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

微生物，如病毒和细菌，能迅速适应强大的选择压力。我们主要针对病毒的进化，建立了贝叶斯层次模型和程序来检测分子进化事件，这些事件对它们的适应性进化起着至关重要的作用。种群遗传学理论表明，选择压力大的濒危种群具有更高的序列进化速度。为了了解加速的序列进化是否会导致其相应蛋白质结构进化的机会更高，我们分析了患者无症状期间HIV-1的V3环。由于不可能在实验上测量蛋白质结构的变化，我们提出了一个序列-结构适应度指数。基于蛋白质结构的数据库，该索引被描述为给定模板结构的序列的可能性。感染后可能性的下降趋势暗示了结构的变化。通过扩展这一指数，我们以似然比的形式提出了两个蛋白质之间结合能力的指数，该指数将蛋白质作为一个复杂的和独立的对进行对比。通过分析流感病毒血凝素的长期趋势，我们观察到与结合区域结合的抗体对宿主细胞受体的逃逸比结合区域邻近结合部位的抗体逃逸更强。最后，我们建立了一个贝叶斯分层模型来检测重组。如果在进化过程中发生重组，现有的序列在基因组区域之间具有不同的系统发育关系(拓扑)。我们开发了一种算法来计算一对拓扑之间的复合距离。通过引入关于相邻区域之间的拓扑距离的先验，可以可靠地估计复合和复合断裂点的数目。我们还开发了一个程序来检测收敛进化。

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.