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Evolutionary analysis of the CAP superfamily of proteins using amino acid sequences and splice sites
Description
Here I document the breadth of the CAP (Cysteine-RIch Secretory Proteins (CRISP), Antigen 5 (Ag5), and the Pathogenesis-Related 1 (PR)) protein superfamily and trace some of the major events in the evolution of this family with particular focus on vertebrate CRISP proteins. Specifically, I sought to study the origin of these CAP subfamilies using both amino acid sequence data and gene structure data, more precisely the positions of exon/intron borders within their genes. Counter to current scientific understanding, I find that the wide variety of CAP subfamilies present in mammals, where they were originally discovered and characterized, have distinct homologues in the invertebrate phyla contrary to the common assumption that these are vertebrate protein subfamilies. In addition, I document the fact that primitive eukaryotic CAP genes contained only one exon, likely inherited from prokaryotic SCP-domain containing genes which were, by nature, free of introns. As evolution progressed, an increasing number of introns were inserted into CAP genes, reaching 2 to 5 in the invertebrate world, and 5 to 15 in the vertebrate world. Lastly, phylogenetic relationships between these proteins appear to be traceable not only by amino acid sequence homology but also by preservation of exon number and exon borders within their genes.
ContributorsAbraham, Anup (Author) / Chandler, Douglas E. (Thesis advisor) / Buetow, Kenneth H. (Committee member) / Roberson, Robert W. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Metabolomics focuses on the study of metabolic changes occurring in varioussystems and utilizes quantitative and semi-quantitative measurements of multiple metabolites in parallel. Mass spectrometry (MS) is the most ubiquitous platform in this field, as it provides superior sensitivity regarding measurements of complex metabolic profiles in biological systems. When combined with MS, multivariate statistics and advanced machine learning algorithms provide myriad opportunities for bioinformatics insights beyond simple univariate data comparisons. In this dissertation, the application of MS-based metabolomics is introduced with an emphasis on biomarker discovery for human disease detection. To advance disease diagnosis using MS-based metabolomics, numerous statistical techniques have been implemented in this research including principal component analysis, factor analysis, partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA, random forest, receiver operating characteristic analysis, as well as functional pathway/enzyme enrichment analyses. These approaches are highly useful for improving classification sensitivity and specificity related to disease-induced biological variation and can help identify useful biomarkers and potential therapeutic targets. It is also shown that MS-based metabolomics can distinguish between clinical and prodromal disease as well as similar diseases with related symptoms, which may assist in clinical staging and differential diagnosis, respectively. Additionally, MS-based metabolomics is shown to be promising for the early and accurate detection of diseases, thereby improving patient outcomes, and advancing clinical care. Herein, the application of MS methods and chemometric statistics to the diagnosis of breast cancer, coccidioidomycosis (Valley fever), and senile dementia (Alzheimer's disease) are presented and discussed. In addition to presenting original research, previous efforts in biomarker discovery will be synthesized and appraised. A Comment will be offered regarding the state of the science, specifically addressing the inefficient model of repetitive biomarker discovery and the need for increased translational efforts necessary to consolidate metabolomics findings and formalize purported metabolic markers as laboratory developed tests. Various factors impeding the translational throughput of metabolomics findings will be carefully considered with respect to study design, statistical analysis, and regulation of biomedical diagnostics. Importantly, this dissertation will offer critical insights to advance metabolomics from a scientific field to a practical one including targeted detection, enhanced quantitation, and direct-to-consumer considerations.
ContributorsJasbi, Paniz (Author) / Johnston, Carol S (Thesis advisor) / Gu, Haiwei (Thesis advisor) / Lake, Douglas F (Committee member) / Sweazea, Karen (Committee member) / Tasevska, Natasha (Committee member) / Arizona State University (Publisher)
Created2022