Matching Items (3)
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- All Subjects: Evolution & development
- All Subjects: X chromosome
- Creators: Rosenberg, Michael S.
Description
The Cape Floral Region (CFR) in southwestern South Africa is one of the most diverse in the world, with >9,000 plant species, 70% of which are endemic, in an area of only ~90,000 km2. Many have suggested that the CFR's heterogeneous environment, with respect to landscape gradients, vegetation, rainfall, elevation, and soil fertility, is responsible for the origin and maintenance of this biodiversity. While studies have struggled to link species diversity with these features, no study has attempted to associate patterns of gene flow with environmental data to determine how CFR biodiversity evolves on different scales. Here, a molecular population genetic data is presented for a widespread CFR plant, Leucadendron salignum, across 51 locations with 5-kb of chloroplast (cpDNA) and 6-kb of unlinked nuclear (nuDNA) DNA sequences in a dataset of 305 individuals. In the cpDNA dataset, significant genetic structure was found to vary on temporal and spatial scales, separating Western and Eastern Capes - the latter of which appears to be recently derived from the former - with the highest diversity in the heart of the CFR in a central region. A second study applied a statistical model using vegetation and soil composition and found fine-scale genetic divergence is better explained by this landscape resistance model than a geographic distance model. Finally, a third analysis contrasted cpDNA and nuDNA datasets, and revealed very little geographic structure in the latter, suggesting that seed and pollen dispersal can have different evolutionary genetic histories of gene flow on even small CFR scales. These three studies together caution that different genomic markers need to be considered when modeling the geographic and temporal origin of CFR groups. From a greater perspective, the results here are consistent with the hypothesis that landscape heterogeneity is one driving influence in limiting gene flow across the CFR that can lead to species diversity on fine-scales. Nonetheless, while this pattern may be true of the widespread L. salignum, the extension of this approach is now warranted for other CFR species with varying ranges and dispersal mechanisms to determine how universal these patterns of landscape genetic diversity are.
ContributorsTassone, Erica (Author) / Verrelli, Brian C (Thesis advisor) / Dowling, Thomas (Committee member) / Cartwright, Reed (Committee member) / Rosenberg, Michael S. (Committee member) / Wojciechowski, Martin (Committee member) / Arizona State University (Publisher)
Created2013
Description
The entire history of HIV-1 is hidden in its ten thousand bases, where information regarding its evolutionary traversal through the human population can only be unlocked with fine-scale sequence analysis. Measurable footprints of mutation and recombination have imparted upon us a wealth of knowledge, from multiple chimpanzee-to-human transmissions to patterns of neutralizing antibody and drug resistance. Extracting maximum understanding from such diverse data can only be accomplished by analyzing the viral population from many angles. This body of work explores two primary aspects of HIV sequence evolution, point mutation and recombination, through cross-sectional (inter-individual) and longitudinal (intra-individual) investigations, respectively. Cross-sectional Analysis: The role of Haiti in the subtype B pandemic has been hotly debated for years; while there have been many studies, up to this point, no one has incorporated the well-known mechanism of retroviral recombination into their biological model. Prior to the use of recombination detection, multiple analyses produced trees where subtype B appears to have first entered Haiti, followed by a jump into the rest of the world. The results presented here contest the Haiti-first theory of the pandemic and instead suggest simultaneous entries of subtype B into Haiti and the rest of the world. Longitudinal Analysis: Potential N-linked glycosylation sites (PNGS) are the most evolutionarily dynamic component of one of the most evolutionarily dynamic proteins known to date. While the number of mutations associated with the increase or decrease of PNGS frequency over time is high, there are a set of relatively stable sites that persist within and between longitudinally sampled individuals. Here, I identify the most conserved stable PNGSs and suggest their potential roles in host-virus interplay. In addition, I have identified, for the first time, what may be a gp-120-based environmental preference for N-linked glycosylation sites.
ContributorsHepp, Crystal Marie, 1981- (Author) / Rosenberg, Michael S. (Thesis advisor) / Hedrick, Philip (Committee member) / Escalante, Ananias (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Rapid advancements in genomic technologies have increased our understanding of rare human disease. Generation of multiple types of biological data including genetic variation from genome or exome, expression from transcriptome, methylation patterns from epigenome, protein complexity from proteome and metabolite information from metabolome is feasible. "Omics" tools provide comprehensive view into biological mechanisms that impact disease trait and risk. In spite of available data types and ability to collect them simultaneously from patients, researchers still rely on their independent analysis. Combining information from multiple biological data can reduce missing information, increase confidence in single data findings, and provide a more complete view of genotype-phenotype correlations. Although rare disease genetics has been greatly improved by exome sequencing, a substantial portion of clinical patients remain undiagnosed. Multiple frameworks for integrative analysis of genomic and transcriptomic data are presented with focus on identifying functional genetic variations in patients with undiagnosed, rare childhood conditions. Direct quantitation of X inactivation ratio was developed from genomic and transcriptomic data using allele specific expression and segregation analysis to determine magnitude and inheritance mode of X inactivation. This approach was applied in two families revealing non-random X inactivation in female patients. Expression based analysis of X inactivation showed high correlation with standard clinical assay. These findings improved understanding of molecular mechanisms underlying X-linked disorders. In addition multivariate outlier analysis of gene and exon level data from RNA-seq using Mahalanobis distance, and its integration of distance scores with genomic data found genotype-phenotype correlations in variant prioritization process in 25 families. Mahalanobis distance scores revealed variants with large transcriptional impact in patients. In this dataset, frameshift variants were more likely result in outlier expression signatures than other types of functional variants. Integration of outlier estimates with genetic variants corroborated previously identified, presumed causal variants and highlighted new candidate in previously un-diagnosed case. Integrative genomic approaches in easily attainable tissue will facilitate the search for biomarkers that impact disease trait, uncover pharmacogenomics targets, provide novel insight into molecular underpinnings of un-characterized conditions, and help improve analytical approaches that use large datasets.
ContributorsSzelinger, Szabolcs (Author) / Craig, David W. (Thesis advisor) / Kusumi, Kenro (Thesis advisor) / Narayan, Vinodh (Committee member) / Rosenberg, Michael S. (Committee member) / Huentelman, Matthew J (Committee member) / Arizona State University (Publisher)
Created2015