Human neurodegenerative disorders are among the most difficult to study. In particular, the inability to readily obtain the faulty cell types most relevant to these diseases has impeded progress for decades. Amyotrophic lateral sclerosis is a late onset neurodegenerative disease in which the upper and lower motor neurons degenerate, leading to paralysis and eventually death. Recent advances in pluripotent stem cell technology now grant access to significant quantities of disease pertinent neurons both with and without predisposing mutations. The two studies described in this thesis demonstrate the feasibility of using MNs, generated from pluripotent stem cell lines harboring known ALS mutations, to establish in-vitro models of the disease. Specifically, we first used gene targeting to establish genetically controlled systems, able to identify causal relationships between a familial ALS mutation and in vitro phenotypes. Next, using transcriptional profiling, we identified novel pathways altered by the mutation and demonstrated functional consequences of these pathways' misregulation. Furthermore, by monitoring the physiology of the pluripotent stem cell derived MNs, we discovered an increased firing rate in the mutant MNs, and identified an FDA-approved drug, retigabine, capable of rescuing this defect. Lastly, to aid in the discovery of additional therapeutic compounds, we combined gene targeting, transcriptional profiling, and a fluorescent reporter human embryonic stem cell line to establish a well-controlled in vitro system capable of identifying genetic modifiers of the phenotypes described herein.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22143号 / 医博第4534号 / 新制||医||1039(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 妻木 範行, 教授 森本 尚樹, 教授 別所 和久 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第21445号 / 医博第4412号 / 新制||医||1032(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 長船 健二, 教授 篠原 隆司, 教授 瀬原 淳子 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

This dissertation focuses on immunological, epidemiological, and economic modeling of HIV, influenza, and fungal meningitis, and includes three research studies. In the first study on HIV, the study objective is to analyze the dynamics of HIV-1, CD4+ T cells and macrophages during the acute, clinically latent and late phases of HIV infection in order to predict their dynamics from acute infection to clinical latency and finally to AIDS in treatment naive HIV-infected individuals. The findings of the study show that the peak in viral load during acute HIV infection is due to virus production by infected CD4+ T cells, while during the clinically latent and late phases of infection infected macrophages dominate the overall viral production. This leads to the conclusion that macrophage-induced virus production is the significant driver of HIV progression from asymptomatic phase to AIDS in HIV-infected individuals. In the second study on influenza, the study objective is to estimate the direct and indirect epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago, and assist in vaccine intervention priorities. Population is distributed among high-risk and non-high risk within 0-19, 20-64 and 65+ years subpopulations. The findings show that based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost-saving for all age and risk groups. In the third study on fungal meningitis, the study objective is to evaluate the effectiveness and cost of the fungal meningitis outbreak response in New River Valley of Virginia during 2012-2013, from the local public health department and clinical perspectives. We estimate the epidemiological effectiveness of this outbreak response to be 153 DALYs averted among the patients, and the costs incurred by the local health department and clinical facilities to be $30,413 and $39,580 respectively. Moving forward, multi-scale analysis of infectious diseases connecting the different scales of evolutionary, immunological, epidemiological, and economic dynamics has good potential to derive meaningful inferences for decision making in clinical and public health practice, and improve health outcomes. / Ph. D.

Emerging infectious diseases (EID) pose a serious threat to global public health. Computational epidemiology is a nascent subfield of public health that can provide insight into an outbreak in advance of traditional methodologies. Research in this dissertation will use fuse nontraditional, publicly available data sources with more traditional epidemiological data to build and parameterize models of emerging infectious diseases. These methods will be applied to avian influenza A (H7N9), Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV), and Ebola virus disease (EVD) outbreaks. This effort will provide quantitative, evidenced-based guidance for policymakers and public health responders to augment public health operations. / Ph. D.

Conventional pre-clinical drug screening, reliant on 2D cell cultures and animal studies, faces challenges—the former lacks biological complexity, and the latter lacks predictability due to differences between animals and humans from genetic to functional levels. Organ-on-chip technologies have evolved to bridge the gap between preclinical and clinical trials, necessitating human cells for precise predictions of human responses. Considering the significance of the vascular system in various diseases, incorporating vascular units into organ-on-chip devices is critical. For effective drug discovery using vessels-on-chips, achieving high-throughput and consistency between samples is crucial. However, many vessels-on-chips are manually handled during preparation and data collection, reducing throughput and increasing sample-to-sample variations. The conventional closed microfluidic chip format further impedes accessibility, hindering automation. This thesis focuses on two high-throughput micro-vessel models replicating vascular functions under perfusion in a 384-well plate format. These open-top models allow automated preparation and examination, enhancing efficiency in compound screening. The first model features a self-assembled perfusable micro-vascular network on a 384-well plate, co-culturing endothelial cells (EC) with stromal cells in a hydrogel. Automated using a robotic system and a fluorescent plate reader, it supports organ-specific functions and enables nanoparticle transport to target tissues. Utilized for testing cancer therapeutic drugs, it demonstrates dose-related responses in vascular permeability and architectures. The second model is dedicated to crafting micro-vessels of consistent quality for biological testing and disease modeling. It employs a sacrificial material for pre-designed tubular shapes for EC seeding. The integration of automated processes and a straight channel design minimizes sample discrepancies. Furthermore, a tri-culture system enhances barrier integrity, enabling effective drug screening that distinguishes between vasculotoxic and non-vasculotoxic agents with notable sensitivity and specificity. Looking ahead, there is potential to further refine these models to encompass a broader range of vascular diseases, which could lead to novel insights and therapeutic targets. / Thesis / Doctor of Philosophy (PhD) / In clinical trials, a staggering 90% of drugs fail during testing in people. Traditional preclinical drug screening methods rely on culturing human cells on flat surfaces or using animal models, both fraught with limitations such as lacking structural complexity or having DNA differences from humans. Addressing this issue could notably reduce efforts and costs. This thesis is dedicated to advancing preclinical drug testing through micro-vessel models. It focuses on constructing 3D vessels using human cells, offering a more accurate representation of human physiology. Two models are discussed: one with self-assembled vessels featuring complex structures, and another emphasizing sacrificial materials to design simpler vascular shapes, ensuring consistency in testing. By leveraging these innovative models, researchers can subject various drugs to micro-vessels constructed in vitro, enabling them to predict their effects in humans. This approach has the potential to transform drug testing methodologies, moving towards the utilization of artificial human organ models.

Adolescents and Young Adults (AYAs) ages 15-39 years with cancer have received little attention in the medical and health fields, resulting in a lack of progress for this age group. Little is known about the unique biologic, epidemiologic, and psychosocial issues that play an integral role in the AYA cancer journey. The purposes of this study were to use the Tennessee Cancer Registry for all new cancer cases from 2004-2008 to determine 1) the main types of cancer that affect AYAs in TN, 2) the predictors of late-stage diagnosis of melanoma, and 3) the factors that predict a total thyroidectomy for cancer treatment. A total of 8,097 cancer cases were diagnosed in AYAs in Tennessee from 2004-2008. The five main cancer types were breast cancers, melanomas, thyroid cancers, lymphomas, and testicular cancers and accounted for over 50% (N=4,269) of cancers in AYAs in Tennessee during the study period. Females were significantly more likely to be diagnosed with melanomas (age adjusted incidence rate (AIR) 14.01, 95% CI 12.96-15.06) and thyroid cancers (AIR 13.39, CI 12.37-14.42) compared to males (AIR 8.08, CI 7.28-8.88 and AIR 3.50, CI 2.98-4.03, respectively). All cancer types increased with age. Individuals with government insurance (OR 8.41, CI 3.04-23.27) and those 15-19 years of age (OR 6.30, CI 1.74-22.86) had the highest risk of late-stage melanoma. Significant predictors of a using total thyroidectomy for thyroid cancer treatment included regional/distant stage cancer at diagnosis (OR 2.80, CI 1.34-5.85) compared to localized stage, papillary carcinoma (OR 2.64, CI 1.02-6.83) and papillary adenocarcinoma (OR 3.56, CI1.37-9.19) histology types compared to follicular adenocarcinoma, and residence in non-Appalachian Tennessee (OR 2.07, CI 1.26-3.42) compared to Appalachian TN. An increased awareness of cancer types that affect AYAs in Tennessee will provide a basis for developing public health campaigns for cancer prevention and control in this population. This research serves as a first step in using state-based cancer registries to identify the unique characteristics of cancer in AYAs and will set the stage for future state-based research in this underserved population.

Type 1 Diabetes (T1D) is an autoimmune disease where the pancreas produces little to no insulin, which is a hormone that regulates blood glucose levels. This happens because the immune system attacks (and kills) the beta cells of the pancreas, which are responsible for insulin production. Higher levels of glucose in the blood could have very negative, long term effects such as organ damage and blindness. To date, T1D does not have a defined cause nor cure, and research for this disease is slow and difficult due to the invasive nature of T1D experimentation. Mathematical modeling provides an alternative approach for treatment development and can greatly advance T1D research. This thesis describes both a single-compartment and multi-compartment model for Type 1 Diabetes.

The multisystemic tumors characteristic of the monogenic neoplastic diseases, tuberous sclerosis complex (TSC) and lymphangioleiomyomatosis (LAM), share common signaling aberrations upon the loss of heterozygosity in either the TSC1 or TSC2 genes. However, their physical manifestations are vastly different and can generally be classified as being either neurological (TSC) or mesenchymal (TSC & LAM; referred to herein as LAM for simplicity) in origin. In this study, I present a comprehensive stem cell model of LAM utilizing multiple TSC2 knockout (TSC2-/-) pluripotent stem cell lines differentiated to the putative cell of origin for mesenchymal tumors, neural crest cells (NCCs). TSC2-/- NCCs faithfully recapitulate LAM phenotypes and temporal RNA-seq analysis of neural and neural crest differentiation was performed to model disease pathogenesis. Analysis revealed immediate activation of stress response signaling resulting in protein aggregation and lysosome and autophagosome accumulation upon neuralization in TSC2-/- cells. This resulted in acute and lasting effects specific to neural progenitor cells (NPCs), that are transient and ameliorated in NCCs. These lineage-specific effects resulted in selective sensitization of NPCs to cell death via proteasome inhibition, suggesting a potential therapeutic avenue for neurological TSC, but not LAM. Thus, a genome-wide CRISPR knockout screen was performed in TSC2-/- NCCs. Analysis of synthetic lethal genes reveals pathways previously targeted for LAM, but provides gene-level resolution to the vulnerable nodes within these pathways. Importantly, 18 novel gene targets were identified that display synthetic lethality to TSC2-/- cells with high specificity. 3 genes within this list were targetable using commercially available small molecule inhibitors, one of which, FGFR1, shows highly selective lethal targeting of TSC2-/- NCCs. Importantly, this model system, paired with the expansive resource of transcriptomic and synthetic lethal data, serves as a foundation for the development of next generation treatment strategies for LAM, and potentially the entire spectrum of TSC manifestations.

COPD is characterized by tissue inflammation and impaired remodeling that suggests fibroblast maintenance of structural homeostasis is dysregulated. Thus, we performed in vitro wound healing experiments on normal and diseased human lung fibroblasts and developed an ABM of fibroblasts closing a scratched monolayer using NetLogo to evaluate differences due to COPD or cigarette smoke condensate exposure. This ABM consists of a rule-set governing the healing response, accounting for cell migration, proliferation, death, activation and senescence rates; along with the effects of heterogeneous activation, phenotypic changes, serum deprivation and exposure to cigarette smoke condensate or bFGF. Simulations were performed to calibrate parameter-sets for each cell type using in vitro data of scratch-induced migration, viability, senescence-associated beta-galactosidase and alpha-smooth muscle actin expression. Parameter sensitivities around each calibrated parameter-set were analyzed. This model represents the prototype of a tool designed to explore fibroblast functions in the pathogenesis of COPD and evaluate potential therapies.