Overall Research Theme: 

The biology: Organisms are comprised of interacting parts. Even within single cells, networks of proteins regulate basic functions. The impact of perturbing one part of an organism – for example, via genetic mutation – can often be modified by perturbation to other parts.12029207276_f449bff05c_c

The problem: This creates obstacles for scientists: how do we predict traits from genetic data when the same mutation can have different impacts? This also presents challenges during evolution: how does an organism adapt or evolve when changing one trait can influence many other traits, resulting in complex tradeoffs?

The solution: To understand the spectrum of effects that a specific perturbation can have on an organism, I measure how yeast cells respond to subtle genetic or environmental changes. Then, I study how cellular responses change when multiple perturbations are combined or when the magnitude of the perturbation is systematically varied.

The impact: Quantifying how cells respond to subtle perturbations is a worthwhile challenge. My research provides insight about how interactions between small-effect genetic variants shape the evolution of complex traits and about the molecular basis of complex disease.

Specific Research Projects: 

1. Quantifying the impact of adaptive point mutations on yeast cell fitness

How many unique ways are there to solve an evolutionary challenge?

The question: In the lab and in nature, we often only get to see the ‘winners’ of an evolutionary challenge: Which bacteria evolved resistance to a particular drug? Which yeast cells grow the fastest when glucose is limited? A recent technology allows us to capture a fuller spectrum of beneficial mutants, in addition to the eventual winners. But how does each beneficial mutant solve the challenge and do all mutants represent the same strategy? Answering these questions is an important step toward predicting or preventing the evolution of drug resistance.

The approach: I have been developing a high-throughput framework to understand the effects of adaptive mutations. This framework utilizes hundreds of yeast lineages (more on the way) each with, on average, a single, sequenced, adaptive mutation, and a DNA-sequencing method that sensitively measures their relative growth rates in many subtly different environments. Based on how their responses cluster, we are learning about different classes of adaptive mutations. Part of this work is being done with the Molecular Biology and Evolution Training Program that I founded to introduce high school students to laboratory research; track our progress by clicking this link!

Paper: Venkataram, Dunn, Li, Agarwala, Chang, Ebel, Geiler-Samerotte, Herrisant, Blundell, Levy, Fisher, Sherlock & Petrov. (2016) Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell Sep 8; 166(6):1585–1596.e22

2. Quantifying the impact of spontaneous mutation on yeast cell morphology

Are there proteins (e.g. HSP90) that make cells robust to spontaneous mutations?  


Summary: The molecular chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations. The potential for Hsp90 and other putative buffers to increase robustness to mutation has had a major impact on disease models, quantitative genetics and evolutionary theoryOpposite expectations, we found that Hsp90 tends to exacerbate, rather than to buffer, mutational effects. This result alters perception of why cryptic (i.e. buffered) genetic variation exists and casts doubt on cancer-treatments aiming to target enhancers of mutational robustness.

We found that natural selection transforms the genetic interactions that persist in genomes leaving a false signal of mutational robustness.

Paper: Geiler-Samerotte, Zhu, Goulet, Hall & Seigal. (2016) Selection transforms the landscape of genetic variation interacting with Hsp90. PLOS Biology 14(10): e2000465

Primer: Schell, Mullis, Ehrenreich. (2016) Modifiers of the Genotype-Phenotype Map: Hsp90 and Beyond. PLOS Biology 14(11): e2001015

Talk: 12 minute presentation at The Allied Genetics Conference (2016) 

3. Quantifying how single-cell morphological traits co-vary 

Why are genetic variants that affect multiple traits common even though they can lead to complex trade-offs?

4. Investigating the mechanistic basis of non-genetic heterogeneity

Why are some clonal populations more heterogeneous than others?

Screenshot 2017-08-29 14.50.48

The question: Increased clonal heterogeneity contributes to drug resistance by increasing the chances that some portion of cells will survive environmental challenges. But the mechanisms that support heterogeneity have been difficult to identify.

The approach: I performed a QTL screen and compared diverse statistical approaches to identify regions of the yeast genome that influence clonal heterogeneity. We are in the process of fine-mapping candidate gene regions.

Paper: Geiler-Samerotte, Bauer, Li, Ziv, Gresham & Siegal. The details in the distributions: How and why to study variability. (2013) Curr Opin Biotechnol 24, 752–759

Talk: 30 minute presentation at the mathematical tools for evolutionary systems biology meeting

5. Quantifying the impact of mild protein misfolding on yeast cell growth rate

Does selection to avoid misfolding impose selection pressure on coding sequences? 

Screenshot 2017-08-29 14.00.56
Costly misfolded protein activities

Summary: Most mutations to coding sequences result in production of misfolded protein. Using a novel method to measure the amount of misfolded protein per cell, mass spectrometry, and a sensitive experimental approach that isolated toxicity from other costs of misfolding, we quantified a per-misfolded-molecule fitness cost. This cost suggests that the toxicity associated with misfolding imposes a widespread evolutionary constraint and contributes to human diseaseThis work was awarded the Walter Fitch Prize at SMBE.

We found that misfolding imposes a pervasive constraint on protein evolution. 

Paper: Geiler-Samerotte, Hashimoto, Dion, Airoldi & Drummond. Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates. (2013) PLoS ONE 8, e75320

Paper: Geiler-Samerotte, Dion, Budnik, Wang, Hartl & Drummond. Misfolded proteins impose a dosage-dependent fitness cost and trigger a cytosolic unfolded protein response in yeast. (2011) Proc. Natl. Acad. Sci. U.S.A. 108, 680–685   

*The first image on this page is from Jonathan McCabe/ Flickr