Elastin is an essential extracellular protein that is a key component of elastic fibres, providing elasticity to cardiac, dermal, and arterial tissues. During the development of the human cardiovascular system, elastin self-assembles before being integrated into fibres, undergoing no significant turnover during the human lifetime. Abnormalities in elastin can adversely affect its self-assembly, and may lead to malformed elastic fibres. Due to the longevity required of these fibres, even minor abnormalities may have a large cumulative effect over the course of a lifetime, leading to late-onset vascular diseases. This thesis project has identified important, over-represented repetitive elements in elastin which are believed to be important for the self-assembly and elastomeric properties of elastin. Initial studies of single nucleotide polymorphisms (SNPs) from the HapMap project and dbSNP resulted in a set of genetic variation sites in the elastin gene. Based on these studies, glycine to serine and lysine to arginine substitutions were introduced in elastin-like polypeptides. The self-assembly properties of the resulting elastin-like polypeptides were observed under microscope and measured using absorbance at 440nm. Assembled polypeptides were also cross-linked to form thin membranes whose mechanical and physical properties were measured and compared. These mutations resulted in markedly different behavior than wild-type elastin-like proteins, suggesting that mutations in the repetitive elements of the elastin sequence can lead to adverse changes in the physical and functional properties of the resulting protein. Using next-generation sequencing, patients with thoracic aortic aneurysms are being genotyped to discover polymorphisms which may adversely affect the self-assembly properties of elastin, providing a link between genetic variation in elastin and cardiovascular disease.

Protein phosphorylation is a prevalent reversible post-translational modification that influences protein functions. The advent of phospho-proteomic technologies now enables proteome-wide quantitative detection of residues phosphorylated under different physiological conditions. The functional consequences of the majority of these phosphorylation events are unknown. This calls for endeavors to characterize their molecular functions and cellular effects. This can be facilitated by systematic approaches to categorize phosphorylation events, interpret their importance and infer their functions. I carried out comparative, evolutionary and integrative analyses on in vivo phosphorylation events to address these challenges. First, I performed cross-species comparative phospho-proteomic analysis to identify evolutionarily conserved phosphorylation events in human. A sequence alignment approach was used to identify phosphorylation events conserved at similar sequence positions across orthologous proteins and a network alignment approach was applied to identify potential evolutionarily conserved kinase-substrate interactions. Conserved human phosphoproteins identified are found enriched for proteins encoded by known cancer- and disease-associated genes. Next, I developed a new approach to analyze the sequence conservation of known phosphorylated residues on human, mouse and yeast proteins that factored in the background mutational rates of protein and phosphorylatable residue. Furthermore, sites were analyzed according to (i) characterized functions, (ii) prevalence, (iii) stoichiometry, their occurrence in (iv) structurally disordered/ordered protein regions, in (v) proteins of various abundance and in (vi) proteins with different protein interaction propensity to identify the factors influencing sequence conservation of phosphorylated residues. Importantly, my analysis suggests that false positives and randomly phosphorylated residues are present in existing phosphorylation datasets and they are more common on high abundance proteins. Lastly, I characterized the theoretical maximum phosphorylation capacity in terms of phosphorylatable residues and discovered that genomic tyrosine frequency correlates negatively and significantly with tyrosine kinase frequency and cell type in metazoan. This observation suggests that fidelity of phosphotyrosine signaling occurred partially through global tyrosine depletion.

Anthracycline- and taxane-based regimens have been the mainstay in treating breast cancer patients using chemotherapy. Yet, the genetic make-up of patients and their tumors may have a strong impact on tumor sensitivity to these agents and to treatment outcome. This study represents a new paradigm assimilating bioinformatic tools with in vitro model systems to discover novel genetic variations that may be associated with chemotherapy response in breast cancer. This innovative paradigm integrates drug response data for the NCI60 cell line panel with genome-wide Affymetrix SNP data in order to identify genetic variations associated with drug resistance. This genome wide association study has led to the discovery of 59 candidate loci that may play critical roles in breast tumor sensitivity to doxorubicin and paclitaxel. 16 of them were mapped within well-characterized genes (three related to doxorubicin and 13 to paclitaxel). Further in silico characterization and in vitro functional analysis validated their differential expression in resistant cancer cell lines treated with the drug of interest (over-expression of RORA and DSG1, and under-expression of FRMD6, SGCD, SNTG1, LPHN2 and DCT). Interestingly, three and six genes associated with doxorubicin and paclitaxel resistance, respectively, are involved in the apoptotic process in cells. A constructed interactome suggested that there is cross-talk at the Nrf-2 oxidative stress pathway between genes associated with resistance to doxorubicin and paclitaxel. This unique GWA approach serves as a proof-of-principle study and systematically investigates targets responsible for variable response to chemotherapy in breast tumor cells and possibly the tumors of breast cancer patients. Overall, the model discovered novel candidate genes that have not been previously associated with doxorubicin and paclitaxel cytotoxicity. Future studies will be directed at illustrating a causative relationship between the observed genomic changes and drug resistance in breast cancer patients undergoing doxorubicin and paclitaxel chemotherapy.

The shikimate pathway links carbohydrate metabolism to the biosynthesis of the aromatic amino acids and an enormous variety of aromatic compounds with essential functions in all kingdoms of life. Aromatic compounds derived from the plant shikimate pathway have substantial biotechnological value and many are essential to the diet of metazoans whose genomes do not encode shikimate pathway enzymes. Despite its importance to the physiology of plants and human health the regulatory mechanisms of the plant shikimate pathway are not well understood. Shikimate kinase (SK) genes encode an intermediate step in the shikimate pathway and were previously implicated in regulation of the plant shikimate pathway. The distribution of SK genes in higher plants was resolved using phylogenetic and biochemical methods. The two SK isoforms of Arabidopsis thaliana, AtSK1 and AtSK2, were functionally characterized. AtSK1 expression is induced by heat stress and the recombinant enzyme was shown to form a homodimer which is important for maintaining the stability and activity of the enzyme at elevated temperatures. The crystal structure of AtSK2, the first reported plant SK structure, identified structural features unique to plant SKs which may perform important regulatory functions. The resolution of bona fide SKs in higher plants led to the discovery of two novel neofunctionalized homologs - Shikimate Kinase-Like 1 (SKL1) and SKL2. These novel genes evolved from SK gene duplicates over 400 million years ago and are found in all major extant angiosperm lineages, suggesting they were important in the development of biological properties required by land plants. The description of albino and variegated skl1 mutants in Arabidopsis thaliana implicate the SKL1 gene product as an important regulator of chloroplast biogenesis. Functional assays were attempted to determine the biochemical function of SKL1 and recombinant constructs of the Arabidopsis thaliana SKL1 protein were crystallized towards structure determination. The results of this thesis further our understanding of the organization and regulation of the plant shikimate pathway. Furthermore, the discovery of SKL1 may yield important insights into chloroplast biogenesis and function. The evolution of the plant SK superfamily highlights the utility of SKs as scaffolds for functional innovation.

The currently known network of human protein-protein interactions (PPIs) is providing new insights into diseases and helping to identify potential therapies. However, according to several estimates, the known interaction network may represent only 10% of the entire interactome - indicating that more comprehensive knowledge of the interactome could have a major impact on understanding and treating diseases. The primary aim of this thesis was to develop computational methods to provide increased coverage of the interactome. A secondary aim was to gain a better understanding of the link between networks and phenotype, by analyzing essential mouse genes. Two algorithms were developed to predict PPIs and provide increased coverage of the interactome: FpClass and mixed co-expression. FpClass differs from previous PPI prediction methods in two key ways: it integrates both positive and negative evidence for protein interactions, and it identifies synergies between predictive features. Through these approaches FpClass provides interaction networks with significantly improved reliability and interactome coverage. Compared to previous predicted human PPI networks, FpClass provides a network with over 10 times more interactions, about 2 times more proteins and a lower false discovery rate. This network includes 595 disease related proteins from OMIM and Cancer Gene Census which have no previously known interactions. The second method, mixed co-expression, aims to predict transient PPIs, which have proven difficult to detect by computational and experimental methods. Mixed co-expression makes predictions using gene co-expression and performs significantly better (p < 0.05) than the previous method for predicting PPIs from co-expression. It is especially effective for identifying interactions of transferases and signal transduction proteins. For the second aim of the thesis, we investigated the relationship between gene essentiality and diverse gene/protein features based on gene expression, PPI and gene co-expression networks, gene/protein sequence, Gene Ontology, and orthology. We identified non-redundant features closely associated with essentiality, including centrality in PPI and gene co-expression networks. We found that no single predictive feature was effective for all essential genes; most features, including centrality, were less effective for genes associated with postnatal lethality and infertility. These results suggest that understanding phenotype will require integrating measures of network topology with information about the biology of the network’s nodes and edges.

Elastin is an essential extracellular protein that is a key component of elastic fibres, providing elasticity to cardiac, dermal, and arterial tissues. During the development of the human cardiovascular system, elastin self-assembles before being integrated into fibres, undergoing no significant turnover during the human lifetime. Abnormalities in elastin can adversely affect its self-assembly, and may lead to malformed elastic fibres. Due to the longevity required of these fibres, even minor abnormalities may have a large cumulative effect over the course of a lifetime, leading to late-onset vascular diseases. This thesis project has identified important, over-represented repetitive elements in elastin which are believed to be important for the self-assembly and elastomeric properties of elastin. Initial studies of single nucleotide polymorphisms (SNPs) from the HapMap project and dbSNP resulted in a set of genetic variation sites in the elastin gene. Based on these studies, glycine to serine and lysine to arginine substitutions were introduced in elastin-like polypeptides. The self-assembly properties of the resulting elastin-like polypeptides were observed under microscope and measured using absorbance at 440nm. Assembled polypeptides were also cross-linked to form thin membranes whose mechanical and physical properties were measured and compared. These mutations resulted in markedly different behavior than wild-type elastin-like proteins, suggesting that mutations in the repetitive elements of the elastin sequence can lead to adverse changes in the physical and functional properties of the resulting protein. Using next-generation sequencing, patients with thoracic aortic aneurysms are being genotyped to discover polymorphisms which may adversely affect the self-assembly properties of elastin, providing a link between genetic variation in elastin and cardiovascular disease.

Protein function is the ultimate expression of the genetic code of every organism, and determining which proteins interact helps reveal their functions. MatrixMatchMaker (MMM) is a computational method of predicting protein-protein interactions that works by detecting co-evolution between pairs of proteins. Although MMM has several advanced features compared to other co-evolution-based methods, these come at the cost of high computation, and so the goal of this research is to improve the performance of MMM. First we redefine the computational problem posed by the method, and then develop a new algorithm to solve it, achieving a total speedup of 570x over the existing MMM algorithm for a biologically meaningful data set. We also develop hardware which has not yet succeeded in further improving the performance of MMM, but could serve as a platform that could lead to further gains.

Medicago sativa (Alfalfa) is an important agricultural plant for animal forage and nitrogen fixation, and has potential value in ligno-cellulosic energy production. In the quest to understand the plant, I generated a draft genome sequence of M. sativa via two reduced representation sequencing approaches: methylation-dependent filtration, and high CoT filtration. Libraries created from each approach were sequenced on an Illumina next-generation sequencing platform yielding approximately 2.5Gb of raw data. A combination of reference-based genome assembly approaches using the closely related species, Medicago truncatula as a reference, and de novo genome assembly approaches were performed to assemble the draft genome. The reference-based assembly generated 312,011 contigs with weighted median contig length (N50) of 247 bases, whereas de novo assembly produced 547,304 contigs with N50 of 275 bases. The creation of the M. sativa draft genome is vital for downstream functional analyses such as genome wide gene mining and gene expression profiling.

Anthracycline- and taxane-based regimens have been the mainstay in treating breast cancer patients using chemotherapy. Yet, the genetic make-up of patients and their tumors may have a strong impact on tumor sensitivity to these agents and to treatment outcome. This study represents a new paradigm assimilating bioinformatic tools with in vitro model systems to discover novel genetic variations that may be associated with chemotherapy response in breast cancer. This innovative paradigm integrates drug response data for the NCI60 cell line panel with genome-wide Affymetrix SNP data in order to identify genetic variations associated with drug resistance. This genome wide association study has led to the discovery of 59 candidate loci that may play critical roles in breast tumor sensitivity to doxorubicin and paclitaxel. 16 of them were mapped within well-characterized genes (three related to doxorubicin and 13 to paclitaxel). Further in silico characterization and in vitro functional analysis validated their differential expression in resistant cancer cell lines treated with the drug of interest (over-expression of RORA and DSG1, and under-expression of FRMD6, SGCD, SNTG1, LPHN2 and DCT). Interestingly, three and six genes associated with doxorubicin and paclitaxel resistance, respectively, are involved in the apoptotic process in cells. A constructed interactome suggested that there is cross-talk at the Nrf-2 oxidative stress pathway between genes associated with resistance to doxorubicin and paclitaxel. This unique GWA approach serves as a proof-of-principle study and systematically investigates targets responsible for variable response to chemotherapy in breast tumor cells and possibly the tumors of breast cancer patients. Overall, the model discovered novel candidate genes that have not been previously associated with doxorubicin and paclitaxel cytotoxicity. Future studies will be directed at illustrating a causative relationship between the observed genomic changes and drug resistance in breast cancer patients undergoing doxorubicin and paclitaxel chemotherapy.

Global quantitative analysis of genetic interactions provides a powerful approach for deciphering the roles of genes and mapping functional relationships amongst path-ways. Using colony size as a proxy for fitness, I developed a method for measuring ge-netic interactions from high-density arrays of yeast double mutants generated by synthet-ic genetic array (SGA) technology. I identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate fitness measurements. I used this scoring method to map quantitative genetic interactions among 5.4 million yeast double mutants and generated the first functionally unbiased genetic interaction map of a eukaryotic cell. My map produced an unprecedented view of the cell in which genes of similar biological processes cluster together in coherent subsets and functionally interconnected bioprocesses map next to each other. We discovered several physiological and evolutionary gene features that are characteristic of genetic interaction hubs, and explored the relationship between genetic and protein-protein interaction networks. In particular, by comparing quantitative single and double mutant phenotypes, we identified specific cases of positive genetic interactions, termed genetic suppression, and constructed a global network of suppression interactions among protein complexes. I also demonstrated that an extensive and unbiased mapping of genetic interactions provides a key for interpreting chemical-genetic interactions and identifying drug targets. In addition, I used genome-wide SGA data to map profiles of genetic linkage along all sixteen yeast chromosomes. These linkage profiles recapitulated previously identified recombination patterns and uncovered an unexpected correlation between chromosome length and the extent of centromere-related recombination repression. These findings suggest a chromosome size-dependent mechanism for ensuring proper chromosome segregation and highlight the SGA methodology as a unique approach for systematic analysis of yeast meiotic recombination.