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Xylose is the second most abundant monomeric sugar in plant biomass. Consequently, xylose catabolism is an ecologically important trait for saprotrophic organisms, as well as a fundamentally important trait for industries that hope to convert plant mass to renewable fuels and other bioproducts using microbial metabolism. Although common across fungi, xylose catabolism is rare within Saccharomycotina, the subphylum that contains most industrially relevant fermentative yeast species. The genomes of several yeasts unable to consume xylose have been previously reported to contain the full set of genes in the XYL pathway, suggesting the absence of a gene-trait correlation for xylose metabolism. Here, we measured growth on xylose and systematically identified XYL pathway orthologs across the genomes of 332 budding yeast species. Although the XYL pathway coevolved with xylose metabolism, we found that pathway presence only predicted xylose catabolism about half of the time, demonstrating that a complete XYL pathway is necessary, but not sufficient, for xylose catabolism. We also found that XYL1 copy number was positively correlated, after phylogenetic correction, with xylose utilization. We then quantified codon usage bias of XYL genes and found that XYL3 codon optimization was significantly higher, after phylogenetic correction, in species able to consume xylose. Finally, we showed that codon optimization of XYL2 was positively correlated, after phylogenetic correction, with growth rates in xylose medium. We conclude that gene content alone is a weak predictor of xylose metabolism and that using codon optimization enhances the prediction of xylose metabolism from yeast genome sequence data.

Human genetic variation associates with the composition of the gut microbiome, yet its influence on clinical traits remains largely unknown. We analyzed the consequences of nearly a thousand gut microbiome-associated variants (MAVs) on phenotypes reported in electronic health records from tens of thousands of individuals. We discovered and replicated associations of MAVs with neurological, metabolic, digestive, and circulatory diseases. Five significant MAVs in these categories correlate with the relative abundance of microbes down to the strain level. We also demonstrate that these relationships are independently observed and concordant with microbe by disease associations reported in case–control studies. Moreover, a selective sweep and population differentiation impacted some disease-linked MAVs. Combined, these findings establish triad relationships among the human genome, microbiome, and disease. Consequently, human genetic influences may offer opportunities for precision diagnostics of microbiome-associated diseases but also highlight the relevance of genetic background for microbiome modulation and therapeutics.

Xylose is the second most abundant monomeric sugar in plant biomass. Consequently, xylose catabolism is an ecologically important trait for saprotrophic organisms, as well as a fundamentally important trait for industries that hope to convert plant mass to renewable fuels and other bioproducts using microbial metabolism. Although common across fungi, xylose catabolism is rare within Saccharomycotina, the subphylum that contains most industrially relevant fermentative yeast species. Several yeasts unable to consume xylose have been previously reported to possess complete predicted xylolytic metabolic pathways, suggesting the absence of a gene-trait correlation for xylose metabolism. Here, we measured growth on xylose and systematically identify XYL pathway orthologs across the genomes of 332 budding yeast species. We found that most yeast species possess complete predicted xylolytic pathways, but pathway presence did not correlate with xylose catabolism. We then quantified codon usage bias of XYL genes and found that codon optimization was higher in species able to consume xylose. Finally, we showed that codon optimization of XYL2, which encodes xylitol dehydrogenase, positively correlated with growth rates in xylose medium. We conclude that gene content cannot predict xylose metabolism; instead, codon optimization is now the best predictor of xylose metabolism from yeast genome sequence data.Significance StatementIn the genomic era, strategies are needed for the prediction of metabolic traits from genomic data. Xylose metabolism is an industrially important trait, but it is not found in most yeast species heavily used in industry. Because xylose metabolism appears rare across budding yeasts, we sought to identify a computational means of predicting which species are capable of xylose catabolism. We did not find a relationship between gene content and xylose metabolism traits. Rather, we found that codon optimization of xylolytic genes was higher in species that can metabolize xylose, and that optimization of one specific gene correlated with xylose-specific growth rates. Thus, codon optimization is currently the only means of accurately predicting xylose metabolism from genome sequence data.