CAREER: Sensitivity of evolution to subtle environmental change

Evolution shapes how organisms respond to stress, influencing challenges that directly affect society, including the rise of drug-resistant pathogens, the stability of agricultural systems, and the resilience of ecosystems under environmental change. However, most studies of evolution focus on extreme conditions, overlooking the subtle environmental differences that organisms routinely experience in nature. This limits our ability to predict how evolution unfolds in realistic settings. This project addresses this gap by examining how small changes in environmental conditions alter which genetic changes are favored during adaptation. By revealing how even slight environmental differences can redirect evolutionary outcomes, the project will improve our ability to anticipate and manage biological responses to stress. In addition to advancing fundamental knowledge, the project integrates research with education through a course-based undergraduate research experience (CURE), providing hands-on training in experimental evolution to the next generation of scientists. The project will also engage the public through outreach activities and openly share all data and tools, contributing to workforce development, scientific literacy, and the broader scientific community. This project uses massively parallel experimental evolution in the model eukaryote Saccharomyces cerevisiae to quantify how adaptive mutations respond to finely resolved environmental gradients. The research will evolve thousands of uniquely barcoded yeast lineages across a series of subtly different temperatures, enabling high-resolution measurement of fitness effects across environmental conditions. Aim 1 will characterize how the genetic basis of adaptation and the distribution of fitness effects (DFE) change across temperature gradients by tracking barcode frequency dynamics and sequencing adaptive lineages. Aim 2 will re-measure the fitness of adaptive mutants across all environments to quantify genotype-by-environment (GxE) interactions and identify patterns in how fitness varies across conditions. Aim 3 will determine the phenotypic basis of these patterns using high-throughput single-cell RNA sequencing to link transcriptomic states to fitness outcomes. Together, these approaches will generate a large, publicly accessible dataset that enables rigorous tests of evolutionary theory, including predictions from Fisher’s geometric model, and supports the development of more accurate models of adaptation. By integrating high-throughput experimentation, single-cell genomics, and data-driven modeling, this project advances the goal of making evolutionary biology a more predictive science. 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. NSF Award ID: 2541235 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Kerry Geiler-Samerotte | Institution: Arizona State University, SCOTTSDALE, AZ | Award Amount: $783,649 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541235 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541235.html