CAREER: Parallel evolution in the microbiome: novel methods for detecting repeated, rapid adaptations in metagenomic data

职业：微生物组的平行进化：检测宏基因组数据中重复、快速适应的新方法

• 批准号: 2240098
• 负责人: Nandita Garud
• 金额: $ 91.7万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240098&HistoricalAwards=false
• 关键词: CAREER; Parallel; evolution; microbiome; novel

Microbiomes experience an influx of new mutations daily, some of which are known to confer essential phenotypes to their hosts or environments. Despite their importance, the vast majority of the adaptive mutations in the microbiome are undetectable, due in large part to the limited power of current approaches, which focus on detecting extreme allele frequency changes in a single sample. These approaches miss mutations that experience subtle frequency changes due to complex forces like pleiotropy and spatial structure. However, the microbiome has attributes that might permit us to understand the importance of these mutations even though they cannot be detected by classical approaches: namely, mutations are so frequent that the same mutation can often be found in the microbiomes of different hosts. Some mutations display dynamics characteristic of adaptation, rising in frequency in parallel across multiple host microbiomes. This parallelism is unlikely to occur by chance, and is indicative of adaptation. Development of new statistical methods that leverage data from many individuals simultaneously may have significantly more power to detect a much broader range of adaptations than have been found by identifying extreme allele frequency changes in one host at a time, hence unlocking the potential to uncover essential adaptive phenotypes that are currently unexplored. Via research and educational efforts at the undergraduate and graduate levels, students will acquire interdisciplinary skills from biology, computer science, and statistics to perform evolutionary genomics research in the microbiome.The project will develop new statistical methods to quantify the pace and targets of adaptation in the microbiome. Specifically, the project will develop statistical methods to (1) identify adaptive single nucleotide variants via parallelism across hosts, (2) identify adaptive gene gain and loss events via parallelism, and (3) detect adaptive events that have spread across multiple host microbiomes. To demonstrate their utility, the methods will be applied to several temporally sampled datasets from humans to obtain insights into the targets and pace of adaptation in microbiomes. Finally, a wholistic approach for recruitment, training and retention of underrepresented STEM students will be piloted via educational opportunities at the undergraduate and graduate levels. Results from the project will be made available at https://garud.eeb.ucla.edu/nsf-career/.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.

微生物组每天都会经历新突变的涌入，其中一些已知会为其宿主或环境赋予必要的表型。尽管它们很重要，但微生物组中的绝大多数适应性突变是无法检测到的，这在很大程度上是由于当前方法的能力有限，这些方法专注于检测单个样本中的极端等位基因频率变化。这些方法错过了由于多效性和空间结构等复杂力量而经历微妙频率变化的突变。然而，微生物组具有的属性可能使我们能够理解这些突变的重要性，即使它们无法通过经典方法检测到：即，突变如此频繁，以至于在不同宿主的微生物组中经常可以发现相同的突变。一些突变显示出适应的动态特征，在多个宿主微生物组中频率平行上升。这种平行性不太可能偶然发生，而是适应性的表现。开发新的统计方法，同时利用来自许多个体的数据，可能比通过一次识别一个宿主中的极端等位基因频率变化发现的更有能力检测更广泛的适应性，从而解锁揭示目前尚未探索的基本适应性表型的潜力。通过本科生和研究生阶段的研究和教育工作，学生将获得生物学、计算机科学和统计学的跨学科技能，以在微生物组中进行进化基因组学研究。该项目将开发新的统计方法，以量化微生物组适应的速度和目标。具体而言，该项目将开发统计方法，以（1）通过宿主之间的并行性识别适应性单核苷酸变体，（2）通过并行性识别适应性基因获得和丢失事件，以及（3）检测已在多个宿主微生物组中传播的适应性事件。为了证明它们的实用性，这些方法将应用于人类的几个时间采样数据集，以深入了解微生物组的目标和适应速度。最后，将通过本科和研究生阶段的教育机会，试行一种招聘、培训和留住代表性不足的STEM学生的整体方法。该项目的结果将在www.example.com上公布https://garud.eeb.ucla.edu/nsf-career/.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。