Browsing by Subject "Gene expression"

The antimicrobial properties of silver have been taken advantage of by societies for thousands of years. Its use has come back in favor in the form of silver nanoparticles, which are highly efficacious antimicrobial agents. Silver nanoparticles are incorporated into a myriad of products specifically designed for clinical use, but also for general use by consumers. Silver nanoparticles can be found in textiles such as clothing and stuffed toys, and in home appliances including washing machines and curling irons. A large number of products specifically marketed for use by children are also available to consumers, including pacifiers, sippy cups, and even breast milk storage bags. The hazards and toxicities associated with silver nanoparticles are not well understood, however modes of toxicity have been reported for ionic silver. It is assumed that the main mechanism of toxicity of silver nanoparticles relates to the release of ionic silver, however studies have indicated an additional nano-effect, likely due to nanoparticle size, differential coatings, and means of sustained dosing of ionic silver. However we are sure that these silver nanoparticles will accumulate in the waste stream, likely arriving during different stages of a product’s lifespan. A main sink of these nanoparticles travelling through both natural and engineered environments is wastewater treatment plants. As a society we rely on the biological removal of nutrients, which takes place primarily in the activated sludge of secondary treatment. Studies have already indicated possible, temporary decreases in removal efficiencies as well as changes in microbial communities, including losses of diversity, following exposure to silver nanoparticles. Therefore, it is of paramount importance to examine the effects of both silver nanoparticles and ionic silver on the community and function of wastewater bacteria.

Sequencing batch reactors were operated to mimic wastewater treatment. They were fed synthetic wastewater and after reaching acclimation, were dosed over time with varying concentrations of both ionic and nanosilver. Cell samples were collected periodically to assess the presence and identity of cultivable silver resistant bacteria and to map the microbial community changes taking place under different treatments using Next Generation Sequencing. Isolates were tested for the presence of known silver resistance (sil) genes as were activated sludge samples from a collection of domestic wastewater treatment plants, by designing TaqMan probe assays and performing quantitative PCR. The silver resistant isolates were also used to test the growth implications, as well as sil gene expression changes, following treatment with ionic silver and a variety of silver nanoparticles with various coatings, all at multiple concentrations. This was accomplished by performing multiple batch experiments and then using the TaqMan assays and reverse transcription-quantitative PCR.

Overall, microbial community changes were observed in the sequencing batch reactors, and there were differences noted based on treatment, including ionic silver versus nanosilver and between the two silver nanoparticle coatings. Most notably, the possibility of nitrification in wastewater treatment being particularly susceptible was strongly indicated. Individual wastewater bacteria isolates all contained sil genes, as did the majority of the wastewater treatment plant activated sludge, however the levels of actual sil gene expression were inconsistent. This particular finding supports a current body of work indicating that there are alternate modes of bacterial silver resistance in play that we are just becoming aware of.

A general goal of biology is to understand how two or more sets of traits in an organism are related - for example, disease state and genetics, physiology and behavior, or phenotypic variation and gene function. Many of the early advancements in statistical analysis dealt with relating measured traits when one could be represented as a single number. However, many traits are inherently multi-dimensional, and technologies are advancing for rapidly measuring many types of such highly complex traits. Making efficient use of these new, larger datasets requires new statistical models for to biological inference. In this thesis, I develop a method for relating two very different types of traits in sea urchin larvae: morphological shape, and developmental gene expression. In particular, I develop an approach for regression modeling using shape as a response variable. I use this method to address the question of whether variation in the expression of regulatory genes during development predicts later morphological variation in the larvae. I propose a hierarchical random effects factor regression model with shape as a response variable for relating morphology and gene expression when the individuals in each dataset are related, but not identical. I fit an approximation to the general model by breaking it into three discrete steps. I find that gene expression can explain ~25% of mean symmetric form variation among cultures of related larvae, and identify several groups of related genes that are correlated with aspects of morphological variation.

Plants respond to changes in their local environment and, at the same time, influence the environment at a global scale. The molecular and physiological mechanisms regulating this interaction are not completely understood and this limits our capacity to predict the response of vegetation to future environmental changes. This dissertation combined tools from genomics, physiology, and ecology to examine the response of plants to environmental change. Specifically, it focused on processes affecting carbon and water exchange in forest trees because (1) trees are long-lived species that might face repeated environmental challenges; (2) relatively little information exists about the genes and the molecular mechanisms regulating structural and physiological traits in adult, long-lived woody plants; and (3) forest trees exchange a significant amount of carbon and water with the atmosphere and are therefore major players in the global carbon and water cycles.

Water flux through forests depends both on environmental conditions (e.g., soil moisture) and on the hydraulic architecture of individual trees. Resistance to xylem cavitation is an important hydraulic trait that is often associated with drought tolerance but potentially at the cost of reduced carbon uptake. The second chapter of this dissertation evaluated the variation in resistance to xylem cavitation, hydraulic conductivity, wood anatomy traits, and leaf gas exchange across 14 co-occurring temperate tree species including both angiosperms and gymnosperms. The relationship between vulnerability to cavitation (ψ₅₀) and hydraulic conductivity within specific organs (i.e. stems and roots) was not significant when considering the phylogenetic association between species. However, even after phylogenetic correction, photosynthetic carbon uptake (A) was positively correlated with both stem and root ψ₅₀, and stomatal conductance (g_s ) was strongly correlated with root ψ₅₀ . These results suggest that there is a trade-off between vulnerability to cavitation and water transport capacity at the whole-plant level, and that this functional relationship reflects an adaptive response to the environment.

Forests are an important component of the global carbon cycle that can be directly impacted by a rise in atmospheric CO₂ concentration.. The third chapter of this dissertation investigated the effects of long-term exposure to elevated CO2 on the gene expression of mature, field-grown loblolly pine trees. Using cDNA microarrays, I compared the expression of 1784 pine transcripts in trees growing under ambient and those under elevated CO₂ at monthly intervals throughout a growing season. Overall, more genes were upregulated than downregulated by elevated CO₂, although the total number of genes differentially expressed varied throughout the season. The pattern of increasing number of differentially expressed genes until the peak of the growing season (July and August) followed by a decrease in that number, matched the seasonal trend of tree growth and photosynthetic response to elevated CO₂ in this species. The seasonal trend also reflected the interaction among multiple abiotic factors intrinsic to field conditions and emphasized the relevance of evaluating the role of genes in their natural environment. Genes consistently upregulated by elevated CO₂ were functionally associated with environmental sensing, cellular signaling, and carbon metabolism, in particular the degradation of carbohydrates through respiration. An increase in carbohydrates degradation is particularly relevant in the context of carbon balance of forest trees because of the potential for enhanced leaf and tree respiration leading to a reduced sink capacity for CO₂.

Loblolly pine produces several flushes of needles throughout the year each with an average lifespan of 19 months. Each year, two age classes of needles contribute to the annual carbon sequestration of the loblolly pine forest. To address the impact of leaf age on the effects of elevated CO₂ in carbon metabolism regulation, I compared the gene expression profiles from trees under ambient and elevated CO₂ conditions in two needle cohorts: one-year-old and current-year. Differential expression under elevated CO₂ was seven times more frequent in current-year than in one-year-old needles. Despite differences in magnitude, many of the patterns within specific groups of genes were similar across age classes. For instance, there was a trend for downregulation of genes involved in the light-reactions of photosynthesis and those in photorespiration in both age classes, while genes associated with dark respiration were largely upregulated by elevated CO₂ in both cases. The difference between the two cohorts was particularly evident in the group of genes related to energy production (ATP synthesis) and the group associated with carbon partitioning (sucrose and starch metabolism). Because sucrose and starch metabolism categories included many genes known to be important regulators of gene expression and plant physiological processes, this suggests that this stage of carbon metabolism might be an important control point in age-dependent foliar responses to elevated CO₂.

This dissertation examined both structural and physiological components of plant water and carbon relations (Chapter 2) across different biological scales of organization (whole-plant level in Chapter 2; gene-level response to ecosystem-level changes in Chapters 3 and 4) and reflecting adjustments at distinct temporal scales (life-span of the organism vs. evolutionary selection of traits). An integrative approach was used to advance our understanding of how plants acclimate and adapt to their environment, and to provide a mechanistic framework for predictive models of plant response to environmental change.

The immune system is constantly faced with the daunting task of protecting the host from a large number of ever-evolving pathogens. In vertebrates, the immune response results from the interplay of two cellular systems: the innate immunity and the adaptive immunity. In the past decades, dendritic cells have emerged as major players in the modulation of the immune response, being one of the primary links between these two branches of the immune system.

Dendritic cells are pathogen-sensing cells that alert the rest of the immune system of the presence of infection. The signals sent by dendritic cells result in the recruitment of the appropriate cell types and molecules required for effectively clearing the infection. A question of utmost importance in our understanding of the immune response and our ability to manipulate it in the development of vaccines and therapies is: "How do dendritic cells translate the various cues they perceive from the environment into different signals that specifically activate the appropriate parts of the immune system that result in an immune response streamlined to clear the given pathogen?"

Here we have developed computational and statistical methods aimed to address specific aspects of this question. In particular, understanding how dendritic cells ultimately modulate the immune response requires an understanding of the subtleties of their maturation process in response to different environmental signals. Hence, the first part of this dissertation focuses on elucidating the changes in the transcriptional

program of dendritic cells in response to the detection of two common pathogen- associated molecules, LPS and CpG. We have developed a method based on Langevin and Dirichlet processes to model and cluster gene expression temporal data, and have used it to identify, on a large scale, genes that present unique and common transcriptional behaviors in response to these two stimuli. Additionally, we have also investigated a different, but related, aspect of dendritic cell modulation of the adaptive immune response. In the second part of this dissertation, we present a method to predict peptides that will bind to MHC molecules, a requirement for the activation of pathogen-specific T cells. Together, these studies contribute to the elucidation of important aspects of dendritic cell biology.

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.

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.

Introduction: This study explores the cytotoxic effects of Per- and polyfluoroalkyl substances (PFAS), specifically focusing on Perfluorooctanoic acid (PFOA) and its industrial substitute, Gen-X, on a set of human cancer cell lines. PFAS are synthetic chemicals with widespread use and environmental persistence, raising concerns over their potential bio accumulative properties and toxicity. Research aims to elucidate the mechanistic impact of PFOA and Gen-X exposure on cell viability, gene expression, and protein signaling pathways including TGF-β/SMAD, p53 signaling across melanoma (A375), renal (SN12C), liver (HepG2), and colon (SW620) cancer cells.

Method: Employing a comprehensive experimental approach, the study assessed cytotoxicity using the CCK-8 assay, protein expression via Western blot analyses, and gene expression changes through RT-PCR. Four human cancer cell lines were exposed to varying concentrations of PFOA and Gen-X to determine their semi-inhibitory concentrations (IC50) and to analyze the subsequent biological effects.

Result: The results highlighted distinct cytotoxic profiles for PFOA and Gen-X across the examined cell lines, revealing variations in cellular susceptibility and resistance. Melanoma cancer cells (A375) displayed high sensitivity to PFOA but greater tolerance to Gen-X, while renal cancer cells (SN12C) showed significant resistance to both compounds. Molecular analyses indicated that exposure to PFOA and Gen-X modulates the TGF-β/SMAD signaling pathway and activates DNA damage response markers. Furthermore, alterations in the expression of genes related to the cell cycle, apoptosis, and metabolic processes were observed, suggesting potential genotoxic and carcinogenic effects.

Conclusion: The study provides insights into the differential cytotoxic effects of PFOA and Gen-X on human cancer cell lines, underscoring the complex interaction between these PFAS and cellular mechanisms. The findings indicate that PFAS exposure can significantly impact cell viability, gene expression, and protein signaling pathways, contributing to our understanding of their toxicological profile. Given the environmental persistence and widespread use of PFAS, these results underscore the need for further research into their biological impacts and the development of targeted intervention strategies to mitigate their health risks.

An important problem in the analysis of gene expression data is the identification of groups of features that are coherently expressed. For example, one often wishes to know whether a group of genes, clustered because of correlation in one data set, is still highly co-expressed in another data set. For some microarray platforms there are many, relatively short, probes for each gene of interest. In this case, it is possible that a given probe is not measuring its targeted transcript, but rather a different gene with a similar region (called cross-hybridization). Similarly, the incorrect mapping of short nucleotide sequences to a target gene is a common issue related to the young technology producing RNA-Seq data. The expression pattern across samples is a valuable source of information, which can be used to address distinct problems through the application of factor models. Our first study is focused on the identification of the presence/absence status of a gene in a sample. We compare our factor model to state-of-the-art detection methods; the results suggest superior performance of the factor analysis for detecting transcripts. In the second study, we apply factor models to investigate gene modules (groups of coherently expressed genes). Variation in the number of copies of regions of the genome is a well known and important feature of most cancers. Copy number alteration is detected for a group of genes in breast cancer; our goal is to examine this abnormality in the same chromosomal region for other types of tumors (Ovarian, Lung and Brain). In the third application, the expression pattern related to RNA-Seq count data is evaluated through a factor model based on the Poisson distribution. Here, the presence/absence of coherent patterns is closely associated with the number of incorrect read mappings. The final study of this dissertation is dedicated to the analysis of multi-factor models with linear and non-linear structure of interactions between latent factors. The interaction terms can have important implications in the model; they represent relationships between genes which cannot be captured in an ordinary analysis.

Although evolution results from differential reproduction and survival at the level of the individual, most research in evolutionary genetics is concerned with comparisons made at the level of divergent populations or species. This is particularly true in work focused on the evolutionary genetics of natural populations. While this level of inquiry is extremely valuable, in order to develop a complete understanding of the evolutionary process we also need to understand how traits evolve within populations, on the level of differences between individuals, and in the context of natural ecological and environmental variation. A major difficulty confronting such work stems from the difficulty of assessing interindividual phenotypic variation and its sources within natural populations. This level of inquiry is, however, the main focus for many long-term field studies. Here, I take advantage of one such field study, centered on the wild baboon population of the Amboseli basin, Kenya, to investigate the possibilities for integrating functional, population, and evolutionary genetic approaches with behavioral, ecological, and environmental data. First, I describe patterns of hybridization and admixture in the Amboseli population, a potentially important component of population structure. Second, I combine field sampling, laboratory measurements of gene expression, and a computational approach to examine the possibility of using allele-specific gene expression as a tool to study functional regulatory variation in natural populations. Finally, I outline an example of how these and other methods can be used to understand the relationship between genetic variation and naturally occurring infection by a malaria-like parasite, Hepatocystis, also in the Amboseli baboons. The results of this work emphasize that developing genetic approaches for nonmodel genetic systems is becoming increasingly feasible, thus opening the door to pursuing such studies in behavioral and ecological model systems that provide a broader framework for genetic results. Integrating behavioral, ecological, and genetic perspectives will allow us to better appreciate the interplay between these different factors, and thus achieve a better understanding of the raw material upon which selection acts.

In an attempt to gain more insight on functional evolution of cAMP-PKA pathway I have taken a comparative approach and examined functional interactions of cAMP-PKA signaling in well-studied yeast developmental programs and closely related Saccharomyces sensu stricto. species. I have shown that variation in cAMP-PKA signaling contributes significantly to variation in developmental responses in S cerevisiae. Variation in pseudohyphal growth and sporulation, two inversely correlated developmental strategies to nutrient limitation in yeast, proportional to variation in intracellular cAMP levels. S. cerevisiae strains proficient in pseudohyphal growth have higher intracellular cAMP concentrations relative to strains that sporulate efficiently. Phenotypic, genetic and signaling data presented here suggest that the cAMP-PKA signaling underlies a phenotypic trade-off between sporulation and pseudohyphal growth in S. cerevisiae.

Further investigation into the role of cAMP-PKA signaling in closely related S paradoxus and S bayanus revealed an antagonistic function of cAMP-PKA signaling for developmental responses in S. bayanus. Unlike in S. cerevisiae, increased cAMP concentrations surprisingly inhibit pseudohyphal response in S. bayanus. Another unanticipated finding in this work is that in S. bayanus. Flo11, required for pseudohyphal differentiation in S. cerevisiae, is dispensable. Additionally, interactions of cAMP-PKA signaling and the general-stress response mechanism appear reversed in S. bayanus. As shown by deletion mutation, gene expression and pharmacological treatment data, altered interactions and alternative targets downstream of cAMP-PKA could critically contribute to alternative regulation of nutrient-induced development in S. bayanus.

Intracellular cAMP concentrations show decaying oscillations upon glucose replenishment in derepressed yeast cells. The quantitative characteristics of oscillations are distinct within and between Saccharomyces species. Given the tight regulation of cAMP levels and its critical role, the variation in cAMP oscillatory dynamics could be reflective of differential interactions of cAMP-PKA signaling that also underlie induction of developmental programs to changing environments. As such, intracellular cAMP levels and dynamics could potentially be used as molecular phenotypes.

hCB-ECs show varying morphology under co-culture conditions. They are known to form networks when co-cultured on matrices like Matrigel, collagen gels and SMCs. Optimizing the co-culture model for the formation of networks can enable better understanding of angiogenesis and can aid in the area of tissue engineered organs while creating a model with no networks can help in producing a smooth layer of cells for tissue engineered blood vessels. Additionally, the study of networks on smooth muscle cell surfaces gives a better approach to understanding the in vivo phenomena.

The main goal of this study was to identify conditions that would support the formation of networks and to study the gene level alterations in the hCB-EC cells between co-culture and monoculture during the formation and absence of networks. To study the morphological changes co-cultures were setup by varying the hCB-EC densities at 26,316 cells/cm2, 52,632 cells/cm2, 80,000 cells/cm2 and 105,263 cells/cm2 on SMCs. Lower seeding densities of hCB-ECs led to network formations while a confluent layer was observed at the highest density. Medium components were altered to identify factors which contributed to network formation and it was found that absence of VEGF led to delayed cell migration and network formation while absence of heparin produced sparser networks in co-culture. Microarray analysis using four different hCB-EC sources plated at the highest and lowest densities resulted in higher expression of ECM remodeling and endothelial cell migration genes consistent to the low density conditions were networks were abundant while high density conditions expressed downregulation of cell cycle associated genes.

Many factors, including gene networks, developmental processes, and the environment mediate the link between the activity of genes and complex phenotypes in higher organisms. While genetic variants are the raw material for evolution, these other factors are critical for determining which variants are actually exposed to natural selection. In this dissertation, I describe three projects in which I investigate how developmental mechanisms and the environment interact to shape phenotypic variation. In each project, I use gene expression as a window into the activity of genes, and as a tool to measure variation in and among developmental mechanisms. Two projects are experimental, focusing on early development in sea urchins, and how environmental stress caused by climate change impacts the expression of genetic variation in phenotypic traits. In these projects, I explicitly incorporate information about the biochemical functions of genes and how they interact in development, and test how such mechanisms shape the impact of genetic and environmental perturbations to development. The third project is methodological, in which I propose a unified statistical framework for inferring previously unknown developmental constraints that may underlie gene expression phenotypes. Together, these projects demonstrate that an understanding of developmental mechanisms can enhance our understanding of the processes that shape variation in populations, and can help predict the biological effects of climate change.

In this thesis, I investigate the influence of three different factors (environment, chromatin regulation, and genome structure) on gene expression in the evolution and development of the sea urchin species Heliocidaris erythrogramma and Heliocidaris tuberculata. This species pair share a recent common ancestor but exhibit different life history modes, and thus are an excellent model for studying how evolution acts over (relatively) short time scales to influence phenotype. In Chapter 1, I provide an overview of the research that has been conducted on this species pair over the past several decades. I also survey the literature on gene expression in the context of evolution and development, and discuss the various mechanisms that influence gene expression. In Chapter 2, I evaluate how gene expression in H. erythrogramma is affected by exposure to low-pH seawater, in an effort to quantify how this species may be affected by ocean acidification (OA) caused by anthropogenic climate change. It has been previously shown that OA from seawater uptake of rising carbon dioxide emissions impairs development in marine invertebrates, particularly in calcifying species. Plasticity in gene expression is thought to mediate many of these physiological effects, but how these responses change across life history stages remains unclear. The abbreviated lecithotrophic development of the sea urchin Heliocidaris erythrogramma provides a valuable opportunity to analyze gene expression responses across a wide range of life history stages, including the benthic, post-metamorphic juvenile. I measured the transcriptional response to OA in H. erythrogramma at three stages of the life cycle (embryo, larva, and juvenile) in a controlled breeding design. The results reveal a broad range of strikingly stage-specific impacts of OA on transcription, including changes in the number and identity of affected genes; the magnitude, sign, and variance of their expression response; and the developmental trajectory of expression. The impact of OA on transcription was notably modest in relation to gene expression changes during unperturbed development and dwarfed by genetic contributions from parentage. The latter result suggests that natural populations may provide an extensive genetic reservoir of resilience to OA. Taken together, these results highlight the complexity of the molecular response to OA, its substantial life history stage specificity, and the importance of contextualizing the transcriptional response to pH stress in light of normal development and standing genetic variation to better understand the capacity for marine invertebrates to adapt to OA. In Chapter 3, I examine the regulation of chromatin accessibility, and investigate how the mechanisms that govern regulatory element accessibility are similar to or different from those that govern gene expression. Chromatin accessibility plays an important role in shaping gene expression patterns across development and evolution, but little is known about the genetic and molecular mechanisms that influence chromatin configuration itself. Because cis and trans influences can both theoretically influence the accessibility of the epigenome, I sought to better characterize the role that both mechanisms play in altering chromatin accessibility in H. tuberculata and H. erythrogramma. Using hybrids of the two species, and adapting a statistical framework previously developed for the analysis of cis and trans influences on the transcriptome, I examined how these mechanisms shape the regulatory landscape at three important developmental stages, and compared my results to similar patterns in the transcriptome. I found extensive cis- and trans-based influences on evolutionary changes in chromatin, with cis effects slightly more numerous and larger in effect. Additionally, I found that genetic mechanisms influencing gene expression and chromatin configuration are correlated, but differ in several important ways. Maternal influences also appear to have more of an effect on chromatin accessibility than on gene expression, persisting well past the maternal-to-zygotic transition. Furthermore, chromatin accessibility near GRN genes appears to be regulated differently than the rest of the epigenome, and indicates that trans factors may play an outsized role in the configuration of chromatin near these genes. Together, these results represent the first attempt to quantify cis and trans influences on evolutionary divergence in chromatin configuration in an outbred natural study system, and suggest that the regulation of chromatin is more genetically complex than was previously appreciated. In Chapter 4, I consider how genomic architecture differs in H. tuberculata, H. erythrogramma, and the outgroup species L. variegatus, and how alterations in genome structure may be tied to gene expression differences between these three species. There is not a strong consensus on the connection between genome architecture and evolution, though many studies have related certain elements of genome structural variation to alterations in gene expression. Using chromosome-level genome assemblies and phylogenetic orthology inference, I found that genome-wide synteny is tied to divergence time, and that genes within syntenic regions tend to be conserved in function and copy number compared genes in non-syntenic regions. I also leveraged existing RNA-seq and ATAC-seq datasets for these species to demonstrate that genes in rearranged regions exhibit larger between-species differences in overall gene expression and altered regulatory architecture relative to genes in syntenic regions. Furthermore, in comparing my between-species comparisons of genome architecture, I was able to evaluate the extent to which genomic structural variation might underlie the gene expression differences seen in the transition to lecithotrophy. I found that “isolated” genes (individual orthologs that fall outside of syntenic blocks) exhibit particularly dramatic differences in expression trajectory within the Heliocidaris comparison; moreover, I demonstrate that the large amount of unique sequence in the H. erythrogramma genome harbors regulatory elements that alter gene expression in this species. Together, these results show that the locations of breakpoints regions in this phylogeny are unlikely to be random, and in fact have measurable influences on gene expression and, potentially, life history strategy. In Chapter 5, I contextualize the conclusions from these three studies by considering how I might add to them in the future, and also provide concluding remarks on the impact of my thesis.

Fluctuations in nutrient availability occur for nearly all species, and adaptation to endure starvation conditions is essential. Genetic pathways involved in regulating starvation resistance are implicated in aging and complex diseases such as cancer, diabetes, and obesity in humans. Consequences of experiencing starvation persist later in life and subsequent generations, suggesting epigenetic regulation. However, much is still unknown about how starvation resistance is regulated and the contributions of different types of regulation. The roundworm Caenorhabditis elegans reversibly arrests development in the absence of food and can endure starvation for several weeks. Here, we investigate how transcriptional, epigenetic, and genetic regulation impact starvation resistance during developmental arrest in C. elegans. Gene expression dynamics change quickly during the first few hours of starvation, and broadly conserved transcription factors are required for starvation survival. However, temporal- and tissue-specific requirements of transcription for supporting starvation survival and recovery are largely unknown. In chapter 2, we used mRNA-seq combined with temporal degradation of RNA Polymerase II in the soma and germline to better understand gene regulation throughout arrest. We find that transcription is required in the soma for survival early in starvation but is dispensable thereafter, and known transcriptional regulators primarily act early in arrest. In contrast, the germline is transcriptionally quiescent throughout starvation, but germline transcripts are relatively stable compared to somatic transcripts. This reveals alternative gene-regulatory strategies in the soma and germline during starvation-induced developmental arrest, with the soma relying on a robust early transcriptional response while the germline relies on mRNA stability to maintain integrity. Phenotypic plasticity is facilitated by epigenetic regulation, and remnants of such regulation may persist after plasticity-inducing cues are gone, even affecting germ cells to impact subsequent generations. However, the relationship between plasticity and transgenerational epigenetic memory is not understood. Dauer diapause provides an opportunity to determine how a plastic response to the early-life environment affects traits later in life and in subsequent generations. In chapter 3, we find that, after extended diapause, postdauer worms initially exhibit reduced reproductive success and greater interindividual variation. In contrast, F3 progeny of postdauers display increased starvation resistance and lifespan, revealing potentially adaptive transgenerational effects. Transgenerational effects are dependent on the duration of diapause, indicating an effect of extended starvation. In agreement, RNA-seq demonstrates a transgenerational effect on nutrient-responsive genes. This work reveals complex effects of nutrient stress over different time scales in an animal that evolved to thrive in feast and famine. Many conserved genes and pathways regulate starvation resistance, but most genetic analysis in C. elegans has been restricted to a single genetic background, potentially restricting identification of additional genes. Hundreds of genetically distinct wild strains of C. elegans have been whole-genome sequenced and can be used for GWAS. In chapters 4 and 5, we implemented two high-throughput sequencing approaches, RAD-seq and MIP-seq, to determine relative starvation resistance of over 100 wild strains over time. We used GWAS to identify QTL associated with starvation resistance, near-isogenic lines to validate QTL, and CRISPR gene editing to modify specific genes within QTL. We focused on genes in the insulin receptor-like domain (irld) family, as this family has been virtually uncharacterized, but the genes share homology with the sole known insulin-like receptor in C. elegans, DAF-2, which is a major regulator of starvation resistance. We found that specific variants in two members of the irld family confers increased starvation resistance in multiple genetic backgrounds, and this is dependent on the transcription factor downstream of insulin signaling, DAF-16/FOXO. Thus, this work shows that natural genetic variation in novel modifiers of insulin-signaling regulates starvation resistance.

My thesis research has centered on the concept of the peripheral blood cell (PBC) as an indicator of disease and environmental exposure. The PBC is not only easily accessible and constantly replenished, but it provides a snapshot of an individual's health. Doctors have long utilized PBCs as indicators of health based on count, morphology or the expression of particular cell surface markers. Using these methods, PBCs can serve as indicators of infection, inflammation or certain types of hematological malignancies. Now PBCs can be characterized as a function of their gene expression profiles in response to disease and toxicant exposure. Advances in cDNA microarray technology have made it possible to analyze global gene expression in small volumes of whole blood, or even in a sorted population of blood cells. The resulting gene expression data can serve as a molecular phenotype, or signature, of disease or toxicant exposure. These signatures serve a twofold purpose. First, they act as biological markers (biomarkers) that can indicate the presence of disease or aid monitoring the response to treatment. Second, they provide insight into the underlying biological mechanisms that are at work, by revealing genes, networks and pathways that are affected by the disease or toxin. This paradigm has been applied in a number of contexts, including infection, inflammation, leukemia, lymphoma, neurological disorders, cardiovascular disease, environmental exposures and solid tumors.

In the work presented here, we describe signatures of lead (Pb) exposure and breast cancer based on peripheral blood gene expression. Our objective in generating a blood-based signature of lead exposure was to develop a potential predictor of past and present exposure. This is particularly relevant because of continued widespread lead exposure through both environmental and occupational sources. Pb causes significant toxicities in a number of different organ systems including the hematological, endocrine, neurological and renal systems. Pb is considered a potential carcinogen due to evidence that it causes cancer in animal models and contributes to an elevated cancer risk in humans. Pb is thought to contribute to cancer risk indirectly through a variety of mechanisms, such as inhibition of DNA synthesis and repair, oxidative damage, interaction with DNA-binding proteins and tumor suppressor proteins, causing chromosomal aberrations and alterations to gene transcription. In addition, it has been shown to exacerbate the effects of other mutagens. Recent work also indicates that even low-level Pb exposure (defined here as levels below the threshold of detection of many common tests or below the level set by the CDC as an "elevated blood lead level" in children, or 10µg/dL) can impact health, especially in children, who are more susceptible to these negative health consequences.

We hypothesized that we could detect subtle and lasting changes in the PBC transcriptome that correlated to Pb exposure. We used a mouse model of per os Pb exposure to generate signatures corresponding to two different doses of Pb. One dose reflected a high-level exposure and the other a low-level exposure. We also analyzed the gene expression changes following removal of the Pb source. We were able to generate robust, dose-specific signatures of Pb exposure. This supports the growing body of evidence that even low levels of Pb exposure can have biological effects, and that there is likely no safe level of exposure. We also utilized a collection of pathway signatures to identify those pathways that were activated or repressed in response to Pb exposure compared to controls. We observed an increase in interferon-gamma pathway activity in response to low-level Pb exposure and an increase in E2F1 pathway activity in response to high-level Pb exposure. These results support previous findings that low-level Pb exposure can increase interferon-gamma production, whereas high-level Pb has been shown to increase DNA synthesis. The Pb signatures we report here were not predictive of a past lead exposure. These results suggest that the effect of Pb exposure on PBC gene expression is transient, perhaps due to the rapid turnover of blood cells and the absorption of Pb by the bones. We have proposed further studies to identify cells in the bone marrow that may serve as indicators of past Pb exposure based previous reports on the lasting effects of genotoxic stress on this tissue.

We also describe a predictor of human breast cancer based on peripheral blood gene expression. The objective of this study was to identify and characterize PBC gene expression patterns associated with the presence of a breast tumor. This work has the potential to make a significant impact on breast cancer screening and diagnosis. Despite the success of mammography in reducing mortality from breast cancer, many cancers go undetected due to factors such as breast density, age of the woman, or type of cancer. A blood-based breast tumor predictor would potentially offer an easy and noninvasive means of detecting primary breast cancer as well as monitoring patients for recurrences or metastases. In addition, the concept of using a blood-based biomarker for cancer detection would have positive implications for other types of cancer. For instance, patients with ovarian cancer are typically diagnosed at a late stage because of the absence of definitive symptoms and the lack of effective screenings methods.

We were able to successfully identify robust predictors of both mouse mammary tumors and human breast tumors based on PBC gene expression. The human breast tumor predictor exhibits a high level of sensitivity and specificity in distinguishing breast cancer patients and controls in an independent validation cohort. However, the true novelty in this study is that it integrates a factor modeling approach and a transgenic mouse model of breast cancer to identify biologically meaningful gene expression changes in the mouse PBC transcriptome. These genes were then used as the starting point for developing a human breast cancer predictor. This establishes an experimental system in which we can address questions that are inherently difficult to answer in human studies, such as whether this predictor is useful in detecting breast tumors early or in monitoring patients for recurrence or metastasis. In fact, our work suggests that tumor-associated gene expression changes in the PBCs can be detected in asymptomatic mice. Our results support those of previous studies, which identified blood gene expression profiles that are associated with a variety of solid tumors, including breast cancer. However, the sensitivity and specificity of our predictor are higher than that of the previously reported breast cancer signature. This may suggest that our strategy of using a mouse model to first identify informative genes allowed us to focus on those genes most relevant to the presence of a breast tumor and overcome the influence of the high degree of variation in blood gene expression in our human population. In order to be clinically useful, the predictor we report here would need to be tested in additional, large validation sets to establish its utility in an early detection setting and its specificity in distinguishing breast cancer from other cancer types as well as other potentially confounding conditions such as infection and inflammation. We describe some preliminary experiments in the mouse model intended to address these important questions.

Glioblastoma (GBM) is a grade IV astrocytoma in which the median overall survival is approximately 15 months at time of diagnosis. Even with the current multi- modal therapeutic approach of surgery, chemotherapy with the DNA alkylating agent temozolomide, and radiation therapy, GBM remains uniformly lethal. Immunotherapeutic interventions are a burgeoning field in many different cancer treatments. They offer the exquisite specificity endowed by the immune system with minimal toxicities and new methods are being developed to enhance the endogenous immune responses.

With the recent identification of human cytomegalovirus (CMV) present within glioblastoma tissue but void in the surrounding normal healthy parenchyma there have been significant efforts aimed at understanding the biologic implications of the presence of the virus within GBM tissues with preliminary work demonstrating several capabilities of the virus to enhance the oncogenic process.

Likewise, a key area of importance in the development and design of effective immunotherapeutic platforms is the identification and targeting of tumor-specific antigens. The success of any immunotherapy platform relies heavily on the ability to selectively target antigens present within tumors but absent on healthy tissue, regardless of its role in tumorigenesis, as well as having robust immunogenic properties.

CMV offers a plethora of possible targets, as it is the largest known DNA virus that infects humans, yet very little is known about its biological significance in glioblastoma pathogenesis as well as the most efficacious and immunogenic targets for immunotherapeutic development.

We have been able to elucidate more thoroughly the feasibility and potency of an immunologic platform targeting CMV within glioblastoma utilizing a multi-antigen multi-component peptide based strategy that demonstrated significant immunogenicity and anti-tumor activity in pre-clinical models utilizing various assays. We have also developed several sensitive and specific detection methodologies including: 1) custom gene expression microarrays, 2) multiplex real time quantitative polymerase chain reaction (RT-qPCR) assays, 3) a massively parallel RNA deep sequencing platform, and 4) immunological assays. We have also successfully determined the capacity for endogenous CMV gene expression to be maintained in primary glioblastoma cell lines as well as examining the preponderance of CMV gene expression in a subpopulation of glioma stem cell-like cells, the slow cycling GBM cells established from primary tumor tissues, in an attempt to illuminate some of the biologic underpinnings of CMV with respect to GBM pathogenesis.

Taken together, these data lay the groundwork for the development of a more efficacious vaccination strategy targeting CMV in GBM. The screening strategies employed throughout this work will allow for an accurate antigenic profile of CMV in GBM which will subsequently permit the design of a more robust peptide vaccine for the next generation of cancer vaccine. We have also begun to describe some of the interesting biologic phenomena associated with CMV in GBM, as our results demonstrate continued viral gene expression in glioma stem cell-like cell populations indicating viral tropism for certain cell types.

The evolution of copper tolerance in the common yellow monkeyflower, Mimulus guttatus, is a classic example of rapid adaptation in plants; however, little is yet known about the genetic, functional, and evolutionary mechanisms involved. How does excess copper cause toxicity in M. guttatus, and how do tolerant plants avoid negative effects? What genes are involved in adaptations to copper mine tailings? How do they contribute to tolerance, and what do they teach us about how copper tolerance evolved? In my dissertation, I address these questions to gain a better understanding of this rapid local adaptation.

To better understand how copper affects M. guttatus, we used RNA-seq to find genes that are differentially expressed between hydroponically-grown tolerant and nontolerant lines of M. guttatus in excess copper. In addition, we used F1 hybrids to examine allele specific expression of candidate genes. We found that copper causes more differential gene expression and greater oxidative stress in the nontolerant genotype than in the tolerant genotype. Additionally, comparisons of differentially expressed genes between genotypes revealed that more genes are constitutively expressed than induced. We identified possible candidate genes and found that most of them are cis-regulated, while genes that were systemic or downstream responses to excess copper are more likely to be trans-regulated. Together, these patterns suggested that the tolerant genotype avoids oxidative stress by either excluding, sequestering, or pumping out copper ions from the cells.

To identify loci that contribute to adaptations to copper mine tailings, we looked for regions that directly contribute to survival in copper-contaminated soil. We first characterized two parental inbred lines to determine whether copy number and gene expression differences of candidate genes are consistent with previous results. We then mapped differential survival of F2s to find loci that correlated with survival and determine their effects on fitness. We compared a list of candidate genes to our results to see if any co-localized with survival. We looked for interactions between loci and tested how these genes contribute to copper tolerance. At least two loci on Scaffolds 4 and 9 contributed to survival on copper soil. Multi-copper oxidase (MCO) and copper ion ATPase transporter (COP) both co-localized with these loci and had large effects on fitness. The MCO locus was required and sufficient for survival, while the COP locus acted additively with MCO. We hypothesized that the causal gene at the MCO region was selected for first in the evolution of copper tolerance, and that these two genes work together to exclude Cu+ ions from the cell. Together, our findings improved our understanding of the genetic, functional, and evolutionary mechanisms of copper tolerance in M. guttatus and provide evidence for at least two genes that are involved in this common adaptation.

Variations in gene expression are potential risk factors for atherosclerosis, which is one of the most common forms of cardiovascular disease. We performed a localized Pearson correlation test in 372 individuals from seven datasets relevant to cardiovascular disease studies. The genomes of samples were separated into 20Mb windows and correlation tests were performed locally in these windows. The localized Pearson correlation test found chr3:115Mb–135Mb was tightly connected by significantly high proportion of highly correlated pairs (P value = 0.0266 with Z-test). LSAMP, GATA2, MBD4, and other genes in the region were considered associated with cardiovascular disease because they were involved in highly correlated pairs. Furthermore, these genes were also associated with cardiovascular disease by having significantly high SNP odds ratios (P value < 0.1) between patients and controls in an independent Duke University Medical Center database. In addition, a permutation test was performed to demonstrate that chr3:115Mb–135Mb might underlie the regulation of cardiovascular disease. Finally, the localized Pearson correlation test also found some other regions that could be associated with cardiovascular disease.

Advances in technologies for gene expression profiling have resulted in an unprecedented abundance of gene expression data. However, computational methods available for the exploratory analysis of such data are limited in their ability to generate an interpretable overview of biologically relevant similarities and differences among samples. This work first introduces the XL-mHG test, a sensitive and specific hypothesis test for detecting gene set enrichment, and discusses its algorithmic and statistical properties. It further introduces GO-PCA, a method for exploratory analysis of gene expression data using prior knowledge. The XL-mHG test serves as a building block for GO-PCA. The output of GO-PCA consists of functional expression signatures, designed to provide an interpretable representation of biologically meaningful variation in the data. The power and versatility of the method is demonstrated on heterogeneous human and mouse expression data. Finally, applications of the proposed methods to carcinoma and lymphoma expression data aim to demonstrate their clinical relevance. The effective utilization of prior knowledge in the exploratory analysis of gene expression data through carefully designed computational methods is essential for successfully harnessing the power of current and future platforms for gene expression profiling, with the aim of generating clinically relevant insights into complex diseases such as cancer.