Development of Bayesian hierarchical models of genetic diversity and adaptive evolution of micro-organisms in the extreme environments

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Micro-organisms such as virus and bacteria adapt promptly to the strong selection pressures. Focusing mostly on viral evolution, we developed Bayesian hierarchical models and procedures to detect events of molecular evolution which play essential role on their adaptive evolution.The theory of population genetics suggests that endangered populations under strong selection pressure have higher rate of sequence evolution. To see if accelerated sequence evolution leads to higher chance of structural evolution of its corresponding protein, we analyzed the V3-loop of HIV -1 during the asymptomatic period in the patients. Since it is impossible to measure the variation of protein structure experimentally, we developed an index of sequence-structure fitness. Based on the database of protein structures, the index is described as a likelihood of a sequence given a template structure. Structural variation is implied by the declining trend of the likelihood after infection. By extending this index, we developed an index of binding ability between two proteins in the form of likelihood ratio which contrasts the proteins as a complex to independent pair. By analyzing the long term trend of influenza virus HA, we observed that the escape from the antibodies bound to the binding region to the receptor of the host cell is stronger than the escape from those bound to the region neighbor to the binding sites.Finally, we developed a Bayesian hierarchical model to detect recombination. If recombination occurs during the evolutionary history, the extant sequences have different phylogenetic relation (topology) between regions of the genomes. We developed an algorithm to calculate the recombination distance between a pair of topologies. By introducing a prior on the distances between the topologies of neighboring regions, it became possible to estimate the number of recombination and recombination break points reliably. We also developed a procedure to detect convergent evolution.