Neisseria meningitidis is a gram negative, and sometimes encapsulated, diplococcus that causes devastating disease worldwide. For the worldwide genetic surveillance of N. meningitidis, the gold standard for profiling the bacterium uses genetic loci found around the genome. Unfortunately, the software for analyzing the data for these profiles is difficult to use for a variety of reasons. This thesis shows my suite of tools called the Meningococcus Genome Informatics Platform for the analysis of these profiling data. To better understand N. meningitidis, the CDC Meningitis Laboratory and other world class laboratories have adopted a whole genome approach. To facilitate this approach, I have developed a computational genomics assembly and annotation pipeline called the CG-Pipeline. It assembles a genome, predicts locations of various features, and then annotates those features. Next, I developed a comparative genomics browser and database called NBase. Using CG-Pipeline and NBase, I addressed two open questions in N. meningitidis research. First, there are N. meningitidis isolates that cause disease but many that do not cause disease. What is the genomic basis of disease associated versus asymptomatically carried isolates of N. meningitidis? Second, some isolates' capsule type cannot be easily determined. Since isolates are grouped into one of many serogroups based on this capsule, which aids in epidemiological studies and public health response to N. meningitidis, often an isolate cannot be grouped. Thus the question is what is the genomic basis of nongroupability? This thesis addresses both of these questions on a whole genome level.

<p dir="ltr">Crohn’s disease and ulcerative colitis are chronic inflammatory bowel diseases (IBD) with a rising global prevalence, influenced by clinical and demographics factors. The pathogenesis of IBD involves complex interactions between gut microbiome dysbiosis, epithelial cell barrier disruption, and immune hyperactivity, which are poorly understood. This necessitates the development of novel approaches to integrate and model multiple clinical and molecular data modalities from patients, animal models, and <i>in-vitro</i> systems to discover effective biomarkers for disease progression and drug response. As sequencing technologies advance, the amount of molecular and compositional data from paired measurements of host and microbiome systems is exploding. While it is become routine to generate such rich, deep datasets, tools for their interpretation lag behind. Here, I present a computational framework for integrative modeling of microbiome multi-omics data titled: Latent Interacting Variable Effects (LIVE) modeling. LIVE combines various types of microbiome multi-omics data using single-omic latent variables (LV) into a structured meta-model to determine the most predictive combinations of multi-omics features predicting an outcome, patient group, or phenotype. I implemented and tested LIVE using publicly available metagenomic and metabolomics data set from Crohn’s Disease (CD) and ulcerative colitis (UC) status patients in the PRISM and LLDeep cohorts. The findings show that LIVE reduced the number of features interactions from the original datasets for CD to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes, clinical variables, and a disease status outcome. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the meta-model integrating -omic data types.</p><p dir="ltr">A novel application of LIVE modeling framework was associated with sex-based differences in UC. Men are 20% more likely to develop this condition and 60% more likely to progress to colitis-associated cancer compared to women. A possible explanation for this observation is differences in estrogen signaling among men and women in which estrogen signaling may be protective against UC. Extracting causal insights into how gut microbes and metabolites regulate host estrogen receptor β (ERβ) signaling can facilitate the study of the gut microbiome’s effects on ERβ’s protective role against UC. Supervised LIVE models<b> </b>ERβ signaling using high-dimensional gut microbiome data by controlling clinical covariates such as: sex and disease status. LIVE models predicted an inhibitory effect on ER-UP and ER-DOWN signaling activities by pairs of gut microbiome features, generating a novel of catalog of metabolites, microbial species and their interactions, capable of modulating ER. Two strongly positively correlated gut microbiome features: <i>Ruminoccocus gnavus</i><i> </i>with acesulfame and <i>Eubacterium rectale</i><i> </i>with 4-Methylcatechol were prioritized as suppressors ER-UP and ER-DOWN signaling activities. An <i>in-vitro</i> experimental validation roadmap is proposed to study the synergistic relationships between metabolites and microbiota suppressors of ERβ signaling in the context of UC. Two i<i>n-vitro</i> systems, HT-29 female colon cancer cell and female epithelial gut organoids are described to evaluate the effect of gut microbiome on ERβ signaling. A detailed experimentation is described per each system including the selection of doses, treatments, metrics, potential interpretations and limitations. This experimental roadmap attempts to compare experimental conditions to study the inhibitory effects of gut microbiome on ERβ signaling and how it could elevate or reduce the risk of developing UC. The intuitive interpretability of the meta-model integrating -omic data types in conjunction with the presented experimental validation roadmap aim to transform an artificial intelligence-generated big data hypothesis into testable experimental predictions.</p>

<p>  </p> <p>Microbiome studies are varied and involve the examination of microorganisms at different levels: individual cells to determine individual functions, populations of specific microorganisms to determine interactions between organisms, and/or communities of microorganisms for a broader investigation of interactions between organism and environment. These studies are typically done within the context of a particular niche or environment. There are two parts to this dissertation, separated by the types of research involved. First, the analysis of bacterial communities using 16S rRNA sequencing and analysis. In this first part the bacterial communities of the reproductive tract of bulls and the gastrointestinal tract of weanling pigs were studied. The reproductive organs of the male, domestic species had not been studied from an ecological perspective prior to the study. As such, the research was mainly focused on characterizing the bacterial communities found within the prepuce of bulls that were considered to be healthy, or that the breeding soundness exam was satisfactory and the bulls had no clinical disease in the urogenital tract. Through this study two distinct types of bacterial communities were found based on the diversity of the observed taxa; the groups were split into a low diversity group identified by the presence of <em>Bradyrhizobium</em> and a high diversity group distinguished by the abundance of mucosal-associated bacteria found in oral, respiratory, and vaginal communities of cattle. Second, the effects of supplementary, soluble fiber on the intestinal bacterial communities of piglets pre- and/or post-weaning were studied. The rationale behind this study was to determine if pre-weaning fiber could alter the microbiome prior to weaning and the change of diet from liquid to solid. Pre-weaning, supplementary, soluble fiber was found to increase short-chain fatty acid concentrations and bacterial taxa potentially involved in their production. Additionally, bacterial taxa implicated in an increased inflammatory response were reduced in groups fed supplementary fiber. Taken together, the two bacterial community studies highlight the gaps in knowledge for reproductive communities in male animals as well as the potential for reducing weaning stress in pigs. Part two of this dissertation focuses on whole genome sequence analysis as a way to study bacterial populations associated with bovine respiratory disease (BRD), a common and potentially fatal disease in cattle. Identification of BRD has low accuracy and the presence of antibiotic resistant bacteria increases the chance of treatment failure. Using machine learning, the prediction of antibiotic resistance in bacterial isolates from animals with BRD was performed to find potential sequences for use in future molecular assays. While using known resistance genes was helpful for some antibiotics, several of the antibiotics used in treating BRD were better predicted using the machine learning models. Model output sequences should be further tested using molecular methods to determine function and importance before using as an assay target. Put together, the contents of this dissertation should serve as an introduction to bacterial ecology as well as how the concepts can be applied to food animal production systems.</p>

<p>  </p> <p>Epstein-Barr virus (EBV) is a complex oncogenic symbiont. The molecular mechanisms governing EBV carcinogenesis remain elusive and the functional interactions between virus and host cells are incompletely defined. Some of the known mechanisms include viral integration into the host genome, expression and mutation(s) of viral genes and the host response to the virus. Despite decades of research there is a lack of effective treatment options for EBV-positive cancer patients underscoring an urgent need to further investigate the mechanisms underlying tumorigenesis as well as explore and develop personalized treatment strategies for patients with EBV-positive cancers. In Chapter 1, I introduce Epstein-Barr Virus (EBV), the two phases of EBV lifecycle and an overview of certain EBV-associated carcinomas. I will also discuss the underlying mechanisms and few current therapeutic strategies against EBV infection. Next, I will discuss some of the preclinical model systems and high-throughput computation techniques that are commonly used by researchers in the field of EBV.  </p> <p>In chapter 2, we have systematically analyzed RNA-sequencing from >1000 patients with 15 different cancer types, comparing virus and host factors of EBV+ to EBV- tissues to reveal novel insights into EBV-positive tumors. First, we observed that EBV preferentially integrates at highly accessible regions of the cancer genome with significant enrichment in super-enhancer architecture. Second, we determined that the expression of twelve EBV transcripts, including LMP1 and LMP2, correlated inversely with EBV reactivation signature. Over-expression of these genes significantly suppressed viral reactivation, consistent with a ‘Virostatic’ function. Third, we identified hundreds of novel frequent missense and nonsense variations in Virostatic genes in cancer samples, and that the variant genes failed to regulate their viral and cellular targets in cancer. Lastly, we were able to dichotomously classify EBV-positive tumors based on patterns of host interferon signature genes and immune checkpoint markers, such as PD-L1 and IDO1. </p> <p>In chapter 3, we probed the lifecycle of EBV on a cell-by-cell basis using single cell RNA sequencing (scRNA-seq) data from six EBV-immortalized lymphoblastoid cell lines (LCL). While the majority of LCLs comprised cells containing EBV in the latent phase of its life cycle, we identified two additional clusters that had distinct expression of both host and viral genes. Both clusters were high expressors of EBV Latent Membrane Protein-1 (LMP1) but differed in their expression of other EBV lytic genes, including glycoprotein gene GP350. We further probed into the transcriptional landscape of these clusters to identify potential regulators which will be discussed in further detail in the chapter. Importantly, I was able to demonstrate enhancing HIF1-a signaling by using Pevonedistat, a compound that stabilized HIF1-a can preferentially induce the transcriptional program specific to one of the three identified clusters. </p> <p>In Chapter 4, I describe some of my recent work. In this project, we have used an intuitive <em>in-silico </em>drug prediction approach to rapidly screen and identify FDA-approved or clinically available compounds that can be repurposed to induce lytic cycle in different EBV+ tumors. Using this strategy, we identified Ciclopirox, an antifungal drug, as a potent inducer of lytic cycle in EBV+ epithelial cancers. We used EBV+ GC cells to determine the effect of Ciclopirox on EBV reactivation as well as identify the underlying mechanisms. In summary, we discovered that reactivation of EBV lytic cycle by Ciclopirox is mediated by multiple pathways, two of the major ones being the HIF1-a and NF-kB pathways. Although, Ciclopirox treatment enhanced the killing effect of antiviral, further investigation is needed to effectively deliver this drug <em>in vivo.</em> Throughout this chapter, I have discussed findings that needs further investigation and proposed necessary experiments. Finally, in Chapter 5 I have summarized my work and described how our work can provide novel insights that can help delineate some of the complexities of host-pathogen interactions in EBV-associated malignancies. </p>

<p dir="ltr">The primary purpose of this thesis is to propose and demonstrate BioGRNsemble, a modular and flexible approach for inferencing gene regulatory networks from RNA-Seq data. Integrating the GENIE3 and GRNBoost2 algorithms, this ensembles-of-ensembles method attempts to balance the outputs of both models through averaging, before providing a trimmed-down gene regulatory network consisting of transcription and target genes. Using a Drosophila Eye Dataset, we were able to successfully test this novel methodology, and our validation analysis using an online database determined over 3500 gene links correctly detected, albeit out of almost 530,000 predictions, leaving plenty of room for improvement in the future.</p>