Comparative genomic approaches for investigating evolution in the microbial and viral populations of the rumen

The rumen is home to a diverse population of microorganisms encompassing all three domains of life: Bacteria, Archaea, and Eukarya. Viruses have also been documented to be present in large numbers; however, little is currently known about their role in the dynamics of the rumen ecosystem. This research aimed to use a comparative genomics approach in order to assess the potential evolutionary mechanisms at work in the rumen environment. We proposed to do this by first assessing the diversity and potential for horizontal gene transfer (HGT) of multiple strains of the cellulolytic rumen bacterium, Ruminococcus flavefaciens, and then by conducting a survey of rumen viral metagenome (virome) and subsequent comparison of the virome and microbiome sequences to ascertain if there was genetic information shared between these populations. We hypothesize that the bacteriophages play an integral role in the community dynamics of the rumen, as well as driving the evolution of the rumen microbiome through HGT. In our analysis of the Ruminococcus flavefaciens genomes, there were several mobile elements and clustered regularly interspaced short palindromic repeat (CRISPR) sequences detected, both of which indicate interactions with bacteriophages. The rumen virome sequences revealed a great deal of diversity in the viral populations. Additionally, the microbial and viral populations appeared to be closely associated; the dominant viral types were those that infect the dominant microbial phyla. The correlation between the distribution of taxa in the microbiome and virome sequences as well as the presence of CRISPR loci in the R. flavefaciens genomes, suggested that there is a “kill-the-winner” community dynamic between the viral and microbial populations in the rumen. Additionally, upon comparison of the rumen microbiome and rumen virome sequences, we found that there are many sequence similarities between these populations indicating a potential for phage-mediated HGT. These results suggest that the phages represent a gene pool in the rumen that could potentially contain genes that are important for adaptation and survival in the rumen environment, as well as serving as a molecular ‘fingerprint’ of the rumen ecosystem.

A study of computational methods to analyze gene expression data

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.