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.

CHARACTERIZATION OF TOBACCO MOSAIC VIRUS DIRECTED REPROGRAMMING OF PHLOEM GENE EXPRESSION TO PROMOTE VIRAL PHLOEM LOADING AND SYSTMEIC MOVEMENT

Vascular phloem loading has long been recognized as an essential step in the establishment of a systemic virus infection. Yet little is known about this process and the mechanisms that control it. In this study, an interaction between the replication protein of Tobacco mosaic virus (TMV) and phloem specific auxin/indole acetic acid (Aux/IAA) transcriptional regulators was found to modulate virus phloem loading. Promoter expression studies show TMV 126/183 kDa interacting Aux/IAAs predominantly express and accumulate within the nuclei of phloem companion cells (CC). Furthermore, CC Aux/IAA nuclear localization is disrupted upon infection with an interacting virus but not during infection with a non-interacting virus. In situ analysis of virus spread shows the inability of TMV variants to disrupt Aux/IAA CC nuclear localization correlates with a reduced ability to load into the vascular tissue. Subsequent systemic movement assays also demonstrate that a virus capable of disrupting Aux/IAA localization is significantly more competitive at systemic movement than a non-interacting virus. Similarly, CC expression and over-accumulation of a degradation-resistant-interacting Aux/IAA protein was found to selectively inhibit TMV accumulation and phloem loading. Transcriptional expression studies demonstrate a role for interacting Aux/IAA proteins in the regulation of salicylic acid and jasmonic acid dependent host defense responses as well as virus specific movement factors including pectin methylesterase that are involved in regulating plasmodesmata size exclusion limits and promoting virus cell-to-cell movement. Further characterization of the phloem environment was done using two phloem specific promoters (pSUC2 and pSULTR2;2) to generate epitope-tagged polysomal-RNA complexes. Immuno-purification using the epitope tag allowed us to obtain mRNAs bound to polysomes (the translatome) specifically in phloem tissue. We found the phloem translatome is uniquely altered during TMV infection with 90% and 88% of genes down regulated in the pSUC2 and pSULTR2;2 phloem translatomes, compared to 31% of genes down regulated in the whole plant p35S translatome. Transcripts down regulated in phloem include genes involved in callose deposition at plasmodesmata, host defense responses, and RNA silencing. Combined, these findings indicate TMV reprograms gene expression within the vascular phloem as a means to enhance phloem loading and systemic spread.