DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

AN EXAMINATION OF HYDROXYL RADICAL: OUR CURRENT UNDERSTANDING OF THE OXIDATIVE CAPACITY OF THE TROPOSPHERE THROUGH EMPIRICAL, BOX, AND GLOBAL MODELING APPROACHES

Hydroxyl radical (OH) is the primary oxidant in the troposphere, initiating the removal of numerous atmospheric species including greenhouse gases, pollutants that are detrimental to human health, and ozone-depleting substances. Because of the complexity of OH chemistry, models vary widely in their OH chemistry schemes and resulting methane (CH4) lifetimes. The current state of knowledge concerning global OH abundances is often contradictory. This body of work encompasses three projects that investigate tropospheric OH from a modeling perspective, with the goal of improving the tropospheric community’s knowledge of the atmospheric lifetime of CH4. First, measurements taken during the airborne CONvective TRansport of Active Species in the Tropics (CONTRAST) field campaign are used to evaluate OH in global models. A box model constrained to measured variables is utilized to infer concentrations of OH along the flight track. Results are used to evaluate global model performance, suggest against the existence of a proposed “OH Hole” in the tropical Western Pacific, and investigate implications of high O3/low H2O filaments on chemical transport to the stratosphere. While methyl chloroform-based estimates of global mean OH suggest that models are overestimating OH, we report evidence that these models are actually underestimating OH in the tropical Western Pacific. The second project examines OH within global models to diagnose differences in CH4 lifetime. I developed an approach to quantify the roles of OH precursor field differences (O3, H2O, CO, NOx, etc.) using a neural network method. This technique enables us to approximate the change in CH4 lifetime resulting from variations in individual precursor fields. The dominant factors driving CH4 lifetime differences between models are O3, CO, and J(O3-O1D). My third project evaluates the effect of climate change on global fields of OH using an empirical model. Observations of H2O and O3 from satellite instruments are combined with a simulation of tropical expansion to derive changes in global mean OH over the past 25 years. We find that increasing H2O and increasing width of the tropics tend to increase global mean OH, countering the increasing CH4 sink and resulting in well-buffered global tropospheric OH concentrations.