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Background: Genome-wide association studies have identified numerous disease-associated variants, but a vast majority are located in non-coding regions, making it challenging to understand their functional impact. This complexity necessitates new techniques to identify causal variants in non-coding regions and elucidate their specific cellular contexts and mechanisms of action. Here we present work i) examining mutations that create nullomers in the human genome to explore its potential utility in identifying pathogenic mutations and ii) a single-cell multi-omic study identifying the transcriptome and regulome of the human and mouse hypothalamus to identify regulatory regions of obesity-associated variants.Methods: (i) We generated all possible mutations of the human genome that can lead to emergence of a nullomer, and examine where in the genome they emerge. (ii) We apply single-cell RNA and ATAC sequencing to adult hypothalamus samples.Results and Conclusions: (i) Our findings highlight CpG hypermutability and methylated cytosines as key elements leading to resurfacing of nullomers in individuals. We also showcase that nullomers can have applications in disease annotation and pathogenic variant identification. (ii) We identified regulatory elements of hypothalamus cell types and mapped obesity-associated variants to cell-type specific peaks. We validated these regions to be enhancers using CRISPR editing and CRISPRi.

Complex traits are intricately intertwined with an organism's genome, a relationship underscored by the dynamic landscape of its transcriptome. Selective gene expression regulates cell type specialization and fluctuation of cell states. The development of RNA sequencing has facilitated the capture of the whole transcriptome of a given sample. However, a bulk approach obscures cell type heterogeneity, impeding the precise dissection of cell-specific effects, including those modulated by genotype, developmental stage, and disease state. In contrast, single-cell and single-nucleus RNA-seq preserves cellular identity, enabling a comprehensive mapping of gene expression across various cell types and states.Here, I describe my work in single-cell transcriptomics to characterize cell types and cell states in mouse. First, I present our long-read single-cell RNA-seq method, benchmarked in the C2C12 mouse myogenic system, which revealed cell type-specific isoform switching in key genes during myogenesis. Next, I characterize 5 mouse tissues at single-nucleus resolution during postnatal development using the ENCODE4 mouse dataset, where I used topic modeling to reveal cell type- and state-specific cellular programs. Lastly, I investigate the impact of genetic variation on gene expression across 8 diverse tissues from 8 mouse genotypes, pinpointing genotype- driven variation in specific celltypes in both wild-derived and classical lab strains. Together, these projects lay the groundwork for cohesive cell type and cell state annotation and comparative analyses, contributing to future characterization of these tissues in other contexts such as human diseases and hybrid mouse genotypes.

Cancer has historically been considered a genetic disease originating from activating mutations of oncogenes or inactivating mutations of tumor suppressors. More recently, appreciation for the contributions of epigenetic mechanisms to carcinogenesis has grown as they help to explain the aberrant gene expression and cellular identity associated with many cancers. For example, changes in DNA methylation at CpG dinucleotides can alter gene expression to promote malignant properties. In pancreatic ductal adenocarcinoma (PDA), the genetic underpinnings of tumorigenesis are known and primarily originate from mutations in KRAS and TP53. Furthermore, transcriptomic studies have identified transcriptional programs that are commonly enriched in subsets of patients, resulting in two main subtypes of PDA with clinical implications: squamous-PDA and progenitor-PDA. An understanding of the genome-wide DNA methylation aberrations in PDA and their contribution to tumor progression and subtype identity is less clear. In this dissertation, the genome-wide DNA methylation changes across PDA stages and subtypes as well as the impacts on metastatic characteristics and subtype differentiation are explored. Chapter 1 is a review of the literature highlighting our current understanding of the DNA methylation landscape in PDA which has primarily been learned from microarray studies that assay a very limited subset of CpGs. In Chapter 2, we characterize the genome-wide DNA methylation landscape of PDA organoids using whole genome bisulfite sequencing (WGBS), identifying and characterizing stage-specific and subtype-specific DNA methylation differences that may be beneficial as prognostic and predictive biomarkers. In Chapter 3, we identify subtype-specific DNA methylation differences in commercially available PDA cell lines and dissect GATA6-mediated epigenetic contributions to progenitor-PDA subtype differentiation. GATA6 is a known master regulator of progenitor-PDA and we found that its binding motifs are enriched in hypermethylated regions of squamous-PDA. While restoration of GATA6 expression in squamous-PDA did upregulate the progenitor-PDA gene signature, it did not reverse the observed hypermethylation suggesting that GATA6 does not directly mediate DNA methylation of its binding regions. Instead, GATA6 may epigenetically regulate transcription of its target genes through its interaction with the SWI/SNF family of chromatin remodelers. Thus, the SWI/SNF complex may be a therapeutically actionable target for treating patients with the progenitor-PDA subtype. In Chapter 4, we perform a combined transcriptomic and DNA methylome analysis of mouse PDA organoid models to identify genes whose methylation-mediated silencing likely contributes to PDA metastasis. We found that hypermethylation of the Pdzrn3 promoter represses Pdzrn3 transcription and promotes metastatic characteristics including increased cell migration and a pro-migratory cell morphology. Chapter 5 summarizes the main findings of this work, applications of the research, and future directions.

Since its inception in the 1970s with Frederick Sanger's pioneering work on Sanger sequencing, DNA sequencing technology has undergone a remarkable evolution. Automated Sanger sequencing in the 1980s and 1990s significantly increased throughput and accuracy, while the 21st century saw the emergence of next-generation sequencing (NGS) technologies like Illumina, revolutionizing genomics research by enabling high-throughput sequencing (van Dijk et al. 2014). However, short-read sequencing techniques, including Illumina, have limitations in accurately resolving repetitive regions and complex structural variations within the genome, hindering comprehensive understanding. In response, third-generation sequencing technologies such as Pacific Biosciences' (PacBio) and Oxford Nanopore Technologies (ONT) have arisen, offering long-read capabilities but facing challenges like DNA polymerase limitations (PacBio) and high error rates (ONT)(Rhoads and Au 2015). Despite these challenges, both PacBio and ONT sequencing technologies continue to advance rapidly, with potential to deepen our understanding of the genome's complexities (Wang et al. 2021). Leveraging the advantages of ONT sequencing, and developing molecular biology methods and bioinformatics tools that overcome the limitations of ONT sequencing to address the unknowns of the adaptive immune system (AIS), is the focus of my graduate work. The diversity of antibody repertoires poses significant challenges for sequencing and annotation. By developing assays that overcome these challenges, my research contributes to a deeper understanding of antibody diversity across vertebrate species, with implications for antibody-based therapy and research applications.

Understanding the genomic architecture of speciation remains a challenge in evolutionary biology. Among broadcast-spawning marine invertebrates, reproductive isolation is thought to be established and maintained by the divergence of gamete recognition proteins located on the surfaces of sperm and egg cells. However, it remains unclear whether gametic isolation has been an effective barrier to gene flow during and/or following speciation. In this dissertation, I characterized the history of introgression among the North Pacific sea urchin species of the family Strongylocentrotidae to deepen our understanding of their diversification and evaluate the importance of gametic isolation in speciation. Using whole-genome sequencing data from each strongylocentrotid species and cutting-edge phylogenomic approaches, I documented widespread introgression in both extant taxa and ancestral lineages, demonstrating that gametic isolation did not effectively limit introgression. I implemented a phylogenetic hidden Markov model to locate the specific regions of the genome affected by introgression, finding evidence of strong selection against introgression across much of the genome. Although introgressed variation has predominantly persisted in slowly evolving, low-divergence genomic regions, numerous protein-coding genes showed both introgression and historical positive selection, suggesting an adaptive role for introgression. Finally, I showed that the two gamete recognition proteins responsible for species-selectivity in sea urchin fertilization, sperm protein bindin and its egg receptor, EBR1, have experienced historical adaptive introgression, a pattern inconsistent with expectations for barrier loci. My findings contribute to the body of literature evaluating the biological consequences of introgression and question the importance of gamete recognition proteins in the evolution of reproductive isolation among incipient strongylocentrotid sea urchin species.

U2AF1S34F, a somatic splicing factor mutation, is frequently recurrent in human neoplasias such as lung adenocarcinoma (ADC). Although U2AF1S34F has been shown to occur early in tumor lineages, the mutation, alone, is insufficient for producing tumors. However, lung ADC patients with U2AF1S34F frequently have co-occurring KRAS mutations and smoking histories. We hypothesized that U2AF1S34F interacts with oncogenic KRAS and environmental stress to promote tumor-forming potential. To elucidate interaction of U2AF1S34F with a co-occurring mutation, we generated human bronchial epithelial cells (HBEC3kts) with U2AF1S34F or with co-occurring U2AF1S34F and KRASG12V. From analyzing short-read transcriptome sequences, we found synergistic effects of co-occurring mutations on gene expression in cell cycle and inflammatory pathways associated with increased tumors in mouse xenografts, anchorage-independent growth, proliferation, and altered cytokine production.nterestingly, HBEC3kts harboring only U2AF1S34F display increased splicing in stress granule protein genesand increased viability in cigarette smoke concentrate. Our results suggest that U2AF1S34F may prime cells for transformation by allowing precancerous cells to survive longer when environmental stress is present, permitting U2AF1S34F cells to accumulate transforming mutations, such as KRASG12V.Next, I sought to further investigate the impact of U2AF1S34F and environmental stress response by profiling the mRNA modification landscape of U2AF1S34F and U2AF1WT HBEC3kts exposed to cigarette smoke concentrate (CSC) using Nanopore direct RNA sequencing (dRNA-seq). Preliminary results show that RNA modificatios in autophagy gene VAMP8 were associated with altered protein expression levels. We also show that CSC and the presence of U2AF1S34F both increase the number of RNA modifications in the transcriptome, with the highest number of modifications occurring in CSC treated U2AF1S34F HBEC3kts. We hypothesize that U2AF1S34F and CSC modify the RNA modification landscape in a synergistic way to increase oncogenic potential.

When divergent populations come into contact and interbreed, incompatible genotypes can result in decreased hybrid fitness and partial reproductive isolation. By studying patterns of gene flow between populations, we can identify the genetic, phenotypic, and ecological components of partial reproductive isolation. Using whole-genome sequence data generated from 1201 yellow-rumped warblers (Setophaga coronata coronata, S. c. auduboni, and their hybrids), we measure variation of a hybrid zone’s position in space and over time, identify genomic regions where gene flow is restricted, and compare the genetic basis of reproductive barriers to the genetic basis of plumage traits. We find that reproductive isolation is generated by the effects of many loci throughout the genome, with a large influence of the sex-chromosomes, that “barrier loci” cluster in some highly differentiated regions, and that gene flow and introgression are broadly asymmetric. Barrier loci show strong linkage disequilibrium, which augments the strength of the barrier to gene flow and may indicate the presence of genetic incompatibilities. Clines in plumage traits and their associated SNPs suggest strong selection against some hybrid phenotypes, although loci with the strongest associations with plumage color traits do not exhibit the strongest barrier to gene flow.

G protein coupled receptors (GPCRs) are the largest family of integral membrane receptor proteins, responsible for mediating numerous signaling pathways that are vital to a wide range of biological processes. As a result, over a third of FDA-approved drugs target GPCRs to treat a variety of conditions. Additionally, dysfunctional GPCR and G protein signaling contributes to several disease states, including cancer. Previous studies have shown that dysregulation of Gαs-coupled protein receptors have an important role in the immunosuppressive properties of the tumor microenvironment (TME). Despite this, the function of GPCRs in the context of cancer immunotherapy remains largely underexplored. Here, we used a bioinformatics analysis pipeline to identify and analyze the immune cells present in the TME of 42 different mouse tumor samples. With this information, we found several GPCRs with conserved upregulation in mouse and human tumor samples. Of these GPCRs, several are targets of FDA-approved drugs, providing several potential targets for future cancer immunotherapeutic drugs.

A major goal of the biomathematics discipline is to optimize mathematical models for biological processes. This optimization can take on various forms; finding the appropriate model that fits available data, allows for accurate inference, and is computationally feasible is no easy task and requires an understanding of both the biological processes at hand and the mathematics behind each potential model or algorithm. In this dissertation, I seek to understand how mathematical modeling choices affect our ability to understand human disease. I study infectious, cancerous, and polygenic disease from a variety of computational perspectives. First, I apply methods for differential sensitivity analysis in biological models for both cancerous and infectious disease spread. I compare prediction accuracy for existing first-order methods and propose a second-order method with enhanced flexibility both in terms of the model for which it is applied and the programming environment available. Second, I compare statistical approaches for uncovering genetics of complex disease in admixed populations, using likelihood ratio tests to understand how to incorporate local ancestry in genome wide association studies to achieve the highest power. Third, I utilize machine learning methods to reduce diagnostic delay for patients across the University of California Health system. I adapt a logistic regression model to find patients likely to have common variable immune deficiencies from one health system to five health systems. I also adapt this algorithm from the immunology realm to the cardiology realm to predict cardiac amyloidosis. Along the way, I use this context to study automated feature selection, longitudinal feature engineering, and observational bias in electronic health record data.

Autoimmune disease arises from dysregulation of the immune system, leading to its attack on the body's own tissues and organs. The clinical heterogeneity of these diseases arises from several sources, such as genetic predisposition, environmental triggers, and aberrant immune responses. One emerging area of interest is the role of transposable elements (TEs) in autoimmune disease pathogenesis because these self-nucleic acids can be mistakenly detected as foreign, which can trigger a chronic immune reaction.There is growing appreciation for the role of TEs in systemic lupus erythematosus (SLE) and studies have found differentially expressed TEs in SLE patients, which suggests a link between TE activity and disease mechanisms. Our work investigated TE expression in four immune cell types from SLE patients, revealing cell-specific and SLE subphenotype-specific differentially expressed TEs, with additional cell-type-specific TE associations in different ancestry groups. TE expression was also associated with host gene expression involved in antiviral and immune responses, supporting the hypothesis that TEs could activate the innate immune system and contribute to chronic inflammation and autoimmunity.This study underscores the importance of TEs in SLE heterogeneity and highlights the need for further exploration of TE expression in normal immune cells and functional studies to understand their role in SLE pathogenesis. Future work to study whether antiretroviral drugs could reduce expression of TEs and mitigate SLE symptoms is warranted, given the potential involvement of TEs in autoimmune disease pathogenesis

With advances in mass spectrometry, there is increasing demand for more effective and adaptive approaches to systematically extract biological insights from glycomic and lipidomic data. This thesis explores the application of Markov modeling in understanding two key cellular processes, N-glycosylation and lipid biosynthesis.In Chapter 2, we demonstrate that a Markov model of N-glycosylation network successfully captures intricate glycosyltransferase interactions by reproducing a set of glycoprofiles from glycoengineered CHO cells producing erythropoietin (EPO). We further validate the model parsimony and accuracy by predicting the glycoprofiles of other glycoengineered drugs from the trained models and their wildtype glycoprofiles. The results attest to the model’s ability to learn glycosyltranferase activities and substrate specificities. To increase the impact of this approach, we also develop GlycoMME to allow broader access to this modeling pipeline, presenting a promising direction for rational glycoengineering. Chapter 3 extends the modeling approach to lipid biosynthesis, introducing the Lipid Synthesis Investigative Markov Model (LipidSIM). As a low-parameter, biologically interpretable framework, LipidSIM proves powerful in leveraging the interdependency in lipidomic data and extracts and quantifies perturbations to lipid biosynthesis reactions, generating hypotheses directly testable by transcriptomic data. This method is showcased in 5 different scenarios with 3 different lipidomic datasets, and the results substantiate LipidSIM as a valuable tool for extracting insights from high-dimensional lipidomic data of different types. In Chapter 4, a Taguchi design is used to systematically characterize the impact of 15 media supplements on potential cellular phenotypes for CHO cells, especially N-glycosylation. The analytical pipeline allows disentangling the impact of individual supplements at different concentration levels with minimal numbers of experimental configurations. This approach answers the demand to find more flexible strategies for controlling N-glycosylation beyond genetic engineering. When applied in conjunction with the Taguchi design, our modeling framework has the potential to facilitate rapid customization of media for the growing market of glycoprotein drugs.This thesis encapsulates the innovative applications of probabilistic modeling in accounting for the biological dependency underlying omics data, offering insights into intricate cellular processes and motivating further exploration in actionable modeling of biological systems.

The study of gene expression, the abundance of RNA transcripts in cells, has played a critical role in expanding knowledge of the molecular underpinnings of cancer and the ways in which treatments affect it. Because genes operate in interlinking pathways with nuanced and context-dependent behavior, deriving the phenotypic implication of changes in abundances of individual genes can be challenging. For this reason, Gene Set Enrichment Analysis (GSEA) was developed. GSEA is a statistical method that quantifies the activation of pathways or processes as represented by a priori annotated gene sets and plays a critical role in constructing pathway-level understandings of cell states in diseases such as cancer. In the use of GSEA and the expansion of its companion database of gene sets, the Molecular Signatures Database (MSigDB), two challenges have emerged. First, some gene sets lack the properties of context-specificity and coordinate regulation, that is, not all their members be collectively more expressed in samples that have a specific phenotype to which the gene set should correspond. Second, the new technologies for measuring gene expression at single cell resolution yield data with different properties than the “bulk” gene expression data for which GSEA was initially developed. I will address the first challenge in Chapter 1, in which I propose a data-driven method for gene set refinement and show that this method yields new gene sets that are both more coordinately regulated and context-specific. I address the second challenge in Chapter 2, in which I present a characterization of the performance of the single sample version of GSEA (ssGSEA) in multiple datasets and propose adaptations which I will show produce enrichment scores that are more stable and certain in the single cell context. Finally, Chapter 3 is an application of gene set based pathway characterization to the problem of understanding the development of chemoresistance in ovarian cancer. Using a cell cycle-aware approach incorporating single sample GSEA, we propose a new framework for modeling the development of resistance to carboplatin. These analyses together lay the groundwork for improving the robustness of GSEA results, adapting it to emerging technologies for gene expression measurement, and applying it to address key challenges in the treatment of cancer.

Biomolecular signaling networks enable cells to decode signals from their environment into the appropriate responses needed for survival and maintaining biological function. Using principles derived from these systems, synthetic approaches such as optogenetics were developed to grant users with precise control over cellular activity. Nonetheless, it remains challenging to engineer synthetic signaling systems towards predefined dynamic behaviors. In this work, we present a quantitative framework that integrates multi-objective optimization, dynamic modeling, and biosensor imaging to streamline the design and implementation of synthetic signaling networks in cells. First, we formulated a general signaling model and applied an evolutionary algorithm to derive a network design capable of decoding two distinct frequency modulated stimuli into separate response channels. Based on this design, we implemented an optogenetic circuit to control protein interactions in cells according to the blue light input pattern. To aid this process, dimerization-dependent fluorescent proteins were used to characterize and select optogenetic components with the required dynamic characteristics. Lastly, we applied our frequency decoder system to regulate downstream biological outputs such as gene transcription, antigen presentation, and antigen triggered CAR-T killing in mice. This framework provides an integrative approach for developing synthetic signaling systems that reduces reliance on trial-and-error.

Insulin resistance is implicated in multiple prevalent metabolic disorders, such as type 2 diabetes, cardiovascular disease, and fatty liver disease. While genome-wide association studies (GWAS) have been employed to identify genes associated with insulin resistance, the difficulty in measuring insulin resistance has led to limited sample sizes and reduced statistical power in previous GWAS studies. Conducting an expansive GWAS with a large sample size is essential to identify novel insulin resistance genes. The triglyceride to high-density lipoprotein ratio (TG/HDL) demonstrates a high correlation with insulin resistance phenotypes and is available in large cohorts, making it a potential surrogate marker for insulin resistance. Performing a GWAS of TG/HDL on 382,129 individuals in the UK Biobank, we identified 251 genomic risk loci, among which 62 displayed heightened significance compared to individual markers (TG or HDL), suggesting them as potential insulin resistance loci. We developed a systematic approach for the comprehensive analysis of these loci, prioritizing those that remain undiscovered and exhibit differential associations across different sexes. Through this analysis, we highlighted TNFAIP8, a gene involved in the regulation of apoptosis and immune responses, as a novel sex-dimorphic factor influencing insulin resistance. Our findings contribute to the genetic understanding of insulin resistance and identify potential sex-specific genes as therapeutic targets.

The regenerative capabilities of the liver represent a paradigm for understanding tissue repair in solid organs. Regeneration after partial hepatectomy in rodent models is well understood, while regeneration in the context of clinically relevant chronic injuries is less studied. Given the growing incidence of fatty liver disease, cirrhosis, and liver cancer, interest in liver regeneration is increasing. Here, we will review the principles, genetics, and cell biology underlying liver regeneration, as well as new approaches being used to study heterogeneity in liver tissue maintenance and repair.