Cell Biology & Molecular Genetics

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    COMPUTATIONAL METHODS IN PROTEIN STRUCTURE, EVOLUTION AND NETWORKS.
    (2013) Cao, Chen; Moult, John; Molecular and Cell Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The advent of new sequencing technology has resulted in the accumulation of a large amount of information on human DNA variation. In order to make sense of these data in the context of biology and medicine, new methods are needed both for analysis and for integration with other resources. In this work: 1) I studied the distribution pattern of human DNA variants across populations using data from the 1000 genomes project and investigated several evolutionary biology questions from the perspective of population genomics. I found population level support for trends previously observed between species, including selection against deleterious variants, and lower frequency of variants in highly expressed genes and highly connected genes. I was also able to show that the correlation between synonymous and non-synonymous variant levels is a consequence of both mutation prevalence variation across the genome and shared selection pressure. 2) I performed a systematic evaluation of the effectiveness of GWAS (Genome Wide Association Studies) for finding potential drug targets and discovered the method is very ineffective for this purpose. I proposed two reasons to explain this finding, selection against variants in drug targets and the relatively short length of drug target genes. I discovered that GWAS genes and drug targets are closely associated in the biological network, and on that basis, developed a machine learning algorithm to leverage the GWAS results for the identification of potential drug targets, making use of biological network information. As a result, I identified some potential drug repurposing opportunities. 3) I developed a method to increase the number of protein structure models available for interpreting the impact of human non-synonymous variants, important for not only the understanding the mechanisms of genetic disease but also in the study of human protein evolution. The method enables the impact of approximately 40% more missense variants to be reliably modeled. In summary, these three projects demonstrate that value of computational methods in addressing a wide range of problems in protein structure, evolution, and networks.
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    Evolution of sex-biased expression in Caenorhabditis
    (2011) Thomas, Cristel Gwenola; Haag, Eric S; Molecular and Cell Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Mating systems have a profound impact on genome structure evolution, both indirectly through their effects on population genetics and directly due to the genetic control of reproductive traits. Most extant Caenorhabditis species are gonochoristic (males and females), while the most studied species, C. elegans and C. briggsae, are androdioecious (self-fertile hermaphrodites and males). The latter two species display an overall reduced ability to mate, suggesting that the selective pressure on maintaining efficient mating was weakened as selfing arose. The genes underlying these traits were likely to have been expressed in a sex-biased fashion in the gonochoristic ancestor, and we hypothesized that as selfing emerged their regulation was modified or they were lost altogether. This hypothesis is especially interesting given that selfing species have consistently smaller genome sizes than their gonochoristic relatives. I sought to address whether a disproportionate loss of genes with sex-biased expression accompanies the loss of mating-related traits in Caenorhabditis hermaphrodites. I first examine sex-biased expression in a gonochoristic species, C. remanei, and identify genes with highly sex-biased expression. I find that these genes are more likely to be missing in selfing species than expected by chance. I then select some of these genes based on their phylogenetic conservation patterns in the genus, and characterize them more thoroughly to shed some light on their functions. Through this study I identify a novel male-associated candidate cis-regulatory element. Lastly, I broaden the scope of the study by determining transcriptome wide sex-biased expression patterns in four Caenorhabditis species. I confirm that C.elegans displays a decrease in the proportion of strong female-biased expression, as well as a modification of the expression of genes with male-biased expression both in males and in hermaphrodites, when compared to gonochoristic Caenorhabditis. Taken together, this study illustrates the transcriptomic consequences of a modification of the mating system, and begins to address its effect on genome structure.
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    EVOLUTIONARY TRANSITIONS BETWEEN STATES OF STRUCTURAL AND DEVELOPMENTAL CHARACTERS AMONG THE ALGAL CHAROPHYTA (VIRIDIPLANTAE)
    (2004-12-10) Lewandowski, Jeffrey David; Delwiche, Charles F; Cell Biology & Molecular Genetics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The Charophyta comprise green plant representatives ranging from familiar complex-bodied land plants to subtle and simple forms of green algae, presumably the closest phylogenetic relatives of land plants. This biological lineage provides a unique opportunity to investigate evolutionary transition series that likely facilitated once-aquatic green plants to colonize and diversify in terrestrial environments. A literature review summarizes fundamental structural and developmental transitions observed among the major lineages of algal Charophyta. A phylogenetic framework independent of morphological and ontological data is necessary for testing hypotheses about the evolution of structure and development. Thus, to further elucidate the branching order of the algal Charophyta, new DNA sequence data are used to test conflicting hypotheses regarding the phylogenetic placement of several enigmatic taxa, including the algal charophyte genera <i>Mesostigma</i>, <i>Chlorokybus</i>, <i>Coleochaete</i>, and <i>Chaetosphaeridium</i>. Additionally, technical notes on developing RNA methods for use in studying algal Charophyta are included.