How populations acquire beneficial mutations is of fundamental importance to evolutionary biology and to the treatment outcomes of diseases such as microbial infection and cancer. This renewal proposal, a collaboration between the Sherlock and Rosenzweig laboratories, will expand our understanding of adaptive evolution by using high-throughput sequencing in novel ways to gain the most granular view yet of the dynamics of a population of evolving cells. In the last funding period, we made fundamental discoveries concerning the dynamics and molecular nature of adaptive evolution and about epistatic interactions between beneficial mutations, using the model eukaryote S. cerevisiae (budding yeast). We propose here to extend these discoveries with major technological innovations that we are pioneering.
The specific aims of this proposal are: 1) to measure the beneficial mutation rate and the distribution of fitness effects for the vast majority of beneficial mutations that will impact our evolving populations;2) to map the adaptive landscape explored by that population through in-depth clonal and population sequencing;and 3) to determine how adaptive mutation rate and the distribution of fitness effects change in relation to ploidy, evolutionary history and environmental stress.
For Aim 1, we have developed a molecular barcode-based lineage tracking system with which we can quantify, to high-resolution, the emergence and establishment of adaptive clones in evolving populations. This novel method greatly improves upon our previous fluorescence-based lineage tracking method: instead of tracking 3 subpopulations, we can now track half a million subpopulations, which we now refer to as lineages. We will use lineage tracking to estimate parameters that have been challenging to measure directly: the beneficial mutation rate and the distribution of selection coefficients for the vast majority of mutations affecting th course of evolution. These estimates will significantly advance evolutionary theory and enrich our understanding of all evolutionary processes driven by the accumulation of beneficial mutations.
In Aim 2, we will sequence selected adaptive clones from our evolving populations, as well as the evolving populations themselves, obtaining detailed genome-wide data about what types of mutations provide an adaptive advantage and how the frequencies of novel beneficial alleles change over time. Finally, in Aim 3, we will use our lineage tracking system to investigate how beneficial mutation rate changes in relation to factors long thought to influence the dynamics of adaptive evolution: ploidy, evolutionary history and stress. Achieving these aims will enable us to see in unprecedented detail not only how beneficial mutations arise in yeast, but also how they arise in any mitotically-proliferating cell line subject to variation in ploidy, historical contingency and stress, including cancer.

Public Health Relevance

We have developed a method that now allows us to observe evolutionary change in yeast at high resolution; this will allow us to develop a robust estimate of the beneficial mutation rate, and ask how ploidy affects that rate. Evolutionary genomics studies using yeast have extraordinary power for elucidating the principles of evolutionary processes, including the process by which cancer-causing mutations in tumors accumulate. Additionally, our system could be applied to other areas of biomedical relevance, such as tracking the emergence of multi-drug resistance in microbial pathogens and in tumors;our discoveries may therefore be expected to positively impact human health.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
2R01HG003328-07A1
Application #
8506520
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Felsenfeld, Adam
Project Start
2004-07-01
Project End
2016-06-30
Budget Start
2013-09-01
Budget End
2014-06-30
Support Year
7
Fiscal Year
2013
Total Cost
$574,265
Indirect Cost
$164,093
Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Sellis, Diamantis; Kvitek, Daniel J; Dunn, Barbara et al. (2016) Heterozygote Advantage Is a Common Outcome of Adaptation in Saccharomyces cerevisiae. Genetics 203:1401-13
Venkataram, Sandeep; Dunn, Barbara; Li, Yuping et al. (2016) Development of a Comprehensive Genotype-to-Fitness Map of Adaptation-Driving Mutations in Yeast. Cell 166:1585-1596.e22
Zhu, Yuan O; Sherlock, Gavin; Petrov, Dmitri A (2016) Whole Genome Analysis of 132 Clinical Saccharomyces cerevisiae Strains Reveals Extensive Ploidy Variation. G3 (Bethesda) 6:2421-34
Levy, Sasha F; Blundell, Jamie R; Venkataram, Sandeep et al. (2015) Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519:181-6
Chiotti, Kami E; Kvitek, Daniel J; Schmidt, Karen H et al. (2014) The Valley-of-Death: reciprocal sign epistasis constrains adaptive trajectories in a constant, nutrient limiting environment. Genomics 104:431-7
Blundell, Jamie R; Levy, Sasha F (2014) Beyond genome sequencing: lineage tracking with barcodes to study the dynamics of evolution, infection, and cancer. Genomics 104:417-30
Rosenzweig, Frank; Sherlock, Gavin (2014) Experimental evolution: prospects and challenges. Genomics 104:v-vi
Kinnersley, Margie; Wenger, Jared; Kroll, Evgueny et al. (2014) Ex uno plures: clonal reinforcement drives evolution of a simple microbial community. PLoS Genet 10:e1004430
Spor, Aymé; Kvitek, Daniel J; Nidelet, Thibault et al. (2014) Phenotypic and genotypic convergences are influenced by historical contingency and environment in yeast. Evolution 68:772-90
Dunn, Barbara; Paulish, Terry; Stanbery, Alison et al. (2013) Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression. PLoS Genet 9:e1003366

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