Doctor of Philosophy, Miami University, 2024, Cell, Molecular and Structural Biology (CMSB)

The dissertation is structured into five chapters. Chapter 1: I provided an overarching introduction to the dissertation, including the dysregulation of repetitive elements (REs) in human cancer genomes and the importance of repeat-derived reverse complementary matches (RCMs) in forming a new type of RNA: circular RNA. Chapter 2: I investigated the dysregulation of transposable elements (TEs) in osteosarcoma (OS) cancer patients by integrative analysis of RNA-seq, whole-genome sequence (WGS), and methylation data. I found that TEs, including LINE-1, Alu, SVA, and HERV-K, are significantly up-regulated in OS tumors at the subfamily level. By filtering polymorphic TE insertions, I discovered that most OS patient-specific TE insertions (3175 out of 3326) are germline insertions associated with genes critical for cancer development. In addition to 68 TE-affected cancer genes, I found recurrent germline TE insertions in 72 non-cancer genes with high frequencies among patients. I also found reduced LINE-1 (young) and Alu methylation levels in OS tumor samples. Finally, with TE activities in OS tumors, I showed that higher TE insertions are associated with a longer event-free survival time. Chapter 3: I determined the differentially expressed REs at locus-specific levels stratified by their genomic context (i.e., genic or intergenic REs) among 12 common cancer types. I found uniquely dysregulated genic REs associated with distinct biological functions and intergenic REs containing important information to cluster different sample types. In addition, I found that genes associated with recurrently up-regulated REs are involved in the cell cycle process, whereas the extracellular matrix is associated with recurrently down-regulated REs. Furthermore, 4 out of 5 REs consistently down-regulated across 12 cancer types are located in the same intronic region of a tumor suppressor gene: TMEM252. TMEM252 is down-regulated in 10 out of 12 cancer types. Finally, with the DNA met (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2024, Molecular and Cellular Biology (Arts and Sciences)

Members of the genus Shigella are Gram-negative, non-spore-forming bacilli that cause shigellosis, a severe bacillary dysentery in humans, mostly affecting children under the age of five, immunocompromised individuals, and people living in developing countries. With an estimated 27,000 annual cases of antibiotic-resistant Shigella infections in the US alone, and no success in vaccine development, Shigella infections pose a serious health concern, creating the need for more targeted therapeutics. Key to the development of novel therapeutics against Shigella is a comprehensive understanding of the diverse molecular strategies that underlie the pathogen's physiology. An emerging phenomenon in bacterial gene expression is that of Toxin-antitoxin (TA) systems. TA systems are dual component genetic loci in bacteria, producing two genes, a toxin gene whose expression is lethal to the organism producing it, and an antitoxin which protects the organism from the unwanted expression of the toxin. There are currently VIII TA systems studied in bacteria, differing based on whether the toxin or antitoxin is a protein or an sRNA, as well as mechanism of action of either the toxin or antitoxin. This dissertation characterizes a Toxin-Antitoxin (TA) system in Shigella flexneri, focusing on the ryf locus, which includes the toxin-encoding ryfA gene and two small RNAs (sRNAs), ryfB and ryfB1, with distinct regulatory functions. The 305-nucleotide toxin RNA, ryfA, inhibits bacterial growth by inducing membrane lysis and ATP depletion. ryfB, approximately 100 nucleotides in length, neutralizes ryfA's toxicity via nucleic acid complementarity, without reducing its transcript abundance. ryfB1, although 77% identical to ryfB, does not function as an antitoxin but modulates global gene expression, mostly metabolism. Initial in silico analyses identified key genetic elements within the ryf locus, including promoters, open reading frames, and Shine-Dalgarno sequences, as well as potential tar (open full item for complete abstract)

Doctor of Philosophy, Case Western Reserve University, 2024, Systems Biology and Bioinformatics

The rapid advancement of personal wearable devices has allowed for the inception of novel applications of deep phenotyping for characterization of disease. The need to advance deep phenotyping and analysis methods for personalized wearable devices is crucial to the advancement of personalized remote patient monitoring. We developed an end-to-end digital health infrastructure designed for fast, secure, and effective patient recruitment, data collection, and analysis reporting. We analyzed the efficacy of patient recruitment through our end-to-end patient interface and found that recruitment methods from traditional means such as through clinical sources and university sources resulted in more consents ([0.015, 0.030]; p << 0.001) and more active patients initially (2 = 23.65; p < 0.005). Additionally, we noted that online recruitment through Facebook advertising and Google advertising produced a more ethnically diverse population compared to regional clinical recruitment (2 = 231.47; p < 0.001). We investigated the use of the previously reported NightSignal algorithm, originally developed for SARS-CoV-2 detection, on the detection of abnormal resting heart rate observations for cardiothoracic surgical patients collected through our infrastructure. We found The NightSignal algorithm had a sensitivity of 81%, a specificity of 75%, a negative predictive value of 97%, and a positive predictive value of 28% for the detection of postoperative events. When compared to patients who did not experience a postoperative event, patients who did experience a postoperative event had a significantly higher proportion of red alerts issued by the NightSignal algorithm during the first 30 days after surgery (0.325 vs. 0.063; p<0.05)]. Finally, we then investigated the potential for latent subgroup identification using physiological parameters generated from personal wearable devices. We found latent subgroups at 30-days, 60-days, and 90-days post-operatively. Each latent group was we (open full item for complete abstract)

PHD, Kent State University, 2024, College of Arts and Sciences / Department of Biological Sciences

Parkinson's Disease (PD) is a multifactorial disease with heterogenous phenotypes that vary across individuals, as well as by age and sex. Therefore, it is likely that multiple interacting factors, such as environmental influences and aging, as well as genetic factors, including dynamic RNA editing, via ADARs (Adenosine Deaminases Acting on RNA), may play a role in PD pathology. Here we explored changes in ADAR editing in PD in two datasets: one consisting of skeletal muscle transcriptomes from a small cohort of male PD patients and controls, including those that engaged in a rehabilitative exercise training program, and a second dataset of 317 transcriptomes of healthy controls, PD and prodromal patients aged 65 years or older, from the Parkinson's Project Markers Initiative dataset. We observed differences in ADAR expression, number of putative ADAR edits, editing index, and the number of high and moderate impact edits between control groups and diseased samples, between sexes, and between PD samples pre- and post-exercise, particularly when ADAR editing is associated with nonsense-mediated decay (NMD). Likewise, differentially expressed genes between comparison groups were linked to NMD-related pathways. NMD is an important process in detecting deleterious nonsense sequences in mRNA transcripts and eliminating them from the cell. Thus, NMD regulation serves an important role in neurodevelopment, neural differentiation, and neural maturation. RNA misprocessing, which includes dysregulation of NMD, is known to play an important role in neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and fronto-temporal dementia. Our results suggest that NMD may also be an important factor in PD physiology.

PhD, University of Cincinnati, 2024, Medicine: Biomedical Informatics

The advent of sequencing technologies has revolutionized our understanding of disease. Researchers can now investigate the complex processes involved in the multi-layered transcription of genetic content, which regulates cell activity, homeostasis, and ultimately the organism's health. A disease can be conceived as a deviation from a homeostatic state, leading to cascading negative effects. A disease state, or more generally a disrupting factor (sometimes called a "perturbagen"), can be characterized by how it impacts the organism. This information constitutes its "signature", such as a list of differentially expressed genes or vectors of abundance of proteins or lipids. Significant efforts have focused on gathering these signatures into connectivity maps (CMAPs), which allow the identification of related disrupting factors based on the similarity of their signatures. CMAPs can overcome some limitations of traditional enrichment analysis. However, challenges remain. The integrative analysis of multi-domain data, as opposed to concurrent or sequential analysis, is still a challenge. The complexity of multi-omics analysis, involving retrieving datasets, annotations, and applying analytical pipelines, requires advanced programming skills, which can be a barrier for researchers without dedicated resources. Additionally, analysis pipelines need to scale up as assays become clinically available and more data is generated. To address these challenges, we developed machine learning tools to predict health outcomes, ranging from sepsis to dementia. Our goal is to build knowledge and expertise about integrative and extensible analytical pipelines for clinical, transcriptomics, and proteomics data. Specifically, we developed a statistical and machine learning model to classify patients by phenotype and predict mortality risk. We analyzed a prospective cohort of sepsis patients, selected predictive features, built and validated models, and then refined a robust model u (open full item for complete abstract)

Doctor of Philosophy, Miami University, 2024, Biology

This dissertation is structured into five chapters. Chapter I: I provide a general introduction to my dissertation, primarily introducing the different influences on intraspecific variation and providing a background on local adaptation. Chapter II: I investigate the effects of environmental conditions and demographic history on populations of desert horned lizards (Phrynosoma platyrhinos). I evaluate the demographic history of P. platyrhinos and identify signatures of selection associated with climate, which may be indicative of local adaptation. I then link signatures of selection to genes and functional genomic elements. Chapter III: I explore the influence of environmental heterogeneity on intraspecific variation of the chisel-toothed kangaroo rat (Dipodomys microps). I discover signals of selection associated with both climate and vegetation. I also find evidence that selective pressures likely vary across the species distribution and develop a permutation test to identify populations that possess more putatively adaptive alleles than expected by chance. I also link signals of selection to genes and biological functions that may be related to previously identified morphological differences between populations. Chapter IV: I perform a meta-analysis to understand general patterns of putative local adaptation in terrestrial chordates. I use previously published datasets and analyze them using a common framework to test theoretical predictions regarding the relationship between environmental and demographic factors and signals of selection. I find that signals of selection follow theoretical predictions, and, importantly, find that constant variation is an important driver of signals of selection. Chapter V: I provide conclusions and future directions from my results.

Doctor of Philosophy, The Ohio State University, 2024, Biomedical Sciences

This dissertation focuses on advancing machine learning applications in genomic sequencing, emphasizing the need for model transparency and reproducibility. We introduce two innovative methods to enhance genomics-guided precision medicine: inferring genetic ancestry using genome sequence data and identifying variants in tumor-only RNA sequencing. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages. Our first method, SNVstory, predicts genetic ancestry from single nucleotide variants with high accuracy across 36 populations. We employ a novel approach to simulate individual samples from aggregated allele frequencies of known populations, thus increasing the number and diversity of training variants. Additionally, detailed feature importance analyses provide insights into the genomic features most influential in ancestry prediction, enhancing its clinical utility. Transitioning to variant calling in RNA sequencing data, we explore its potential to enhance our understanding of pediatric cancer biology. Our second method, VarRNA, processes tumor-only RNA sequencing data to call variants and accurately classify them as germline, somatic, or artifacts. Applying VarRNA to pediatric cancer samples revealed crucial insights into allele-specific expression patterns in key cancer-driving genes. VarRNA represents a groundbreaking approach that integrates expression data with variant identification, opening new avenues for cancer biology research and clinical care. These transparent and reproducible methods significantly broaden the utility and impact of genomics-guided precision medicine, promising substantial advancements in the field.

Doctor of Philosophy, The Ohio State University, 2024, Biomedical Sciences

This dissertation addresses the critical need for scalable variant interpretation in the diagnosis of rare genetic diseases (RGDs) by developing and validating novel computational methods for the interpretation of genome sequencing (GS) data. We introduce Clinical Assessment of Variant Likelihood Ratios (CAVaLRi), a robust algorithm that uses a modified likelihood ratio framework to prioritize diagnostic germline variants. CAVaLRi effectively integrates phenotypic data with variant impact predictions and family genotype information to achieve superior performance over existing prioritization tools in multiple clinical cohorts. CAVaLRi-informed reanalysis was able to uncover eight diagnoses in a cohort of RGD patients who had previously received non-diagnostic GS test results, demonstrating utility in ending diagnostic odysseys. Complementing CAVaLRi, we developed CNVoyant, an advanced tool designed to classify and prioritize copy number variants (CNVs) by incorporating machine learning techniques with genomic features to identify disease causal CNVs. CNVoyant's integration into the CAVaLRi framework allows for a unified approach to handle multiple variant types, thus providing a comprehensive solution for genetic diagnostics. The combined utility of CAVaLRi and CNVoyant offers significant improvements in diagnostic yield and accuracy, facilitating timely and precise genetic diagnosis in clinical settings. These tools represent a scalable approach to meet the growing demands of GS testing, thereby expediting the diagnostic process for patients with undiagnosed RGDs and supporting the broader application of genomics in personalized medicine.

Master of Sciences, Case Western Reserve University, 2024, Systems Biology and Bioinformatics

Alzheimer's disease (AD) is the most common neurodegenerative disease and the leading cause of dementia. Cerebrospinal fluid (CSF) is a neuroprotector fluid that carries brain metabolites away from the blood-brain barrier. It is an optimal sample for studying neuroinflammation in central nervous system diseases. However, the role of cells carried in CSF in remains underexplored. In this thesis, we investigated the single-cell RNA sequencing data of leukocytes in CSF. The ratio of CD11B+ cells versus T cells increased in amyloid-healthy individuals and gradually decreased with AD progression. Differential expression analysis of the same leukocyte subtype in different AD stages showed that CCL3 and its variants are up-regulated in monocytes from MCI to AD. IL1B is down-regulated in IM and NCM in MCI patients vs healthy individuals. Pathways enrichment analysis shows that interferon-gamma response, interferon-alpha response, and allograft rejection pathways are up-regulated through AD progress in most cell types.

Doctor of Philosophy, Case Western Reserve University, 2024, Nutrition

Drug resistance in both cancer and infectious disease is a major driver of mortality across the globe. In infectious disease, the emergence of antimicrobial resistance (AMR) outpaces our ability to develop novel drugs, and within-host evolution confounds the use of previously effective drugs during the course of treatment. In cancer, while targeted therapies have improved outcomes for some, many patients continue to face metastatic, drug-resistant disease, with limited therapeutic options available. As both disease types are driven by clonal evolution, a complementary approach to treatment that leverages tools and ideas from evolutionary biology has been beneficial. However, this evolutionary-inspired therapy has thus far been limited in its consideration of drug variation in time and space within a patient (pharmacokinetics) and variable pathogen response to drug (pharmacodynamics). In this dissertation, we describe novel computational and experimental approaches that integrate pharmacokinetics and pharmacodynamics to allow for more physically realistic models of the evolution of drug resistance. We apply these approaches to gain novel insights into drug dosing regimens and drug diffusion in tissue. In Chapters 1 and 2, we briefly review integrated pharmacokinetics and pharmacodynamics in the study of drug resistance and survey the current evidence of fitness costs to drug resistance in cancer. In Chapter 3, we developed a novel, fluorescence-based time-kill protocol for estimating drug dose-dependent death rates in bacteria. In Chapter 4, we described a software package, FEArS, that allows for efficient agent-based simulation of evolution under time-varying drug concentration. In Chapter 5, we leverage both of these methods to gain insight into why some antimicrobial treatments fail using computational modeling and simulated clinical pharmacokinetics. In Chapter 6, we use spatial agent-based modeling to examine how drug diffusion in tissue can promote tumor hetero (open full item for complete abstract)

Doctor of Philosophy, Miami University, 2024, Cell, Molecular and Structural Biology (CMSB)

Lens epithelial explants serve as a valuable in vitro model for studying cellular processes related to lens development and differentiation. Despite significant research, key mechanisms underlying lens fiber cell differentiation and related signaling pathways remain unclear. This dissertation aims to address this gap by investigating the roles of key genes, transcription factors, and microRNAs in lens development and fiber cell differentiation through multiple studies involving RNA-sequencing, ATAC-sequencing, and other molecular biology techniques. Chapter 2 focuses on the influence of vitreous humor on lens epithelial explants, revealing that it increases chromatin accessibility and upregulates genes related to lens fiber cell differentiation while downregulating those associated with lens epithelial cells. The study's unbiased analysis indicated that RUNX, SOX, and TEAD transcription factors might drive these gene expression changes, providing a basis for further exploration. Chapter 3 investigates the role of Fgfrs and Pten in lens fiber cell differentiation and immune responses using RNA-sequencing on explants lacking Fgfrs, Pten, or both. The results show that the loss of Fgfr signaling impairs vitreous-induced fiber differentiation and immune responses, while the loss of Pten can partially rescue these effects. Gene set enrichment analysis suggested that PDGFR-signaling might mediate this rescue, which was confirmed with immunohistochemistry showing beta crystallin expression, indicating fiber cell differentiation. Chapter 4 explores the functional roles of specific microRNAs in lens development. A comprehensive analysis of miRNA transcripts revealed that the loss of miR-26 leads to postnatal cataracts and significant changes in gene expression, with abnormal increases in genes related to neural development, inflammation, and epithelial-to-mesenchymal transition. This demonstrates that miR-26 is crucial for normal lens development and cataract prevention. (open full item for complete abstract)

Master of Science (MS), Wright State University, 2024, Physiology and Neuroscience

ETV1 is a member of the ETS transcription factor family that has been implicated in the development of the nervous system, electrical signaling in the heart, and cancer. Despite being implicated in normal development and pathological states, little is known about the genes regulated by ETV1. As a transcription factor, ETV1 binds to specific sequences of DNA, and this study sought to identify putative binding sites for ETV1 in the promoters and regulatory units of more than 300 genes identified in an expression screen to be differentially expressed in ETV1 knockout tissue. We found ETV1 the number of binding sites does not correlate with the degree of differential gene expression. Tools developed in this study make it possible to efficiently identify transcription factor binding sites in conserved regulatory units.

Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, 2024, College of Sciences and Health Professions

Colorectal cancer is the third leading cause of cancer-related mortality worldwide. Prognosis is favorable if detected early, however, current screening methods have major limitations. There is an urgent need to develop new non-invasive screening tests that are more sensitive and specific to improve outcomes at every stage. Advances in mass spectrometry-based proteomics provide insights into molecular changes driving CRC and can uncover potential biomarker candidates that have diagnostic or prognostic utility. Mass spectrometry is an ideal platform for the development of assays to serve as first line screening tests and to monitor disease progression and response to treatment. The aim of this research was to develop a sensitive and specific mass spectrometry-based method to quantify two protein biomarkers of colorectal cancer in stool that exceeds the performance of current biochemical methods for the quantification of these proteins. The second part of this work aimed to uncover predictive markers that identify individuals at risk of relapse for patients with stage II colon adenocarcinoma. A proteolysis assisted workflow was optimized to quantify peptides of hemoglobin and calprotectin in human stool samples by LC-MS/MS. The multiplexed LC-MS/MS assay for fecal hemoglobin and calprotectin had sensitivities 2 ng and 0.5 ng, respectively and demonstrated linearity from 20 – 1000 µg/g feces for hemoglobin and 6 – 800 µg/g feces for calprotectin. The assay is accurate and precise over the analytical measuring range and highly correlated with measured values from the enzymatic immunoassays. The LC-MS/MS assay was able to distinguish colorectal cancer stool samples from healthy controls with 100% sensitivity and 100% specificity with the combination of both hemoglobin and calprotectin. An untargeted analysis of proteomic data of stage II colon adenocarcinoma tissue samples was conducted. Overexpression of eosinophil peroxidase was associated with lymphovascular invasi (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Biological Sciences

This dissertation addresses the critical need for bioinformatics tools to analyze and annotate the growing number of RNA and ribosome structures available in public databases. RNA molecules, key players in gene regulation, protein synthesis, and catalytic reactions, adopt complex three-dimensional structures that are pivotal for their diverse functions. However, the analysis of these structures is hindered by the redundancy and variability in the datasets, making efficient comparison across different structures a challenging task. To overcome these limitations, this research introduces a novel automated approach for the comparison of RNA motifs 3D structures and the functional annotation of ribosome structures. A new web service, the RNA 3D Motif Correspondence Server (R3DMCS), has been developed to enable the automated comparison and visualization of structural variations within RNA motifs, facilitating a deeper understanding of their functional implications. We illustrate the utility of this web service by examining several RNA 3D motifs in the bacterial small ribosomal subunit. The second part of this dissertation involves automating annotation of ribosome structures based on the occupancy of binding sites for tRNAs, mRNA, protein factors, and ligands. The annotations enhance the identification and comparison of ribosome functional states, which will enable detailed analyses of structural interactions in various states. As the deposition of new RNA structures continues to accelerate, these tools will become increasingly valuable, providing essential capabilities to the scientific community for keeping pace with the expanding frontier of RNA research.

BS, Kent State University, 2024, College of Arts and Sciences / Department of Biological Sciences

The American pika (Ochotona princeps) is an herbivorous mammal that inhabits rocky, mountainous regions across the western United States. Although they share a common ancestor with rabbits and other species in the order Lagomorpha, American pikas have a specialized diet due to an inability to migrate from their narrow habitat range. Gene families are made up of genes similar in sequence and function among species that share a common ancestor. Increases in gene copy number due to random duplication results in gene family expansion, whereas gene deletion results in family contraction. Evolutionary divergence can result in functional and genetic differences in the way that pikas and other lagomorphs digest their food with the help of enzymes. We hypothesized that American pikas have undergone lineage-specific expansions or contractions in gene families encoding enzymes (particularly digestive enzymes), allowing the species to digest available food in their narrow and changing habitat. Using the computational tool, OrthoFinder, protein sequences of the American pika were compared to seven distantly related taxa to identify gene families. CAFE5 analysis identified copy number evolution compared to the most recent common ancestor of all eight species. Functional enrichment analysis with PANTHER showed gene families related to digestive enzymes are significantly expanded in the American pika compared to other species. This indicates that protease digestive enzymes are more highly expressed in the American pika, possibly contributing to their metabolism of plants that inhabit their habitat.

Master of Engineering, Case Western Reserve University, 2024, Biomedical Engineering

Radiomic analysis of individual regions or acquisitions has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI. We present a novel multi-plane, multi-region radiomics framework for exploiting intuitive clinical and biological aspects of rectal tumor response on MRI. Using a multi-institutional cohort of 151 baseline T2-weighted axial and coronal rectal MRIs, 2D texture features were extracted from multiple regions of interest (tumor, tumor-proximal fat) across both axial and coronal planes, with machine learning analysis to identify descriptors predictive of complete response to neoadjuvant therapy. Our multi-plane, multi-region radiomics model was found to significantly outperform single-plane or single-region feature sets with a discovery area under the ROC curve (AUC) of 0.765±0.054, and hold-out validation AUCs of 0.700 and 0.759. This suggests multi- region, multi-plane radiomics could enable detailed phenotyping of treatment response on MRI and thus personalization of therapeutic and surgical interventions in rectal cancers.

Doctor of Philosophy, The Ohio State University, 2024, Microbiology

Microbes are engines of ocean biogeochemical processes. Viruses influence and shape microbial communities via lysis, horizontal gene transfer, and metabolic reprogramming. Viral lysis facilitates the export of carbon from the surface into the deep ocean via aggregates of sinking particles. In fact, they outperform prokaryotes and eukaryotes as the strong predictor for carbon fluxes in the oligotrophic ocean. Viruses also impact the gene flow of their hosts, and the genes transferred from virus-host interactions can be fixed in viral genomes. Viruses are known to carry and express host-derived auxiliary metabolic genes (AMGs) that directly reprogram metabolisms within virus-infected cells, termed virocells. However, viral communities are poorly characterized in the oligotrophic ocean, and their AMG-driven metabolic reprogramming lacks systematic descriptions from the global oceans. The Sargasso Sea is highly stratified and nutrient-depleted each year in the summer months. This seasonal pattern makes the Sargasso Sea one of the ideal model ecosystems to study oligotrophic oceans. In the Sargasso Sea, abundance of viral-like particles has seasonal and depth-associated structuring patterns. Here, to better survey the Sargasso Sea viruses, we apply sequencing approaches to characterize viral communities via metagenomics and uncover their biogeographical and ecological structures locally and globally in the ocean. As described in Chapter 2, comparison with global viral metagenomics revealed that Sargasso Sea viruses were similar across warm oligotrophic oceanic regions but not represented globally. They form discrete populations in the viral and cellular fractions at the viral maximum (80m) and mesopelagic (200m) depths. Inclusion of long-read data captured 1,257 viral genomes in addition to the 1,044 viral genomes derived from short-read assemblies, resulting in the identification of ecologically important and microdiverse viral genomes. Having established lo (open full item for complete abstract)

MA, Kent State University, 2024, College of Arts and Sciences / Department of Anthropology

Genotyping animals is necessary for various field-based applications that require precise knowledge of the sampled individuals. Though feces are considered a low-quality source of host DNA, molecular techniques are increasingly prioritizing its usage for field-based noninvasive projects. Here, we describe a reproducible workflow to genotype individuals using a whole-genome sequencing approach with the portable, high throughput MinION MK1B and the BWA-GATK variant calling pipeline. After filtering, only 4 of the original 5,394 SNPs passed the filtering criteria, leading to an unsuccessful attempt to generate an informative multiloci SNP panel to confidently and accurately differentiate animals. In the filtered SNPs, 5 samples were entirely void of genotyping data. The majority of SNPs exhibited allelic dropout and a lack of called heterozygote genotypes, leading to the presumable false genotypes of the sampled individuals. On average, approximately 97% of the genome remained unsequenced, with only about one read covering each base in the mapped regions. Despite the limitations of employing a whole-genome sequencing approach to differentiate individuals with the MinION using feces, for species lacking known variants, this strategy may be an effective way to initially identify SNPs for subsequent resequencing and genotyping. Future studies are necessary to validate the authenticity of the identified SNPs and to assess their ability to discriminate individuals effectively with enrichment and targeted sequencing techniques.

Master of Science in Computer Engineering, University of Dayton, 2024, Electrical and Computer Engineering

This study aims to incorporate some advantages of computed tomographic data into the chest X-ray deep lung segmentation paradigm. We do this by training a deep convolutional neural network on chest radiographs (a.k.a. X-rays) with manually drawn ground truth and an identical network on radiographs digitally reconstructed from computed tomographic data with ground truth generated for the given computed tomographic image using an automated morphological 3D lung segmentation algorithm. The resulting twin-network ensemble generates pairs of lung image segmentation labels for chest X-rays: 1) a “traditional” segmentation of the lungs encompassing the apparently low-density tissue and 2) a novel, “full” lung segmentation encompassing an expanded view of the lungs' position in a chest X-ray including those regions obscured by the heart, ribs, and viscera, in essence, a 2D projection of any portion of the 3D lung. These networks perform consistently, with mean Intersection-Over-Union scores of > 90% and > 95%, respectively, across five trials. By subjective analysis, the proposed lung segmentation approach shows satisfactory ability to generalize onto genuine check X-ray images. The proposed technique's high performance and robustness establish a precedent for applying computed tomographic data to automatic chest X-ray segmentation and present an opportunity to further refine existing computer-aided detection and diagnostic tools by considering the full lung.

Doctor of Philosophy, Case Western Reserve University, 2024, Biochemistry

Gene expression in higher eukaryotic cells involves carefully orchestrated interactions between RNA-binding proteins (RBPs) and RNAs. Understanding the rules that govern the interactions between RBPs and RNAs is a central question in biology, and increasingly, in medicine. This thesis investigates the cellular RNA- RBP interactions in two ways. First, we develop colocalization CLIP, a method that combines CrossLinking and ImmunoPrecipitation (CLIP) with proximity labeling, to explore in-depth the subcellular RNA interactions of the RNA-binding protein HuR. Using this new method, we uncover HuR's unique binding preferences in the nucleus, cytoplasm, and stress granule, during mock and arsenite stress conditions. Second, using available datasets that describe RBP binding in vitro and in cells, we devise two quantitative metrics to directly compare RBP binding behaviors: inherent specificity and mutational sensitivity. These new metrics provide a new quantitative framework to characterize the binding behaviors of an RBP.