Scientific Publications

Quantifying fitness distributions and phenotypic relationships in recombinant yeast populations.

Publication TypeJournal Article
AuthorsPerlstein, EO, Deeds EJ, Ashenberg O., Shakhnovich EI, and Schreiber SL
AbstractStudies of the role of sex in evolution typically involve a longitudinal comparison of a single ancestor to several intermediate descendants and to one terminally evolved descendant after many generations of adaptation under a given selective regime. Here we take a complementary, statistical approach to sex in evolution, by describing the distribution of phenotypic similarity in a population of yeast F1 meiotic recombinants. By applying graph theory to fitness measurements of thousands of Saccharomyces cerevisiae recombinants treated with 10 mechanistically distinct, growth-inhibitory small-molecule perturbagens (SMPs), we show that the network of phenotypic similarity among F1 recombinants exhibits a scale-free degree distribution. F1 recombinants are often phenotypically unique and sometimes exceptional, and their fitness strengths are unevenly distributed across the 10 compound treatments. By contrast, highly phenotypically similar F1 recombinants constitute failing hubs that display below-average fitness across all compound treatments and are candidate substrates for purifying selection. Comparison of the F1 generation with the parental strains reveals that (i) there is a specialist more fit in any given single condition than any of the parents but (ii) only rarely are there generalists that exhibit greater fitness than both parental strains across a majority of conditions. This analysis allows us to evaluate and to gain better theoretical understanding of the costs and benefits of sex in the F1 generation.
Year of Publication2007
JournalProceedings of the National Academy of Sciences of the United States of America
Volume104
Issue25
Pages10553-8
Date Published (YYYY/MM/DD)2007/06/19
ISSN Number0027-8424
DOI10.1073/pnas.0704037104
PubMedhttp://www.ncbi.nlm.nih.gov/pubmed/17566105?dopt=Abstract