Indiana University-Purdue University Indianapolis (IUPUI) / Complex biological systems are composed of many regulatory components, which can be measured with the advent of genomics technology. Each molecular assay is normally designed to interrogate one aspect of the cell state. However, a comprehensive understanding of the regulatory mechanism requires characterization from multiple levels such as genome, epigenome, and transcriptome. Integration of multi-omics data is urgently needed for understanding the global regulatory mechanism of gene expression. In recent years, single-cell technology offers unprecedented resolution for a deeper characterization of cellular diversity and states. High-quality single-cell suspensions from tissue biopsies are required for single-cell sequencing experiments. Tissue biopsies need to be processed as soon as being collected to avoid gene expression changes and RNA degradation. Although cryopreservation is a feasible solution to preserve freshly isolated samples, its effect on transcriptome profiles still needs to be investigated. Investigation of multi-omics data at the single-cell level can provide new insights into the biological process. In addition to the common method of integrating multi-omics data, it is also capable of simultaneously profiling the transcriptome and epigenome at single-cell resolution, enhancing the power of discovering new gene regulatory interactions. In this dissertation, we integrated bulk RNA-seq with ATAC-seq and several additional assays and revealed the complex mechanisms of ER–E2 interaction with nucleosomes. A comparison analysis was conducted for comparing fresh and frozen multiple myeloma single-cell RNA sequencing data and concluded that cryopreservation is a feasible protocol for preserving cells. We also analyzed the single-cell multiome data for mesenchymal stem cells. With the unified landscape from simultaneously profiling gene expression and chromatin accessibility, we discovered distinct osteogenic differentiation potential of mesenchymal stem cells and different associations with bone disease-related traits. We gained a deeper insight into the underlying gene regulatory mechanisms with this frontier single-cell mutliome sequencing technique.

The ovarian hormone 17β-estradiol (E2) is one of the central regulators of the female reproductive system. E2 is also a pleiotropic regulator since it can exert its non-reproductive role on other organ systems. E2 is neuroprotective, it maintains body's energy homeostasis, participates in various repair mechanism and is required for neural development. However, there is a substantial evidence suggesting that there might be a molecular reprogramming of E2's action when it is supplied exogenously after E2 deprivation. Though the length of E2 deprivation and age has been linked to this phenomenon, the molecular components and how they activate this reprogramming is still elusive. Our main goal was to perform global proteomics and metabolomics study to identify the molecular components and their interaction networks that are being altered in the brain and serum after a short-term E2 treatment following ovariectomy (OVX) in Sprague Dawley rats. One of the strength of our global study is that it gave us extensive information on the brain proteome itself by identification of a wide number of proteins in different brain sections. By analyzing the differentially expressed proteins, our proteomics study revealed 49 different networks to be altered in 7 sections of the brain. Most of the perturbed networks were involved in cell metabolism, neural development, protein synthesis, cellular trafficking and degradation, and several stress response signaling pathways. We assessed the neuroenergetic status of the brain based on E2's response to various energy generating pathways, including glycolysis, TCA cycle, and oxidative phosphorylation, and several signaling pathways. All energetics pathways were shown to be downregulated in E2 treatment, which suggests that E2 exerts its neuroprotective role by restoring energy homeostasis in OVX rat model by regulating complex signaling and metabolic networks. Our second focus was to determine the metabolite response (amino acids and lipids) after E2 treatment in the brain and serum by employing targeted metabolomics study. We have found that in rat brain cortex there was significant upregulation of a wide number of amino acids suggesting alternate route of metabolism. Another alternate explanation is that E2 replacement replenished the amino acid pool in the tissue. Pathway enrichment analysis revealed upregulation of several pathways, including amino sugar metabolism, purine metabolism, and glutathione metabolism. By combining proteomics and metabolomics in two different biological matrices we were able to gather a vast array of information on how E2 replacement after E2 deprivation can confer neuroprotection. Our findings will help to create a foundation of basic science to be used for developing potentially effective hormone therapies.

The advent of advanced high-throughput biological technologies provides opportunities to measure the whole genome at different molecular levels in biological systems, which produces different types of omic data such as genome, epigenome, transcriptome, translatome, proteome, metabolome and interactome. Biological systems are highly dynamic and complex mechanisms which involve not only the within-level functionality but also the between-level regulation. In order to uncover the complexity of biological systems, it is desirable to integrate multi-omic data to transform the multiple level data into biological knowledge about the underlying mechanisms. Due to the heterogeneity and high-dimension of multi-omic data, it is necessary to develop effective and efficient methods for multi-omic data integration. This thesis aims to develop efficient approaches for multi-omic data integration using machine learning methods and network theory. We assume that a biological system can be represented by a network with nodes denoting molecules and edges indicating functional links between molecules, in which multi-omic data can be integrated as attributes of nodes and edges. We propose four network-based approaches for multi-omic data integration using machine learning methods. Firstly, we propose an approach for gene module detection by integrating multi-condition transcriptome data and interactome data using network overlapping module detection method. We apply the approach to study the transcriptome data of human pre-implantation embryos across multiple development stages, and identify several stage-specific dynamic functional modules and genes which provide interesting biological insights. We evaluate the reproducibility of the modules by comparing with some other widely used methods and show that the intra-module genes are significantly overlapped between the different methods. Secondly, we propose an approach for gene module detection by integrating transcriptome, translatome, and interactome data using multilayer network. We apply the approach to study the ribosome profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose an approach for feature selection by integrating transcriptome and interactome data using network-constrained regression. We develop a more efficient network-constrained regression method eGBL. We evaluate its performance in term of variable selection and prediction, and show that eGBL outperforms the other related regression methods. With application on the transcriptome data of human blastocysts, we select several interested genes associated with time-lapse parameters. Finally, we propose an approach for classification by integrating epigenome and transcriptome data using neural networks. We introduce a superlayer neural network (SNN) model which learns DNA methylation and gene expression data parallelly in superlayers but with cross-connections allowing crosstalks between them. We evaluate its performance on human breast cancer classification. The SNN provides superior performances and outperforms several other common machine learning methods. The approaches proposed in this thesis offer effective and efficient solutions for integration of heterogeneous high-dimensional datasets, which can be easily applied to other datasets presenting the similar structures. They are therefore applicable to many fields including but not limited to Bioinformatics and Computer Science.

No description available.

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

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.

The advent of the high throughput era has resulted in rapid growth in the availability of large biological datasets. These massive datasets are organized in public or private repositories, encompassing not only DNA but also multiple biomolecules that represent different layers of biological information. The examination and quantification of one such layer are commonly known as "omics," which include the genome, proteome, transcriptome, and metabolome. Currently, it has become commonplace to conduct association analyses between a single omics and a specific phenotype. This practice has significantly enhanced our comprehension of both biological mechanisms and disease, particularly Mendelian disorders. However, the study of a single omics often fails to capture the entirety of variations within a multi-layered mechanism, as well as the interplay between different biological layers, thus not accurately characterizing changes in complex disorders and regulatory systems. Hence, the integration of information from multiple omics has emerged as the prevailing approach, leading to the development of computational tools for conducting multi-omics analyses. These tools are essential for further unraveling the underlying causes of complex diseases. However, the landscape of multi-omics analysis software is highly diverse, offering researchers a wide range of options in terms of purposes, data types, integration methods, and development techniques. This diversity provides tailored pipelines that cater to specific research needs. Yet, it also poses challenges, as the multitude of software options often lacks standardized practices and protocols. Consequently, a universally accepted gold standard is absent, impeding result reproducibility and comparability across different research efforts. To address this issue, we have developed MOUSSE, a novel modular omicsgeneric pipeline for unsupervised data integration. The characteristic of our tool is to use rank-based subject-specific signatures as input to derive from each omics a subject similarity network. This network maintains the informative content of the input data while reducing its size and allows for a graph-based integration of multiple omics. Using the resulting integrated network, the pipeline clusters the subjects andallows researchers to identify biomarkers for each cluster. One aspect that sets MOUSSE apart from other techniques is that it require almost no data preprocessing, making it more robust to noise in the data and more suitable to novel and not yet fully characterized data types. We tested our tool by analyzing ten publicly available benchmark datasets for different types of cancer. Each dataset contained data from three separate omics, namely transcriptome, methylome and miRNAome. The aim of our analysis was two-folded. First, we wanted to demonstrate that MOUSSE was able to identify the different phenotypes of cancers as clusters, second, we aimed to demonstrate that the pipeline was also able to identify biomarkers for each cancer type or progression. Moreover, we compared MOUSSE clustering performance against tenmulti-omics tools tested on the same data, achieving the highest median classification score. Finally, we performed an additional analysis on the biomarkers selected by the pipeline for a selected number of cancer phenotypes, showing that MOUSSE was able to identify the markers underlying disease progression and differential survival rate between cancer phenotypes. Collectively, these results showed that MOUSSE clustering and biomarker identification can be reliable even when the disease is changing. Finally, we successfully compiled and implemented MOUSSE as an R-package. To enhance the pipeline, we incorporated an additional omics dataset. This integration allowed us to optimize the selection of subject-specific signatures and introduced the capability of iteratively running the tool. This means that users can refine their clustering results while reducing the size of candidates, therefore enhancing the overall effectiveness of the software.

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.