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.

细菌通常生活在复杂的多物种群落中，它们不仅可以从前几代获得基因，还可以通过自然转化过程从邻居甚至环境中的游离 DNA 中获取新的遗传元素。目前尚不清楚哪些序列最容易通过转化转移。了解基因组和群落规模的自然转化需要开发和应用新的算法来解决这个先前未探索的空间。该项目将开发计算方法来识别和预测哪些遗传功能可能会发生变化。通过自然方式在细菌种群中进行遗传传播这些计算方法的应用将有助于填补分类学鉴定可能忽视的细菌群落遗传进化的空白，这反过来又可以更好地评估与细菌群落成员相关的风险或收益，并改进合成细菌群落的规划。项目工作流程将为校园内多个 STEM 专业的本科生提供新的顶点研究体验。由于缺乏基因组水平的实验和研究，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得到探索。 PI的实验室开发了实验分子遗传工具来收集和绘制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态的计算方法却没有。与合作伙伴的合作将使用实验数据生成基因组规模的统计算法和计算工具，用于（i）控制基因转移事件的分子和生物参数的全局分析，以及（ii）预测具体产生的基因转移事件。自然的该项目将使工作范围超越目前自然转化的分子遗传分析，扩展到基因组和群落规模。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。