- Aug 2024
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www.biorxiv.org www.biorxiv.org
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The magnitude of the raw difference is typically much larger than that of the posterior effects. The difference is likely caused by LD, in that the raw difference of a single mutation contains contributions from other linked mutations, which may inflate the estimates.
Could you constrain this analysis to mutations that are in LE with other de-novo mutations to test this hypothesis?
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Here we employ a classical line-cross strategy with MA lines, to break down the linkage disequilibrium among the accumulated mutations. We then combine whole-genome sequencing with high-throughput competitive fitness assays to estimate the DFE of a set of 169 spontaneous mutations.
I greatly enjoyed reading this paper. True experimental estimates of the DFE in MA studies are super valuable and provide a very interesting comparison for pop-gen based DFE methods as pointed out by the authors.
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Averaged over all RI(AI)Ls, accounting for variation among assay blocks and removing two outlying lines, the regression of W on number of mutations is not significantly different from 0 (slope = −0.0051, F1,509=1.83, P>0.17), although the trend suggests that mutations are deleterious, on average.
Is there a chance that false negative mutations (i.e. incorrectly unobserved events in the MA lines) could contribute to this result?
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The simplest way to infer the mutational effect at a locus is to calculate the mean value of all lines with a mutant allele and all lines with an ancestral allele at that locus; the difference is the raw difference (uRAW) of the mutation at that locus. As a sanity check, we plotted the inferred Bayesian posterior effect against the raw difference; ideally, the correlation should be +1. The correlations were positive, but well below 1 in all three cases (Figure 4). The magnitude of the raw difference is typically much larger than that of the posterior effects. The difference is likely caused by LD, in that the raw difference of a single mutation contains contributions from other linked mutations, which may inflate the estimates.
Two quick thoughts for further sanity checks. 1) Does this regression look any different for SNPs vs indels? 2) Do the individual mutation specific effects conform to expectations one might have based on the functional annotations available for these mutational events?
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- Jul 2024
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www.biorxiv.org www.biorxiv.org
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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 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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