Reassessing Microbial Evolution in Light of Horizontal Gene Transfer

水平或横向基因转移（HGT）是指生物体之间的遗传信息交换，而不是垂直遗传，即基因从亲本细胞传递到子代细胞。本项目将研究HGT和垂直遗传对微生物进化的相对贡献。首先，将探讨基因内嵌合的程度。由于重组，单个基因可以镶嵌。初步数据表明，在核糖体RNA的情况下，基因内嵌合可能不是一个罕见的事件。将开发统计测试来评估微生物进化过程中基因内镶嵌现象发生的频率。这些测试将利用模拟序列演化，结合不同的HGT和重组量模型。其次，将模拟基因组进化以解决以下问题：确定检测到的HGT实例是否只是冰山一角，其中大部分仍低于检测极限？或者许多所谓的转移事件是由于成分偏见、趋同进化和未被识别的基因复制造成的人工产物？模拟将包括不同的重组频率，并将使用不同的模型来模拟序列演化。考虑到水平基因转移和基因内嵌合的检测水平的模拟将使水平基因转移的研究超越轶事证据的汇编。第三，利用松弛分子钟模型分析原核生物的早期演化。将获得单个基因家族的时间估计及其可信区间。从兼容数据集估计的关键进化事件的日期将结合起来，以获得更准确的分化时间估计

Horizontal or lateral gene transfer (HGT) denotes the exchange of genetic information between organisms as opposed to vertical inheritance, i.e. the passing of genes from parent to daughter cells. This project will study the relative contributions of HGT and vertical inheritance to microbial evolution. First, the extent of intra-gene mosaicism will be explored. Individual genes can be mosaic as a result of recombination. Preliminary data indicate that in the case of ribosomal RNA intra-gene mosaicism might not be a rare event. Statistical tests will be developed to assess how frequently intra-gene mosaicism occurred during microbial evolution. These tests will utilize simulated sequence evolution incorporating different models with varying amounts of HGT and recombination. Second, genome evolution will be simulated to address the following question: Are the confidently detected instances of HGT the tip of an iceberg, the bulk of which remains below the detection limit? Or are many of the alleged transfer events artifacts due to compositional bias, convergent evolution and unrecognized gene duplications? The simulations will include different frequencies of recombination and will utilize different models to simulate sequence evolution. Simulations that take the detection levels for horizontal gene transfer and intra-gene mosaicism into consideration will move the study of horizontal gene transfer beyond a compilation of anecdotal evidence. Third, a relaxed molecular clock model will be used to analyze the early evolution of prokaryotic life. Time estimates and their credibility intervals will be obtained for individual gene families. Dates for key evolutionary events estimated from compatible datasets will be combined to achieve more accurate divergence time estimates