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This dissertation focuses on the reduction of biological complexity using marine cyanobacteria Prochlorococcus as the model system. New computational methods have been developed for the understanding of genomic characteristics, for the exploration of environmental metagenomic data, and for the inference of evolutionary forces driving genome reduction in Prochlorococcus. The first part of the dissertation presents basic genomic characteristics in Prochlorococcus MED4. Known as the smallest and the most numerically abundant photosynthetic organism in the ocean, it shares many genomic characteristics with chloroplasts and bacterial endosymbionts. Orthologous genes from Prochlorococcus and a closely related marine cyanobacteria group Synechococcus, are profiled to show the gradients in genome sizes, GC% content, and the genome-wide acceleration of protein sequence evolution. The second part of the dissertation introduces new computational approaches for exploring environmental metagenomic data. The profiling of relative sequence abundance in the Sargasso Sea data has motivated the development of a phylogenetic focus group-based sequence filtering framework that takes into account of limitations in general purposed sequence similarity search, variations in evolutionary rates, as well as the context of phylogeny. A sequence trimming and segmentation mechanism has been proposed to facilitate downstream analysis. The integrated framework of sequence filtering and trimming performs better than general purpose methods, and benefits the exploration study of environmental metagenomic data. The third part of the dissertation tests a hypothesis about the relative strength of genetic drift vs. natural selection, formulated based on similarities between Prochlorococcus and endosymbionts. The hypothesis conjectures that Prochlorococcus has been experiencing a relative higher level of genetic drift, resulting in a relaxation in selection efficiency, leading to genome reduction and genome wide accelerated protein evolution. The evaluation of the hypothesis is performed by comparing the evolutionary profiles of Prochlorococcus with Synechococcus. Results from the complete genomes and the metagenomic data indicate that the average pairwise dN/dS ratios in the high-light adapted Prochlorococcus ecotypes are significantly lower than that in Synechococcus, i.e., Prochlorococcus is actually experiencing stronger selection genome-wide. The hypothesis is thus rejected, opening up space for constructing new hypotheses regarding the evolution of Prochlorococcus.

Small bacterial genomes are believed to be evolutionarily derived from larger genomes through massive loss of genes and are usually associated with symbiotic or pathogenic lifestyles. It is therefore intriguing that a similar phenomenon of genome reduction has been reported within a group of free-living phototrophic marine cyanobacteria Prochlorococcus. Here I have investigated the roles of natural selection and mutation rate in the process of Prochlorococcus genome size reduction. Using a data set of complete cyanobacterial genomes including 12 Prochlorococcus and a sister group of 5 marine Synechococcus , I first reconstructed the steps leading to Prochlorococcus genome reduction in a phylogenetic context. The result reveals that small genome sizes within Prochlorococcus were largely determined by massive gene loss shortly after the split of Prochlorococcus and Synechococcus (a process we refer to as early genome reduction). A maximum likelihood approach was then used to estimate changes in both selection effect and mutation rate in the evolutionary history of Prochlorococcus. I also examined the effect of selection and functional importance of a subset of ancestor-derived genes those are lost in Prochlorococcus but are still retained in the genomes of its sister Synechococcus group. It appears that the effect of purifying selection was strongest when a large number of small effect genes were deleted from nearly all functional categories. And during this period, mutation rate also accelerated. Based on these results, I propose that shortly after Prochlorococcus diverged from its common ancestor with marine Synechococcus, its population size increased quickly and thus the efficacy of selection became very high. Due to limited nutrients and relatively constant environment, selection favored a streamlined genome for maximum economies in material and energy, causing subsequent reduction in genome size and possibly also contributing to the observed higher mutation rate.

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Annotation Enrichment Analysis is a widely used analytical methodology to process data generated by high-throughput genomic and proteomic experiments such as gene expression microarrays. The analysis uncovers and summarizes discriminating background information for sets of genes identified by the previous processing stages (e.g., a set of differentially expressed genes, a cluster). Enrichment analysis algorithms attach annotations to the genes and then discover statistical fluctuations of individual annotation terms in a given gene subset. The annotation terms represent different aspects of biological knowledge and come from databases such as GO, BIND, KEGG. Typical statistical models used to detect enrichments or depletions of annotation terms are hypergeometric, binomial and X2. At the end, the discovered information is utilized by human experts to find biological interpretations of the experiments. The main drawback of AEA is that it isolates and tests for overrepresentation of isolated individual annotation terms or groups of similar terms. As a result, AEA is limited in its ability to uncover complex phenomena involving relationships between multiple annotation terms from various knowledge bases. Also, AEA assumes that annotations describe the whole object of interest, which makes it difficult to apply it to sets of compound objects (e.g., sets of protein-protein interactions) and to sets of objects having an internal structure (e.g., protein complexes). To overcome this shortcoming, we propose a novel logic-based Annotation Concept Synthesis and Enrichment Analysis (ACSEA) approach. In this approach, the source annotation information, experimental data and uncovered enriched annotations are represented as First-Order Logic (FOL) statements. ACSEA uses the fusion of inductive logic reasoning with statistical inference to uncover more complex phenomena captured by the experiments. The proposed paradigm allows a synthesis of enriched annotation concepts that better describe the observed biological processes. The methodological advantage of Annotation Concept Synthesis and Enrichment Analysis is six-fold. Firstly, it is easier to represent complex, structural annotation information. Information already captured and formalized in OWL and RDF knowledge bases can be directly utilized. Secondly, it is possible to synthesize and analyze complex annotation concepts. Thirdly, it is possible to perform the enrichment analysis for sets of aggregate objects (such as sets of genetic interactions, physical protein-protein interactions or sets of protein complexes). Fourthly, annotation concepts are straightforward to interpret by a human expert. Fifthly, the logic data model and logic induction are a common platform that can integrate specialized analytical tools (e.g. tools for numerical, structural and sequential analysis). Sixthly, used statistical inference methods are robust on noisy and incomplete data, scalable and trusted by human experts in the field. In this thesis we developed and implemented the ACSEA approach. We evaluate it on large-scale datasets from several microarray experiments and on a clustered genome-wide genetic interaction network using different biological knowledge bases. Also, we define a statistical model of experimental and annotation data and evaluate ACSEA on synthetic datasets. The discovered interpretations are more enriched in terms of P- and Q-values than the interpretations found by AEA, are highly integrative in nature, and include analysis of quantitative and structured information present in the knowledge bases. The results suggest that ACSEA can significantly boost the effectiveness of the processing of high-throughput experiment data.

Alzheimer’s disease (AD) is a neurodegenerative disease classified pathologically by the presence of tau tangles and amyloid plaques. The largest genetic risk factor for AD is the APOE ε4 allele, while the APOE ε2 allele has been linked to a protective effect for AD. Recent studies demonstrated that APOE genotypes are linked to unique omics signatures and pathological features relating to AD, such as blood-brain barrier breakage. To investigate the role of APOE genotype in AD, I analyzed different levels of omic data in blood and brain. I analyzed transcriptomic data derived from autopsied brains using network and differential gene expression approaches to identify genes and pathways involved in the APOE ε2 protective mechanism for AD. Additionally, I identified APOE genotype-specific pathways and networks involved in both blood and brain function in AD using blood and brain tissue gene expression from mostly the same individuals. Lastly, I analyzed the association of methylation of DNA from blood and brain samples with AD to identify APOE and AD specific methylation signatures and potential drug targets. Collectively, this thesis emphasizes the utility of investigating APOE genotypes individually to identify novel pathways and potential drug targets within AD subpopulations.

A common feature of eukaryotic genomes is the abundance of simple sequences. Single amino acid repeats, which is one kind of simple sequences, are characterized by tandem recurrence of only one amino acid within the proteins and are broadly found among almost all genomes of eukaryotic species. Combined with its abundance, the lack of deterministic function of SAAR makes it intriguing to study on its evolution. In this study, 34 eukaryotic genomes are used and an abundance of SAARs on X/Z chromosomes is observed. Also, amino acid composition and codon usage bias is different between SAARs and non-repetitive regions. We also observe that the conserved number of SAARs is linearly correlated with logarithm of divergence time. / Thesis / Master of Science (MSc)