Codon Optimization Improves the Prediction of Xylose Metabolism from Gene Content in Budding Yeasts

Genomic factors shaping codon usage across the Saccharomycotina subphylum

Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. The biased use of synonymous codons has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. The Saccharomycotina, the fungal subphylum containing the yeasts Saccharomyces cerevisiae and Candida albicans , has been a model system for studying codon usage. We characterized codon usage across 1,154 strains from 1,051 species to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns across the subphylum. We found evidence of a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is also distinct between the 12 orders within the subphylum to such a degree that yeasts can be classified into orders with an accuracy greater than 90% using a machine learning algorithm trained on codon usage. We also characterized the degree to which codon usage bias is impacted by translational selection. Interestingly, the degree of translational selection was influenced by a combination of genome features and assembly metrics that included the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs. The order contains 24 species, and 23 are computationally predicted to lack tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that extreme avoidance of the CGN codons is associated with a decline in arginine tRNA function. Codon usage bias within the Saccharomycotina is generally consistent with previous investigations in fungi, which show a role for both genomic features and GC bias in shaping codon usage. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons.

Machine learning enables identification of an alternative yeast galactose utilization pathway

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype-phenotype map, and their application will uncover novel biology, even in well-studied traits.

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.

Oxygenation influences xylose fermentation and gene expression in the yeast genera Spathaspora and Scheffersomyces

BACKGROUND

Cost-effective production of biofuels from lignocellulose requires the fermentation of D-xylose. Many yeast species within and closely related to the genera Spathaspora and Scheffersomyces (both of the order Serinales) natively assimilate and ferment xylose. Other species consume xylose inefficiently, leading to extracellular accumulation of xylitol. Xylitol excretion is thought to be due to the different cofactor requirements of the first two steps of xylose metabolism. Xylose reductase (XR) generally uses NADPH to reduce xylose to xylitol, while xylitol dehydrogenase (XDH) generally uses NAD+ to oxidize xylitol to xylulose, creating an imbalanced redox pathway. This imbalance is thought to be particularly consequential in hypoxic or anoxic environments.

RESULTS

We screened the growth of xylose-fermenting yeast species in high and moderate aeration and identified both ethanol producers and xylitol producers. Selected species were further characterized for their XR and XDH cofactor preferences by enzyme assays and gene expression patterns by RNA-Seq. Our data revealed that xylose metabolism is more redox balanced in some species, but it is strongly affected by oxygen levels. Under high aeration, most species switched from ethanol production to xylitol accumulation, despite the availability of ample oxygen to accept electrons from NADH. This switch was followed by decreases in enzyme activity and the expression of genes related to xylose metabolism, suggesting that bottlenecks in xylose fermentation are not always due to cofactor preferences. Finally, we expressed XYL genes from multiple Scheffersomyces species in a strain of Saccharomyces cerevisiae. Recombinant S. cerevisiae expressing XYL1 from Scheffersomyces xylosifermentans, which encodes an XR without a cofactor preference, showed improved anaerobic growth on xylose as the primary carbon source compared to S. cerevisiae strain expressing XYL genes from Scheffersomyces stipitis.

CONCLUSION

Collectively, our data do not support the hypothesis that xylitol accumulation occurs primarily due to differences in cofactor preferences between xylose reductase and xylitol dehydrogenase; instead, gene expression plays a major role in response to oxygen levels. We have also identified the yeast Sc. xylosifermentans as a potential source for genes that can be engineered into S. cerevisiae to improve xylose fermentation and biofuel production.

Natural trait variation across Saccharomycotina species

Among molecular biologists, the group of fungi called Saccharomycotina is famous for its yeasts. These yeasts in turn are famous for what they have in common-genetic, biochemical, and cell-biological characteristics that serve as models for plants and animals. But behind the apparent homogeneity of Saccharomycotina species lie a wealth of differences. In this review, we discuss traits that vary across the Saccharomycotina subphylum. We describe cases of bright pigmentation; a zoo of cell shapes; metabolic specialties; and species with unique rules of gene regulation. We discuss the genetics of this diversity and why it matters, including insights into basic evolutionary principles with relevance across Eukarya.

Taxogenomic analysis of a novel yeast species isolated from soil, Pichia galeolata sp. nov

A novel budding yeast species was isolated from a soil sample collected in the United States of America. Phylogenetic analyses of multiple loci and phylogenomic analyses conclusively placed the species within the genus Pichia. Strain yHMH446 falls within a clade that includes Pichia norvegensis, Pichia pseudocactophila, Candida inconspicua, and Pichia cactophila. Whole genome sequence data were analyzed for the presence of genes known to be important for carbon and nitrogen metabolism, and the phenotypic data from the novel species were compared to all Pichia species with publicly available genomes. Across the genus, including the novel species candidate, we found that the inability to use many carbon and nitrogen sources correlated with the absence of metabolic genes. Based on these results, Pichia galeolata sp. nov. is proposed to accommodate yHMH446T (=NRRL Y-64187 = CBS 16864). This study shows how integrated taxogenomic analysis can add mechanistic insight to species descriptions.

Ploidy evolution in a wild yeast is linked to an interaction between cell type and metabolism

Ploidy is an evolutionarily labile trait, and its variation across the tree of life has profound impacts on evolutionary trajectories and life histories. The immediate consequences and molecular causes of ploidy variation on organismal fitness are frequently less clear, although extreme mating type skews in some fungi hint at links between cell type and adaptive traits. Here, we report an unusual recurrent ploidy reduction in replicate populations of the budding yeast Saccharomyces eubayanus experimentally evolved for improvement of a key metabolic trait, the ability to use maltose as a carbon source. We find that haploids have a substantial, but conditional, fitness advantage in the absence of other genetic variation. Using engineered genotypes that decouple the effects of ploidy and cell type, we show that increased fitness is primarily due to the distinct transcriptional program deployed by haploid-like cell types, with a significant but smaller contribution from absolute ploidy. The link between cell-type specification and the carbon metabolism adaptation can be traced to the noncanonical regulation of a maltose transporter by a haploid-specific gene. This study provides novel mechanistic insight into the molecular basis of an environment-cell type fitness interaction and illustrates how selection on traits unexpectedly linked to ploidy states or cell types can drive karyotypic evolution in fungi.

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

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Paradigms proposed to explain this variation either invoke trade-offs between performance efficiency and breadth or underlying intrinsic or extrinsic factors. We assembled genomic (1,154 yeast strains from 1,049 species), metabolic (quantitative measures of growth of 843 species in 24 conditions), and ecological (environmental ontology of 1,088 species) data from nearly all known species of the ancient fungal subphylum Saccharomycotina to examine niche breadth evolution. We found large interspecific differences in carbon breadth stem from intrinsic differences in genes encoding specific metabolic pathways but no evidence of trade-offs and a limited role of extrinsic ecological factors. These comprehensive data argue that intrinsic factors driving microbial niche breadth variation.