Distributions of Mutational Effects and the Estimation of Directional Selection in Divergent Lineages of Arabidopsis thaliana

Unmatched Level of Molecular Convergence among Deeply Divergent Complex Multicellular Fungi

Convergent evolution is pervasive in nature, but it is poorly understood how various constraints and natural selection limit the diversity of evolvable phenotypes. Here, we analyze the transcriptome across fruiting body development to understand the independent evolution of complex multicellularity in the two largest clades of fungi-the Agarico- and Pezizomycotina. Despite >650 My of divergence between these clades, we find that very similar sets of genes have convergently been co-opted for complex multicellularity, followed by expansions of their gene families by duplications. Over 82% of shared multicellularity-related gene families were expanding in both clades, indicating a high prevalence of convergence also at the gene family level. This convergence is coupled with a rich inferred repertoire of multicellularity-related genes in the most recent common ancestor of the Agarico- and Pezizomycotina, consistent with the hypothesis that the coding capacity of ancestral fungal genomes might have promoted the repeated evolution of complex multicellularity. We interpret this repertoire as an indication of evolutionary predisposition of fungal ancestors for evolving complex multicellular fruiting bodies. Our work suggests that evolutionary convergence may happen not only when organisms are closely related or are under similar selection pressures, but also when ancestral genomic repertoires render certain evolutionary trajectories more likely than others, even across large phylogenetic distances.

Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae

Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the 10 genes surveyed and are inconsistent with normality. For example, all 10 distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene's expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.

Accelerating structure-function mapping using the ViVa webtool to mine natural variation

Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of these data remain inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project (http://1001genomes.org). ViVa facilitates data mining on the gene, gene family, or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well-studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well-studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less-studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project ( http://snp-seek.irri.org/) and human genetic variation ( https://www.ncbi.nlm.nih.gov/clinvar/).