Codon optimization, not gene content, predicts XYLose metabolism in budding yeasts

Abstract

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.

Genomic and ecological factors shaping specialism and generalism across an entire subphylum

“…Importantly, growth on xylose was included in our carbon classification, and the xylose metabolism genes XYL1 (Step 2 in Fig. 4D), XYL2 (Step 3), and XYL3 (Step 8) were all identified as important features (with XYL1 falling within the top 100), suggesting that xylose metabolism genes are promiscuous and may have multiple metabolic capabilities (57). This result also supports the hypothesis that intrinsic genetic factors contribute to niche breadth by connecting pathways.…”

“…We placed sterile water around the perimeter of the wells to limit evaporation. We previously published a subset of this data for specific carbon sources and species (comprising <5% of the total dataset) in smaller-studies (57,108). Growth rates were calculated in R using the grofit (v. 1.1.1-1) package (109).…”

“…4D) was only partially annotated by KEGG. To annotate XYL3, we used previously published reference sequences (57). An hmm profile was built from these sequences and then used to search the remaining yeast protein annotations using hmmscan (hmmer v3.3.2.)…”

“…Step 8 was in the top features used in the machine learning analysis, despite the fact that KEGG only partially annotated this gene. The xylulokinase encoded in yeasts, XYL3, is well studied (57). Therefore, we re-annotated the XYL3 gene and have shown the relative abundance of this gene (red star).…”

An adaptive interaction between cell type and metabolism drives ploidy evolution in a wild yeast

“…Resolving the genotype-to-phenotype map remains a central goal in genetics and evolutionary biology, but it has frequently proven challenging, even in microbes. While gene content is generally correlated with metabolic traits across budding yeasts [25], regulatory nuances in organisms that are not traditional models can confound superficial inferences of phenotypes from genotypes [63,66,145]. In the taxonomic type strain of S. eubayanus , structural maltose metabolism genes in canonical MAL clusters are exquisitely repressed or induced hundreds-fold in response to carbon source [58], which is similar to their S. cerevisiae homologs [146].…”