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Rokas, A.

Publications and source records attributed to Rokas, A..

At least 37 records · Page 2Linked to original sources

Convergent expansions of keystone gene families drive metabolic innovation in a major eukaryotic clade

Many remarkable innovations 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 14,710 gene families from 993 species of Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we discovered that gene family expansion and contraction was significantly linked to trait gain and loss in 45/56 (80%) of traits. While 601/746 (81%) of 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. These results indicate that metabolic innovations in yeasts are governed by a narrow set of major genetic elements and mechanisms.

evolutionary biology↗

Specialization restricts the evolutionary paths available to yeast sugar transporters

Functional innovation at the protein level is a key source of evolutionary novelties. The constraints on functional innovations are likely to be highly specific in different proteins, which are shaped by their unique histories and the extent of global epistasis that arises from their structures and biochemistries. These contextual nuances in the sequence-function relationship have implications both for a basic understanding of the evolutionary process and for engineering proteins with desirable properties. Here, we have investigated the molecular basis of novel function in a model member of an ancient, conserved, and biotechnologically relevant protein family. These Major Facilitator Superfamily sugar porters are a functionally diverse group of proteins that are thought to be highly plastic and evolvable. By dissecting a recent evolutionary innovation in an -glucoside transporter from the yeast Saccharomyces eubayanus, we show that the ability to transport a novel substrate requires high-order interactions between many protein regions and numerous specific residues proximal to the transport channel. To reconcile the functional diversity of this family with the constrained evolution of this model protein, we generated new, state-of-the-art genome annotations for 332 Saccharomycotina yeast species spanning approximately 400 million years of evolution. By integrating phylogenetic and phenotypic analyses across these species, we show that the model yeast -glucoside transporters likely evolved from a multifunctional ancestor and became subfunctionalized. The accumulation of additive and epistatic substitutions likely entrenched this subfunction, which made the simultaneous acquisition of multiple interacting substitutions the only reasonably accessible path to novelty.

evolutionary biology↗

Pathogenicity is associated with population structure in a fungal pathogen of humans

Aspergillus flavus is a clinically and agriculturally important saprotrophic fungus responsible for severe human infections and extensive crop losses. We analyzed genomic data from 250 (95 clinical and 155 environmental) A. flavus isolates from 9 countries, including 70 newly sequenced clinical isolates, to examine population and pan-genome structure and their relationship to pathogenicity. We identified five A. flavus populations, including a new population, D, corresponding to distinct clades in the genome-wide phylogeny. Strikingly, > 75% of clinical isolates were from population D. Accessory genes, including genes within biosynthetic gene clusters, were significantly more common in some populations but rare in others. Population D was enriched for genes associated with zinc ion binding, lipid metabolism, and certain types of hydrolase activity. In contrast to the major human pathogen Aspergillus fumigatus, A. flavus pathogenicity in humans is strongly associated with population structure, making it a great system for investigating how population-specific genes contribute to pathogenicity.

evolutionary biology↗

Convergent reductive evolution in bee-associated lactic acid bacteria

AbstractDistantly related organisms may evolve similar traits when exposed to similar environments or engaging in certain lifestyles. Several members of the Lactobacillaceae (LAB) family are frequently isolated from the floral niche, mostly from bees and flowers. In some floral LAB species (henceforth referred to as bee- associated), distinctive genomic (e.g., genome reduction) and phenotypic (e.g., preference for fructose over glucose or fructophily) features were recently documented. These features are found across distantly related species, raising the hypothesis that specific genomic and phenotypic traits evolved convergently during adaptation to the floral environment. To test this hypothesis, we examined representative genomes of 369 species of bee-associated and non-bee-associated LAB. Phylogenomic analysis unveiled seven independent ecological shifts towards the floral niche in LAB. In these bee-associated LAB, we observed pervasive, significant reductions of genome size, gene repertoire, and GC content. Using machine leaning, we could distinguish bee-associated from non-bee-associated species with 94% accuracy, based on the absence of genes involved in metabolism, osmotic stress, or DNA repair. Moreover, we found that the most important genes for the machine learning classifier were seemingly lost, independently, in multiple bee-associated lineages. One of these genes, adhE, encodes a bifunctional aldehyde-alcohol dehydrogenase associated with the evolution of fructophily, a rare phenotypic trait that was recently identified in many floral LAB species. These results suggest that the independent evolution of distinctive phenotypes in bee- associated LAB has been largely driven by independent loss of the same set of genes. ImportanceSeveral lactic acid bacteria (LAB) species are intimately associated with bees and exhibit unique biochemical properties with potential for food applications and honeybee health. Using a machine-learning based approach, our study shows that adaptation of LAB to the bee environment was accompanied by a distinctive genomic trajectory deeply shaped by gene loss. Several of these gene losses occurred independently in distantly related species and are linked to some of their unique biotechnologically relevant traits, such as the preference of fructose over glucose (fructophily). This study underscores the potential of machine learning in identifying fingerprints of adaptation and detecting instances of convergent evolution. Furthermore, it sheds light onto the genomic and phenotypic particularities of bee-associated bacteria, thereby deepening the understanding of their positive impact on honeybee health.

evolutionary biology↗

Unique trajectory of gene family evolution from genomic analysis of nearly all known species in an ancient yeast lineage

Gene gains and losses are a major driver of genome evolution; their precise characterization can provide insights into the origin and diversification of major lineages. Here, we examined gene family evolution of 1,154 genomes from nearly all known species in the medically and technologically important yeast subphylum Saccharomycotina. We found that yeast gene family and genome evolution are distinct from plants, animals, and filamentous ascomycetes and are characterized by small genome sizes and smaller gene numbers but larger gene family sizes. Faster-evolving lineages (FELs) in yeasts experienced significantly higher rates of gene losses--commensurate with a narrowing of metabolic niche breadth--but higher speciation rates than their slower-evolving sister lineages (SELs). Gene families most often lost are those involved in mRNA splicing, carbohydrate metabolism, and cell division and are likely associated with intron loss, metabolic breadth, and non-canonical cell cycle processes. Our results highlight the significant role of gene family contractions in the evolution of yeast metabolism, genome function, and speciation, and suggest that gene family evolutionary trajectories have differed markedly across major eukaryotic lineages.

evolutionary biology↗

The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of Evolution

The functions of cells and proteins depend on their biochemical microenvironment. To understand how biochemical constraints shaped protein structural evolution, we coupled the extensive genetic and metabolic data from the Saccharomycotina subphylum with the capability of AlphaFold2 to systematically predict protein structures from sequence. Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties. For example, structural evolution depends on each enzymes reaction mechanism, on the variability rather than the amount of metabolic flux, and on biosynthetic cost. Evolutionary cost-optimization is stronger on highly abundant enzymes and acts differently on different structural domains, with the exception of small-molecule binding sites, which are prioritised over other structural domains and lack cost-optimisation. Finally, while enzyme surfaces are less constrained, surface residues can also be exposed to positive selection for the co-evolution of protein-protein interaction sites. Accessing AlphaFolds power to predict protein structures systematically and across species barriers, facilitating the integration of protein structures with functional genomics, we were thus able to map biological constraints which shape protein structural evolution at scale and over long timelines.

systems biology↗

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.

genomics↗

Strain heterogeneity in a non-pathogenic fungus highlights factors contributing to virulence

Fungal pathogens exhibit extensive strain heterogeneity, including variation in virulence. Whether closely related non-pathogenic species also exhibit strain heterogeneity remains unknown. Here, we comprehensively characterized the pathogenic potentials (i.e., the ability to cause morbidity and mortality) of 16 diverse strains of Aspergillus fischeri, a non-pathogenic close relative of the major pathogen Aspergillus fumigatus. In vitro immune response assays and in vivo virulence assays using a mouse model of pulmonary aspergillosis showed that A. fischeri strains varied widely in their pathogenic potential. Furthermore, pangenome analyses suggest that A. fischeri genomic and phenotypic diversity is even greater. Genomic, transcriptomic, and metabolomic profiling identified several pathways and secondary metabolites associated with variation in virulence. Notably, strain virulence was associated with the simultaneous presence of the secondary metabolites hexadehydroastechrome and gliotoxin. We submit that examining the pathogenic potentials of non-pathogenic close relatives is key for understanding the origins of fungal pathogenicity.

evolutionary biology↗

Genome-wide patterns of non-coding sequence variation in the major fungal pathogen Aspergillus fumigatus

A. fumigatus is a deadly fungal pathogen, responsible for >400,000 infections/year and high mortality rates. A. fumigatus strains exhibit variation in infection-relevant traits, including in their virulence. However, most A. fumigatus protein-coding genes, including those that modulate its virulence, are shared between A. fumigatus strains and closely related non-pathogenic relatives. We hypothesized that A. fumigatus genes exhibit substantial genetic variation in the non-coding regions immediately upstream to the start codons of genes, which could reflect differences in gene regulation between strains. To begin testing this hypothesis, we identified 5,812 single-copy orthologs across the genomes of 263 A. fumigatus strains. A. fumigatus non-coding regions showed higher levels of sequence variation compared to their corresponding protein-coding regions. Specifically, we found that 1,274 non-coding regions exhibited <75% nucleotide sequence similarity (compared to 928 protein-coding regions) and 3,721 non-coding regions exhibited between 75% and 99% similarity (compared to 2,482 protein-coding regions) across strains. Only 817 non-coding regions exhibited [&ge;]99% sequence similarity compared to 2,402 protein-coding regions. By examining 2,482 genes whose protein-coding sequence identity scores ranged between 75% and 99%, we identified 478 total genes with signatures of positive selection only in their non-coding regions and 65 total genes with signatures only in their protein-coding regions. 28 of the 478 non-coding regions and 5 of the 65 protein-coding regions under selection are associated with genes known to modulate A. fumigatus virulence. Non-coding region variation between A. fumigatus strains included single nucleotide polymorphisms and insertions or deletions of at least a few nucleotides. These results show that non-coding regions of A. fumigatus genes harbor greater sequence variation than protein-coding regions, raising the hypothesis that this variation may contribute to A. fumigatus phenotypic heterogeneity.

evolutionary biology↗

Functional and evolutionary integration of a fungal gene with a bacterial operon

Siderophores are crucial for iron-scavenging in microorganisms. While many yeasts can uptake siderophores produced by other organisms, they are typically unable to synthesize siderophores themselves. In contrast, Wickerhamiella/Starmerella (W/S) clade yeasts gained the capacity to make the siderophore enterobactin following the remarkable horizontal acquisition of a bacterial operon enabling enterobactin synthesis. Yet, how these yeasts absorb the iron bound by enterobactin remains unresolved. Here, we demonstrate that Enb1 is the key enterobactin importer in the W/S-clade species Starmerella bombicola. Through phylogenomic analyses, we show that ENB1 is present in all W/S clade yeast species that retained the enterobactin biosynthetic genes. Conversely, it is absent in species that lost the ent genes, except for Starmerella stellata, making this species the only cheater in the W/S clade that can utilize enterobactin without producing it. Through phylogenetic analyses, we infer that ENB1 is a fungal gene that likely existed in the W/S clade prior to the acquisition of the ent genes and subsequently experienced multiple gene losses and duplications. Through phylogenetic topology tests, we show that ENB1 likely underwent horizontal gene transfer from an ancient W/S clade yeast to the order Saccharomycetales, which includes the model yeast Saccharomyces cerevisiae, followed by extensive secondary losses. Taken together, these results suggest that the fungal ENB1 and bacterial ent genes were cooperatively integrated into a functional unit within the W/S clade that enabled adaptation to iron-limited environments. This integrated fungal-bacterial circuit and its dynamic evolution determines the extant distribution of yeast enterobactin producers and cheaters.

evolutionary biology↗

The evolution of gestation length in eutherian mammals

Gestation length, or the duration of pregnancy, is a critical component of mammalian reproductive biology1. Eutherian mammals exhibit striking variation in their gestation lengths2-5, which has traditionally been linked to and allometrically scales with variation in other life history traits, including body mass and lifespan5-8. How the phenotypic landscape of gestation length variation, including its associations with body mass and lifespan variation, changed over mammalian evolution remains unknown. Phylogeny-informed analyses of 845 representative extant eutherian mammals showed that gestation length variation substantially differed in both whether and how strongly it was associated with body mass and lifespan across mammalian clades. For example, gestation length variation in Chiroptera and Cetacea was not associated with lifespan or body mass but was strongly associated only with body mass in Carnivora. We also identified 52 adaptive shifts in gestation length variation across the mammal phylogeny and 14 adaptive shifts when considering all three life history traits; the placements of six adaptive shifts are common in the two analyses. Notably, two of these shifts occurred at the roots of Cetacea and Pinnipedia, respectively, coinciding with the transition of these clades to the marine environment. The varying dynamics of the phenotypic landscape of gestation length, coupled with the varying patterns of associations between gestation length and two other major life history traits, raise the hypothesis that evolutionary constraints on gestation length have varied substantially across mammalian phylogeny. This variation in constraints implies that the genetic architecture of gestation length differs between mammal clades.

evolutionary biology↗

A genome-scale Opisthokonta tree of life: toward phylogenomic resolution of ancient divergences

AbstractAncient divergences within Opisthokonta--a major lineage that includes organisms in the kingdoms Animalia, Fungi, and their unicellular relatives-- remain contentious, hindering investigations of the evolutionary processes that gave rise to two kingdoms and the repeated emergence of iconic phenotypes like multicellularity. Here, we use genome-scale amounts of data to reconstruct the most taxon-rich Opisthokonta tree of life to date (348 species) and place divergences in geologic time, suggesting a Mesoproterozoic origin ([~] 1.11 billion years ago). By dissecting multiple dimensions of phylogenomic error, such as the influence of taxon sampling and model complexity, we found that deep divergences within Holozoa remain unresolved and suggest Pluriformea is either sister to Ichthyosporea and Filozoa (Pluriformea-sister hypothesis) or is monophyletic to Ichthyosporea, forming the Teretosporea lineage (Teretosporea-sister hypothesis). A combination of information theory and sensitivity analyses revealed that the inferred unicellular Holozoa relationships are largely robust to common sources of analytical error, such as insufficient model complexity, and suggest that previous reports likely suffered from insufficient taxon sampling. Our study presents a robust Opisthokonta phylogenomic framework, highlights the challenges in resolving the relationships of unicellular Holozoa, and paves the way for illuminating ancient evolutionary episodes concerning the origin of two kingdoms.

evolutionary biology↗

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↗

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

microbiology↗

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↗

A phylogenetic approach to explore the Aspergillus fumigatus conidial surface-associated proteome and its role in pathogenesis

Aspergillus fumigatus, an important pulmonary fungal pathogen causing several diseases collectively called aspergillosis, relies on asexual spores or conidia for initiating host infection. Here, we used a phylogenomic approach to compare proteins in the conidial surface of A. fumigatus, two closely related non-pathogenic species, Aspergillus fischeri and Aspergillus oerlinghausenensis, and the cryptic pathogen Aspergillus lentulus. After identifying 62 proteins uniquely expressed on the A. fumigatus conidial surface, we deleted 42 genes encoding conidial proteins. We found deletion of 33 of these genes altered susceptibility to macrophage killing, penetration and damage to epithelial cells, and cytokine production. Notably, a gene that encodes glycosylasparaginase, which modulates levels of the host pro-inflammatory cytokine IL-1{beta}, is important for infection in an immunocompetent murine model of fungal disease. These results suggest that A. fumigatus conidial surface proteins and effectors are important for evasion and modulation of the immune response at the onset of fungal infection.

microbiology↗

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↗

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↗