Low-dimensional genotype-fitness mapping across divergent environments suggests a limiting functions model of fitness
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (Arcadia Science)
Abstract
A central goal in evolutionary biology is to be able to predict the effect of a genetic mutation on fitness. This is a major challenge because fitness depends both on phenotypic changes due to the mutation, and how these phenotypes map onto fitness in a particular environment. Genotype, phenotype, and environment spaces are all extremely complex, rendering bottom-up prediction unlikely. Here we show, using a large collection of adaptive yeast mutants, that fitness across a set of lab environments can be well-captured by top-down, low-dimensional linear models that generate abstract genotype-phenotype-fitness maps. We find that these maps are low-dimensional not only in the environment where the adaptive mutants evolved, but also in more divergent environments. We further find that the genotype-phenotype-fitness spaces implied by these maps overlap only partially across environments. We argue that these patterns are consistent with a “limiting functions” model of fitness, whereby only a small number of limiting functions can be modified to affect fitness in any given environment. The pleiotropic side-effects on non-limiting functions are effectively hidden from natural selection locally, but can be revealed globally. These results combine to emphasize the importance of environmental context in genotype-phenotype-fitness mapping, and have implications for the predictability and trajectory of evolution in complex environments.
Article activity feed
-
To measure the relative fitness of all mutants in thelibrary for a single environmental condition, we uti-lize previously established methods that leverage DNAbarcoding and high-resolution lineage tracking. Wecompete a pool of barcoded strains against a highly-abundant reference strain, the wild type (WT) ances-tor, which takes up 95% of the culture. We quantifythe frequencies of barcodes over time via amplicon se-quencing. Based on these frequency trajectories, wecan infer the relative fitness of thousands of strains(see Methods for more details) (5, 22, 56).
I appreciate this well-structured and efficient experimental design! However, I wonder about the potential for metabolic interactions between strains potentially confounding the results of this pooled competition set-up. Cells harboring mutations (particularly those in metabolic …
To measure the relative fitness of all mutants in thelibrary for a single environmental condition, we uti-lize previously established methods that leverage DNAbarcoding and high-resolution lineage tracking. Wecompete a pool of barcoded strains against a highly-abundant reference strain, the wild type (WT) ances-tor, which takes up 95% of the culture. We quantifythe frequencies of barcodes over time via amplicon se-quencing. Based on these frequency trajectories, wecan infer the relative fitness of thousands of strains(see Methods for more details) (5, 22, 56).
I appreciate this well-structured and efficient experimental design! However, I wonder about the potential for metabolic interactions between strains potentially confounding the results of this pooled competition set-up. Cells harboring mutations (particularly those in metabolic regulation pathways like Ras and TOR) might show altered secretion of nutritious or toxic compounds that could affect neighboring cells. This seems particularly relevant for interpreting fitness effects in the context of your "limiting functions" model. Perhaps these effects are not as concerning as I am flagging here since the local environment of neighboring cells for each mutant will differ across batch replicates, which could help counter some of the confounding nature of the pooled design.
-
