Thesis (Ph.D.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / Similarities between patterns of differential gene expression can be used to establish connections between the experimental and biological conditions that give rise to them. The growing volume of gene expression data in repositories such as NCBI's Gene Expression Omnibus (GEO) presents an opportunity to identify such similarities on a large scale across a diverse collection of datasets. In this work, I have developed a pattern-based approach, named openSESAME, to identify datasets enriched in samples displaying coordinate differential expression of a query signature. Importantly, openSESAME performs this search without knowledge of the experimental groups in the datasets being searched, which allows it to identify perturbations of gene expression due to attributes that may not have been recorded. First, I demonstrated the utility of openSESAME using two gene expression signatures to query a set of more than 75,000 human expression profiles obtained from GEO. A query using a signature of estradiol treatment identified experiments in which estrogen signaling was perturbed and also discriminated between estrogen receptor-positive and -negative breast cancers. A second query using a signature of silencing of the transcription factor p63 (a key regulator of epidermal differentiation) identified datasets related to stratified squamous epithelia or epidermal diseases such as melanoma. Next, to improve the utility of openSESAME, I expanded the collection of profiles to include samples from mouse and rat, and automatically translated expression signatures for cross-species queries. Furthermore, I processed the sample annotation associated with these samples in GEO, extracting informative words and phrases and continuous (e.g., age) and categorical (e.g., disease state) variables. I have also recorded sample-specific dates and quality metrics to assess whether batch effects or outliers are affecting individual query results. Finally, I used openSESAME to query this repository with over 800 gene expression signatures from the Broad Institute's Molecular Signatures Database (MSigDB). I then used the scores of the association of each signature with each sample in the repository to build a network of the relatedness of these signatures to each other. This "constellation" of signatures can be used to determine the relationship of a query signature to other biological and experimental perturbations. / 2031-01-02

The advent of large-scale genomic sequencing is providing researchers with an unparalleled wealth of information which can be used to elucidate the evolutionary relationships of living organisms. The newly available genome sequence data have enabled the use of comparative genomic techniques for the identification of novel molecular signatures, shared uniquely by evolutionarily related groups of organisms: conserved signature indels (CSIs) and conserved signature proteins (CSPs). These signatures allow for the unambiguous delineation of the prokaryotic taxa, independent of gene and genome based phylogenetic trees, and provide insights into novel aspects of their evolutionary relationships. The phylum Spirochaetes and the class Betaproteobacteria are large, diverse groups of bacteria, containing many important pathogenic and environmental organisms, which are classified primarily on the basis of 16S rRNA gene analysis. Here, I describe phylogenetic analyses of the phylum Spirochaetes based on genome derived molecular signatures. These analyses have yielded substantial evidence for differentiation between the three main sequenced groups of organisms within the phylum Spirochaetes and between the genus Borrelia from other closely related Spirochaetes. These findings have prompted a proposal to create three new orders and a new family within the phylum. These analyses have also supported the differentiation of two clinically distinct groups within the genus Borrelia and a proposal to divide the genus Borrelia into two genera. The use of molecular signatures and phylogenetic analysis of major groups within the class Betaproteobacteria are also described. The analyses of the order Neisseriales within this class resulted in a division of the order into two families, while the analyses of the genus Burkholderia supported the differentiation of the clinically relevant members of the genus Burkholderia from the plant-beneficial and environmental Burkholderia and a proposal to divide the genus into two genera. I also describe the use of phylogenomic techniques and molecular signatures to differentiate the seven main groups within the order Enterobacteriales and the integrated software pipeline used to produce the supermatrix based phylogenomic tree and genome distance calculations in the analysis of the order Enterobacteriales. The molecular signatures described in this thesis represent powerful new tools for evolutionary and systematic studies. Additionally, due to their taxon specificity, these molecular signatures are novel diagnostic markers for their specified group. Further analyses of these molecular signatures should lead to the discovery of novel functions and biological characteristics, mediated by CSIs and CSPs, which will provide important insights into the physiology, evolution, and adaptations of these groups. / Thesis / Doctor of Philosophy (PhD)

No description available.

Analysis of genome sequences is enabling identification of numerous novel characteristics that provide valuable means for genetic and biochemical studies. Of these characteristics, Conserved Signature Indels (CSIs) in proteins which are specific for a given group of organisms have proven particularly useful for evolutionary and biochemical studies. My research work focused on using comparative genomics techniques to identify a large number of CSIs which are distinctive characteristics of fungi and other important groups of organisms. These CSIs were utilized to understand the evolutionary relationships among different proteins (species), and also regarding their structural features and functional significance. Based on multiple CSIs that I have identified for the PIP4K/PIP5K family of proteins, different isozymes of these proteins and also their subfamilies can now be reliably distinguished in molecular terms. Further, the species distribution of CSIs in the PIP4K/PIP5K proteins and phylogenetic analyses of these protein sequences, my work provides important insights into the evolutionary history of this protein family. The functional significance of one of the CSI in the PIP5K proteins, specific for the Saccharomycetaceae family of fungi, was also investigated. The results from structural analysis and molecular dynamics (MD) simulation studies show that this 8 aa CSI plays an important role in facilitating the binding of fungal PIP5K protein to the membrane surface. In other work, we identified multiple highly-specific CSIs in the phosphoketolase (PK) proteins, which clearly distinguish the bifunctional form of PK found in bifidobacteria from its homologs (monofunctional) found in other organisms. Structural analyses and docking studies with these proteins indicate that the CSIs in bifidobacterial PK, which are located on the subunit interface, play a role in the formation/stabilization of the protein dimer. We have also identified 2 large CSIs in SecA proteins that are uniquely found in thermophilic species from two different phyla of bacteria. Detailed bioinformatics analyses on one of these CSIs show that a number of residues from this CSI, through their interaction with a conserved network of water molecules, play a role in stabilizing the binding of ADP/ATP to the SecA protein at high temperature. My work also involved developing an integrated software pipeline for homology modeling of proteins and analyzing the location of CSIs in protein structures. Overall, my thesis work establishes the usefulness of CSIs in protein sequences as valuable means for genetic, biochemical, structural and evolutionary studies. / Dissertation / Doctor of Philosophy (PhD)