Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed the landscape of aging and disease biology. They have revealed novel molecular markers of aging, disease state, and drug response. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and the underlying biology of aging and disease is heterogeneous and complex. Methods from integrative computational biology can help diminish these challenges by creating new analytical methods and software tools that leverage the large and diverse quantity of publicly available HTP data. In this thesis I report on four projects that develop and apply strategies from integrative computational biology to identify improved biomarkers and therapeutics for aging and disease. In Chapter 2, I proposed a new network analysis method to identify gene expression biomarkers of aging, and applied it to study the pathway-level effects of aging and infer the functions of poorly-characterized longevity genes. In Chapter 4, I adapted gene-level HTP chemogenomic data to study drug response at the systems level; I connected drugs to pathways, phenotypes and networks, and built the NetwoRx web portal to make these data publicly available. And in Chapters 3 and 5, I developed a novel meta-analysis pipeline to identify new drugs that mimic the beneficial gene expression changes seen with calorie restriction (Chapter 3), or that reverse the pathological gene changes associated with lung cancer (Chapter 5). The projects described in this thesis will help provide a systems-level understanding of the causes and consequences of aging and disease, as well as new tools for diagnosis (biomarkers) and treatment (therapeutics).

Increasing gene dosage provides a powerful means of probing gene function, as it tends to cause a gain-of-function effect due to increased gene activity. In the budding yeast, Saccharomyces cerevisiae, systematic gene overexpression studies have shown that in wild-type cells, overexpression of a small subset of genes results in an overt phenotype. However, examining the effects of gene overexpression in sensitized cells containing mutations in known genes is a powerful means for identifying functionally relevant genetic interactions. When a query mutant phenotype is rescued by gene overexpression, the genetic interaction is termed dosage suppression. I comprehensively investigated dosage suppression genetic interactions in yeast using three approaches. First, using one of two novel plasmid libraries cloned by two colleagues and myself, I systematically performed dosage suppression screens and identified over 130 novel dosage suppression genetic interactions for more than 25 essential yeast genes. The plasmid libraries, called the molecular barcoded yeast ORF (MoBY-ORF) 1.0 and 2.0, are designed to streamline dosage analysis by being compatible with high-throughput genomics technologies that can monitor plasmid representation, including barcode microarrays and next-generation sequencing methods. Second, I describe a detailed analysis of the novel dosage suppression interactions, as well as of literature-curated interactions, and show that the gene pairs exhibiting dosage suppression are often functionally related and can overlap with physical as well as negative genetic interactions. Third, I performed a systematic categorization of dosage suppression genetic interactions in yeast and show that the majority of the dosage suppression interactions can be assigned to one of four general mechanistic classifications. With this comprehensive analysis, I conclude that systematically identifying dosage suppression genetic interactions will allow for their integration into other genetic and physical interaction networks and should provide new insight into the global wiring diagram of the cell.

Development of genome-wide reagents has allowed systematic analysis of gene function. The experimental accessibility of budding yeast makes it a test-bed for technology development and application of new functional genomic tools and resources that pave the way for comparable efforts in higher eukaryotes. In this Thesis, I describe a two-color GFP-RFP reporter system I developed to assess the consequences of genetic perturbations on a promoter of interest. The dual-reporter system is compatible with the synthetic genetic array methodology, an approach that enables marked genetic elements to be introduced into arrays of yeast mutants via an automated procedure. I use this approach to probe cell cycle-regulation of histone gene transcription by introducing an HTA1 promoter-GFP reporter gene construct into an ordered array of ~4500 yeast deletion mutants. I scored defects in reporter gene expression for each mutant, generating a quantitative analysis of histone promoter activity. The results of my screen motivated a number of follow-up experiments, including chromatin immunoprecipitation, transcript profiling and genome-wide analysis of nucleosome positions, which revealed a previously unappreciated pathway that specifies regions of repressed chromatin in a cell cycle-sensitive manner. A novel aspect of this pathway is that it involves histone chaperones and a chromatin boundary element. Specifically, we discovered that the histone chaperone Rtt106 works with two other chaperones, Asf1 and the HIR complex, to create a repressive chromatin structure at histone promoters which is bound by the protein Yta7. It was clear from previous work that Asf1 and HIR repress transcription at HTA1 and that HIR localizes to and functions through a specific element in histone promoters. However, there was no previous data demonstrating a role for Rtt106 in cell cycle-dependent gene transcription. In sum, I describe a new genomic screen that I used to discover a novel pathway regulating cell cycle-dependent transcription. While I examined histone gene expression as proof-of-principle, my screening system could be applied to virtually any pathway for which a suitable reporter can be devised. I anticipate this methodology will enable yeast researchers to collect quantitative data on hundreds of gene expression pathways.

Increasing gene dosage provides a powerful means of probing gene function, as it tends to cause a gain-of-function effect due to increased gene activity. In the budding yeast, Saccharomyces cerevisiae, systematic gene overexpression studies have shown that in wild-type cells, overexpression of a small subset of genes results in an overt phenotype. However, examining the effects of gene overexpression in sensitized cells containing mutations in known genes is a powerful means for identifying functionally relevant genetic interactions. When a query mutant phenotype is rescued by gene overexpression, the genetic interaction is termed dosage suppression. I comprehensively investigated dosage suppression genetic interactions in yeast using three approaches. First, using one of two novel plasmid libraries cloned by two colleagues and myself, I systematically performed dosage suppression screens and identified over 130 novel dosage suppression genetic interactions for more than 25 essential yeast genes. The plasmid libraries, called the molecular barcoded yeast ORF (MoBY-ORF) 1.0 and 2.0, are designed to streamline dosage analysis by being compatible with high-throughput genomics technologies that can monitor plasmid representation, including barcode microarrays and next-generation sequencing methods. Second, I describe a detailed analysis of the novel dosage suppression interactions, as well as of literature-curated interactions, and show that the gene pairs exhibiting dosage suppression are often functionally related and can overlap with physical as well as negative genetic interactions. Third, I performed a systematic categorization of dosage suppression genetic interactions in yeast and show that the majority of the dosage suppression interactions can be assigned to one of four general mechanistic classifications. With this comprehensive analysis, I conclude that systematically identifying dosage suppression genetic interactions will allow for their integration into other genetic and physical interaction networks and should provide new insight into the global wiring diagram of the cell.

No description available.

Unicellular freshwater cyanobacterium Synechococcus elongatus PCC 7942 is the model organism for studying the circadian clock in cyanobacteria. Despite tremendous work over the last decade in identification of clock-related loci and elucidation of molecular mechanisms of the central oscillator, many details of the basic steps in generating circadian rhythms of biological processes remain unsolved and many components are still missing. A transposon-mediated mutagenesis and sequencing strategy has been adopted to disrupt essentially every locus in the genome so as to identify all of the loci that are involved in clock function. The complete genome sequence has been determined by a combination of shotgun sequences and transposon-mediated sequences. The S. elongatus PCC 7942 genome is 2,695,903 bp in length, and has a 55.5% GC content. Automated annotation identified 2,856 protein-coding genes and 51 RNA coding loci. A system for community refinement of the annotation was established. Organization and characteristic features of the genome are discussed in this dissertation. More than 95% of the PCC 7942 genome has been mutagenized and mutants affected in approximately 30% of loci have been screened for defects in circadian function. Approximately 70 new clock loci that belong to different functional categories have been discovered through a team effort. Additionally, functional analysis of insertion mutants revealed that the Type-IV pilus assembly protein PilN and the RNA chaperon Hfq are involved in transformation competence of S. elongatus cells. Functional analysis of an atypical short period kaiA insertional mutant showed that the short period phenotype is caused mainly by the truncation of KaiA by three amino acid residues. The interaction between KaiC and the truncated KaiA is weakened as shown by fluorescence anisotropy analysis. Deletion analysis of pANL, the large endogenous plasmid, implies that two toxin-antitoxin cassettes were responsible for inability to cure cells of this plasmid. In summary, the results indicate that this functional genomics project is very promising toward fulfilling our goal to assemble a comprehensive view of the cyanobacterial circadian clock. The mutagenesis reagents and dataset generated in this project will also benefit the greater scientific community.

Giant pandas are unique animals because of their digestive system is similar to carnivores but they have in fact adapted to a plant diet with bamboo as their main food source. According to fossils records, giant pandas were omnivorous approximately 7 million years ago, becoming almost vegetarian after 4.6 to 5 million years of evolution. However, their genome and anatomical structure do not favor bamboo digestion. For more than a decade, researchers have been questioning the underlying mechanism of their ability to digest bamboo. In 2010, the genome of giant panda was completed, which confirmed that their genome had no gene encoding for cellulolytic enzymes. Thus, the gut microbiota of giant panda, which has been hypothesized to play a key role in bamboo digestion, has garnered unprecedented attention. Researchers are also interested in the giant panda’s gut microbes due to their potential application in biomass conversion. In Chapters 2 and 3 of this thesis, the microbial catalog of the giant panda’s gut microbiota was characterized, showing possible age-related microbial dysbiosis. Moreover, the microbiota, both bacterial and fungal was highly individualized because very few operational taxonomic units were shared among the four pandas in this study. Novel homoacetogens were also identified in the giant panda using functional gene clone-library sequencing. Using metagenomic sequencing, I uncovered the first evidence of human and animal related viruses in the giant panda’s gut. In addition to the community structure, I also determined the metabolic pathways of the microbiome. From KEGG annotation, a metabolic pathway for both cellulose and hemicellulose metabolism was identified. Comparative metagenomic analysis indicated that the giant panda’s gut microbiome was taxonomically and functionally distinct from those in mammals. In Chapters 4 and 5, a total of 97 species of bacteria were isolated and identified using biochemical assays. Four of these bacteria showed powerful cellulolytic and hemicellulolytic activities on solid media. The gram-positive bacteria (HKUOP_BS) and the gram-negative bacteria (HKUOP_A14) were found to be rod shaped, facultative anaerobes that had the ability to powerfully hydrolyze both cellulose and hemicellulose using intracellular and extracellular enzymes respectively. In Chapter 6, I determined the complete genome of a cellulolytic bacterium, Klebsiella oxytoca HKUOPL1, from giant panda and further described the annotated virulence, drug resistant, functional and potential horizontal transferring genes. The phylogenomic tree indicated that K. oxytoca HKUOPL1 closely resembled the K. oxytoca KCTC 1686 strain commonly used in 2,3-butanediol production. In captive giant pandas, a mucous excretion episode usually occurs with mild to severe colic. To understand the host-microbial interactions during this episode, bacterial communities were compared between mucous excreta and normal feces. The shifts in community abundance (especially flooding of Clostridia) may be associated with the mucous excretion episode. This study provides a better understanding of the microbial community structure and function in the giant panda’s gut. / published_or_final_version / Biological Sciences / Doctoral / Doctor of Philosophy

No description available.

Genomic sequence data from the three domains of life have revealed a remarkable diversity of genome architectures. The relative contributions of adaptive versus non-adaptive processes in shaping this diversity are poorly understood and hotly debated. This thesis investigates the evolution of genome architecture in the Chloroplastida (i.e., green algae and land plants), with a particular focus on the mitochondrial and plastid genomes of chlamydomonadalean algae (Chlorophyceae, Chlorophyta). Much of the work presented here describes unprecedented extremes in: i) genome compactness (i.e., the fraction of noncoding DNA in a genome), ii) genome conformation (e.g., circular vs. linear vs. linear fragmented genomes), iii) intron and repeat content; and iv) nucleotide-composition landscape (e.g., GC-rich vs. AT-rich genomes). These data are then combined with intra-population nucleotide diversity data to explore the degree to which non-adaptive forces, such as random genetic drift and mutation rate, have shaped the organelle and nuclear genomes of the Chloroplastida. The major conclusions from this dissertation are that chlamydomonadalean algae show a much greater variation in organelle genome architecture than previously thought — this group boasts some of the most unusual mitochondrial and plastid genomes from all eukaryotes — and that the majority of this variation can be explained in non-adaptive terms.

This thesis is an investigation of the concepts and tools required for the successful application of genome ecology on real genomic transposable element (TE) data. Beginning with the formation of an interdisciplinary working group to examine the distinction between ecology and evolution within genome ecology. By establishing these definitions it was possible to account for the relative effect of proxies for these processes in explaining the variation with the TE communities in a group of genomes. This resulted in the finding that ecological processes were could only explain variation in closely related groups of genomes. Thus ecological methods, developed for examining species distributions along a linear transect, were used to analyze the 30 B.taurus chromosomes. This resulted in the identification of 8 TE species responsible for explaining the spatial distribution in the B.taurus chromosomes. This successful application of ecological methods on TE data promises to inspire many other promising interdisciplinary studies. / OGS