CD8+ T cells are a crucial component of the adaptive immune system and are required for optimal protection from most pathogens and cancer. They function by secreting pro-inflammatory cytokines and by directly eliminating infected and malignant cells. In order to be effective, CD8+ T cells must be activated through a complex sequence of steps involving engagement of the antigen-specific T cell receptor (TCR) and other receptors, which orchestrate transcriptional, epigenetic, and metabolic changes that direct the differentiation of the responding cells. Following optimal activation, naive CD8+ T cells rapidly undergo clonal expansion and effector differentiation that enables prompt resolution of infection. Following pathogen clearance, a fraction of effector CD8+ T cells differentiate into long-lived memory CD8+ T cells that provide robust protection from re-challenge with the same microbe. However, in the context of persistent abundance of antigen and inflammation, such as in chronic infections and in cancer, the T cells instead become gradually more dysfunctional – a state known as T cell exhaustion. The overarching goal of this thesis is to identify the cardinal features and molecular mechanisms associated with three main states in which CD8+ T cells exist: T cell memory, T cell exhaustion, and T cell effector differentiation. I used two complementary approaches to examine CD8+ T cells at the different states in vivo. First, I used classical immunology techniques including knockout mice and cellular phenotypic analyses to examine the role of cell surface molecules PD-1 and CD39 on CD8+ T cells in the context of memory and exhaustion, respectively. Secondly, I developed a novel experimental platform that enables gene perturbation in naive CD8+ T cells in vivo during their differentiation. I used this approach to systematically interrogate the transcriptional programming of activated CD8+ T cells and to identify novel regulators of effector differentiation. In a proof of concept study, I used this system to further define how the transcription factor BATF regulates CD8+ T cell activation. Additionally, I used this experimental platform to systematically interrogate the functional role of a set of ~80 transcription factors in CD8+ T cell differentiation, and identified TGIF1 as a novel regulator of this process. The role of the co-inhibitory receptor PD-1 on CD8+ T was examined in mice using an acute respiratory infection model. PD-1 is a co-inhibitory receptor that is up-regulated on T cells following activation and recruits SHP1/2 phosphatases to directly antagonize signals through the TCR and this way inhibit the activation of T cells. It is down-regulated following the resolution of an acute infection but remains persistently expressed on CD8+ T cells in chronic infections and cancer. As such, PD-1 has been exhaustively studied for its contribution to the functional exhaustion of T cells. However, its role in acute infections remains less defined. We found that this receptor prevents over-activation and over-expansion of CD8+ T cells following initial differentiation, and is crucial for optimal differentiation of effector CD8+ T cells into functional memory cells. Exhausted CD8+ T cells express several markers distinctive of the state. Some, like PD-1, Tim-3, and Lag-3 are well known. However, genome-wide transcriptional studies identified numerous additional genes that are differentially expressed in the exhausted state. Thus, we hypothesized that additional markers may provide characteristic features of the exhausted cell state and may function in chronic infections. We investigated one such gene – ENTPD1 – that encodes for CD39. This cell surface molecule is an ectonucleotidase that hydrolyzes extracellular ATP into ADP and AMP, which can be further broken down to immunosuppressive adenosine by CD73. In the context of the immune system, CD39 has largely been studied on CD4+ regulatory T cells, which use CD39 as a mechanism to suppress immune responses. However, surprisingly, we found that CD8+ T cells can also express CD39, but its expression is largely restricted to terminally exhausted CD8+ T cells. These cells are most dysfunctional as measured by the most limited proliferative capacity and ability to produce pro-inflammatory cytokines. We have observed this biology in both human and mouse chronic viral infections. Additional studies further demonstrated the importance of CD39 and the purinergic pathway in regulating lethal immunopathology associated with chronic LCMV infection in mice. In addition to interrogating memory and exhaustion fates of CD8+ T cells, we also examined the initial regulatory programs involved in CD8+ T cell differentiation in vivo through gene silencing. Gene perturbation in naive T cells without prior cellular stimulation has been a continuous challenge in the field. To circumvent this limitation, we engineered a novel experimental platform that enables inducible gene knock-down in any immune cell in mice in vivo without prior manipulation of these cells. Initially, I validated this system by knocking down BATF and confirmed its essential role in CD8+ T cell responses to acute LCMV infection. Additionally, leveraging the inducible nature of the platform, I showed that BATF functions in the early stages of T cell activation but becomes dispensable once its transcriptional program is initiated. Several other transcription factors such as T-bet, Eomes, Bcl6, and Blimp-1 have been described to regulate CD8+ T cell differentiation. However, numerous additional transcription factors may function in this process based on their rapid up-regulation following T cell activation. I used the novel platform to systematically test the functional relevance of ~80 additional transcription factors in a pooled setting. These experiments identified several novel candidate regulators of this process. We validated one such gene – Tgif1 – to confirm its role in the effector CD8+ T cell differentiation following acute LCMV infection and provide clues to the potential mechanism in which it may function. The above projects have benefited significantly from genome-wide transcriptional datasets of cells at various states or of different genotypes that we generated or that originate from published studies. One particularly powerful approach to examine differences between different groups is gene set enrichment analysis (GSEA) that examines coordinate up- or down-regulation of sets of genes rather than assessing differential expression of specific genes. This is particularly important because changes in biological processes are often guided by relative small changes of groups of genes that act in concert rather than by a robust expression change of a single gene. This approach, however, is only informative if a relevant gene-set collection is used to analyze the data. Existing collections are largely centered around cancer biology and general biological processes but no extensive gene-set collection existed that contained information describing immune processes. Thus, we created ImmuneSigDB – the largest collection of immunology-focused gene sets to date by identifying, annotating, and reanalyzing ~400 published immunology studies. To show its broad use, we used it to examine the cross-species conservation of transcriptional responses in the immune system. We focused on analyzing transcriptional data from systemic responses to sepsis using GSEA and a novel approach, called leading edge metagene analysis. Using these approaches, we uncovered shared and species-specific biology in mouse and human transcriptional responses to sepsis. Deciphering CD8+ T cell biology is key for conceptualizing new medical interventions that may boost their activation, memory development, and rejuvenation from functional exhaustion. We have determined that PD-1 is essential for optimal CD8+ T cell memory responses, and that BATF is a key transcription factor initiating effector T cell transcriptional programming. We also identified CD39 as a new marker of terminally exhausted CD8+ T cells and uncovered a key role for purinergic signaling in regulating lethal immunopathology in LCMV Clone 13 infection in mice. Furthermore, we developed a new experimental platform that enables systematic interrogation of gene function in any hematopoietic cell type by inducible knock-down of genes and identified TGIF1 as a novel negative regulator of CD8+ T cell responses. We have also developed a new computational resource to improve analyses of transcriptional profiles in the immune system. Together, the body of work presented in this thesis advances our knowledge of major states of CD8+ T cell biology, uncovering both novel mechanisms underlying CD8+ T cell function, as well as highlighting potential novel therapeutic targets that may be transformative in creating better vaccines, treating infections, or fighting cancer. / Medical Sciences

Since their divergence from a common ancestor some 200 million years ago, mammals have undergone significant diversification in physiology, morphology, habitat, size, and longevity. The maximum lifespan of mammalian species ranges from under 3 to over 200 years, but the molecular basis of such variation is poorly understood. While many genes, pathways, dietary interventions, and pharmacological compounds have been shown to influence the lifespan of model organisms, it is not known whether the same mechanisms are responsible for the longevity variation across different species. This thesis presents the analyses of gene expression and the levels of metabolites, chemical elements, and/or proteins, across multiple organs and tissues of up to 42 species of mammals, as well as the analyses of 5 long-lived mouse models, 22 natural isolates of yeast, and 16 species of fruit flies, to identify the molecular patterns and signatures associated with species longevity. The results show that longer-lived mammals up-regulate ribosomal proteins and genes involved in DNA repair, and down-regulate ubiquitin-mediated proteolysis and apoptotic functions. Some of the metabolic changes in long-lived mammals, such as higher levels of sphingomyelins and glycerophospholipids but lower levels of polyunsaturated triacylglycerols, were also observed in long-lived mouse models. Yeast strains of varying replicative lifespan differed in their aerobic respiration capacity, attributable to different protein composition in mitochondria. Long-lived fruit flies overexpressed the genes involved in lipid metabolism but suppressed the genes involved in neuronal development. Many genes previously implicated in lifespan control in model organisms also showed the expected correlation with the longevity traits across species. This thesis presents the snapshots of the complex changes associated with species natural lifespan variation and offers new insights into the mechanisms of longevity control and potential lifespan extension strategies. / Medical Sciences

Staphylococcus aureus is a gram positive coccoid pathogen that causes intractable infections in hospitals and communities around the world, and tens of thousands of people die of these infections every year. In order to combat these antibiotic-resistant infections, we need to better understand the genes involved in resistance to the cell stress caused by antibiotic treatment, which will enable the discovery of new antimicrobials and the development of novel therapeutic strategies. We chose to use an approach to this problem that utilizes a new phage-based high frequency of transposition system. In this work, we adapted this system so that transposon mutant libraries can be made and sequenced using next-generation sequencing (NGS) in any strain of S. aureus. We validated our new platform by performing a temperature screen and identifying mutants that are significantly resistant or sensitive to temperature-stress. Next, we created transposon libraries in two MRSA strains to show that this system can be broadly applied to other S. aureus strains, and we used one of these libraries to identify a new interaction between two genes involved in the secretion of sortase-anchored surface proteins. To better understand antibiotic-resistance, we performed Tn-Seq on transposon libraries treated with a small panel of six different antibiotics to identify intrinsic resistance factors to these antibiotics. We identified two new intrinsic resistance factors, SAOUHSC_01025 and SAOUHSC_01050, that sensitize to many cell envelope targeting antibiotics and may be involved in hemolysin regulation. Finally, we expanded this approach to sequence transposon libraries treated with 25 different antibiotics. Based on these data, we were able to develop methods for predicting the mechanism of action of new antibiotics. These methods involve identifying genes upregulated by transposon insertion and applying machine learning algorithms to identify similarities to a curated panel of well-studied antibiotics with known mechanisms of action. This work will enable many new functional genomics studies in S. aureus, and it will allow us to gain a better understanding of antibiotic resistance in this dangerous pathogen. / Chemical Biology

The research goal was to assess the current capabilities of a metabolic modeling environment to support exploration of inborn errors of metabolism (IEMs); and to assess whether, drawing on evidence from published studies of EMs, the current capabilities of this modeling environment correlate with clinical measures of energy production, fatty acid oxidation, accumulation of toxic by-products of defective metabolism, and mitigation via therapeutic agents. IEMs comprise several hundred disorders of energy production, often with significant impact on morbidity and mortality. Despite advances in genomic medicine, currently the majority of therapeutic options for IEMs are supportive only, and most only weakly evidenced. Metabolic modeling could potentially offer an in silico alternative for exploring therapeutic possibilities. This research established models of two inborn errors of metabolism (IEMs), carnitine palmitoyltransferase (CPT) II deficiency and respiratory chain complex I deficiency, allowing exploration of combinations of IEMs at different degrees of enzyme deficiency. It utilized a modified version of the human metabolic network reconstruction, Recon 2, which includes known metabolic reactions and metabolites in human cells, and which allows constraint-based modeling within a computational and mathematical representation of human metabolism. It utilized the Matlab-based COBRA (Constraint-based Reconstruction and Analysis) Toolbox 2.0, and a customized suite of functions, to model ATP production, long-chain fatty acid oxidation (LCFA), and acylcarnitine accumulation in response to varying defect levels, inputs and a simulated candidate therapy. Following significant curation of the metabolic network reconstruction and customization of COBRA/Matlab functions, this study demonstrated that ATP production and LCFA oxidation were within expected ranges, and correlated with clinical data for enzyme deficiencies, while acylcarnitine accumulation inversely correlated with the degree of enzyme deficiency; and that it was possible to simulate upregulation of enzyme activity with a therapeutic agent. Results of the curation effort contributed to development of an updated version of the metabolic reconstruction Recon 2. Customization of modeling approaches resulted in a suite of re-usable Matlab functions and scripts usable with COBRA Toolbox methods available for further exploration of IEMs. While this research points to potentially greater suitability of kinetic modeling for some aspects of metabolic modeling of IEMs, it helps to demonstrate potential viability of constraint-based steady state modeling as a means to explore some clinically relevant measures of metabolic function for single and combined inborn errors of metabolism.

Breast cancer, the most commonly diagnosed cancer in American women, is a heritable disease with nearly one hundred known genetic risk factors. Using next generation sequencing, we explored the contribution of genetics at 12 GWAS-identified loci to breast cancer susceptibility in a multi-ethnic breast cancer case-control study. Methods: The study population consists of 4,611 breast cancer cases and controls (2,316 cases and 2,295 controls) from four mutually exclusive ethnicities: African, Latina, Japanese, or European American.We conducted rare variant association testing between sequenced genotypes and simulated phenotypes to compare the performance of several approaches for assessing rare variant associations across multiple ethnicities and the statistical performance of different ethnic sampling fractions, including single-ethnicity studies and studies that sample up to four ethnicities. Findings from simulation of causal rare variant penetrance models were then applied to a non-synonymous protein-coding rare variant association study of breast cancer. Next, we applied variance partitioning methods to determine what proportion of breast cancer heritability is explained by rare and common, coding and non-coding, and the complete set of sequenced genetic variants. Results: Variance component-based tests were better powered in several scenarios. Multi-ethnic studies were well powered, with inclusion of African Americans providing the largest gains in statistical power. Rare variation in several genes was nominally associated (alpha=0.05) with breast cancer risk. Common variants explained a significant amount of breast cancer heritability (5%; SE=2%). Total breast cancer heritability from all sequenced SNVs from all 12 loci was approximately 11% (S.E.=4%), a substantial portion of breast cancer heritability which ranges from 27% to 32% in European familial studies. Conclusion: Our findings suggest that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups. We demonstrate practices with the potential to uncover and localize gene-based associations using gene-based rare variant association testing at 12 GWAS-identified breast cancer susceptibility loci. We also present strong evidence that just this subset of previously-identified loci explains a substantial portion of heritability which suggests that all GWAS-identified loci may explain more breast cancer heritability than the 17% previously reported. / Epidemiology

Inferring ancestral gene orders in a phylgenomic tree is an important topic in comparative genomics. In this thesis, three different approaches have been used to infer ancestors, first, using common intervals in a model-free approach and extending it to using common clusters and neighbourhood parameter; second, using double cut and join operation (DCJ); third, using breakpoint distance. A statistically fair comparison between the performance of DCJ and breakpoint criteria ends the thesis. Away from any assumptions or considerations, probabilistic or combinatorial, about specific processes involved in rearranging genomes, we present a new phylogenetic reconstruction method based solely on common intervals. The objective function to be optimized is simply the sum over the tree branches of the symmetric difference between the two sets of intervals associated with the genomes at the two ends of the branch. To achieve this goal, we use dynamic programming optimization to determine the presence of common intervals at the ancestral nodes of the phylogeny. Noticing the drawback that the concept of common intervals suffers from, we introduce the concept of generalized adjacency to find common clusters using a neighborhood parameter that turns out to be closely related to the bandwidth parameter of a graph. Our focus will be on how this parameter affects the characteristics of clusters: how numerous they are, how large they are, how rearranged they are and to what extent they are preserved from ancestor to descendant in a phylogenetic tree. Again, we use dynamic programming optimization to determine the presence of individual edges at the ancestral nodes of the phylogeny. The DCJ (double cut and join) operation introduced by Yancopoulos et al. in 2005 is the most inclusive operation to date as it can generate all the movement rearrangements. One year later, Bergeron et al. restated the DCJ model and produced a simplified (linear) algorithm, which is now the most general existing algorithm to transform one genome into another using genome rearrangements events. Motivated by both, the most inclusive operation, DCJ, and its most general algorithm, we study the small phylogeny problem in the space of multichromosomal genomes under the DCJ metric. This is similar to the existing MGR (multiple genome rearrangements) approach, but it allows, in addition to inversion and reciprocal translocation, operations of transposition and block interchange. Thanks to Tannier et al., the first polynomial solution to the median problem has been found in only one context, namely the case of breakpoint distance on multichromosomal genoms where chromosomes are unconstrained as to linearity or circularity. This motivated us to study the small phylogeny problem using breakpoint median as a third approach, that is different both biologically and computationally from the common intervals and DCJ approaches, and then to compare statistically the performance of both criteria, breakpoint and DCJ. Keywords: phylogenetic tree, genome rearrangment, inversion, reciprocal translocation, transposition, block interchange, common intervals, generalized adjacency, neighborhood parameter, graph bandwidth, multiple genome rearrangement (MGR), double cut and join (DCJ), breakpoint (BP), excess explanatory rate.

<p> X-ray absorption spectromicroscopy combines microscopy and spectroscopy to provide rich information about the chemical organization of materials down to the nanoscale. But with richness also comes complexity: natural materials such as biological or environmental science specimens can be composed of complex spectroscopic mixtures of different materials. The challenge becomes how we could meaningfully simplify and interpret this information. Approaches such as principal component analysis and cluster analysis have been used in previous studies, but with some limitations that we will describe. This leads us to develop a new approach based on a development of non-negative matrix approximation (NNMA) analysis with both sparseness and spectra similarity regularizations. We apply this new technique to simulated spectromicroscopy datasets as well as a preliminary study of the large-scale biochemical organization of a human sperm cell. NNMA analysis is able to select major features of the sperm cell without the physically erroneous negative weightings or thicknesses in the calculated image which appeared in previous approaches.</p>

High turnover in the haematopoietic system is sustained by stem and progenitor cells, which divide and mature to produce the range of cell types present in the blood. This complex system has long served as a model of differentiation in adult stem cell systems and its study has important clinical relevance. Maintaining a healthy blood system requires regulation of haematopoietic cell fate decisions, with severe dysregulation of these fate choices observed in diseases such as leukaemia. As transcriptional regulation is known to play a role in this regulation, the gene expression of many haematopoietic progenitors has been measured. However, many of the classic populations are actually extremely heterogeneous in both expression and function, highlighting the need for characterising the haematopoietic progenitor compartment at the level of individual cells. The first aim of this work was to chart the single-cell transcriptional landscape of the haematopoietic stem and progenitor cell (HSPC) compartment. To build a comprehensive map of this landscape, 1,654 HSPCs from mouse bone marrow were profiled using single-cell RNA-sequencing. Analysis of these data generated a useful resource, and reconstructed changes in gene expression, cell cycle and RNA content along differentiation trajectories to three blood lineages. To investigate how single-cell gene expression can be used to learn about regulatory relationships, data measuring the expression of 41 genes (including 31 transcription factors) in 2,167 stem and progenitor cells were used to construct Boolean gene regulatory network models describing the regulation of differentiation from stem cells to two different progenitor populations. The inferred relationships revealed positive regulation of Nfe2 and Cbfa2t3h by Gata2 that was unique to differentiation towards megakaryocyte-erythroid progenitors, which was subsequently experimentally validated. The next study focused on investigating the link between transcriptional and functional heterogeneity within blood progenitor populations. Single-cell profiles of human cord blood progenitors revealed a continuum of lympho-myeloid gene expression. Culture assays performed to assess the functional output of single cells found both unilineage and bilineage output and, by investigating the link between surface marker expression and function, a new sorting strategy was devised that was able to enrich for function within conventional lympho-myeloid progenitor sorting gates. The final project aimed to study changes to the HSPC compartment in a perturbed state. A droplet-based single-cell RNA-sequencing dataset of 44,802 cells was analysed to identify entry points to eight blood lineages and to characterise gene expression changes in this transcriptional landscape. Mapping single-cell data from W41/W41 Kit mutant mice highlighted quantitative shifts in progenitor populations such as a reduction in mast cell progenitors and an increase towards more mature progenitors along the erythroid trajectory. Differential gene expression identified upregulation of stress response and a reduction of apoptosis during erythropoiesis as potential compensatory mechanisms in the Kit mutant progenitors. Together this body of work characterises the HSPC compartment at single-cell level and provides methods for how single-cell data can be used to discover regulatory relationships, link expression heterogeneity to function, and investigate changes in the transcriptional landscape in a perturbed environment.

<p>Cis-elements govern the key step of transcription to regulate gene expression within a cell. Identification of utilized elements within a particular cell line will help further our understanding of individual and cumulative effects of trans-acting factors. These elements can be identified through an assay leveraging the ability of DNaseI to cut DNA that is in an open and accessible state making it hypersensitive to cleavage. Here we develop and explore computational techniques to measure open chromatin from sequencing and microarray data. We are able to identify 94,925 DNaseI hypersensitive sites genome-wide in CD4+ T cells. Interestingly, only 16%-20% of these sites were found in promoters. We also show that these regions are associated with different chromatin modifications. We found that DNaseI data can also be used to identify precise 'footprints' indicating protein-DNA interaction sites. Footprints for specific transcription factors correlate well with ChIP-seq enrichment, reveal distinct conservation patters, and reveal a cell-type specific arrangement of transcriptional regulation. These footprints can be used in addition to or in lieu of ChIP-seq data to better understand genomic regulatory systems.</p> / Dissertation

<p>Alternative mRNA processing can result in the generation of multiple, qualitatively different RNA transcripts from the same gene and is a powerful engine of complexity in higher organisms. Recent deep sequencing studies have indicated that essentially all human genes containing more than a single exon generate multiple RNA transcripts. Functional roles of alternative processing have been established in virtually all areas of biological regulation, particularly in development and cancer. Changes in alternative mRNA processing can now be detected from over a billion dollars' worth of conventional gene expression microarray data archived over the past 20 years using a program we created called SplicerAV. Application of SplicerAV to publicly available microarray data has granted new insights into previously existing studies of oncogene over-expression and clinical cancer prognosis.</p> <p>Adaptation of SplicerAV to the new Affymetrix Human Exon arrays has resulted in the creation of SplicerEX, the first program that can automatically categorize microarray detected changes in alternative processing into biologically pertinent categories. We use SplicerEX's automatic event categorization to identify changes in global mRNA processing during B cell transformation and show that the conventional U133 platform is able to detect 3' located changes in mRNA processing five times more frequently than the Human Exon array.</p> / Dissertation