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Many remarkable phenotypes have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times, many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,785 gene families from 993 Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we found that gene family expansion and contraction were significantly linked to trait gain and loss in 45/56 (80%) traits. While 595/739 (81%) significant gene families were associated with only one trait, we also identified several “keystone” gene families that were significantly associated with up to 13/56 (23%) of all traits. Strikingly, most of these families are known to encode metabolic enzymes and transporters, including all members of the industrially relevantMALtose fermentation loci in the baker’s yeastSaccharomyces cerevisiae. These results indicate that convergent evolution on the gene family level may be more widespread across deeper timescales than previously believed. 

Five yeast isolates belonging to a candidate for novel species were obtained from rotting wood and the gut of a passalid beetle larva in a site of Amazonian rainforest biome in Brazil. Sequence analysis of the Internal Transcribed Spacer (ITS)-5.8S region and the D1/D2 domains of the large subunit rRNA gene showed that the isolates represent a novel species of the genusVanderwaltozyma. The closest relative of the novel species isVanderwaltozyma huisunica. These species differs due to 44 nt substitutions and 21 indels in the sequences of the ITS region, as well as by 15 substitutions and four indels in the sequences of the D1/D2 domains. A phylogenomic analysis of theVanderwaltozymaspecies with genomes sequenced showed that this novel species is an outgroup to the other species of this genus. We propose the nameVanderwaltozyma urihicolasp. nov. (CBS 18107^T, MycoBank MB 856975) to accommodate these isolates. The species is homothallic, producing one to two ascospores per ascus. The habitat ofV. urihicolais rotting wood in the Brazilian Amazonian rainforest biome. 

Abstract BackgroundCost-effective production of biofuels from lignocellulose requires the fermentation ofd-xylose. Many yeast species within and closely related to the generaSpathasporaandScheffersomyces(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. ResultsWe 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 expressedXYLgenes from multipleScheffersomycesspecies in a strain ofSaccharomyces cerevisiae. RecombinantS. cerevisiaeexpressingXYL1fromScheffersomyces xylosifermentans, which encodes an XR without a cofactor preference, showed improved anaerobic growth on xylose as the primary carbon source compared toS. cerevisiaestrain expressingXYLgenes fromScheffersomyces stipitis. ConclusionCollectively, 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 yeastSc. xylosifermentansas a potential source for genes that can be engineered intoS. cerevisiaeto improve xylose fermentation and biofuel production. 

ABSTRACT Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine‐learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum. 

High yield isobutanol production is experimentally demonstrated. The solvent-to-biomass ratio during pretreatment and enzyme production during hydrolysis are identified as the major economic drivers of the biorefinery. 

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning–based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall–degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle—and perhaps more generally lifestyles favoring thermotolerance—might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity. 

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 yeastSaccharomyces eubayanusexperimentally 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. 

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 theGALactose 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 pathogenCandida aurisand the genusOgataea, respectively), have an alternative galactose utilization pathway because they lack theGALgenes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonicalGALpathway. 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.