Measuring the statistics of pleiotropic and epistatic fitness effects of beneficial mutations during microbial adaptation across environments

Harvard University, Cambridge MA

Investigators

Linked publications, trials & patents

Abstract

Project Summary. Two main sources of uncertainty can combine to make the outcome of adaptive evolution unpredictable: how the effects of mutations on evolutionary fitness depend on either the environment (i.e. pleiotropy) or genotype (i.e. epistasis) of the organism. By conferring substantial context dependence to the fitness effects of mutations, epistasis and pleiotropy independently and jointly impact the repeatability?and thus predictability?of evolution at phenotypic and genotypic levels. Yet we lack an empirical and conceptual basis for predicting their general importance in evolution, leaving a fundamental question unanswered: how much of the future depends on the past? The answers may be as pertinent to the causes of biological diversification and speciation as they are to predicting cancer progression, parasite host switches, and the emergence of drug resistant pathogens? adaptive processes for which predictability is greatly needed. When studied in detail, pleiotropy and epistasis reveal important facets of how the genetic architecture of organismal traits interacts with the environment to affect the outcomes of adaptation. This work moves beyond case-studies by proposing to systematically measure the distribution of fitness effects of beneficial mutations across environments, as well as how these effects change when paired with other mutations in the genome. Progress on these fronts has been impeded by practical combinatorial limits to the gene?gene and gene? environment interactions that can be assayed at once, as well as by the limited availability of study systems allowing controlled manipulation of recombination to isolate its effect on the evolution of such interactions. Using budding yeast as a model for microbial adaptation, the proposed work removes the combinatorial upper limit that has for so long prevented estimation of the statistics of epistatic and pleiotropic mutational effects, and permits an analysis of how recombination affects their role in evolution. This work uses innovative sequencing-based approaches to track the fitness of thousands of genotypes via DNA barcodes in bulk across a wide range of environments, and will reveal the statistical context in which important forms of pleiotropy (e.g. trade-offs) actually tend to arise. Additionally, this work tests how the effects, and genetic basis, of epistasis and pleiotropy evolve differently in sexual versus asexual populations, by using DNA barcode fitness tracking of thousands of recombinant offspring across multiple environments. Together, this work will provide a comprehensive study of classic but poorly measured evolutionary quantities that impact the genetic and phenotypic dynamics of adaptation and its predictability.