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

MCA：自然转化细菌中基因转移的基因组算法和统计模型

• 批准号: 2221039
• 负责人: Briana Burton
• 金额: $ 40.99万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221039&HistoricalAwards=false
• 关键词: MCA; Genomic; algorithms; statistical; models

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.

经常生活在复杂多物种社区中的细菌不仅限于上几代人的基因。使用称为自然转化的过程，细菌Canalso从邻居那里获得了新的遗传元素，甚至从环境中的自由DNA中获取了新的遗传元素。目前尚不清楚哪些遗传序列最容易通过转化来转化。了解在基因组和社区规模上的自然转化需要新算法的开发和应用，以解决以前出乎意料的空间。该项目将采用计算方法来识别和预测哪些遗传功能可能通过自然转化传播细菌种群。这些计算方法的应用将有助于填补了解细菌群落中遗传进化的空白，这可能会被分类学识别所忽略。反过来，这可以更好地评估与细菌社区成员相关的风险轨道和改进的合成群社区计划。该项目工作流程将使校园多个STEM专业的新盖石研究经验能够从校园内的多个STEM研究中进行研究。由于缺乏基因组水平的实验和计算分析，因此在细菌自然转化过程中驱动全基因组循环的分子和生物学参数之间的连接是出乎意料的。 PI的实验室开发了经验丰富的分子遗传工具，以收集和绘制自然转化基因转移基因转移位点，包括供体和受体细菌。但是，计算方法赋予了驱动功能参数，并最终尚不存在​​与自然转化相关的基因转移动态。与合作伙伴的合作将使用实验数据来生成基因组量表统计算法和计算工具，用于（i）对基因转移事件的分子和生物学参数进行全球分析，以及（ii）对自然转化的专门产生的基因转移事件的预测。这项项目将使工作能够扩展到当前的自然转化分子遗传分析到基因组和社区规模之外。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来评估。