Chronic infection and tumorigenesis are both evolutionary processes, in which adaptation is constrained by the rate at which beneficial mutations occur, and by whether those mutations are still beneficial in a changing environment. Many beneficial mutations exhibit pleiotropy, causing a """"""""ripple effect"""""""" that alters many phenotypes, some of which may increase fitness in one environment, but decrease fitness in another. This phenomenon, antagonistic pleiotropy (AP), is thought to underlie senescence, evolutionary trade-offs, and the persistence of deleterious alleles at high frequency in human populations. Recent genome-wide association studies have shown that many common human SNPs are linked via AP to certain cancers, metabolic syndromes, and immune mediated disorders. AP has also been shown theoretically to severely reduce the mean selective value of beneficial mutations, reducing their likelihood of fixation. Despite their importance in medicine and evolutionary biology, the beneficial mutation rate, the distribution of their fitness effects, nd the extent to which beneficial mutations show AP have yet to be measured empirically in a systematic, prospective and unbiased manner. This proposal will fill these knowledge gaps, using innovations in molecular barcoding, which will provide the most detailed view yet of evolutionary dynamics. Using the model eukaryote, Saccharomyces cerevisiae, our groups have made fundamental discoveries concerning the molecular bases of adaptation, epistasis and introgression, and how AP causes physiological trade-offs. We propose here to extend these discoveries.
Our Specific Aims are: 1) to evolve and measure the fitness effects of beneficial mutations in one environment, then measure their fitness in other environments;2) to determine the molecular basis of adaptation and antagonistic pleiotropy;and 3) to determine at the molecular level how lineages can adaptively escape from the harmful effects of antagonistic pleiotropy.
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 thousands of adaptive clones in an evolving population. We will use lineage tracking to estimate the beneficial mutation rate and the distribution of selection coefficients for these new mutants in multiple environments, which will reveal the extent of AP.
In Aim 2, we will sequence hundreds of clones that either do or do not demonstrate AP, in order to determine the underlying molecular nature of the responsible mutations, and define the adaptive mutational spectrum for the evolutionary condition in greater detail than has ever been possible. Finally, in Aim 3 we will again use our lineage tracking system to discover the mutational routes by which novel beneficial mutants adaptively """"""""escape"""""""" from AP, eliminating its cost, while preserving the original benefit. The identity of these secondary mutations will provide qualitatively new information about the underlying wiring of, and weak or strong points in metabolic networks, and shed light upon epistatic interactions and the role they play in adaptive evolution.

Public Health Relevance

When a gene is mutated, the new allele of that gene is sometimes beneficial under certain circumstances, but harmful under others;this phenomenon, termed Antagonistic Pleiotropy (AP), is thought to underlie aging as well as explain why certain alleles persist at high frequency in human populations, even when they cause hereditary disorders and can be linked to cancer. Notwithstanding its medical importance, we know little about the prevalence of AP, which genes are subject to AP under what conditions, and still less about the specific nature of alleles that exhibit AP. Here, we propose to use a new molecular barcoding system in conjunction with experimental evolution to fill these knowledge gaps, thereby improving our prospects for understanding the relationship between pleiotropic human alleles and the various conditions to which they contribute.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genetic Variation and Evolution Study Section (GVE)
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Eckstrand, Irene A
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Stanford University
Schools of Medicine
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Jaffe, Mia; Sherlock, Gavin; Levy, Sasha F (2017) iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast. G3 (Bethesda) 7:143-153
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