Reassessing Microbial Evolution in Light of Horizontal Gene Transfer

水平或横向基因转移（HGT）表示生物体之间遗传信息的交换，而不是垂直遗传，即基因从亲代细胞传递到子代细胞。 该项目将研究 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，单个基因可以是嵌合的。 初步数据表明，就核糖体 RNA 而言，基因内嵌合现象可能并不罕见。 将开发统计测试来评估微生物进化过程中基因内嵌合发生的频率。 这些测试将利用模拟序列进化，结合具有不同数量的 HGT 和重组的不同模型。 其次，将模拟基因组进化来解决以下问题：可靠检测到的 HGT 实例是否只是冰山一角，其中大部分仍低于检测限？或者，许多所谓的转移事件是否是由于成分偏差、趋同进化和未被识别的基因重复而造成的？ 模拟将包括不同频率的重组，并将利用不同的模型来模拟序列进化。 考虑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，将使用宽松的分子钟模型来分析原核生命的早期进化。 将为各个基因家族获得时间估计及其可信区间。 根据兼容数据集估计的关键进化事件的日期将被合并，以实现更准确的分歧时间估计

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