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Pervasive function and evidence for selection across standing genetic variation in S. cerevisiae | Nature Communications
Results.Mapping QTN that drive complex metabolic innovation.S. cerevisiae strains have recently adapted to a wide range of ecological niches, having shared an ancestor with S. paradoxus only some 5–10 million years ago16,17,18,19. Of the traits thought to be responsible for such adaptation, for instance to anaerobic fermentation or to the host mucosa, one of the most important10,11,12 is metabolic innovation. To dissect the genetic basis of growth on diverse carbon sources, we examined a highly inbred cross between parents derived from two different ecological niches: RM11-1a, from a vineyard in California20, and YJM975, from an immunocompromised patient in Italy21. The two strains differ by only 12,054 polymorphisms despite their distinct niches. We previously sequenced the genomes of 1125 F6 haploid progeny of this cross, enabling high-resolution genetic mapping with single-gene, and often single-nucleotide, resolution8. We examined the growth of these progeny in quadruplicate on a diverse set of carbohydrates that included sugars and nonfermentable carbon sources: glucose, galactose, raffinose, maltose, glycerol, ethanol, and sucrose.We next determined the QTLs responsible for growth on each carbon source. Because complex traits are driven by many alleles of small effect, it can be challenging to identify the underlying QTLs using classical approaches22. To map the causal variants for each trait, we used a forward selection procedure, followed by fine mapping using in silico reciprocal hemizygote analysis [Fig.?1a]8. Of 195 causal loci identified, we could resolve 62.1% (N?=?121) to single genes (that is, within 1?kbp) and 54.9% (N?=?107) to single nucleotides [Supplementary Figure?1]. The QTNs responsible for these diverse traits included missense and regulatory variants, but also many synonymous variants in coding regions [Fig.?1b].Fig. 1Mapping complex metabolic traits to single-nucleotide resolution. a Schema of the crossing strategy, phenotyping conditions, genetic mapping, and phylogenetic analysis procedure employed herein. b Fraction of QTN of each functional class for each growth condition tested; “all other” includes all other types of polymorphisms, e.g., premature stop codons, frameshifts, loss of a start codon, etc. Indicated at top is the number of QTNs identified for each trait. c Variance explained per QTL normalized to the maximum variance explained for each growth condition tested (pink); analogous data for growth in the presence of various drug and other stressors are included as a reference for the complexity of drug-resistance traits8 (gray). Also indicated is the mean?±?s.e.m. of number of QTLs identified per trait for each class. d Effect of selected example QTNs in conditions as indicated. Shown are normalized, Z-scored colony size for each allele. Line indicates the mean. Blue: RM11 allele; orange: YJM975 allele. Source data are provided as a Source Data fileFull size image The statistical power of our approach enabled us to readily identify causal polymorphisms explaining as little as 0.3% of phenotypic variance [Fig.?1c, Supplementary Figure?1]. Furthermore, because we could identify many QTLs for each trait, we could explain up to 72% of the total phenotypic variance despite the genetic complexity of the traits we examined [Supplementary Figure?1]. Metabolic phenotypes were comparably, and more consistently, complex as compared to growth in the presence of a battery of drugs and other chemical insults (27.7?±?5.65 QTLs for metabolic traits; 26.0?±?22.0 QTLs for other traits; mean?±?s.e.m.; p??C transition at position ?6 in the 5′ UTR that explained up to 12.8% of the phenotypic variance, on the other hand, was more surprising [Fig.?2a]. In concordance with the apparently highly deleterious effect of each polymorphism for sugar metabolism, both were rare across a very large collection of more than 1000 sequenced S. cerevisiae isolates [Fig.?2b].Fig. 2Neighboring QTNs in a compound QTL. a) Growth phenotypes of segregants with the parental ditypes (SUC2?6T/394A, blue; SUC2?6C/394fs, orange) and nonparental ditypes (SUC2?6T/394fs and SUC2?6T/394fs, pink). Data shown are mean?±?s.e.m of normalized, Z-scored colony size for each ditype. b Prevalence of the causal SUC2 variants shown in (a) across 1011 sequenced S. cerevisiae isolates28. c Growth of representative segregants with genotypes as in (a). Shown are technical quadruplicates arrayed in squares; panels are representative of N?=?8 biological replicates. d Normalized invertase activity of representative segregants with genotypes as in (a) when grown in media containing raffinose (blue) or sucrose (red), as indicated. Data shown are N?=?3 biological replicates; bars show the mean. Source data are provided as a Source Data fileFull size image To confirm that both QTNs impinged on Suc2 activity and to link this activity to the observed phenotype, we selected representative segregants to examine in detail. To avoid confounding effects from other segregating QTLs, these were chosen to be isogenic at the other major QTL for growth in raffinose, located at the ATG19 gene. We first confirmed the growth phenotypes of the segregants on glucose, raffinose, and sucrose: while there was no evident growth defect on glucose, both QTNs were associated profound growth defects on both sucrose and raffinose [Fig.?2c]. This is consistent with a defect in sucrose catabolism, as the trisaccharide raffinose is first decomposed into galactose and sucrose. To further confirm that Suc2 activity was the molecular phenotype responsible for the growth defect, we assayed the total cellular invertase activity in each of the segregants when propagated in raffinose and sucrose. In concordance with our hypothesis, segregants with either or both QTNs exhibited greatly reduced invertase activity [Fig.?2d]. Indeed, segregants bearing the nonparental ditypes at the two loci were comparably compromised as compared to the YJM975 ditype. While we did not exhaustively survey the genetic variation in our mapping panel for such interactions, our finding of a strong compound QTL in our limited search suggests that the phenomenon may be common. Continued improvements in the resolution of genetic mapping approaches will likely reveal many more examples of compound QTLs, as were recently described for genetic variation segregating in the BY4741 and RM11 strains27.Evidence of positive selection on metabolic traits.Although the traits we examined are likely to be important in many environments, it is impossible to completely recapture in the laboratory the selective forces that have driven S. cerevisiae evolution in the wild. We therefore turned to a powerful statistical test for selection, inspired by Orr and predicated on the idea that positive selection on a given trait in one lineage should enrich it for QTLs of coherent effect28,29. This test is implemented by calculating the fraction of variants from the same parent that have a coherent effect as a function of genetic distance and comparing this enrichment to the random expectation of no coherence. We observed a striking enrichment for nearby variants from the same parent to have the same effect on phenotype (p?5%; N?=?27) of all QTN we have identified thus far (in a limited survey of growth conditions) have been subject to selection in natural S. cerevisiae populations. This estimate is a conservative lower bound on the fraction of variants in our cross that have been subject to selection, but still suggests RM11?×?YJM975 alone harbors at least 600 ecologically relevant variants, and likely many more.Selection for adaptation to ecological niche.Finally, we investigated connections between the phylogenetic evidence for selection and the actual selective forces at play in nature. If adaptation to ecological niche is a relevant selective pressure, variants adaptive in a certain niche should be enriched in isolates from that environment. Therefore, we evaluated the enrichment of alternate alleles in strains isolated from particular ecological niches. For every shared variant, we assessed whether the multiple occurrences were more likely to have occurred in strains isolated from the same ecological environment (N?=?26,671 and N?=?20,089 variants present in two and three strains, respectively) [Fig.?7a]. Indeed, both doubly and triply occurring alternate alleles were far more likely to have occurred solely within a single niche than would be expected by chance (p?
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