Evolution and population genetics in microbial populations

/ABSTRACT The overall goal of my research program is to understand evolution in microbial populations, using a combination of mathematical models and laboratory evolution experiments in budding yeast. Here, we aim to pursue this overall goal by characterizing the statistical structure of complex and high-dimensional genotype- phenotype landscapes, and by analyzing how microbial and viral populations evolve across these landscapes. In the short term, evolution depends primarily on the most immediate aspect of the landscape: the distribution of fitness effects of individual mutations. However, on longer timescales interactions between the effects of multiple mutations (epistasis) can be crucial. Similarly, mutations often have different fitness effects in different environments (pleiotropy). This is essential to evolution in fluctuating environments. Recent work shows that epistasis and pleiotropy are strong and common among specific sets of mutations in many microbial systems. However, these studies of specific limited sets of mutations cannot fully explain how epistasis and pleiotropy constrain the rate, repeatability, or dynamics of adaptation. And even given a complete set of epistatic and pleiotropic interactions, we are still often unable to predict how evolution will act. This severely limits our ability to understand the evolution of complex phenotypes, such as compensated antibiotic resistance, multiple mutations required for immune escape, or multiple gene knockouts enabling cancer evolution. The first main research direction in this proposal will examine the role of epistasis and pleiotropy in the evolution of microbial populations. Rather than characterizing specific examples, we propose to survey the overall statistics of epistasis and pleiotropy that are relevant for constraining microbial adaptation. Given some structure of the genotype-phenotype landscape, we next aim to understand evolutionary dynamics and population genetics in microbial populations. Extensive previous work has addressed this question, but these methods are primarily constrained to the analysis of one or a few loci at a time and break down when selection acts simultaneously at many linked loci across the genome. The basic problem is that when natural selection is widespread, there is too much happening at once: mutations arise constantly in a variety of combinations linked together in physical chromosomes, and selection can only act on these combinations as a whole. Thus the dynamics of mutations at different loci are intertwined, creating complex correlations between sites. In recent years, it has become increasingly clear that these effects, known as linked selection, are pervasive in microbial and viral evolution. Yet despite their potentially broad importance, we have limited understanding of how we expect linked selection to affect evolutionary dynamics or observable patterns of genetic variation. The second main research direction in this proposal will use a combination of mathematical models and laboratory evolution experiments to analyze evolutionary dynamics and population genetics in these large and rapidly evolving populations, where linked selection is widespread. Project Number: 1R35GM161612-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Michael Desai | Institution: HARVARD UNIVERSITY, CAMBRIDGE, MA | Award Amount: $463,375 | Activity Code: R35 | Study Section: Special Emphasis Panel[ZRG1 MGG-F (55)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11260366