry out on the promise of aging research: to provide better understandings of biological systems and perhaps to bring us one step closer to extending lifespan and benefiting health in humans.
4
Materials and Methods
All experiments were conducted in the Orr/Radyuk fly lab in Dallas, TX for maximally controlled conditions across treatments and experiments.
Fly Strains and Procedures As described in Radyuk et al. [5], flies under-expressing both dPrx3 and dPrx5 were generated in yellow-white (yw) background by expressing the RNAi-dPrx3 hairpin construct (5ds), using the ubiquitous Da-GAL4 driver in the dPrx5 –/– mutant background. The genotypes of the generated flies were yw; yw/da,dPrx5, and dPrx5/5ds/da.dPrx5. The Da,dPrx5 and dPrx5, RNAi-dPrx3 (5ds) configurations were obtained by recombination. In all experimental studies, flies were collected within 1–2 days after hatching and reared on a standard sucrose-cornmeal medium at 25 ◦C. Flies were kept at environmentally controlled conditions, and media were changed daily until the age indicated for each experiment.
Temperature-Sensitive Paralysis Flies were aged according to the procedure above until the appropriate age for each experiment. Ten flies each were transferred to watertight 3 mL vials and submerged in water at 45.5 ◦C for 30 seconds. Flies were removed from the vial and time to recovery was measured by the time it took for each fly to fully stand upright. Double mutant flies were tested at 3, 6, 9, 10 and 11 days. Wt/da,dPrx5 flies were tested at 13, 29, and 45 days. Yellow-white flies were tested at 13, 30, 41 and 47 days. Sample size was ≈100 flies (10 vials) per time point.
RNAseq RNA samples from two biological replicates of dPrx3/dPrx5 double mutants and background-matched wt/dPrx5 controls were treated with Promega RQ1 DNAse (≈1 u/µg RNA), then submitted for RNAseq library preparation by Illumina TruSeq mRNA-seq and sequencing by the University of Rochester Genomics Resource Center (URGRC). The URGRC performed differential expression analysis by cuffdiff2 [8] with an FDR curoff of 0.05 (95% confidence interval).
References [1] Finch, C. E., and Austad, S. N. Primate aging in the mammalian scheme: the puzzle of extreme variation in brain aging. Age (Dordr) (Jan 5 2012). [2] Girardot, F., Lasbleiz, C., Monnier, V., and Tricoire, H. Specific age related signatures in drosophila body parts transcriptome. BMC Genomics 7, 1 (2006), 69. [3] Heemels, M.-T. Ageing. Nature 464, 7288 (03 2010), 503–503.
[4] Huang, D. W., Sherman, B. T., and Lempicki, R. A. Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat. Protocols 4, 1 (12 2008), 44–57. [5] Radyuk, S. N., Rebrin, I., Klichko, V. I., Sohal, B. H., Michalak, K., Benes, J., Sohal, R. S., and Orr, W. C. Mitochondrial peroxiredoxins are critical for the maintenance of redox state and the survival of adult drosophila. Free Radical Biology and Medicine 49, 12 (2010), 1892 – 1902. [6] Reenan, R. A., and Rogina, B. Acquired temperature-sensitive paralysis as a biomarker of declining neuronal function in aging drosophila. Aging Cell 7, 2 (2008), 179–186. [7] Sohal, R. S., and Orr, W. C. The redox stress hypothesis of aging. Free Radical Biology and Medicine 52, 3 (2012), 539 – 555. [8] Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., and Pachter, L. Differential analysis of gene regulation at transcript resolution with rna-seq. Nat Biotech 31, 1 (01 2013), 46–53.
High-Throughput Screening System for P-glycoprotein Inhibition Collette Marchesseault
High-Throughput Screening System for P-glycoprotein Inhibition Collette Marchesseault cmarchesseau@smu.edu Abstract Many scientists are studying the membrane protein P-glycoprotein (Pgp), because of its role multi-drug resistance, causing some cancer cells to become resistant to chemotherapeutics. Search for a new drug that would inhibit this membrane pump is underway; such a drug would re-sensitize tumors that were considered untreatable by chemotherapy. However, it is expensive and arduous to study this membrane protein in human cell lines. Therefore, it was proposed to transfer the P-gp gene to a strain of E. coli that is better suited to rigorous experimentation. This clone was successfully generated in a plasmid, proved by DNA sequencing. Further work to induce expression of P-gp in E. coli has begun.
1
Introduction and Motivation
Chemotherapeutics are among the strongest drugs developed in recent years and have saved countless lives that otherwise would have been claimed by cancer. However, this treatment is useless if the cells develop chemotherapeutic resistance. The protein that is responsible for about 40% of chemotherapy failures in recurring cancers is the multi-drug resistance P-glycoprotein. The protein’s function in cellular transport is exporting toxins, here chemotherapeutics, out of the cell. P-glycoprotein (P-gp) is comprised of a single polypeptide with two trans-membrane domains, each comprised of six helices which together bind and discharge compounds outside the cell. While expression is necessary in maintaining the blood brain barrier and performing other functions, the overexpression of this protein leads to cells resistant to chemotherapeutics. If there are copious amounts of the protein in the cell membrane, any chemotherapeutic that enters the cell will potentially be pumped out, thereby lowering the intracellular concentration of the therapeutic to levels too low to negatively affect the cancer cell. Higher and higher doses of chemotherapeutics are then necessary to suppress the growth of the tumor eventually reaching the point where the patient can no longer withstand the treatment. This is especially common with recurring cancers, as recurrences often correlate with higher expression of P-gp. The need today is to find a way
to re-sensitize cancer cells to chemotherapeutics in order to allow patients with recurring metastases a viable treatment option. One approach to this problem has been to search for inhibitors of P-gp that could be administered to a patient in combination with a chemotherapeutic. If P gp activity was inhibited, then the chemotherapeutic would remain in the cell and the cancer cells would die. The main complication in finding an effective inhibitor is that P-gp has a non-specific toxin or drug binding site. This allows it to bind and remove many chemically different toxins. Though useful when the protein is protecting the body from toxins, this is problematic when attempting to design an inhibitor to block this action. High doses of inhibitory drugs must be administered in order to achieve competitive inhibition, which in the past has produced serious detrimental side effects in clinical trials. To reduce the dosage needed for effective inhibition, the drug could target the ATPase binding site (the power unit for P gp). If ATP is unable to bind or be utilized because of the presence of a drug, then the pump has no energy source and is effectively inhibited. Because this is non-competitive inhibition at the toxin binding site, a significantly smaller dose would be sufficient to inhibit P-gp. Dr. John Wise at Southern Methodist University has spearheaded the effort to screen possible P-gp inhibitors through the use of targeted molecular dynamic techniques. The computer modeling has unearthed possible inhibitors of Pgp that have begun to be tested using mammalian cells. However, given the high financial costs, as well as many hours needed, I propose to find a more efficient way to screen compounds for inhibition of P-gp. This would drastically increase the number of compounds that could be screened, providing a larger and wider data set with which to search for effective inhibitors. A high-throughput screening method would be highly beneficial moving forward in the search of P-gp inhibitors and could exponentially speed both my own further research, as well as well as the work of several graduate students in our lab and would eliminate the tedious process that our undergraduates (myself included) perform in purifying P-gp from yeast cells. Since the fall of 2012, I have been working on the project of transforming the P-gp gene into E. coli under the guidance of Dr. John Wise. This means that the E. coli would express the human protein so that we could study it in a simpler model. By using a bacterial model, the speed and number of compounds that could be screened would increase dramatically. However, due to the complexity of the P-gp gene and other factors, basic cloning techniques had proved unsuccessful. Although a clone was generated, the gene appeared to become scrambled after it entered the bacterial cell. Therefore, this summer I shifted my efforts to attack the problem from a different angle.
2
Methodology and Results
The goal was to clone the MDR1 (human P-gp) into a bacterial model to create the high-throughput screening system. Because of the size and composition
of the MDR1 gene, it was beneficial to create a fusion protein with a glycerol uptake facilitator protein, called glpF. GlpF has been used as fusion protein to assist in cloning difficult genes. The glpF gene was inserted first into the plasmid and then the MDR1 sequence aligned directly behind the glpF gene. Therefore, when the glpF gene is transcribed, the P-gp gene will also be transcribed. This will also aid in making sure that P-gp is inserted into the membrane during translation. The cloning was completed using a bacterial plasmid, or small piece of circular DNA, in this case the pet24a plasmid. The plasmid and the glpF gene were cut with the same enzymes, rendering the ends complementary. The plasmid and glpF gene were then mixed together, allowing the plasmid to take up the glpF gene and re-circularize. The glpF gene was checked for proper insertion by transformation into E. coli cells, and plasmid isolation. DNA sequencing by an outside company verified the results. This plasmid was named CM1 and can be seen in Figure 1. The same techniques were then repeated using the CM1 plasmid and MDR1 gene. Because of the large size of the MDR1 gene, almost 4000 nucleotides long, the DNA sequencing had to be performed in a series of smaller experiments. For each sequencing run, a PCR reaction was run to generate enough DNA of appropriate length. To do this, eight sets of DNA primers were designed to amplify sequential pieces of DNA throughout MDR1. They were also used to direct the sequencing to the correct place and direction in the DNA. The sequence data, in the form of chromatograms, was analyzed and compared the data to the known sequence of MDR1, shown in Figure 3. Three mutations in the DNA were identified, with no large deletions or insertions. Two of these mutations were identified as natural variants of MDR1, and the third was a mutation from glutamic acid to cysteine designed in the DNA to render the protein inactive. Inactive protein is not selected against when the bacteria grow, therefore it is beneficial to clone DNA of an inactive protein. The sequence data proved that the MDR1 sequence was complete and correct in the newly engineered bacterial plasmid, named CM2, shown in Figure 2. This work was done under the supervision of Dr. John Wise and Dr. Pia Vogel in the Department of Biological Sciences at SMU.
3
Future Work
Experimentation beyond the cloning has already begun in two areas. First, a mutagenesis experiment was begun to mutate the cysteine back to a glutamic acid in order to produce active protein. Second, experimentation to induce of the E. coli cells to produce the glpF-MDR1 fusion protein was started. In order to do this, the bacteria must be grown in the presence of IPTG, a molecule that binds to the repressor which had kept the glpf-MDR1 sequence from being transcribed. With the repressor removed from the DNA, the fusion protein should be produced. The cells must then be broken open to isolate the membranes with the fusion protein in them. Using a SDS-Page gel and a Western Blot, the protein can be isolated and a series of antibodies used to stain the protein for
Figure 1: Plasmid CM1 showing the pet24a plasmid with the glpF gene in between the enzyme cut sites for NheI and SacI.
Figure 2: Plasmid CM2 showing the pet24a plasmid with the glpF gene in between the enzyme cut sites for NheI and SacI and the MDR1 gene in between the SacI and XhoI cut sites.
Figure 3: Chromatogram data of the DNA sequence of a portion of the MDR1 gene. Each color correlates to a different nucleotide, labelled at the top: Adenine, Guanine, Cytosine or Thymine.
visual analysis. As a scientist interested in a career in biomedical research, it is highly important to remember the end goal of the research, which in this case, is the development of a pharmaceutical drug that could save the lives of those who have been battling cancer. My research could be influential in this process, given the speed at which a large number of compounds could be tested against P-glycoprotein.
References [1] Delannoy, S., Urbatsch, I. L., Tombline, G., Senior, A. E., and Vogel, P. D. Nucleotide binding to the multidrug resistance p-glycoprotein as studied by esr spectroscopy. Biochemistry 44, 42 (2005), 14010–14019. PMID: 16229490. [2] Hoffman, A. D., Urbatsch, I. L., and Vogel, P. D. Nucleotide binding to the human multidrug resistance protein 3, MRP3. Protein J. 29, 5 (Jul 2010), 373–379. [3] Neophytou, I., Harvey, R., Lawrence, J., Marsh, P., Panaretou, B., and Barlow, D. Eukaryotic integral membrane protein expression utilizing the Escherichia coli glycerol-conducting channel protein (GlpF). Appl. Microbiol. Biotechnol. 77, 2 (Nov 2007), 375–381. [4] Wise, J. G. Catalytic transitions in the human mdr1 p-glycoprotein drug binding sites. Biochemistry 51, 25 (2012), 5125–5141. PMID: 22647192.
Trekking through the Trees:
Forest Succession at the Trinity River Audubon Center E. Jewel Lipps
Bonnie F. Jacobs
Shannon M. Hart
Trekking through the Trees: Forest Succession at the Trinity River Audubon Center E. Jewel Lipps elipps@smu.edu
Bonnie F. Jacobs bjacobs@smu.edu
Shannon M. Hart shart@smu.edu
Abstract The Trinity River Audubon Center in Dallas, TX (TRAC) was established in an effort to restore land that had a long history of intense human disturbance. From the 1960s to 1990s, the site was a large-scale illegal landfill operation in an old gravel mine. More than 15 hectares of streamside (riparian) forest along the Trinity River are now protected and publicly accessible since TRAC’s opening on the remediated land in 2008. This study assessed forest composition and successional stages (relative forest age) to inform conservation strategies and educational goals at TRAC. Six forest stands were delineated based upon existing fragmentation using aerial photography, and were surveyed in spring 2014 by the random plot method. Published bottomland forest succession models were consulted as standards with which to compare species importance values in the study area. Stands exhibit mid-successional, transitioning to late successional, and late successional stages. None of the study areas is yet in transition to old growth forest. For all stand data combined, sugarberry, ash, pecan, and cedar elm trees had the highest importance values. Overall, the forest area accessible through TRAC can be characterized as mid-succession transitioning to the late succession sugarberry-American elm-green ash community. The Center’s conservation strategy should account for expected changes in forest composition and species dominance, particularly along established trails. Results will be developed into a tree ID guide for Center visitors to learn about riparian forests.
1
Introduction
Environmental restoration of the Great Trinity Forest is an initiative of the City of Dallas for possible recreational and ecological benefits. Since the city’s settlement in 1841, much of the forest has been disturbed by gravel mining, agriculture, channelization, flood control, and urban development. The Deepwood Dump operated illegally and hazardously from the 1960s to the 1990s on forested land zoned for residential use in South Dallas. Residents of the nearby neighborhoods experienced severe adverse effects from the landfill operation and carried out legal action for the dump’s closure. Remediation occurred in the
2000s, and the Trinity River Audubon Center (TRAC) opened on the site in 2008 for continued restoration, community use, and educational outreach. Dallas County is naturally a blackland prairie ecosystem. However, within the prairie region, forests may be found beside streams and rivers. These floodplain forests are termed bottomland hardwood forest or riparian forest. The Great Trinity Forest is at least 6000 acres of continuous bottomland hardwood forest in South Dallas, and possibly the largest urban riparian forest in the nation. Urban forest research suggests that conserved forests provide cities with improved air quality, water quality, and lower summer temperatures, and offer a potential recreational asset for the public. Besides restoring and conserving forest land, the Trinity River Audubon Center serves as a gateway to the Great Trinity Forest, providing recreational and educational opportunities [3]. This project provides ecological data necessary for conservation planning and education about the riparian forest areas accessible through the Trinity River Audubon Center. This study assessed forest composition to determine which tree species are currently most important. After disturbances, forests regenerate in successional stages. The importance of certain tree species changes as the forest grows older. Some important trees grow only during the early or mid successional stage of a forest during recovery from disturbance, and are replaced by different trees by the late stage. Old growth forests are composed of tree species that replace themselves, and the same tree species remain the most important trees until the next disturbance [6]. The successional stages of forest stands near the Center were determined by comparing species’ importance values to published models of bottomland hardwood forest succession. Since the Great Trinity Forest has been disturbed in several ways, its conservation strategies must consider that some important trees will decline and be replaced by different tree species. Forest composition and successional stage describe wildlife habitat. For example, bird communities are influenced by bottomland hardwood forest successional stage [2]. The Trinity River Audubon Center provides access to the Trinity River and Great Trinity forest through several trails. Most of the forest stands reported on here are accessible through a public trail, thus study results pertain to forest areas that Center visitors may enjoy.
2 2.1
Methods Site Selection
To assess forest composition and successional stage, forest stands of different disturbance history were selected for the study. Survey results from sample plots were used to calculate species importance values. Using Google Earth aerial images from 1995 through 2013, six forest stands were distinguished for the Trinity River Audubon Center and adjacent property (Figure 1). The aerial images indicated different vegetation cover and disturbance histories for the chosen stands. Overlook Trail (Stand A) is separated
Figure 1: Aerial photo of the Trinity River Audubon Center from October 18, 2013. Forest stand study areas and vegetation plots are designated.
from Forest Trail (Stand B) by the entrance road into TRAC. These stands are most accessible to visitors and farthest from the river. All other stands border the Trinity River. TRAC Forest (Stand C) is completely within Audubon Center borders. The stand has no maintained trail or public access. In aerial images since 1995, the TRAC Forest stand appears distinctly lighter and thinner than the surrounding vegetation, with which it forms a sharp boundary. McCommas Bluff West (Stand D) is immediately east of the TRAC Forest stand. It is not accessible to the public by trail. Aerial images appear dark green and thick. Trinity River Trail (Stand E) is on the south side of the river. Aerial photos from 1995 through 2001 show the stand as mostly cleared. Vegetation cover appears to increase from 2007 to 2013. McCommas Bluff East (Stand F) is the only stand to the east of the concrete trail maintained by the city of Dallas. The aerial images appear similar to McCommas Bluff West. The McCommas Bluff stands are the property of the City of Dallas, as a 111-acre preserve established in 1985 under the Open Space System. ArcGIS was used to select geographic (GPS) coordinates for vegetation sample plots. Using the software, the stands were overlaid with a grid. Each grid square represented a potential sample plot and had an assigned number. Five percent of the potential plots in each stand were selected randomly through the Microsoft Excel random number function. The randomly generated number was used to retrieve the GPS coordinates for the center of the grid square in ArcGIS. Extra plots were randomly selected as back up, in case a selected plot exhibited
extreme inaccessibility or unsafe location. Coordinate data for each sample plot were loaded into a GPS device and named so they could be retrieved in the field.
2.2
Field Procedure
The center coordinates of sample plots were located with Garmin GPSmap 60CSx unit. General observations about the site condition, wildlife, groundcover, seedlings, and any other notable features were written in a field notebook. Percent cover of the invasive species Chinese privet was recorded. Pictures were taken from the center of the plot in a clockwise rotation with about 2% overlap. Ten by ten meter square plots were measured with meter tape and marked with survey flags and flag tape. Each plot was 100 square meters and aligned with cardinal directions. Within the plot, diameter at breast height (d.b.h.) was recorded in centimeters for every tree greater than 1.4 meters tall. Each measured tree was identified to species. Unknowns were sampled and placed in a plant press for later identification using botanical resources.
2.3
Calculation of Importance Values
The forest composition of each stand is determined with importance values (Figure 2). Relative frequency, relative density, and relative dominance were calculated for each species by stand. For a given species, frequency (plots observed/total plots) was the proportion of plots in which the species was observed divided by the total number of plots in the stand. The frequencies of all species found within a stand were summed. The sum was used to determine relative frequency (0-100%) by dividing each species’ frequency by the sum. Density (trees/hectare) for a given species was the quantity of individual trees within each stand divided by the sampled stand area. The densities of all species were summed and used to determine relative density (0-100%). Relative dominance was derived from the d.b.h. value. Measured d.b.h. was converted to meters and used to calculate basal area (square meters) for each individual sampled. Basal areas of the same species within a stand were summed and divided by the sampled stand area, resulting in species dominance (square meters/hectare). Dominance values for all species in a stand were summed. The sum was used to find relative dominance (0-100%) for each species. Importance value for each species is the sum of relative frequency, relative density, and relative dominance. It ranges from 0 to 300% with larger numbers indicating higher importance of the tree species in the stand.
3
Results and Discussion
The Trinity River Audubon Center accessible forest areas contain at least 21 different tree species. In total, 2600 square meters were surveyed for the study, yielding a sample of 625 trees within the plot areas. Of the 625 trees, 464 were
Figure 2: Important trees in the forest near the Trinity River Audubon Center.
saplings (less than 10 cm d.b.h.). Sugarberry, Green Ash, Pecan, and Cedar Elm have the highest importance values when all data are combined (Figure 2). Two tree species (Paper Mulberry and Silk Tree) are nonnative, but they were uncommon and have low importance values. Successional stage can be determined by comparison with other studies and published models. The bottomland hardwood forest near Denton, TX on the Elm Fork of the Trinity River between Lake Ray Roberts and Lake Lewisville has been well studied. It is called the Greenbelt Corridor (GBC). Part of the GBC was classified as transitional old growth. The top five most important trees were Sugarberry, Cedar Elm, Green Ash, Black Walnut, and Bur Oak [1]. The sugarberry-elm-ash community represents late succession, while black walnut and oaks represent old growth. Nixon studied succession in the South Dallas Great Trinity Forest [4]. Abandoned gravel pits of known ages were surveyed. In the 5 year old pit, black willow and eastern cottonwood were most important. In the 47 year old pit, juniper and sugarberry were most common. In the unexcavated forest, winged elm, post oak, and Mexican plum were most common. Bottomland hardwood forest succession is described by [6] and [5]. Additionally, the U.S. Forest Service classifies juniper as an early/mid succession species, and they have designated sugarberry-elm-ash association. Using these sources, Figure 3 illustrates forest succession in the Great Trinity Forest. The study results indicate that the forest area as a whole is transitioning from mid to late succession. There is a relatively high importance of midsuccession species like pecan, box elder, and juniper, although sugarberry and green ash are most important. When each forest stand is considered separately, different tree species are the most important in each (Table 1). The McCommas Bluff preserve, protected since 1985, represents the oldest and least disturbed forest area near the Center. No oaks were recorded in this area, thus the association will remain for the long term. The TRAC Forest will likely have an increase in sugarberry and elm importance in the coming decades.
Figure 3: Bottomland hardwood forest succession model.
Importance Value
Successional Stage
Stand
Tree Species
Overlook Trail
Cedar Elm Juniper (sapling)
129 68
Mid
Forest Trail
Juniper
132
Mid
TRAC Forest
Green Ash
118
Mid–Late
McCommas Bluff West
Sugarberry Cedar Elm Green Ash
72 69 52
Late
Trinity River Trail
Pecan
124
Mid
McCommas Bluff East
Sugarberry American Elm Green Ash
90 80 64
Late
Table 1: Results for forest stands.
In the Forest Trail stand, many juniper trees already appear to be in decline. Juniper should be allowed to die out, to be replaced by green ash and sugarberry. Only the Overlook Trail stand had juniper saplings of high importance, and may remain in mid-succession. The Trinity River Trail stand had the highest percentage of saplings. Many will die as they grow and competition increases, changing the composition in the coming decades. Trinity River Audubon Center conservation strategy should expect juniper decline and the increased importance of sugarberry, elms, and green ash. The riparian forest should be monitored for invasive species that may negatively affect forest growth. Chinese privet should be removed where it exists. Monitoring for Dutch Elm disease, pests, and other health concerns for the most important species will ensure forest longevity and stability of the Great Trinity Forest sugarberry-elm-ash community.
References [1] Barry, D., and Kroll., A. J. A phytosociological description of a remnant bottomland hardwood forest in denton county, texas. Texas Journal of Science 51 (Nov 1999), 309–316. [2] Buffington, J. M., Kilgo, J. C., Sargent, R. A., Miller, K. V., and Chapman, B. R. Comparison of breeding bird communities in bottomland hardwood forests of different successional stages. The Wilson Bulletin 109, 2 (1997), pp. 314–319. [3] City of Dallas Urban Forest Advisory Committee. Great trinity forest. http://dallastrees.org/?page_id=85. [4] Nixon, E. S. Successional stages in a hardwood bottomland forest near dallas, texas. The Southwestern Naturalist 20, 3 (1975), pp. 323–335. [5] Rijal, R. Soil and forest variation by topography and succession stages in the Greenbelt Corridor, floodplain of the Elm Fork of the Trinity River, North Texas. PhD thesis, University of North Texas, August 2011. [6] Stanturf, J. A., Schoenholtz, S. H., Schweitzer, C. J., and Shepard, J. P. Achieving restoration success: Myths in bottomland hardwood forests. Restoration Ecology 9, 2 (2001), 189–200.
Forecasting Error in TV Ratings Hal Hoeppner
Forecasting Error in TV Ratings Hal Hoeppner hhoeppner@smu.edu
1
Introduction
Your attention is a commodity and it is for sale. Television networks sell their viewers in the form of commercial airtime. Advertisers buy this time, hoping that it will lead to high future sales. One of the most important factors in determining the price of commercial space is the rating, or audience size, of the show it occurs during. However, 80% of advertising space is sold months in advance [1]. The ratings from the previous season can act as guidelines for returning programs, but little is known about new shows at this time. Accurately forecasting ratings— and thus setting the price—for these new shows with minimal information (the schedule and trailer) is important: too low and you’ve lost out on profit, too high and you may lose confidence in future dealings. A show’s ratings are measured two ways in this paper: share and A18-49. Share is the average percentage of TV-watching adults ages 18-49 that watched the program and A18-49 is the average percent of TV-owning adults ages 18-49 that watched the program. The two are similar and are the most commonly cited Nielson ratings.
2
The Article
In 2001, Philip Napoli looked at the error inherent in forecasting ratings for new primetime shows in his article “The Unpredictable Audience” [1]. He describes what he calls ‘uncertainty factors’, variables that contribute to error: inheritance effects, competitive scheduling, quantity of new shows, and the increasing use of VCRs (which were replaced in favor of DVRs). Inheritance effects come from the show directly before (lead-in) and after (lead-out) the selected program and are based on the idea that viewers tend to remain on the same channel through a sitting. Competitive scheduling is a method that networks use to ensure that their programs will attract a different segment of the audience and thus minimize the direct competition between programs. Napoli derived five hypotheses from his uncertainty factors: 1. Shows with returning lead-ins will have less forecasting error than those without.
2. Shows with returning lead-outs will have less forecasting error than those without. 3. A higher percentage of new programs will lead to higher forecasting error. 4. The number of new programs a network is trying to produce will also lead to higher forecasting error. 5. Forecasting error is increasing over time. He was also curious to see if FOX’s error would be different from the ‘Big Three’ networks (CBS, NBC, ABC) since it is the newest and targets a younger audience [1]. To test these hypotheses, Napoli used multivariate regression analysis to look at 157 programs premiering during the 1993–98 seasons from CBS, NBC, ABC, and FOX, though 17 were removed due to schedule shifts. The forecasted ratings came from an annual report in Broadcasting & Cable. He chose to use the average share from episodes two through five1 , reported in weekly Nielsen reports, as the show’s actual rating [1]. Error, the response variable, was measured as a percentage of the actual rating. The absolute value of this percentage was used because Napoli was not interested in which direction the error occurred but simply how large the value was. Dummy (i.e. Boolean) variables represented whether a show’s lead-in and lead-out were returning shows (0=no, 1=yes). Additional dummy variables were included to denote if the show had a lead-in or lead-out (0=no, 1=yes); shows at the beginning or end of primetime do not. Competitive scheduling, or ‘counterprogramming’, was included as the percent of half-hour blocks taken up by new shows out of the available competing primetime half-hour blocks. The quantity of new shows was simply measured by the number of new programs that season [1]. Napoli reports finding the type of lead-in and lead-out to be statistically significant predictors of error at the 0.05 level, and that the error was increasing over time, giving support to hypotheses 1, 2, and 5. The number of new shows and percentage of new show programming were not found to be statistically significant, providing no support for hypotheses 3 and 4. He also found that while FOX’s mean error was significantly lower than the other three networks, its dummy variable was not significant in the multivariate regression analysis, providing an unclear response to his original question [1].
3
My Statistics
I wanted to see if Napoli’s findings would hold up over a decade later. Unfortunately, Broadcasting & Cable no longer publishes an annual report of forecasted 1 Napoli excluded the premiere, believing it not to be an accurate predictor of overall season performance. He did not directly state why he only used four episodes. It may have been to reduce bias against shows cancelled mid-season.
Year
N
Variable
Mean
SD
Min
Max
2011
24
error % error
0.63 29.14
0.42 21.69
0.08 2.86
1.73 85.19
2012
18
error % error
0.65 39.34
0.41 26.92
0.00 0.00
1.65 87.50
2013
22
error % error
0.48 35.46
0.29 27.75
0.00 0.00
1.15 94.59
Table 1: Breakdown of absolute error and absolute percent error of A18-49 by year.
Network ABC CBS FOX NBC
N 21 13 12 18
Mean 32.30 35.64 32.15 36.68
SD 27.07 26.57 23.61 25.35
Table 2: Mean absolute percent error of A18-49 by network.
ratings. I was only able to find predicted ratings for the fall seasons of 2011, 2012, and 2013 at ShowBuzzDaily.com [2]. Their estimates are put together by Mitch Metcalf, former head of scheduling at NBC; Ted Frank, NBC’s former head of current programming; and Mitch Salem. They are based on the announced fall schedules and trailers for new shows and are reported as A18-49 rather than share. For the actual rating, I have calculated an average for episodes two through five from figures on SpottedRattings.com. I do not have the data necessary to look at lead-outs and won’t include them in my analysis. There were a total of 64 new shows during the fall seasons of 2011, 2012, and 2013. However, four moved in the schedule before their fifth episode (Appendix A). One moved only for the fifth episode, and using the sixth episode in its place did not affect the average rating. For the other three, only ratings for the episodes that aired in the show’s original time slot will be used. The same method will be used for shows that were cancelled and pulled from the schedule before their fifth episode. Napoli used this same approach to reduce bias against poorly performing programs [1]. There were 24 new shows in 2011 taking up 25.69% of available programming time, 18 in 2012 at 18.75% of the time, and 22 in 2013 at 22.93% of the time. 20.31% of shows were scheduled such that they did not have lead-ins, 54.69% of shows had returning lead-ins, and 25% of shows had new programs as their lead-ins. From a cursory glance at the data summary, error rates do not appear to vary with network or increase with time (Table 1 and Table 2). On average, the forecasted ratings overestimated audience size by 21.45%. This is greater than the 15% Napoli reports, suggesting that there may be a longer pattern of increasing error rates that requires data from more than three years to observe.
(a) Residuals vs fitted
(b) Normal Q-Q
(c) Histogram and normal function
Figure 1: Fit diagnostics for percent error.
(a) Residuals vs fitted
(b) Normal Q-Q (c) Histogram and normal function
Figure 2: Fit diagnostics for log of percent error.
4
Assumption Check
Following Napoli’s method, linear regression will be used to test the significance of these factors on forecasted ratings error. Regression requires four assumptions to be met: linearity between dependent and independent variables, independence, constant variance of residuals, and normally distributed residuals. Since the industry accepts that lead-ins affect ratings, there is a breach of independence for the 16 shows (25%) that have new programs, which would also be included in the data, as their lead-in. It should also be noted that my sample size is smaller than one would hope. The ‘rule of thumb’ would be to have at least 104 observations (100 + k, where k is the number of predictors) to ensure the proper power; unfortunately, this wasn’t possible due to limited access to rating predictions. The variance of residuals across predicted values (Figure 1a) is not constant; a wave pattern can be seen in the negative residuals. As for normality, the residuals are not too far off the wanted diagonal line (Figure 1b). At this point I cannot fix a problem with independence, but performing a transformation on the data may help with other assumptions.
4
Variable Intercept Leadin Rleadin NewSeas PercNew
Parameter estimate 60.882 3.887 2.534 -1.468 3.289
Standard error 32.07 9.68 7.83 1.41 15.81
Pr > |t| 0.063 0.689 0.747 0.303 0.836
β 0 0.062 0.050 -0.141 0.028
pr2 0.0027 0.0018 0.0179 0.0007
VIF 0 1.47 1.47 1.13 1.09
Table 3: Results of regression of percent error. ‘Leadin’ represents whether a show has a lead-in or not. ‘Rleadin’ is the dummy variable for if the lead-in is a returning show. ‘NewSeas’ is the number of new shows in a same season. ‘PercNew’ is the percent of competing time taken up by new shows.
Statistic Model Pr > F R2 Adjusted R2
Value 0.718 0.034 -0.031
Table 4: Validity of regression model.
Taking the logarithm of percent error has improved the normality of residuals (Figure 2b, Figure 2c). However, it has exacerbated the problem with nonconstant residual variance (Figure 2a): what I thought was a wave is now a large empty space, though this may be purely due to having few observations in that area. I will continue with the analysis, regardless of issues with non-constant variance and independence, using the absolute value of percent error.
5
Regression
Regression was used to test the null hypothesis that all uncertainty factors’ slopes are zero, meaning that they have no significant effect on the percent of error when forecasting ratings. None of the factors explored are statistically significant regardless of whether all variables are included or a stepwise selection is used. The model itself is also not statistically significant (Table 4); thus, I fail to reject the null hypothesis. A post-hoc calculation of power results in an acceptable 0.865, meaning that my sample size should be large enough for the model to have picked up on any effects present, even though it is smaller than the suggested amount [3]. It appears that at least for these three fall seasons, the lead-in type, number of new shows, and scheduling against new shows did not affect the percent error of forecasted ratings.
6
Conclusion
Napoli had a good idea: uncertainty is inherent in television and continues to grow today with increasing DVR use, cable use, and fragmentation of audiences. Perhaps with more data and research these uncertainty factors may prove to be important to advertisers looking for the most secure bet. For now though, focus should be placed on improving the underlying predictions.
References [1] Napoli, P. M. The unpredictable audience: An exploratory analysis of forecasting error for new prime-time network television programs. Journal of Advertising 30, 2 (2001), pp. 53–60. [2] Salem, M. THE SKED’s Fall TV Ratings Predictions. ShowBuzz Daily. http://www.showbuzzdaily.com/articles/the-sked/tvratings/ predictions-tvratings/page/3. [3] Soper, D. Free post-hoc statistical power calculator for multiple regression. http://www.danielsoper.com/statcalc3/calc.aspx?id=9.
A
Shows that moved on or prior to 5th episode
Year 2011
Title How to Be a Gentleman (CBS)
2011
I Hate My Teenage Daughter (FOX)
2011
Grimm (NBC)
Change Moved from Thursday to Saturday on 3rd episode. Moved from Wednesday to Tuesday on 5th episode. 5th episode aired on Thursday instead of Wednesday; rest of season on schedule.