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Indiana University-Purdue University Indianapolis (IUPUI) / Non-coding RNAs across the genome have been associated with various biological processes, ranging from regulation of splicing to remodeling of chromatin. Amongst the repertoire of non-coding sequences lies a critical species of RNAs called long non-coding RNAs (lncRNAs). LncRNAs significantly contribute to a large spectrum of human phenotypes, including cancers, Heart failure, Diabetes, and Alzheimer’s disease. This dissertation emphasizes the need to characterize the functional role of lncRNAs to improve our understanding of human diseases. This work consolidates a resource from multiple computational genomics and natural language processing-based approaches to advance our ability to functionally annotate hundreds of lncRNAs and their interactions, providing a one-stop lncRNA functional annotation and dynamic interaction network and multi-facet omics data visualization platform. RNA interactions are vital in various cellular processes, from transcription to RNA processing. These interactions dictate the functional scope of the RNA. However, the multifaceted functional nature of RNA stems from its ability to form secondary structures. Therefore, this work establishes a computational method to characterize RNA secondary structure by integrating SHAPE-seq and long-read sequencing to enhance further our understanding of RNA structure in modulating the post-transcriptional regulatory processes and deciphering the influence at several layers of biological features, ranging from structure composition to consequent protein occupancy. This study will potentially impact the research community by providing methods, web interfaces, and computational pipelines, improving our functional understanding of long non-coding RNAs. This work also provides novel integration methods of technologies like Oxford Nanopore-based long-read sequencing, RNA structure-probing methods, and machine learning. The approaches developed in this dissertation are scalable and adaptable to investigate further the functional and regulatory role of RNA and its structure. Overall, this study accelerates the development of RNA-based diagnostics and the identification of therapeutic targets in human disease.

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A mild traumatic brain injury (mTBI), commonly referred to as a concussion, is when the brain experiences an abrupt acceleration and/or deceleration that sends shock waves through the brain tissue, upsetting its structure and function. A mTBI is a heterogeneous condition with acute and chronic outcomes for patients. The chronic form of mTBI can lead to a wide range of neurological, behavioral, and cognitive symptoms. Critically, this injury is not defined by a simple process or pathophysiological event but rather biomechanical and neurological brain damage that can trigger highly complex physiological cascades. These further lead to a wide range of cellular, molecular, and functional changes that alter genes and associated metabolites. These changes, if specifically characterized, could be used to predict a patient’s outcome and recovery timeline. Recently, genetic studies showed that specific genotypes could increase an individual’s risk of more severe injury and impaired recovery following mTBI. Consequently, an improved understanding of gene alteration and genetic changes is necessary to develop personalized diagnostic approaches which can guide the design of novel treatments. The current study proposes utilizing bioinformatic tools, biological networks, and databases to identify potential genes and microRNAs associated with the mTBI in order to aid the early diagnosis of mTBI and track recovery for individual patients. With bioinformatic techniques, we were able to identify and compare genetic and epigenetic data associated with mTBI, as well as understand the various aspects of molecular changes after brain injury. Ultimately, we analyzed and cataloged the biological pathways and networks associated with this injury. A critical search of online bioinformatics databases was performed to determine interactions between mTBI-related genes, and relevant molecular processes. The major finding was that APOE, S100B, GFAP, BDNF, AQP4, COMT, MBP, UCHL1, DRD2, ASIC1, and CACNA1A genes were significantly associated with mTBI outcome. Those genes are primarily involved in different neurological tasks and neurological pathways such as neuron projection regeneration, regulation of neuronal synaptic plasticity, cognition, memory function, neuronal cell death and the dopaminergic pathway. This study predicted specific miRNAs linked to mTBI outcomes and candidate genes (hsa-miR-204-5p, hsa-miR-16-5p, hsa-miR-10a-5p, has-miR-218-5p, has-miR-34a-5p), and RNA-seq analysis on the GSE123336 data revealed that one miRNA found (hsa-miR-10a-5p) matched our predictions related to mTBI outcomes. Pathway analysis revealed that the predicted miRNA targets were mainly engaged in nervous system signaling, neuron projection and cell differentiation. These findings may contribute to developing diagnostic procedures and treatments for mTBI patients who are still experiencing symptoms, but validation of these genetic markers for mTBI assessment requires patient participation and correlation with advanced personalized MRI methods that show concussion related changes. / Thesis / Master of Applied Science (MASc) / Traumatic brain injury (TBI) is a highly prevalent neurological injury affecting millions of individuals globally. Mild TBI (mTBI), sometimes called concussion, makes up over 85% of TBI cases. A mTBI is a heterogeneous condition with acute and chronic outcomes for patients and involves complex cascades of cellular and molecular events that can lead to functional changes in genes and associated metabolites. In recent genetic studies, it has been shown that certain genotypes are associated with a higher risk of experiencing a more serious injury and a slower recovery after mTBI. These genes can be utilized as crucial biomarkers to predict how long it will take for a person to recover from a concussion. The purpose of this study was to find potential biomarkers that could help in the early detection of mTBI and the monitoring of individual patients’ recovery. It was hypothesized that genes and miRNAs (and their associated proteins) involved in neuronal body, axonal and myelin integrity and regeneration would be identified as important markers of severity.

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Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder and the third leading cause of mortality. In this thesis we performed a clustering analysis of four specific immune cells in the GSE136831 dataset, using the default recommended parameters of the Seurat package in R, and obtained 16 subclasses with various COPD and cell-type proportions. Clusters 3, 7 and 9 had more pronounced independence and were all composed of macrophage-dominated control samples. The results of the pseudo-time analysis based on Monocle 3 package in R showed three different patterns of cell evolution. All started with a high percentage of COPD states, one ended with a high rate of Control states, and the other two still finished with a high percentage of COPD states. The results of differentially expressed gene analysis corroborated the existence of finer clusters and provided support for their rational categorization based on the similar marker genes. The gene ontology (GO) enrichment analysis for cluster 0 and cluster 6 provided feedback on enriched biological process terms with significant and unique characteristics, which could help explore latent novel COPD treatment directions. Finally, some top-ranked potential pharmaceutical molecules were searched via the connectivity map (cMAP) database. / Graduate / 2023-08-12

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Research into the molecular pathology of prevalent public health epidemics such as neurodegenerative diseases including frontotemporal dementia (FTD) and Alzheimer’s Disease (AD), non-medical and illicit opioid use (OU), and Human Immunodeficiency Virus-1 (HIV-1) has been hindered by a lack of systems that allow for rapid and high-throughput modeling of the complex multifactorial conditions in a human context. In this thesis we have addressed this challenge using a multi-pronged approach that encompasses single cell RNA sequencing (scRNA-seq) of three-dimensional (3D) human induced pluripotent stem cell (hiPSC) assembloid culture models and patient derived peripheral blood mononuclear cell (PBMC) samples. We describe the development of an iPSC derived neuron-astrocyte assembloid model of tauopathies, including FTD and AD (AstTau), that rapidly recapitulates propagation of toxic human oligomeric tau (oTau) and cell type specific pathology including misfolded, phosphorylated, oligomeric, and fibrillar tau, strong neurodegeneration, and reactive astrogliosis. scRNA-seq identified vulnerable excitatory neuron specific inflammatory pathways and a heat shock response in astrocytes, recapitulating transcriptomic signatures of adult neurodegeneration and supporting a hypothesis of cell type specific neuroinflammation in tau pathogenesis. To more completely model AD, we incorporated amyloid precursor protein (APP) mutant iPSCs into the assembloid model. These iPSCs contained the familial AD APP V717I mutation or the isogenic CRISPR corrected control, and were used to derived neurons, astrocytes, and microglia. This advanced combinatorial system (AstAD and MAstAD) enabled selective microglial incorporation, APP mutation expression, and oTau seeding allowing us to identify discrete contributions to AD pathogenesis. Ast/MAstAD developed extracellular amyloid-β (Aβ) and microglial activation in addition to the pathology observed in AstTau. scRNA-seq identified divergent microglial activation in response to Aβ and oTau pathology, with APP V717I mutation and oTau seeding synergistically exacerbating AD phenotypes. These assembloid models enable study of the cellular and molecular inflammatory mechanisms in multifactorial neurodegenerative diseases. To better understand disease signatures at the crossroads of multifactorial OU, HIV-1, and antiretroviral (ART) viral suppression we also produced a scRNA-seq data set of more than 100,000 peripheral blood mononuclear cells (PBMCs) from 75 study participants. We identified chronic immune activation and T cell activation dysfunction driven by interferon transcriptomic signatures that were elevated in people with HIV (PWH) with opioid use as compared to PWH without OU. We also identified a functional natural killer cell subtype that was depleted with OU in PWH. Cessation of OU reduced these potentially deleterious inflammatory transcriptomic profiles, supporting the hypothesis that OU in PWH amplifies a state of chronic immune activation. Taken together, single cell transcriptomic resolution has enabled the identification of cell type specific disease signatures in complex pathophysiologies. These data demonstrate the dynamic range of inflammatory signaling across multifactorial disease states and emphasize the need for disease- and cell- type specific approaches to therapeutic development. / 2025-02-05T00:00:00Z

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24197号 / 医博第4891号 / 新制||医||1060(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 平井 豊博, 教授 万代 昌紀, 教授 伊達 洋至 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM