Large-scale analyses reveal the contribution of adaptive evolution in pathogenic and non-pathogenic fungal species

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Genome studies of fungal pathogens have presented evidence for exceptionally high rates of evolution. It has been proposed that rapid adaptation is a hallmark of pathogen evolution that facilitates the invasion of new host niches and the overcoming of intervention strategies such as fungicide applications and drug treatments. To which extent high levels of genetic variation within and between species correlate with adaptive protein evolution in fungi more generally has so far not been explored. In this study, we addressed the contribution of adaptive evolution relative to genetic drift in 20 fungal species, hereby exploring genetic variation in 2,478 fungal genomes. We reannotated positions of protein-coding genes to obtain a high-quality dataset of 234,427 full-length core gene and 25,612 accessory gene alignments. We applied an extension of the McDonald-Kreitman test that models the distributions of fitness effects to infer the rate of adaptive (ω A ) and non-adaptive (ω NA ) non-synonymous substitutions in protein-coding genes. To explore the relevance of recombination on local adaptation rates, we inferred the population genomic recombination rate for all 20 species. Our analyses reveal extensive variation in rates of adaptation and show that high rates of adaptation are not a hallmark of a pathogenic lifestyle. Up to 83% of non-synonymous substitutions are adaptive in the species Parastagonospora nodorum . However, non-synonymous substitutions in other species, including the prominent rice-infecting pathogen Magnaporthe oryzae , are predominantly non-adaptive (neutral or slightly deleterious). Correlating adaptation measures with effective population size and recombination rate, we show that effective population size is a primary determinant of adaptive evolution in fungi. At the genome scale, recombination rate variation explains variation in both ω A and ω NA . Finally, we demonstrate the robustness of our estimates using simulations. We underline the value of population genetic principles in studies of fungal evolution, and we highlight the importance of demographic processes in adaptive evolution of pathogenic and non-pathogenic species.

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  1. Materials and Methods

    I'm realizing as I read through the methods that it is not described here how πS was actually calculated. This is so crucially important for the paper, so these details really must be included in detail. For instance, reading the methods it's unclear to me whether πS was calculated in sliding windows across the genome, using the core genes, or something else. The details may be outlined elsewhere in the manuscript, but the really must be here in the methods. It's quite difficult to evaluate the results otherwise.

  2. We used bootstraps to estimate the variation in each gene set’s adaptation rates.

    I would suggest being more specific about what you mean here - which specific parameters are you referring to as "adaptation rates"?

  3. The distribution of fitness effects (DFE) was determined based on the SFS information of synonymous and non-synonymous sites of concatenated genes using the GRAPES software (3,14).

    Why were these methods applied to the concatenated genes? Would it not be more informative to fit these models on a gene-by-gene basis so as to gain more insight into how the DFE varies within and among species? Alternatively, if the issue is a matter of statistical power, could this be instead be done on the concatenations of the three datasets described above? (non-secreted, secreted, and effectors)

  4. we inferred the distribution of fitness effects (DFE) of mutations across species using six models (Figure 2B; Supplementary table S9).

    I would be curious to see whether Ne or the recombination rate vary in some predictable way among the species corresponding to each model.

  5. we estimated πS reflecting the neutral genetic variation in the population and here considered a relative proxy for the effective population size

    It seems to me that accurate estimates of Ne will be critical to your interpretations throughout the manuscript, will it not? If so, and given the incredible dataset you're working with here, I can't help but feel that using πs here rather than actual estimates of Ne is surprising.

    At the very least, I might suggest using a method such as GONE ( to estimate Ne for each species - at the very least to demonstrate that the two approaches leads to comparable or equivalent solutions. The method performs quite well for estimating contemporary effective population sizes from modest numbers of samples. a comparison of this and other related methods (including those that better infer historical pop sizes) can be found here: