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

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

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.