Biology subjects

Wolters, J. F.

Publications and source records attributed to Wolters, J. F..

8 recordsLinked to original sources

bioRxiv2023

Diverse signatures of convergent evolution in cacti-associated yeasts

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 [~]17 times. Using machine-learning, we further found that cactophily can be predicted with 76% accuracy from functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which is likely associated with duplication and 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 through disparate molecular mechanisms. Remarkably, multiple cactophilic lineages and their close relatives are emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle--and perhaps more generally lifestyles favoring thermotolerance--may 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.

evolutionary biology↗

bioRxiv2023

Saccharomycotina yeasts defy longstanding macroecological patterns

The Saccharomycotina yeasts ("yeasts" hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth1; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild2. Here, we trained machine learning models on 12,221 occurrence records and 96 environmental variables to infer global distribution maps for 186 yeast species ([~]15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversification. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many longstanding macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and latitude-dependent range size (Rapoports rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

ecology↗

bioRxiv2023

Mitochondrial Genome Diversity across the Subphylum Saccharomycotina

Eukaryotic life depends on the functional elements encoded by both the nuclear genome and organellar genomes, such as those contained within the mitochondria. The content, size, and structure of the mitochondrial genome varies across organisms with potentially large implications for phenotypic variance and resulting evolutionary trajectories. Among yeasts in the subphylum Saccharomycotina, extensive differences have been observed in various species relative to the model yeast Saccharomyces cerevisiae, but mitochondrial genome sampling across many groups has been scarce, even as hundreds of nuclear genomes have become available. By extracting mitochondrial assemblies from existing short-read genome sequence datasets, we have greatly expanded both the number of available genomes and the coverage across sparsely sampled clades. Comparison of 353 yeast mitochondrial genomes revealed that, while size and GC content were fairly consistent across species, those in the genera Metschnikowia and Saccharomyces trended larger, while several species in the order Saccharomycetales, which includes S. cerevisiae, exhibited lower GC content. Extreme examples for both size and GC content were scattered throughout the subphylum. All mitochondrial genomes shared a core set of protein-coding genes for Complexes III, IV, and V, but they varied in the presence or absence of mitochondrially-encoded canonical Complex I genes. We traced the loss of Complex I genes to a major event in the ancestor of the orders Saccharomycetales and Saccharomycodales, but we also observed several independent losses in the orders Phaffomycetales, Pichiales, and Dipodascales. In contrast to prior hypotheses based on smaller-scale datasets, comparison of evolutionary rates in protein-coding genes showed no bias towards elevated rates among aerobically fermenting (Crabtree/Warburg-positive) yeasts. Mitochondrial introns were widely distributed, but they were highly enriched in some groups. The majority of mitochondrial introns were poorly conserved within groups, but several were shared within groups, between groups, and even across taxonomic orders, which is consistent with horizontal gene transfer, likely involving homing endonucleases acting as selfish elements. As the number of available fungal nuclear genomes continues to expand, the methods described here to retrieve mitochondrial genome sequences from these datasets will prove invaluable to ensuring that studies of fungal mitochondrial genomes keep pace with their nuclear counterparts.

genetics↗

bioRxiv2023

Machine learning illuminates how diet influences the evolution of yeast galactose metabolism

How genomic differences contribute to phenotypic differences across species 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 subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. In recent years, machine learning has been successfully used in diverse analyses of biological big data. Using a random forest classification algorithm trained on these genomic, metabolic, and/or environmental data, we predicted growth on several carbon sources and conditions with high accuracy from presence/absence patterns of genes and of growth in other conditions. Known structural genes involved in assimilation of these sources were important features contributing to prediction accuracy, whereas isolation environmental data were poor predictors. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.6%) or growth data in 120 other conditions (83.3%) but not from isolation environment data (65.7%). When we combined genomic and growth data, we noted that prediction accuracy was even higher (93.4%) and that, after the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol. These data raised the hypothesis that several species in two orders, Serinales and Pichiales (containing Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays of several of these species confirmed that they utilize galactose through an oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. We conclude that machine learning is a powerful tool for investigating the evolution of the yeast genotype-phenotype map and that it can help uncover novel biology, even in well-studied traits.

evolutionary biology↗

bioRxiv2023

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. One-Sentence SummaryA nearly complete genomic catalog of the yeast subphylum illuminates the evolution of their diverse ecologies and metabolisms.

evolutionary biology↗

bioRxiv2022

Aim18p and Aim46p are CHI-domain-containing mitochondrial hemoproteins in Saccharomyces cerevisiae.

Chalcone isomerases (CHIs) have well-established roles in the biosynthesis of plant flavonoid metabolites. Saccharomyces cerevisiae possesses two predicted CHI-like proteins, Aim18p (encoded by YHR198C) and Aim46p (YHR199C), but it lacks other enzymes of the flavonoid pathway, suggesting that Aim18p and Aim46p employ the CHI fold for distinct purposes. Here, we demonstrate that Aim18p and Aim46p reside on the mitochondrial inner membrane and adopt CHI folds, but they lack select active site residues and possess an extra fungal-specific loop. Consistent with these differences, Aim18p and Aim46p lack chalcone isomerase activity and also the fatty acid-binding capabilities of other CHI-like proteins, but instead bind heme. We further show that diverse fungal homologs also bind heme and that Aim18p and Aim46p possess structural homology to a bacterial hemoprotein. Collectively, our work reveals a distinct function and cellular localization for two CHI-like proteins, introduces a new variation of a hemoprotein fold, and suggests that ancestral CHI-like proteins were hemoproteins.

biochemistry↗

bioRxiv2022

Mapping mitonuclear epistasis using a novel recombinant yeast population

Natural genetic variation in mitochondrial and nuclear genomes can influence phenotypes by perturbing coadapted mitonuclear interactions. Mitonuclear epistasis, i.e. non-additive phenotype effects of interacting mitochondrial and nuclear alleles, is emerging as a general feature in eukaryotes, yet very few mitonuclear loci have been identified. Here, we present a novel advanced intercrossed population of S. cerevisiae yeasts, called the Mitonuclear Recombinant Collection (MNRC), designed explicitly for detecting mitonuclear loci contributing to complex traits, and use this population to map the genetic basis to mtDNA loss. In yeast, spontaneous deletions within mtDNAs lead to the petite phenotype that heralded mitochondrial research. We show that in natural populations, rates of petite formation are variable and influenced by genetic variation in nuclear, mtDNAs and mitonuclear interactions. We then mapped nuclear and mitonuclear alleles contributing mtDNA stability using the MNRC by integrating mitonuclear epistasis into a genome-wide association model. We found that associated mitonuclear loci play roles in mitotic growth most likely responding to retrograde signals from mitochondria, while associated nuclear loci with main effects are involved in genome replication. We observed a positive correlation between growth rates and petite frequencies, suggesting a fitness tradeoff between mitotic growth and mtDNA stability. We also found that mtDNA stability was influenced by a mobile mitochondrial GC-cluster that is expanding in certain populations of yeast and that selection for nuclear alleles that stabilize mtDNA may be rapidly occurring. The MNRC provides a powerful tool for identifying mitonuclear interacting loci that will help us to better understand genotype-phenotype relationships and coevolutionary trajectories. Author SummaryGenetic variation in mitochondrial and nuclear genomes can perturb mitonuclear interactions and lead to phenotypic differences between individuals and populations. Despite their importance to most complex traits, it has been difficult to identify the interacting loci. Here, we created a novel population of yeast designed explicitly for mapping mitonuclear loci contributing to complex traits and used this population to map genes influencing the stability of mitochondrial DNA (mtDNA). We found that mitonuclear interacting loci were involved in mitotic growth while non-interacting loci were involved in genome replication. We also found evidence that selection for mitonuclear loci that stabilize mtDNAs occurs rapidly. This work provides insight into mechanisms underlying maintenance of mtDNAs. The mapping population presented here is an important new resource that will help to understand genotype/phenotype relationships and coevolutionary trajectories.

evolutionary biology↗

bioRxiv2022

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

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.

evolutionary biology↗