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.

Functional Neuroimaging of the Social Regulation of Emotion in Schizophrenia

Negative symptoms in schizophrenia are characterized by deficits in normative experiences and expression of emotion. Social anhedonia (diminished pleasure from social experiences) is one negative symptom that may impact patients’ motivation to engage in meaningful social relationships. Past research has begun to examine the mechanisms that underlie social anhedonia, but it is unclear how this lack of social interest may impact the typically positive effects of social buffering and social baseline theory whereby social support attenuates stress. The present pilot study examines how social affiliation through hand holding is related to subjective and neural threat processing, negative symptoms, and social functioning. Twenty-one participants (14 controls; 7 schizophrenia) developed social affiliation with a member of the research staff who served as the supportive partner during the threat task. Participants displayed greater subjective benefit to holding the hand of their partner during times of stress relative to being alone or with an anonymous experimenter, as indicated by self-reported increased positive valence and decreased arousal ratings. When examining the effects of group, hand holding, and their interaction on the neurological experience of threat during the fMRI task, the results were not significant. However, exploratory analyses identified preliminary data suggesting that controls experienced small relative increases in BOLD signal to threat when alone compared to being with the anonymous experimenter or their partner, whereas the schizophrenia group results indicated subtle relative decreases in BOLD signal to threat when alone compared to either of the hand holding conditions. Additionally, within the schizophrenia group, more positive valence in the partner condition was associated with less severe negative symptoms, better social functioning, and more social affiliation, whereas less arousal was correlated with more social affiliation. Our pilot study offers initial insights about the difficulties of building and using social affiliation and support through hand holding with individuals with schizophrenia during times of stress. Further research is necessary to clarify which types of support may be more or less beneficial to individuals with schizophrenia who may experience social anhedonia or paranoia with others that may challenge the otherwise positive effects of social buffering and maintaining a social baseline.

STRUCTURAL AND FUNCTIONAL STUDIES OF CYCLIC K48-LINKED DIUBIQUITIN

K48-linked di-ubiquitin exists in a dynamic equilibrium between open and closed states. The structure of K48-Ub2 in the closed conformation features a hydrophobic interface formed between the two Ub domains. The same hydrophobic residues at the interface are involved in binding to ubiquitin-associated (UBA) domains. Cyclization of K48-Ub2 should limit the range of conformations available for such interactions. Interestingly, cyclic K48-linked Ub2 (cycUb2) has been found in vivo and can be isolated in vitro to study its structure and dynamics. In this study, a crystal structure of cycUb2 was obtained, and the dynamics of cycUb2 were characterized by solution NMR. The crystal structure of cycUb2, which is in agreement with solution NMR data, is closed with the hydrophobic patches of each Ub domain buried at the interface. Despite its structural constraints, cycUb2 was still able to interact with UBA domains, albeit with lower affinity.