Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)
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Abstract
While the terms “gene-by-gene interaction” (GxG) and “gene-by-environment interaction” (GxE) are commonplace within the fields of quantitative and evolutionary genetics, “environment-by-environment interaction” (ExE) is a term used less often. In this study, we find that environment-by-environment interactions are a meaningful driver of phenotypes, and that they differ across different genotypes (suggestive of ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. We show that the effectiveness of a drug combination, relative to single drugs, often varies across different drug resistant mutants. Even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that better predicts the direction and magnitude of ExE interactions for some mutants. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes, but also for broader efforts to understand the cell biology underlying these interactions and to resolve the source of phenotypic variance across populations. The relevance of ExExG interactions have been largely omitted from canon in evolutionary and population genetics, but these fields and others stand to benefit from perspectives that highlight how interactions between external forces craft the complex behavior of living systems.
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An important caveat is that, although the DE framework makes reasonable fitness predictions for these two drug pairs, it fails in many other environments and for many other genotypes, again highlighting the prevalence of ExExG.
The DE approach seems quite powerful especially since it adds a 'benign E' reference line for fitness comparisons. I would love to see how the prediction from this model lines up with true fitness in figure 2 for all lines tested.
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In terms of synergy vs antagonism, our results suggest that a small number of mutations can change a drug combination from having a synergistic to an antagonistic effect. For example, figure 2C shows a case where LRLF acts synergistically on a yeast strain harboring a single nucleotide mutation to the HDA1 gene, but acts antagonistically on a different evolved yeast mutant. Similarly, figure 3 shows cases where a drug pair changes from having a synergistic to an antagonistic effect across different mutants.
It seems from figure 2 and 3, the dominant pattern in the dataset is that of antagonistic interactions (at least in respect to the additive model). This made me wonder two things: 1) Are there are general biological explanations for such a pattern or considerations for why this might be expected? I'm thinking of the GxG equivalent …
In terms of synergy vs antagonism, our results suggest that a small number of mutations can change a drug combination from having a synergistic to an antagonistic effect. For example, figure 2C shows a case where LRLF acts synergistically on a yeast strain harboring a single nucleotide mutation to the HDA1 gene, but acts antagonistically on a different evolved yeast mutant. Similarly, figure 3 shows cases where a drug pair changes from having a synergistic to an antagonistic effect across different mutants.
It seems from figure 2 and 3, the dominant pattern in the dataset is that of antagonistic interactions (at least in respect to the additive model). This made me wonder two things: 1) Are there are general biological explanations for such a pattern or considerations for why this might be expected? I'm thinking of the GxG equivalent where we know for example that diminishing returns epistasis is a common feature of adaptive populations, and this can be linked to theoretical models of fitness landscapes in the context of Fisher's geometric model etc. 2) Is this the correct biological null model to use? Certainly in the quant-gen world the additive approach would be the go-to starting point, but is this relevant for the context of these fitness estimates? My first gut feeling was that the average null model should be more relevant. Not sure if a pop-gen multiplicative approach is another potential null.
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Here, we take a large collection of roughly 1,000 antifungal drug-resistant yeast mutants evolved using this method and ask how often fitness in multidrug environments is predicted by fitness in single drug environments (Figure 1D)
I enjoyed reading this paper and the novel ExExG framing of the study! This is a great dataset, I hope more genomic data can be attached to it in the future enabling even more mutation specific questions to be asked.
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Four different models (horizontal axis) are used to calculate expected fitness for each of roughly 1000 mutants per drug pair
It would be useful to get a short description of these models here (aside from the methods) for clarity.
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