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.

HIV Reverse Transcriptase Fidelity And Inhibition Are Modulated By Divalent Cations In A Concentration-Dependent Manner In Vitro

Human immunodeficiency virus (HIV) rapidly evolves through generation and selection of mutants that can escape drug therapy. This process is fueled, in part, by the presumably highly error prone polymerase reverse transcriptase (RT). Fidelity of polymerases can be influenced by cation co-factors. Physiologically, magnesium (Mg2+) is used as a co-factor by RT to perform catalysis, however, alternative cations including manganese (Mn2+), cobalt (Co2+), and zinc (Zn2+) can also be used. I demonstrate here that fidelity and inhibition of HIV RT can be influenced differently, in vitro, by divalent cations depending on their concentration. The reported mutation frequency for purified HIV RT in vitro is typically in the 10-4 range (per nucleotide addition), making the enzyme several-fold less accurate than most polymerases. Paradoxically, results examining HIV replication in cells indicate an error frequency that is ~10 times lower than the error rate obtained in the test tube. Here, I reconcile, at least in part, these discrepancies by showing that HIV RT fidelity in vitro is in the same range as cellular results, in physiological concentrations of free Mg2+ (~0.25 mM). At low Mg2+, mutation rates were 5-10 times lower compared to high Mg2+ conditions (5-10 mM). Alternative divalent cations also have a concentration-dependent effect on RT fidelity. Presumed promutagenic cations Mn2+ and Co2+ decreases the fidelity of RT only at elevated concentrations, and Zn2+, when present in low concentration, increases the fidelity of HIV-1 RT by ~2.5 fold compared to Mg2+. HIV-1 and HIV-2 RT inhibition by nucleoside (NRTIs) and non-nucleoside RT inhibitors (NNRTIs) in vitro is also affected by the Mg2+ concentration. NRTIs lacking 3'-OH group inhibited both enzymes less efficiently in low Mg2+ than in high Mg2+; whereas inhibition by the “translocation defective RT inhibitor”, which retains the 3ʹ-OH, was unaffected by Mg2+ concentration, suggesting that NRTIs with a 3ʹ-OH group may be more potent than other NRTIs. In contrast, NNRTIs were more effective in low vs. high Mg2+ conditions. Overall, the studies presented reveal strategies for designing novel RT inhibitors and strongly emphasize the need for studying HIV RT and RT inhibitors in physiologically relevant low Mg2+ conditions.

KINESIN MOTOR PROTEINS ARE ESSENTIAL FOR MALE GAMETOPHYTE DEVELOPMENT IN MARSILEA VESTITA

The male gametophyte of the semi-aquatic fern, Marsilea vestita, produces multiciliated spermatozoids in a rapid developmental sequence that is controlled post-transcriptionally when dry microspores are placed in water. Development can be divided into two phases, mitosis and differentiation. During the mitotic phase, a series of nine successive division cycles produce 7 sterile cells and 32 spermatids in 4.5-5 hours. During the next 5-6 hours, each spermatid differentiates into a corkscrew-shaped motile spermatozoid with ~140 cilia. This document focuses on the role of motor proteins in the regulation of male gametophyte development and during ciliogenesis. In order to study the mechanisms that regulate spermatogenesis, RNAseq was used to generate a reference transcriptome that allowed us to assess the abundance of transcripts at different stages of development. Over 120 kinesin-like sequences were identified in the transcriptome that represent 56 unique kinesin transcripts. Members of the kinesin-2, -4, -5, -7, -8, -9, -12, -13, and -14 families, in addition to several plant specific and ‘orphan’ kinesins are present. Most (91%) of these kinesin transcripts change in abundance throughout gametophyte development, with 52% of kinesin mRNAs enriched during the mitotic phase and 39% enriched during differentiation. Functional analyses show that the temporal regulation of kinesin transcripts during gametogenesis directly correlates with kinesin protein function. Specifically, Marsilea makes one kinesin-2 (MvKinesin-2) and two kinesin-9 (MvKinesin-9A and MvKinesin-9B) transcripts, which are present during spermatid differentiation and ciliogenesis. Silencing experiments showed that MvKinesin-2 and MvKinesin-9A are required for ciliogenesis and motility in the Marsilea male gametophyte; however, these kinesins display atypical roles during these processes. In contrast, spermatozoids produced after the silencing of MvKinesin-9B exhibit normal morphology. MvKinesin-2 is necessary for cytokinesis as well as for regulating ciliary length and MvKinesin-9A is needed for the correct orientation of basal bodies, events not typically associated with these proteins. In addition, Marsilea makes motile, ciliated gametophytes without the help of IFT dynein, outer arm dynein, or the BBsome. These results are the first to investigate the kinesin-linked mechanisms that regulate ciliogenesis in a land plant.

Shaping the Dicot Fruit: Molecular and Genomic Approaches to Fruit Development

The fruit is one of the most complex and important structures produced by flowering plants, and understanding the development and maturation process of fruits in different angiosperm species with diverse fruit structures is of immense interest. In the work presented here, molecular genetics and genomic analysis are used to explore the processes that form the fruit in two species: The model organism Arabidopsis and the diploid strawberry Fragaria vesca. One important basic question concerns the molecular genetic basis of fruit patterning. A long-standing model of Arabidopsis fruit (the gynoecium) patterning holds that auxin produced at the apex diffuses downward, forming a gradient that provides apical-basal positional information to specify different tissue types along the gynoecium’s length. The proposed gradient, however, has never been observed and the model appears inconsistent with a number of observations. I present a new, alternative model, wherein auxin acts to establish the adaxial-abaxial domains of the carpel primordia, which then ensures proper development of the final gynoecium. A second project utilizes genomics to identify genes that regulate fruit color by analyzing the genome sequences of Fragaria vesca, a species of wild strawberry. Shared and distinct SNPs among three F. vesca accessions were identified, providing a foundation for locating candidate mutations underlying phenotypic variations among different F. vesca accessions. Through systematic analysis of relevant SNP variants, a candidate SNP in FveMYB10 was identified that may underlie the fruit color in the yellow-fruited accessions, which was subsequently confirmed by functional assays. Our lab has previously generated extensive RNA-sequencing data that depict genome-scale gene expression profiles in F. vesca fruit and flower tissues at different developmental stages. To enhance the accessibility of this dataset, the web-based eFP software was adapted for this dataset, allowing visualization of gene expression in any tissues by user-initiated queries. Together, this thesis work proposes a well-supported new model of fruit patterning in Arabidopsis and provides further resources for F. vesca, including genome-wide variant lists and the ability to visualize gene expression. This work will facilitate future work linking traits of economic importance to specific genes and gaining novel insights into fruit patterning and development.

Exploring the Function and Regulation of Arabidopsis EIN2 in Ethylene Signaling

Ethylene is an essential plant hormone involved in nearly all stages of plant growth and development. EIN2 (ETHYLENE INSENSITIVE2) is a master positive regulator in the ethylene signaling pathway, consisting of an N-terminal domain and a C-terminal domain. The EIN2 N-terminal domain localizes to the endoplasmic reticulum (ER) membrane and shows sequence similarity to Nramp metal ion transporters. The cytosolic C-terminal domain is unique to plants and signals downstream. There have been several major gaps in our knowledge of EIN2 function. It was unknown how the ethylene signal gets relayed from the known upstream component CTR1 (CONSTITUTIVE RESPONSE1) a Ser/Thr kinase at the ER, to EIN2. How the ethylene signal was transduced from EIN2 to the next downstream component transcription factor EIN3 (ETHYLENE INSENSITIVE3) in the nucleus was also unknown. The N-terminal domain of EIN2 shows homology to Nramp metal ion transporters and whether EIN2 can also function as a metal transporter has been a question plaguing the ethylene field for almost two decades. Here, EIN2 was found to interact with the CTR1 protein kinase, leading to the discovery that CTR1 phosphorylates the C-terminal domain of EIN2 in Arabidopsis thaliana. Using tags at the termini of EIN2, it was deduced that in the presence of ethylene, the EIN2 C-terminal domain is cleaved and translocates into the nucleus, where it could somehow activate downstream ethylene responses. The EIN2 C-terminal domain interacts with nuclear proteins, RTE3 and EER5, which are components of the TREX-2 mRNA export complex, although the role of these interactions remains unclear. The EIN2 N-terminal domain was found to be capable of divalent metal transport when expressed in E. coli and S. cerevisiae leading to the hypothesis that metal transport plays a role in ethylene signaling. This hypothesis was tested using a novel missense allele, ein2 G36E, substituting a highly conserved residue that is required for metal transport in Nramp proteins. This G36E substitution did not disrupt metal ion transport of EIN2, but the ethylene insensitive phenotype of this mutant indicates that the EIN2 N-terminal domain is important for positively regulating the C-terminal domain. The defect of the ein2 G36E mutant does not prevent proper expression or subcellular localization, but might affect protein modifications. The ein2 G36E allele is partially dominant, mostly likely displaying haploinsufficiency. Overexpression of the EIN2 N-terminal domain in the ein2 G36E mutant did not rescue ethylene insensitivity, suggesting the N-terminal domain functions in cis to regulate the C-terminal domain. These findings advance our knowledge of EIN2, which is critical to understanding ethylene signaling.