MCA: Genomic algorithms and statistical models for gene transfer in naturally transformable bacteria

细菌通常生活在复杂的多物种群落中，并不局限于从前几代获得基因。利用一种称为自然转化的过程，细菌还可以从邻居甚至环境中的游离DNA中获得新的遗传元件。目前还不知道哪些基因序列最容易通过转化进行转移。在基因组和群落规模上理解自然转化需要开发和应用新的算法来解决这个以前未探索的空间。该项目将开发计算方法来识别和预测哪些遗传功能可能通过自然转化在细菌种群中传播。这些计算方法的应用将有助于填补分类鉴定中可能忽略的细菌群落遗传进化的空白。这反过来可以导致更好地评估与细菌群落成员相关的风险或益处，并改善合成细菌群落的规划。该项目的工作流程将为来自校园内多个STEM专业的本科生带来新的顶点研究体验。由于缺乏基因组水平的实验和计算分析，在细菌自然转化过程中驱动全基因组基因转移偏好的分子和生物参数之间的联系尚未被探索。PI的实验室已经开发了实验分子遗传工具，以收集和绘制跨越不同供体和受体细菌对的自然转化基因转移位点。然而，量化驱动功能参数并最终预测与自然转化相关的基因转移动力学的计算方法还不存在。与合作伙伴的合作将使用实验数据来生成基因组规模的统计算法和计算工具，用于（i）对控制基因转移事件的分子和生物学参数进行全局分析，以及（ii）预测特定于自然转化的基因转移事件。该项目使工作扩展到目前的分子遗传学分析的自然transformation到基因组和社区scale.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Bacteria, which often live in complex multi-species communities, are not limited to acquiringtheir genes from prior generations. Using a process called natural transformation, bacteria canalso acquire new genetic elements from neighbors and even from free DNA in the environment.It is currently unknown which genetic sequences are most readily transferred via transformation.Understanding natural transformation at the genome and community scale requires developmentand application of novel algorithms to address this previously unexplored space. This project willdevelop computational methods to identify and predict which genetic functions are likely to spreadthrough bacterial populations by natural transformation. Application of these computationalmethods will help fill a void in understanding genetic evolution in bacterial communities which maybe overlooked by taxonomic identification. This in turn can lead to better assessment of risks orbenefits associated with bacterial community members and improved planning of syntheticbacterial communities. The project workflow will enable new capstone research experiences forundergraduates from multiple STEM majors on campus.The connections between molecular and biological parameters that drive genome-wide genetransfer preferences during bacterial natural transformation have been unexplored due to lack ofgenome level experimental and computational analyses. The PI’s laboratory has developedexperimental molecular genetic tools to collect and map natural transformation gene transfer sitesacross diverse pairs of donor and recipient bacteria. However, the computational methods toquantify the driving functional parameters and ultimately to predict gene transfer dynamicsassociated with natural transformation do not yet exist. Collaboration with the partner will use theexperimental data to generate genome scale statistical algorithms and computational tools for (i)global analysis of molecular and biological parameters governing gene transfer events, and (ii)prediction of gene transfer events that result specifically from natural transformation. This projectwill enable expansion of the work beyond current molecular genetic analysis of naturaltransformation to the genome and community scale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.