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Both genetic and environment factors influence susceptibility to substance use disorders. However, the genetic basis of these disorders is largely unknown. We previously identified Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene for reduced methamphetamine (MA) stimulant sensitivity. Mutation (heterozygous deletion of a small region in the first coding exon) in Hnrnph1 also decreased MA reinforcement, reward, and dopamine release. 5’UTR genetic variants in Hnrnph1 support reduced 5’UTR usage and hnRNP H protein expression as a molecular mechanism underlying the reduced MA-induced psychostimulant response. Interestingly, Hnrnph1 mutant mice show a two-fold increase in hnRNP H protein in the striatal synaptosome with no change in whole tissue level. Proteome profiling of the synaptosome identified an increase in mitochondrial complex I and V proteins that rapidly decreased with MA in Hnrnph1 mutants. In contrast, the much lower level of basal mitochondrial proteins in the wild-type mice showed a rapid, MA-induced increase. Altered mitochondrial proteins associated with the Hnrnph1 mutation may contribute to reductions in MA behaviors. hnRNP H1 is an abundant RNA-binding protein in the brain, involved in all aspect of post-transcriptional regulation. We examined both baseline and MA-induced changes in hnRNP H-RNA interactions to identify targets of hnRNP H that could comprise the neurobiological mechanisms of cellular adaptations occurring following MA exposure. hnRNP H post-transcriptionally regulates a set of mRNA transcripts in the striatum involved in psychostimulant-induced synaptic plasticity. MA treatment induced opposite changes in binding of hnRNP H to these mRNA transcripts between Hnrnph1 mutants versus wild-types. RNA-binding, transcriptome, and spliceome analyses triangulated on hnRNP H binding to the 3’UTR of Cacna2d2, an upregulation of Cacna2d2 transcript, and decreased 3’UTR usage of Cacna2d2 in response to MA in the Hnrnph1 mutants. Cacna2d2 codes for a presynaptic, voltage-gated calcium channel subunit that could plausibly regulate MA-induced dopamine release and behavior. The multi-omics datasets point to a dysregulation of mitochondrial function and interrelated calcium signaling as potential mechanisms underlying MA-induced dopamine release and behavior in Hnrnph1 mutants.

Alzheimer’s disease (AD) is a complex neurodegenerative disease characterized by progressive memory loss and caused by a combination of genetic, environmental, and lifestyle factors. AD susceptibility is highly heritable at 58-79%, but only about one third of the AD genetic component is accounted for by common variants discovered through genome-wide association studies (GWAS). Rare variants may contribute to some of the unexplained heritability of AD and have been demonstrated to contribute to large gene expression changes across tissues, but conventional analytical approaches pose challenges because of low statistical power even for large sample sizes. Recent studies have demonstrated by expression quantitative trait locus (eQTL) analysis that changes in gene expression could play a key role in the pathogenesis of AD. However, regulation of gene expression has been shown to be context-specific (e.g., tissue and cell-types), motivating a context dependent approach to achieve more precise and statistically significant associations. To address these issues, I applied a strategy to identify new AD risk or protective rare variants by examining mutations occurring only in cases or only controls, observing that different mutations in the same gene or variable dose of a mutation may result in distinct dementias. I also evaluated the impact of rare variation on expression at the gene and gene pathway levels in blood and brain tissue, further strengthening the rare variant findings with functional evidence and finding evidence for a large immune and inflammatory component to AD. Lastly, I identified cell-type specific eQTLs in blood and brain tissue to explain underlying genetic associations of common variants in AD, and also discovered additional evidence for the role of myeloid cells in AD risk and potential novel blood and brain AD biomarkers. Collectively, these findings further explain the genetic basis of AD risk and provide insight about mechanisms leading to this disorder. / 2022-01-21T00:00:00Z

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL). This thesis discusses the revolutionary field of ML/DL and its applications in cancer metastasis prediction. We are raising the question of whether there is a better way to improve the prediction of metastasis? We effectively addressed this by reviewing strides made in this regard in current literature to draw some conclusions based on a comprehensive review. Then, we used this knowledge to develop multiple ML/DL models using different omics data types that can accurately and cost-effectively predict if the cancer is in the metastatic state and suggest the metastasis site. Beyond that, we show the biological functions that the DL model uses to perform the prediction. We proved that ML/DL could improve efficiency and diagnostic accuracy and can be used to develop novel predictors of prognosis despite some existing challenges.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by breathlessness due to airflow obstruction. COPD is the third leading cause of death worldwide. So far, none of the existing pharmacological treatments for COPD can stop the progressive decline in lung function. Drug repurposing is the application of existing approved therapeutic compounds for new disease indications, which may reduce the cost and time of new drug development. So far, there is not any systematic multi-omics data integration for drug repurposing in COPD. The goal of this project is to develop a systems biology pipeline for the identification of biological-relevant gene targets with drug repurposing potential for COPD treatment using multi-omics integration. Here we implemented a computational methodology to identify drug repurposing targets for COPD. We integrated multi-omics COPD data including, genome, transcriptome, proteome, metabolome, interactome data, and drug-target information. A distance-based network model was created to rank the potential candidate genes. Fifty genes were prioritized as COPD signature genes for their overall proximity to signature genes identified at all omics levels. Forty of them may be considered as “druggable” targets. Literature search reported CRCX4 – Plerixafor as one prioritized targets-gene pair for drug repurposing. The bone marrow stimulant Plerixafor is currently being evaluated for COPD treatment in clinical trials, suggesting that our pipeline is finding promising drug repurposing targets. Our work, for the first time, applied a systematic approach integrating multiple omics data to find drug repurposing targets for COPD. / Pharmaceutical Sciences

Molecular profiling of blood for several purposes, 1) To identify prostate cancer biomarkers, 2) to identify commonalities between asthma and mood disorders, and 3) to identify mRNAs that may be involved in psychobiotic changes to behaviour. / Every cell of the body has the opportunity to secrete molecules into the blood. These molecules: proteins, RNAs, and DNAs, can be secreted freely, or within extracellular vesicles (EV). The complement of specific molecules secreted by cells can vary in accordance with changes to their immediate environment, such as disease in a particular organ. Cells of the immune system which circulate in the blood may also change the rates at which they produce these molecules in response to a disease or unusual event occurring somewhere within the body. The full complement of proteins, RNAs, or DNAs from all sources within the blood can therefore be measured to garner information about disease states and communication between every tissue of the body. In this body of work, we leveraged this to address three separate challenges within medical science. First, we utilized blood as a source of biomarkers for disease and disease severity; isolating EVs from the blood of prostate cancer patients and healthy subjects and characterized their proteins with mass spectrometry to identify potential biomarkers for prostate cancer and its stages. Next, we explored the ability of blood to identify commonalities between distinct but often comorbid diseases; here we utilized publicly available datasets to identify transcripts or gene sets potentially facilitating the relationship between PTSD, MDD, and asthma. Finally, we utilized differential gene and gene sets expression to gain mechanistic insight into microbiota-gut-brain axis; investigating the hippocampus and blood of mice fed one of two psychobiotic bacteria: Lactobacillus rhamnosus JB1, Lactobacillus reuteri 6475. The analysis identified several mRNA expression differences potentially responsible for the mood-altering characteristics of these psychobiotic bacteria. This body of work illustrates the utility of blood omics data for addressing many problems within medical science, and highlights the large scale of information stored within the blood. / Thesis / Doctor of Philosophy (Medical Science) / Every cell of the body has the opportunity to secrete molecules into the blood. These molecules: proteins, ribonucleic acids (RNAs), and deoxyribonucleic acids (DNAs), can be secreted freely, or within small membrane compartments called extracellular vesicles (EV). Specific molecules are secreted more or less by cells depending on changes to their immediate environment, such as disease in a particular organ. We leveraged this to the benefit of medical science in three separate scenarios: 1) using the molecular contents of EVs to determine when someone has prostate cancer, and at what stage; 2) examining RNAs of the blood to determine why so many with asthma also have depression or PTSD; 3) measuring RNAs in the blood and hippocampus of mice to better understand how certain bacteria in the gut can alleviate depression. This work illustrates the utility of blood in tackling many challenging problems within medical science.

Parkinson’s disease (PD) is a neurological disorder characterized by the lack of functional dopaminergic neurons in the nigrostriatal pathway in the brain. Current therapeutic strategies for the disease provide temporary symptomatic relief. Gene therapy has the potential to improve dopamine production in Parkinson’s disease patients. Adeno-associated viruses (AAV) are the vectors of choice in gene therapy for PD, due to their well-characterized safety and efficacy profiles, with all primary receptors being glycans. The problem with using AAV in PD gene therapy is that the aged brain is resistant to transduction of the virus, while PD primarily occurs with age. Thus, in Aim 1 we characterize the age-related changes in glycan receptors in the nigrostriatal pathway as a baseline to address current challenges in gene delivery in Parkinson’s disease. To make these measurements from specific regions of tissue, we develop a platform that incorporates on-slide digestion followed by LC-MS/MS for integrated glycomics and proteomics. Further, we apply this to understand aging- and PD-related changes in the human pre-frontal cortex in Aims 2 and 3, to understand normal and pathological aging processes as well as integrate this information with transcriptomics data, to assess risk factors that may contribute to Parkinson’s disease. Finally, we also apply the method to investigate cancer premalignancy and heterogeneity. Our on-slide method, used herein to study aging, Parkinson’s disease and cancer, can be applied to any precious biopsy specimens to enable glycomic and proteomic profiling in diverse diseases, and thus may have a broad impact on biomedical research.

Ideally, a nutritional biomarker serves as an objective measure of the intake of a particular food or nutrient, may provide a reflection of health and disease processes, and can aid in the development of personalized nutritional recommendations. However, few food biomarkers have been validated and most have yet to be critically appraised in the literature. With the increased use of metabolomics in population-based studies, it is important to identify the sources of variability in nutritional biomarkers that may be attributed to intrinsic physiologic characteristics and extrinsic factors so that exposure-outcome associations can be examined more accurately. Additionally, circulating metabolites are associated with obesity-related changes in gut microbiome but there has been limited integration of metabolomics with microbiome in childhood obesity, and even less is known in non-white populations. This dissertation presents a series of studies that provide direct support for utility of nutritional biomarkers in population-based studies. The first study, presented in Chapter 2, contributes to the growing literature on food-based biomarkers by generating a comprehensive list of metabolites associated with a comprehensive list of all individual foods and food groups, and rated the evidence based on interstudy repeatability and study design. Chapter 3 identifies sources of variability in serum metabolite concentrations in White Europeans and South Asian pregnant women, thereby guiding appropriate statistical modeling when utilizing metabolomics in nutritional epidemiological studies. Chapter 4 provides results from a multi-omics integration analysis of serum metabolites and amplicon sequence variants of 16S ribosomal RNA genes to identify biomarkers that discriminate children with and without obesity. Collectively, the results showed that a specific food/food group may give rise to many metabolites, however in several cases, a single metabolite can be a good indicator of food intake. Dietary factors explained the highest proportion of variability in exogenous food-based biomarkers relative to non-dietary factors, whereas the contribution of non-dietary factors was either similar or lower for metabolites that can either be produced endogenously, biotransformed by gut microbiota, and/or derived from more than one food source. Most of the circulating metabolites differed by ethnicity (South Asian and White Europeans). Biomarkers with good evidence can be considered direct surrogates for food intake, however, they can be influenced by several non-dietary factors, which require appropriate consideration during the statistical analyses of the data. Finally, the results showed notable differences in serum metabolome and specific gut bacterial species, and between specific metabolites and bacterial species related to childhood obesity. Obesity related metabolic pathways such as glutamate and carnitine metabolism may provide insight into the metabolic processes related to early onset of obesity in childhood. / Dissertation / Doctor of Philosophy (Medical Science)

Microbiota-released extracellular vesicles (MEVs) have emerged as key players in intercellular signaling in host-microbiome communications. However, their role in gut-brain axis signaling has been poorly investigated. Here, we performed deep multi-omics profiling of MEVs generated ex-vivo and from stool samples to gain insight into their role in gut-brain-axis signaling. Metabolomics unveiled a wide array of metabolites embedded in MEVs, including many neurotransmitter-related compounds such as arachidonyl-dopamine (NADA), gabapentin, glutamate, and N-acylethanolamines. To test the biodistribution of MEVs from the gut to other parts of the body, Caco-2, RIN-14B, and hCMEC/D3 cells showed the capacity to internalize labeled MEVs through an endocytic mechanism. Additionally, MEVs exhibited dose-dependent paracellular transport through Caco-2 intestinal cells and hCMEC/D3 brain endothelial cells. Overall, our results revealed the capabilities of MEVs to cross the intestinal and blood-brain barriers to delivering their cargo to distant parts of the body.

Single-cell RNA sequencing (scRNA-seq) has been a powerful technique for characterizing transcriptional heterogeneity related to tumor development and disease pathogenesis. Despite the advances of technology, there is still a lack of software to systematically and easily assess the quality and different types of artifacts present in scRNA-seq data and a statistical framework for understanding heterogeneity in the gene programs of cancer cells. In this dissertation, I first introduced novel computational software to enhance and streamline the process of quality control for scRNA-seq data called SCTK-QC. SCTK-QC is a pipeline that performs comprehensive quality control (QC) of scRNA-seq data and runs a multitude of tools to assess various types of noise present in scRNA-seq data as well as quantification of general QC metrics. These metrics are displayed in a user-friendly HTML report and the pipeline has been implemented in two cloud-based platforms. Most scRNA-seq studies only profiled a small number of tumors and provided a narrow view of the transcriptome in tumor tissue. Next, I developed a novel framework to perform a large-scale meta-analysis of cancer cells from 12 studies with scRNA-seq data from patients with non-small-cell lung cancer (NSCLC). I discovered interpretable gene co-expression modules with celda and demonstrated that the activity of gene modules accounted for both inter- and intra-tumor heterogeneity of NSCLC samples. Furthermore, I used CaDRa to determine that the levels of some gene modules were significantly associated with combinations of underlying genetic alterations. I also showed that other gene modules are associated with immune cell signatures and may be important for communication with the cancer cells and the immune microenvironment. Finally, I presented a novel computational method to study the association between copy number variation (CNV) and gene expression at the single-cell level. The diversity of the CNV profile was identified in tumor subclones within each sample and I discovered cis and trans gene signatures which have expression values associated with specific somatic CNV status. This study helped us prioritize the potential cancer driver genes within each CNV region. Collectively, this work addressed the limitation in the quality control of scRNA-seq data and provided insights for understanding the heterogeneity of NSCLC samples.