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The primary amino acid sequences of the L, M, and H photosynthetic reaction center peptide subunits from a number of purple non-sulfur bacteria, including Rhodopseudomonas viridis, Rhodobacter sphaeroides, and Rhodobacter capsulatus have been previously shown to be highly homologous, and detailed X-ray crystallographic analyses of reaction centers from two species of purple non-sulfur bacteria, Rps. viridis and R. sphaeroides have shown that all recognized structural and functional features are conserved. Experiments were undertaken to determine whether genes encoding reaction center and light harvesting peptide subunits from one species could be functionally expressed in other species. Plasmid-borne copies of R sphaeroides and Rps. viridis pigment binding-peptide genes were independently introduced into a photosynthetically incompetent R. capsulatus mutant host strain, deficient in all known pigment-binding peptide genes. The R. sphaeroides puf operon, which encodes the L and M subunits of the reaction center as well as both peptide subunits of light harvesting complex I, was shown to be capable of complementing the mutant R. capsulatus host. Hybrid reaction centers, comprised of R. sphaeroides-encoded L and M subunits and an R. capsulatus-encoded H subunit, were formed in addition to the R. sphaeroides-encoded LHI complexes. These hybrid cells were capable of photosynthetic growth, but their slower growth rates under low light conditions and their higher fluorescence emission levels relative to cells containing native complexes, indicated an impairment in energy transduction. The Rps. viridis puf operon was found to be incapable of functional expression in the R. capsulatus mutant host. Introduction of a plasmid-borne copy of the Rps. viridis puhA gene, which encodes the H subunit of the reaction center, into host cells already containing the Rps. viridis puf operon, such that all structural peptides of the Rps. viridis reaction center were present, still did not permit stable assembly of Rps. viridis photosynthetic complexes. RNA blot analysis demonstrated that the barrier to functional expression was not at the level of transcription. Differences between Rps. viridis and R. sphaeroides that may account for their differing abilities to complement the R. capsulatus mutant host strain are discussed. / Science, Faculty of / Microbiology and Immunology, Department of / Graduate

The tumor suppressor protein p53, known as the ‘guardian of the genome’, transcriptionally regulates the expression of numerous genes, both coding and non-coding, in response to diverse forms of cellular stress. While numerous reports have been published characterizing the protein coding genes that are transcriptionally regulated by p53, the non-coding targets of p53 are less well-studied. In this thesis, high throughput transcriptome sequencing of cell lines was performed following treatment with different drugs in order to induce p53. Utilizing a combination of de novo transcriptome discovery and mapping to a comprehensive annotation of transcripts named the MiTranscriptome, an extensive catalog of long non-coding RNAs (lncRNAs) was identified. This set of lncRNAs, called p53LTCC (p53 LncRNA Transcriptome from Cultured Cells) are derived from an integrative analysis of RNA-Seq and ChIP-Seq data. It has been previously shown that while the mutation status of p53 may not be a significant predictor of cancer patient survival, a mutant p53 gene expression signature is associated with poor prognosis in many types of cancer. Moreover, the use of attractor metagenes has revealed that the increased expression of metagenes associated with epithelial-mesenchymal transition (EMT), mitotic instability (chromosomal/genomic instability) and lymphocyte infiltration are associated with poor prognosis. Since the p53 pathway is impaired in one way or the other in most tumors, a classifier based on a p53 metagene derived from our p53LTCC was developed that could differentiate between tumor and normal samples based on gene expression. Using machine learning approaches, diagnostic classifiers that could distinguish tumor and normal samples with a high degree of accuracy were developed. Also, while expression of individual long non-coding RNAs had low correlation with patient survival in different cancers, a lncRNA signature that was derived from the catalog of p53 targets had significant prognostic utility for cancer patient survival. Since p53 plays a central role in cancer etiology and it is mutated in over 50% of all cancers, we hypothesized that the lncRNA targets of p53 may have vital functions in effectuating the p53 pathway. Indeed, functional studies of two of the lncRNA targets of p53 showed that they play a role in p53-mediated regulation of cell cycle progression in response to DNA damage and are associated with the regulation of reactive oxygen species (ROS) levels in response to oxidative stress. Although the focus of the experimental studies was to elucidate the role of lncRNAs in the p53 pathway, careful analysis of the transcriptome sequencing results revealed insights into the role of different p53 targets (both coding and non-coding) in different contexts to enable a versatile response to diverse stresses. Not only were we able to identify novel targets of p53, the data showed that there are many p53 targets that are unique to each type of stress. There is also a core transcriptional lncRNA program that is activated by p53 regardless of the context. Finally, during the course of my computational studies, I made numerous observations from bioinformatics analysis of high throughput datasets from different sources that has allowed me to validate many of the experimental results derived by my colleagues (in cell-culture based assays) using cancer patient derived datasets. In order to streamline the workflow of such analysis, I have developed a tool for rapid exploratory data visualization of high throughput datasets for cancer genomics (REDVis) that enables users with minimal programming skills to quickly visualize gene expression, mutation, survival or other clinical, demographic or molecular characterization data for the analysis.

Obesity, or the presence of an excessive amount of body fat is a major public health problem in the United States and, increasingly, the rest of the world. The apparent drivers of the increased prevalence of obesity over the past several decades are environmental changes, e.g., dietary and lifestyle changes that interact with the individual’s genetic susceptibility for weight gain. In humans, obesity appears to be driven primarily by increases of energy intake relative to expenditure; that is, to uncompensated hyperphagia. The heritability of adiposity, i.e., the extent to which differences in adiposity among individuals living in the same environment can be attributed to genetic differences is estimated by twin and other studies to be about 50%. Large scale population-based association studies (e.g., GWAS) have suggested that genetic variants (e.g., SNPs) associated with susceptibility or resistance to obesity affect primarily the development and regulation of the central nervous system (CNS). In particular, SNPs in genes that play a role in brain cellular structures and molecular pathways known to regulate energy homeostasis, most notably, the leptin-melanocortin signaling pathway, are among the most highly associated with human obesity. For example, SNPs around the melanocortin receptor, MC4R, are associated with increased adiposity and mutations in MC4R represent the most prevalent genetic variations associated with monogenic obesity. Ultimately, however, relatively little is understood about the biological mechanisms by which an individual’s genetic sequence confers susceptibility or resistance to weight gain in a specific environment. Such understanding could open new avenues for the prevention and treatment of obesity and would advance our understating of genetic predisposition to other complex diseases. The goal of this research is to identify genomic regions contributing to susceptibility and resistance to hyperphagic obesity by analysis of whole genome sequence and hypothalamic gene expression data from two genetically related cohorts of Sprague-Dawley rats – the ‘Levin Rat’. Dr. Levin developed these animals by successive generations of selective breeding for differences in adiposity resulting from exposure to a calorically dense, highly palatable diet (described in detail in Chapter 2). These selectively bred diet-induced obese (DIO) and diet-resistant (DR) Levin rats have been the topic of a large body of physiological research (reviewed in Chapter 1) showing potentially important similarities to the physiology of human obesity. In particular, implication of diet-sensitive hyperphagia as the primary driver for the differential susceptibility of DIO (diet-induced obese) animals to gain weight in response to palatable diet; neuroanatomical and functional differences between DIO and DR in hypothalamic nuclei (e.g., ARH, PVH) and leptin signaling, prior to the development of obesity; and, neurophysiological differences between DIO and DR (diet-resistant) in ‘reward circuit’ nuclei (e.g., NAc) and their differential responses to pharmacological stimuli, e.g., cocaine, as well as palatable diet. These findings established the Levin rat as an interesting model for aspects of the biology of human obesity. Importantly, the genetic bases for these Levin rat phenotypes have remained unknown. Our efforts to elucidate the underlying genetics of this model system are, therefore, of potential relevance to human obesity. We obtained phenotypic, whole genome sequence (WGS) and hypothalamic gene expression (RNA-Seq) data from selected Levin rats and analyzed these data to identify several loci that are highly associated with the body weight phenotype in the Levin cohorts, as well as in a confirmation cohort of genetically related progeny being studied for phenotypes related to addictive behaviors. In Chapter 2, I describe our methods and approaches to collecting the relevant phenotypic and genetic data, and to selecting primary and confirmation cohorts for the WGS and RNA-Seq studies. In Chapter 3, I describe our bioanalytical and statistical approaches to the WGS data designed to detect genomic loci likely inherited Identical by Descent (IBD) from common ancestors of the DIO, or DR animals; such loci constitute candidate obesity susceptibility loci for the Levin rat. In Chapter 4, I present the phenotypic differences between the DIO and DR animals used for the study, the results of IBD analysis of the WGS, and indicate genes we found to be differentially expressed or spliced in their respective hypothalami. I also show, using confirmation groups (CG) of animals from the primary cohorts of Levin rats (not used for IBD mapping), that the identified susceptibility loci are highly associated with DIO/DR lineage. In Chapter 5 I show, using representative SNPs from the loci with the highest association to the obesity susceptibility phenotype, significant association between the ‘DIO’ genotype and increased body weight in an independent cohort of Levin rat progeny [designated Obesity Prone (OP) and Obesity Resistant (OR)] maintained by Dr. Ferrario (U. Michigan). Using gene set enrichment analyses (GSEA), I show that the candidate susceptibility loci are enriched in CNS genes and genes previously associated with obesity traits in human GWAS. I also analyzed the genes implicated by differential expression and/or splicing (RNA-Seq) in relation to the genetic susceptibility map (IBD analyses) and identified several genes whose hypothalamic differential regulation (e.g., expression, splicing) may be linked to genetic sequence variations between the DIO and DR animals. Interestingly, essentially, all of the identified genes have been previously implicated in body weight regulation in other species, including humans. Finally, I propose future studies to build upon this work in order to further refine the genetic susceptibility map, test the roles of putative candidate genes, and ultimately elucidate the genetic bases for the differences in body weight in this genetically complex mammalian model of diet-sensitive obesity: the Levin DIO/DR rats. In summary, the conclusions from these studies are that: - The 15 susceptibility loci we identified, span in total ~35 Mb, or 1.15% of the rat genome, likely contain a significant portion of the causal alleles underlying the Levin rat phenotype. - The majority of genetic variants in these susceptibility loci are ‘non-coding,’ e.g., intronic, in untranslated regions, or intergenic – similar to common obesity SNPs. - Our susceptibility map is enriched for genes governing CNS function and development, and its human syntenic genes/loci are enriched in obesity SNPs, identified by GWAS. Therefore, Levin rat obesity risk may have a genetic architecture similar to that in humans. - We identified several genes that are differentially expressed or spliced in the hypothalamus of DIO/DR animals, and are also implicated by our genetic map: Zfr, Slc24a2, Fhit, Adarb1, Lrp2. Interestingly, human orthologs of all of these genes have been implicated in obesity by GWAS or familial linkage studies, enhancing them as putative candidates for a role in the Levin rat obesity phenotypes. - Our studies further establish the Levin rat as a model system for polygenic human obesity and lay the foundation for further studies to elucidate the genetic basis of this interesting and important complex trait of hyperphagic diet-sensitive obesity.

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