Divergent patterns of selection on metabolite levels and gene expression.

BACKGROUND: Natural selection can act on multiple genes in the same pathway, leading to polygenic adaptation. For example, adaptive changes were found to down-regulate six genes involved in ergosterol biosynthesis-an essential pathway targeted by many antifungal drugs-in some strains of the yeast Saccharomyces cerevisiae. However, the impact of this polygenic adaptation on metabolite levels was unknown. Here, we performed targeted mass spectrometry to measure the levels of eight metabolites in this pathway in 74 yeast strains from a genetic cross. RESULTS: Through quantitative trait locus (QTL) mapping we identified 19 loci affecting ergosterol pathway metabolite levels, many of which overlap loci that also impact gene expression within the pathway. We then used the recently developed v-test, which identified selection acting upon three metabolite levels within the pathway, none of which were predictable from the gene expression adaptation. CONCLUSIONS: These data showed that effects of selection on metabolite levels were complex and not predictable from gene expression data. This suggests that a deeper understanding of metabolism is necessary before we can understand the impacts of even relatively straightforward gene expression adaptations on metabolic pathways.

Tissue-Specific cis-Regulatory Divergence Implicates eloF in Inhibiting Interspecies Mating in Drosophila.

Reproductive isolation is a key component of speciation. In many insects, a major driver of this isolation is cuticular hydrocarbon pheromones, which help to identify potential intraspecific mates [1-3]. When the distributions of related species overlap, there may be strong selection on mate choice for intraspecific partners [4-9] because interspecific hybridization carries significant fitness costs [10]. Drosophila has been a key model for the investigation of reproductive isolation; although both male and female mate choices have been extensively investigated [6, 11-16], the genes underlying species recognition remain largely unknown. To explore the molecular mechanisms underlying Drosophila speciation, we measured tissue-specific cis-regulatory divergence using RNA sequencing (RNA-seq) in D. simulans × D. sechellia hybrids. By focusing on cis-regulatory changes specific to female oenocytes, the tissue that produces cuticular hydrocarbons, we rapidly identified a small number of candidate genes. We found that one of these, the fatty acid elongase eloF, broadly affects the hydrocarbons present on D. sechellia and D. melanogaster females, as well as the propensity of D. simulans males to mate with them. Therefore, cis-regulatory changes in eloF may be a major driver in the sexual isolation of D. simulans from multiple other species. Our RNA-seq approach proved to be far more efficient than quantitative trait locus (QTL) mapping in identifying candidate genes; the same framework can be used to pinpoint candidate drivers of cis-regulatory divergence in traits differing between any interfertile species.

Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing.

A major challenge in genetics is to identify genetic variants driving natural phenotypic variation. However, current methods of genetic mapping have limited resolution. To address this challenge, we developed a CRISPR-Cas9-based high-throughput genome editing approach that can introduce thousands of specific genetic variants in a single experiment. This enabled us to study the fitness consequences of 16,006 natural genetic variants in yeast. We identified 572 variants with significant fitness differences in glucose media; these are highly enriched in promoters, particularly in transcription factor binding sites, while only 19.2% affect amino acid sequences. Strikingly, nearby variants nearly always favor the same parent's alleles, suggesting that lineage-specific selection is often driven by multiple clustered variants. In sum, our genome editing approach reveals the genetic architecture of fitness variation at single-base resolution and could be adapted to measure the effects of genome-wide genetic variation in any screen for cell survival or cell-sortable markers.