Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

  1. Grant Kinsler
  2. Kerry Geiler-Samerotte
  3. Dmitri A Petrov

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Abstract

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular — affecting a small number of phenotypes that matter to fitness in the environment where they evolved — yet globally pleiotropic — affecting additional phenotypes that may reduce or improve fitness in new environments.

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  1. Version published to 10.7554/elife.61271 on eLife
  2. eLife

    ###Reviewer #3:

    Kinsler et al measure the fitness of 292 mutants, which were recovered from previously performed experimental evolution in glucose limited batch culture condition, using barseq in 45 different conditions. They analyze the matrix of individual fitness measurements in different conditions using dimensionality reduction (singular value decomposition) and then study the explanatory power of the matrix decomposition. Although 95% of the variance is explained by the first vector, they identify 7 additional orthogonal vectors that explain a significant fraction of the remaining 5% of variance. They find that this reduced dimensionality representation of fitness profiles is able to predict mutant fitness in conditions similar to that in which the evolution experiment was performed and in environments that differ from the original selection experiment. They observe that different adaptive mutations have different effects across environments despite having similar fitness effects in the selective environment. From these findings the authors conclude that adaptive mutations affect a small number of phenotypes in the condition in which they are selected, but that they have the potential to affect additional phenotypes across conditions concluding that adaptive mutations are locally modular, but globally pleiotropic.

    This experimental study is well performed and the data analysis is clear and comprehensive. The authors have done an exemplary job in describing their study with clear and scholarly writing.

    However, the central question is whether the conclusions of the study are justified. The authors goal is to establish a "genotype-phenotype-fitness" map, but as they state "our phenotypic dimensions are not necessarily comparable to what people traditionally think of as a "phenotype". Indeed, I agree that what the authors have identified are not phenotypes at all but are instead properties of the genotype-fitness map assayed in different conditions. These properties are themselves interesting; however, describing them as phenotypes - observable and measurable traits of an organism -, or even inferring the number of phenotypes they represent, is incorrect. Therefore, I am not convinced that the authors have achieved their goal of defining a genotype-phenotype-fitness map.

    Key points that the authors should consider:

    -The central conclusion is not supported. The authors claim that adaptive mutations affect a small number of phenotypes in the evolved conditions, but many phenotypes over different conditions. But, this conclusion cannot be drawn from the results. Why is a scenario in which hundreds of "phenotypes" (e.g. the expression of 100 genes) underlies enhanced fitness in the adapted environment, but a change in the environment means that only 10 of those genes are expressed (i.e. fewer "phenotypes") and thus the fitness effect is different in that environment incompatible with the results? In that scenario the overall conclusion would be completely the opposite. Perhaps constructing a mechanistic model and performing simulations that explore these different possibilities would strengthen the argument.

    -A primary result of the study is that mutations that are beneficial in one condition are frequently deleterious in other conditions. This phenomenon of antagonistic pleiotropy has been described innumerable times in the experimental evolution literature - indeed, it seems to be the rule rather than the exception - and these prior observations should be more clearly described.

    -The extent to which the results are dependent on the number of environments is not investigated. For example, reducing the number of "similar" environments would likely decrease the variance explained by the first singular value as would increasing the diversity of environments that are studied. How does this variation impact the results and interpretation?

    -In figure 2, it looks like fitness is defined relative to the most fit genotype. Typically, in experimental evolution fitness is defined relative to the ancestor. Perhaps defining ancestral fitness as zero for the SVD is necessary, but this is atypical based on similar studies and may be a source of confusion for readers.

    -In figure 2C an idea of the variance is given for the EC conditions, but not for the other conditions. Some measure of uncertainty for fitness in each condition would help (give the 2-4 replicates of each).

    -Why not use an ancestral strain without a barcode for competition assays, rather than having to digest the ancestral barcode with restriction enzymes?

    -cutoff of 1000 reads for a times point with 400 strains seems really low (or is it supposed to be reads/strain?).

    -The arrows in figure 2C are unexplained.

  3. eLife

    ###Reviewer #2:

    In the manuscript titled "A genotype-phenotype-fitness map reveals local modularity and global pleiotropy of adaptation," the authors describe an approach for uncovering the phenotypic complexity that underlies fitness by tracking hundreds of experimentally-evolved adaptive mutants across a range of environments. This approach yields a genotype-phenotype-fitness map without actually naming and measuring the phenotypes themselves. Instead, by perturbing environmental conditions and measuring mutant fitness across environments, the authors develop a model that reveals a collection of abstract phenotypes that contribute significantly to fitness. The authors find that a low-dimensional phenotypic model is sufficient for capturing fitness of the panel of mutants across subtle environmental perturbations - which suggests that only a few phenotypes contribute to fitness near the evolution conditions. Further, the model accurately predicts fitness in environments that deviate from the evolution condition, often through components that contribute little to fitness near the evolution condition - which suggests that adaptive mutants have latent phenotypic effects that only impact fitness in distant environments. These findings lead the authors to conclude that adaptive mutations are locally modular yet globally pleiotropic, thereby lending valuable insight into our understanding of how adaptive mutations affect the complex physiological interconnectedness of the cell.

    Overall, I am very impressed with the work described in the manuscript. The manuscript is well-written, especially considering the conceptual depth of the topic and novelty of the approach. The experiments were elegantly designed and adopt a variety of molecular tools developed recently within the field. The figures are appealing and present the data in a clear manner. The conclusions are justified by the data, and the findings represent a significant contribution to the field.

  4. eLife

    ###Reviewer #1:

    The distribution of pleiotropic effects of mutations selected in a particular environment is of broad and fundamental significance. We've known for a while from large and even larger-scale screens of beneficial genetic variation that the rising tide of these mutants in the focal environment often lifts other boats in neighboring conditions, but not in orthogonal conditions, where outcomes are unpredictable. This beautifully written, executed, and analyzed study shows that we actually can gain predictability if the number of environments scales to dozens, mutants scale to hundreds, and most importantly, multidimensional analyses are taken seriously enough to derive the most salient predictor variables. Here, the magic number is 8 parameters, and the authors do a great job of justifying this decision given the noise of batch effects and the surprising power of the few, less explanatory parameters in the selective environment to explain variation in the more foreign environments.

  5. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 3 of the manuscript.

    ###Summary:

    The distribution of pleiotropic effects of mutations selected in a particular environment is of broad and fundamental significance. We've known for a while from large and even larger-scale screens of beneficial genetic variation that the rising tide of these mutants in the focal environment often lifts other boats in neighboring conditions, but not in orthogonal conditions, where outcomes are unpredictable. This well written, executed, and analyzed study shows that we actually can gain predictability if the number of environments scales to dozens, mutants scale to hundreds, and most importantly, multidimensional analyses are taken seriously enough to derive the most salient predictor variables. The authors find that a low-dimensional phenotypic model is sufficient for capturing fitness of the panel of mutants across subtle environmental perturbations - which suggests that only a few phenotypes contribute to fitness near the evolution conditions. Further, the model accurately predicts fitness in environments that deviate from the evolution condition, often through components that contribute little to fitness near the evolution condition - which suggests that adaptive mutants have latent phenotypic effects that only impact fitness in distant environments.

  6. Version published to 10.1101/2020.06.25.172197v3 on bioRxiv
  7. Version published to 10.1101/2020.06.25.172197v2 on bioRxiv
  8. Version published to 10.1101/2020.06.25.172197v1 on bioRxiv