Kerry Geiler-Samerotte
Arizona State University Campus Immersion, AZ·Ecology & Evolution·Early-career
Can we predict what a mutation will do? The answer often depends on context.
- Fields
Ecology & Evolution
Genetics
Biology
- Topics
experimental evolution
Saccharomyces cerevisiae
budding yeast
- Grants
- 2 known grants, through May 2031
What they work on
- Evolve thousands of DNA-barcoded yeast lineages across subtly different temperatures to measure how the genetic basis of adaptation and the distribution of fitness effects (DFE) shift along the gradient.
- Re-measure the fitness of adaptive yeast mutants in every temperature to quantify genotype-by-environment (GxE) interactions and test predictions of Fisher's geometric model of adaptation.
- Use high-throughput single-cell RNA sequencing to connect the gene-expression states of adaptive yeast mutants to their fitness across environments.
- Trace how mutation effects travel through a yeast gene regulatory network by measuring correlations between traits across genetic backgrounds and environments.
- Build an abstract genotype-phenotype map from shared mutant behavior to predict how the fitness of drug-resistant yeast changes across subtly different drug concentrations and combinations.
- Use engineered yeast mutant libraries to find scaling relationships linking misfolded protein and ribosome levels to cell growth and fitness.
More about the research
The same DNA change can help in one environment and hurt in another. Its effects can also ripple through networks of linked traits inside a cell. Both projects use budding yeast (Saccharomyces cerevisiae), a single-celled fungus that shares much of its basic biology with human cells. They measure huge numbers of mutations across many conditions at once and interpret the data with mathematical models and machine learning. The shared goal is to make biology, and evolution in particular, more predictable.
Why it matters
Mutations behave differently depending on conditions, which makes it hard to predict disease risk from genetic data or how drug resistance will arise. Better models of how mutation effects change across environments could improve forecasts of drug-resistant pathogen evolution. They could also inform predictions about hereditary and infectious disease and about how organisms in farms and ecosystems respond to environmental change.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Liming Li
Northwestern University, IL·Genetics
Identify prion-specific changes in gene expression and cell-to-cell variation in yeast carrying [SWI+], [MOT3+] or both, using high-throughput single-cell RNA sequencing.
Michael Manhart
Rutgers University-New Brunswick, NJ·Ecology & Evolution
Test whether cross-feeding different nutrient types (amino acids, vitamins, carbon intermediates) changes the fitness effects of mutations differently, using engineered E. coli auxotroph partnerships and DNA-barcoded mutant libraries.
Patricia Wittkopp
University of Michigan, MI·Genetics
Identify the genes and specific DNA changes behind trait differences within and between fruit fly (Drosophila) species, and determine how they alter molecular, cellular and developmental processes.
The school
Arizona State University Campus Immersion: 36 labs on PI Pages, $1.0B a year on research, 625 research doctorates a year.
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