Understanding coral disease dynamics within the heterogeneous populations in which they act is critical for predicting how the structure of reefs may change as a result of enzootic or epizootic levels of these important sources of mortality. This work focused on combining field studies and the development and testing of a spatially-explicit, individual-based epizootiological computer model with the aim of gaining a greater understanding of the dynamics and impact of white plague, a significant source of mortality on reef-building corals in the Caribbean region. Field studies focused on the incidence and distribution of all sources of coral mortality, including suspect white plague in situ, at two locations; the Florida Keys (United States of America) and Little Cayman Island (Cayman Islands, British West Indies). Results indicated that in both regions disease was the most significant source of mortality during the monitoring time periods, and that suspect white plague type II in Cayman is likely contributing to major structural changes. In Florida, observations made during a mass bleaching event indicated that a significant relationship exists between bleaching severity and disease incidence, and that mortality during the event was largely the result of disease and not bleaching. The simulation model was developed using a long-term data set from Little Cayman, and results of calibration indicated that suspect white plague type II on these reefs is transmissible between colonies within a limited field and require a yearly input from an outside source, and that host susceptibility to infection is low and likely not variable among species. Parameters describing the distribution and composition of the coral population were varied, and results indicated a significant effect of colony density, aggregation, and mean size on the impact of disease. Scenario testing of various disease management strategies indicated that should local prevention measures be developed in the future, it is they, and not treatment, that will likely be the most effective in limiting the impact of disease.

Many important factors affect the spread of childhood infectious disease. To understand better the fundamental drivers of infectious disease spread, several researchers have estimated seasonal transmission coefficients using discrete-time models. This research addresses several shortcomings of the discrete-time approaches, including removing the need for the reporting interval to match the serial interval of the disease using infectious disease data from three major cities: New York City, London, and Bangkok. Using a simultaneous approach for optimization of differential equation systems with a Radau collocation discretization scheme and total variation regularization for the transmission parameter profile, this research demonstrates that seasonal transmission parameters can be effectively estimated using continuous-time models. This research further correlates school holiday schedules with the transmission parameter for New York City and London where previous work has already been done, and demonstrates similar results for a relatively unstudied city in childhood infectious disease research, Bangkok, Thailand.

Angiotensinogen is the only known precursor in the renin-angiotensin system, a hormonal system best known as an essential regulator of blood pressure and fluid homeostasis. Angiotensinogen is sequentially cleaved by renin and angiotensin- converting enzyme to generate angiotensin II. As the major effector peptide, angiotensin II mainly function through angiotensin type 1 receptor. Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and more recently renin inhibitors are widely known as the 3 classic renin-angiotensin system inhibitory drugs against hypertension and atherosclerosis. Here, we developed an array of regents to explore the effects of angiotensinogen inhibition. First, we demonstrated that genetic deficiency of angiotensinogen not only protected against hypercholesterolemia- induced atherosclerosis but also prevented diet-induced obesity. Then we found weekly intraperitoneal injection of antisense oligonucleotides against angiotensinogen remarkably surpressed body weight gain in mice fed a western diet, which was absent from classic renin-angiotensin system inhibition. The suppressed body weight gain was attributable to diminished body fat mass gain and enhanced energy expenditure. More excitingly, angiotensinogen antisense oligonucleotides regressed body weight gain on obese mice. Together, our findings revealed a unique feature of angiotensinogen inhibition beyond classic renin angiotensin inhibition and demonstrated therapeutic potentials of angiotensinogen antisense oligonucleotides against hypertension, atherosclerosis, and obesity. We also developed an in vivo system to explore the functional consequences of disrupting a conserved Cys18-Cys137 disulfide bridge in angiotensinogen. The formation of this disulfide bridge could trigger conformational changes in angiotensinogen, thereby facilitating renin cleavage of angiotensinogen. It was predicted that the redox-sensitive disulfide bridge might change the efficiency of angiotensinogen/renin reaction to release angiotensin II, thus modulate angiotensin II-dependent functions. We determined effects of the presence and absence of the disulfide bridge on angiotensin II concentrations and responses in mice expressing either native angiotensinogen or Cys18Ser, Cys137Ser mutated angiotensinogen in liver via adeno-associated viral vectors. Contrary to the prediction, disruption of Cys18-Cys137 disulfide bridge in angiotensinogen had no discernible effects on angiotensin II production and angiotensin II-dependent functions in mice.

HIV/AIDS is one of the largest health problems the world is currently facing. Even with anti-retroviral therapies (ART), many resource-constrained countries are unable to meet the treatment needs of their infected populations. ART-distribution methods need to be created that prevent the largest number of future HIV infections. We have developed a compartment model that tracks the spread of HIV in multiple two-sex populations over time in the presence of limited treatment. The model has been fit to represent the HIV epidemic in rural and urban areas in Uganda. With the model we examine the spread of HIV among urban and rural regions and observe the effects of preferential treatment to rural areas on the spread of HIV in the country as a whole. We also investigate the effects of preferentially treating women on the spread of HIV. We find that preferentially treating urban women produces the most dramatic effect in reducing the number of infected male and females in rural and urban areas.

The most common causes of childhood interstitial lung disease (chILD) are autosomal recessive mutations in the gene encoding ATP Binding Cassette A3 (ABCA3) protein, a lamellar body (LB) associated lipid transporter exclusively expressed within the alveolar epithelial type II cells (AEC2s) in the lung. Instability of primary AEC2s in culture has prevented studies of ABCA3 mutations, resulting in limited understanding of disease pathogenesis. To overcome this challenge, we developed AEC2-like cells from human pluripotent stem cells (PSCs) in vitro, allowing study of normal ABCA3 function and perturbations that result from ABCA3 mutations. To develop an AEC2 model that would recapitulate ABCA3 biology, we targeted human PSC lines with a knock-in GFP fusion reporter (ABCA3:GFP). Differentiations of PSCs into AEC2s (iAEC2s) resulted in exclusive expression of ABCA3:GFP in iAEC2s and intracellular localization to LAMP3+ vesicles, reminiscent of endogenous ABCA3. Moreover, we find these ABCA3:GFP+ iAEC2s express LBs, process surfactant proteins, and secret surfactant lipids, indicative of preserved ABCA3 function. To study the effects of ABCA3 mutations using our model, we generated two sets of PSC reporter lines: 1) two patient-derived iPSC lines carrying rare homozygous E690K and W308R ABCA3 mutations predicted to affect ABCA3 function or trafficking, respectively, and their two syngeneic gene-corrected lines each targeted with the AEC2-specific knock-in fluorescent reporter SFTPCtdTomato; and 2) three syngeneic ABCA3:GFP knock-in iPSC lines encoding wildtype, E690K, or W308R proteins. Directed differentiation of patient iPSCs into iAEC2s revealed attenuated secretion of surfactant-specific lipids, recapitulating clinical findings of surfactant deficiency. Examination of ABCA3 protein trafficking using the ABCA3:GFP fusion reporter revealed retained E690K and W308R mutant ABCA3 protein processing and trafficking compared to the wildtype protein by confocal microscopy and western blot analyses, however mutant iAEC2s exhibited smaller LBs, indicative of defective ABCA3-dependent lipid transport. Bulk RNA sequencing of mutant and gene-corrected SFTPCtdTomato- or ABCA3:GFP-expressing iAEC2s revealed enrichment of the TNF𝛼-NF𝜅B pathway in both W308R and E690K mutant iAEC2s, validated by lentiviral reporter assays and secretion of NF𝜅B-driven cytokines. Thus, we provide insights into how ABCA3 mutations alter AEC2 physiology and developed a platform to study other genetic AEC2 diseases through our ABCA3:GFP reporter system. / 2021-05-26T00:00:00Z

Hereditary transthyretin amyloidosis (ATTR amyloidosis) is a multi-system protein folding disorder that results from >100 described mutations in the transthyretin (TTR) gene. In the disease, non-natively folded TTR, originally produced by the liver, travels throughout circulation and deposits extracellularly at downstream target organs. The multi-tissue etiology of the disease makes it difficult to study in vitro, while no mouse model accurately recapitulates disease pathology. Therefore, we utilized patient-specific induced pluripotent stem cells (iPSCs) to test the hypothesis that production of and exposure to destabilized TTRs results in distinct cellular and molecular changes. The liver’s contribution to the deposition of TTR at distal tissues is understudied. As a result, in Aim 1 we sought to assess the effects of destabilized TTR production on effector hepatic cells. To this end, we utilized gene editing to generate isogenic, patient iPSCs expressing either mutant or wild-type TTR. Combining this tool with single cell RNAseq, we identified hepatic proteostasis factors, including unfolded protein response (UPR) pathways, whose expression coincided with the production of destabilized TTR. Enhancing endoplasmic reticulum (ER) proteostasis within patient hepatic cells via exogenous activation of adaptive UPR signaling, we demonstrated preferential reduction in the secretion of pathogenic TTR. In turn, we demonstrated that production of disease-associated TTR correlates with expression of proteostasis factors capable of regulating TTR secretion and in turn downstream pathogenesis. ATTR amyloidosis patients exhibit extreme phenotypic variation (e.g. TTR fibril deposits at cardiac tissue and/or peripheral nerves). In Aim 2, we sought to define responses of target cell types to pathologically-diverse TTRs. To accomplish this, we profiled transcriptomic changes resulting from exposure to a variety of destabilized TTRs to determine 1) target cell response to TTR exposure and 2) how this response changes across diverse variants and cell types. In doing so, we found that TTR exposure elicits distinct variant- and cell type-specific transcriptional responses. Herein, we addressed our central hypothesis by profiling destabilized TTR production within hepatic cells and TTR exposure at target cell types. Collectively, these data may result in the discovery of unidentified and potentially druggable pathologically-associated pathways for ATTR amyloidosis and other systemic amyloid diseases.

Commonly used simulation models for predicting outbreaks of re-emerging infectious diseases (EIDs) take an individual-level or a population-level approach to modeling contact dynamics. These approaches are a trade-off between the ability to incorporate individual-level dynamics and computational efficiency. Agent-based network models (ABNM) use an individual-level approach by simulating the entire population and its contact structure, which increases the ability of adding detailed individual-level characteristics. However, as this method is computationally expensive, ABNMs use scaled-down versions of the full population, which are unsuitable for low prevalence diseases as the number of infected cases would become negligible during scaling-down. Compartmental models use differential equations to simulate population-level features, which is computationally inexpensive and can model full-scale populations. However, as the compartmental model framework assumes random mixing between people, it is not suitable for diseases where the underlying contact structures are a significant feature of disease epidemiology. Therefore, current methods are unsuitable for simulating diseases that have low prevalence and where the contact structures are significant. The conceptual framework for a new simulation method, Evolving Contact Network Algorithm (ECNA), was recently proposed to address the above gap. The ECNA combines the attributes of ABNM and compartmental modeling. It generates a contact network of only infected persons and their immediate contacts, and evolves the network as new persons become infected. The conceptual framework of the ECNA is promising for application to diseases with low prevalence and where contact structures are significant. This thesis develops and tests different algorithms to advance the computational capabilities of the ECNA and its flexibility to model different network settings. These features are key components that determine the feasibility of ECNA for application to disease prediction. Results indicate that the ECNA is nearly 20 times faster than ABNM when simulating a population of size 150,000 and flexible for modeling networks with two contact layers and communities. Considering uncertainties in epidemiological features and origin of future EIDs, there is a significant need for a computationally efficient method that is suitable for analyses of a range of potential EIDs at a global scale. This work holds promise towards the development of such a model.

Resistance to glucocorticoid (GC) therapy results in poor prognosis for acute lymphoblastic leukemia (ALL) patients. Utilizing a whole genome shRNA screen our lab identified several novel mechanisms of GC resistance. My thesis work established that an orphan nuclear receptor, the Estrogen Related Receptor Beta (ESRRB), is critical for induction of apoptotic genes following treatment with the GC dexamethasone. ESRRB has mostly been implicated in maintenance of pluripotency in mouse embryonic stem cells. We find that repression of ESRRB results in GC resistance in ALL and define ESRRB as a novel cooperating transcription factor in GC-induced gene expression. We also show that agonists to ESRRB synergize with dexamethasone and increase dexamethasone induced apoptosis in relapse ALL patient samples. Interestingly, our shRNA screen identified another factor important in stem cell maintenance: SOX2. While we originally hypothesized that ESRRB and SOX2 may cooperate in ALL, RNA-sequencing studies revealed that these factors mediate GC resistance by independent mechanisms. Our data define SOX2 as a repressor of key signaling pathways in ALL. Upon SOX2 knockdown, we observe activation of pro-survival gene expression including activation of the MAPK pathway, which has previously been implicated in GC resistance. MAPK activation may be explained by an increase in EGFR expression observed in Sox2 knockdown cells and GC resistant patients, suggesting EGFR inhibitors may re-sensitize patients to GCs. Overall my thesis work identifies mechanisms of GC resistance in ALL and utilizes these findings to define novel therapeutic strategies for GC resistant ALL patients.

The kinetics of tumor growth and progression are governed by the interaction between tumor cells, the non-malignant stroma and both innate and adaptive immune cell lineages. Innate immunity has a critical role in the control of tumor cell growth and metastasis. The microenvironment of many tumors is populated with innate immune cells, including regulatory natural killer (NK) cells and dendritic cells (DCs), tumor associated macrophages, and myeloid derived suppressor cells, that suppress normal immune function. Much of our understanding of interactions between tumors and the innate immune system is based on experimental studies performed in mouse “syngenic” models. However, there is clear need for a mechanistic understanding of the human innate immune system within the tumor microenvironment. The goal of my thesis is to characterize the interactions between human innate immune cells and tumors and to define specific pathways and cell lineages that are targets for immune modulation. A central focus of my thesis is the use of cutting-edge humanized mouse models based on the immunodeficient NOD-scid IL2Rgnull (NSG) mouse strain to study human immuno-oncology. In the first section of my thesis I describe studies that evaluate the influence of inflammatory stimuli on innate immune control of tumors. Agents that induce inflammation have been used since the 18th century for the treatment of cancer. The inflammation induced by agents such as toll-like receptor (TLR) agonists is thought to stimulate tumor-specific immunity in patients and augment control of tumor burden. While NSG mice lack murine adaptive immunity (T and B cells), these mice maintain a residual murine innate immune system that responds to TLR agonists. Here I describe a novel NSG mouse strain lacking TLR4 that fails to respond to lipopolysaccharide (LPS). NSG-Tlr4null mice support human immune system engraftment and enables the study of human specific responses to TLR4 agonists. My data demonstrate that specific stimulation of TLR4 activates human innate immune system and promotes regression of human patient derived xenograft (PDX) tumors. In the second section of my thesis I describe the development of an NSG mouse strain that constitutively expresses human Interleukin 15 (IL15) and supports the development of functional human NK cells. Using humanized NSG-IL15 transgenic mice (NSG-Tg(Hu-IL15), my data clearly demonstrate a critical role for human NK cells in limiting growth of a PDX melanoma. In the third section of my thesis I describe, the use of the bone marrow/liver/thymus (BLT) humanized mouse model to study the interactions between the human immune system and PDX melanoma and to evaluate the response of the melanoma to immunotherapy modalities. My results collectively suggest that mice engrafted with human immune systems and bearing human tumors can be harnessed as translational models, which are critically needed as tools to study tumor immunotherapy. These humanized mouse models are an ideal translational tool to advance our understanding of human immuno-oncology and for development and testing of novel immune therapies for the treatment of malignancies.

When attempting to mitigate the spread of an epidemic without the use of a vaccine, many measures may be made to dampen the spread of the disease such as physically distancing and wearing masks. The implementation of an effective test and quarantine strategy on a population has the potential to make a large impact on the spread of the disease as well. Testing and quarantining strategies become difficult when a portion of the population are asymptomatic spreaders of the disease. Additionally, a study has shown that randomly testing a portion of a population for asymptomatic individuals makes a small impact on the spread of a disease. This thesis simulates the transmission of the virus that causes COVID-19, SARSCoV- 2, in contact networks gathered from real world interactions in five different environments. In these simulations, several testing and quarantining strategies are implemented with a varying number of tests per day. These strategies include a random testing strategy and several uniform testing strategies, based on knowledge of the underlying network. By modeling the population interactions as a graph, we are able to extract properties of the graph and test based on those metrics, namely the degree of the network. This thesis found many of the strategies had a similar performance to randomly testing the population, save for testing by degree and testing the cliques of the graph, which was found to consistently outperform other strategies, especially on networks that are more dense. Additionally, we found that any testing and quarantining of a population could significantly reduce the peak number of infections in a community.