Final Report Summary - ADAPT (Origins and factors governing adaptation: Insights from experimental evolution and population genomic data)
Examples of adaptation are available from the fossil record and from extant populations. Genomic studies have supplied many instances of genomic regions exhibiting footprint of natural selection favoring new variants. Despite ample proof that adaptation happens, we know little about beneficial mutations– the raw stuff enabling adaptation. Is adaptation mediated by genetic variation pre-existing in the population, or by variation supplied de novo through mutations? We know even less about what factors limit rates of adaptation. Answers to these questions are crucial for Evolutionary Biology, but also for believable quantifications of the evolutionary potential of populations. The overarching aim of the ADAPT project is to develop models and new methods and analyse data to enable the systematic study of the type of genetic variation enabling adaptation and factors that limit rates of adaptation in natural populations. The project is timely given the surge of new data stemming both from re-sequencing of evolution experiment conducted in the labs and data coming from the re-sequencing of individuals from naturally occurring populations. We have investigated theoretically a model –so called Fisher geometric model -- that predicts the likely effect of novel mutations on organismal fitness in a given environment. Predictions from this model have been tested with experimental data from model organism such as bacteria and we have shown what aspects of the data are well or conversely poorly predicted by theory. Overall the model performs well but fitness landscape underlying evolution in extremely harsh environments --such as ones experienced by micro organisms confronted to anti microbial drugs – exhibit empirical properties poorly predicted.
We have also developed methods to infer the distribution of fitness effects of new mutations using data from the survey of variation between individuals at the DNA level. Methods using that type of data from broadly applicable, as this type of data is a lot more accessible to non-model organisms. Understanding how selection shapes genomes will also enhance our general ability to detect genomic variants with functional importance.
Ultimately, the project provided methods for genetic data analysis and theoretical building blocks for future models addressing the role of adaptation in communities comprising several adapting and competing species. They may also be a valuable strategy to develop methods for making sense of metagenomics surveys exploring patterns of bacterial and viral / diversity in a variety of environments and possibly over time.
We have also developed methods to infer the distribution of fitness effects of new mutations using data from the survey of variation between individuals at the DNA level. Methods using that type of data from broadly applicable, as this type of data is a lot more accessible to non-model organisms. Understanding how selection shapes genomes will also enhance our general ability to detect genomic variants with functional importance.
Ultimately, the project provided methods for genetic data analysis and theoretical building blocks for future models addressing the role of adaptation in communities comprising several adapting and competing species. They may also be a valuable strategy to develop methods for making sense of metagenomics surveys exploring patterns of bacterial and viral / diversity in a variety of environments and possibly over time.