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

Abstract

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.

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

“…A) Phylogenetic tree of the Saccharomycotina yeast subphylum highlighting the 27 species from which metabolic enzyme sequences were extracted and used for structural prediction, coloured as indicated by phylogenetic order, numbering counterclockwise starting at C. albicans . Branch lengths and topology are from the species time tree as calculated in (Opulente et al, 2024), except for the branch for the outgroup species, S. pombe which is not drawn to scale. B) Illustration of our analysis pipeline.…”

“…At the same time, they are still characterised by a remarkable metabolic diversity, including gene losses preventing growth on specific substrates (Opulente et al, 2024; Shen et al, 2018), a major whole-genome hybridization that corresponds with the emergence of the ability to ferment in the presence of oxygen (Crabtree effect) (Hagman et al, 2014; Hagman and Piškur, 2015; Marcet-Houben and Gabaldón, 2015), and limited horizontal gene transfers from other yeast and bacteria that confer new metabolic capabilities (Gonçalves et al, 2018; Gonçalves and Gonçalves, 2022; Kominek et al, 2019; Marsit et al, 2015). The clade has been comprehensively sequenced and characterised at the molecular and metabolic level (Kurtzman et al, 2011; Opulente et al, 2024; Riley et al, 2016; Shen et al, 2018; Steenwyk et al, 2022; Wolters et al, 2023; Wu et al, 2017), and includes the most prevalent human fungal pathogen Candida albicans, and several industrially important yeast species ( Kluyveromyces marxianus, Komatagella pastoris, Yarrowia lipolytica) as well as the model single celled eukaryote and food and beverage industry workhorse, Saccharomyces cerevisiae. Previous work has integrated phenotypic and genomic evidence to understand the evolution of metabolism in the yeast subphylum (Opulente et al, 2024; Shen et al, 2018), including reconstruction of individual genome scale metabolic models for hundreds of species (Li et al, 2022; Lu et al, 2021).…”

“…The clade has been comprehensively sequenced and characterised at the molecular and metabolic level (Kurtzman et al, 2011; Opulente et al, 2024; Riley et al, 2016; Shen et al, 2018; Steenwyk et al, 2022; Wolters et al, 2023; Wu et al, 2017), and includes the most prevalent human fungal pathogen Candida albicans, and several industrially important yeast species ( Kluyveromyces marxianus, Komatagella pastoris, Yarrowia lipolytica) as well as the model single celled eukaryote and food and beverage industry workhorse, Saccharomyces cerevisiae. Previous work has integrated phenotypic and genomic evidence to understand the evolution of metabolism in the yeast subphylum (Opulente et al, 2024; Shen et al, 2018), including reconstruction of individual genome scale metabolic models for hundreds of species (Li et al, 2022; Lu et al, 2021).…”

“…We speculated that protein structures could augment our understanding of these relationships, by providing information on the co-localization of residues within proteins, distinguish surface from core residues, and enable the identification of functional sites, such as active sites, binding pockets for allosteric regulators, and protein-protein interaction sites (Studer et al, 2013) that might be exposed to specific selective constraints. In order to identify the metabolic constraints affecting enzyme structural evolution, we used a combination of comparative evolutionary genomic and comparative structural biology, exploiting the extensive metabolic and genetic characterisation of the Saccharomycotina subphylum, which contains the biotechnologically and medically relevant yeasts Saccharomyces cerevisiae and Candida albicans (Kurtzman et al, 2011; Lu et al, 2021; Opulente et al, 2024; Shen et al, 2018), as well as recent advances in computational structural biology that allow us to predict high quality protein structure at scale and across species barriers using Alphafold2 (Jumper et al, 2021). To test how the divergent metabolic properties between these species, their metabolic pathways, and the different enzyme reaction mechanisms represented in the metabolic network acted on protein structural evolution, we used AlphaFold2 to assemble structures of 11,269 enzymes from a selection of 27 species, which evolved over 400 million years (Opulente et al, 2024; Shen et al, 2018).…”

“…In order to identify the metabolic constraints affecting enzyme structural evolution, we used a combination of comparative evolutionary genomic and comparative structural biology, exploiting the extensive metabolic and genetic characterisation of the Saccharomycotina subphylum, which contains the biotechnologically and medically relevant yeasts Saccharomyces cerevisiae and Candida albicans (Kurtzman et al, 2011; Lu et al, 2021; Opulente et al, 2024; Shen et al, 2018), as well as recent advances in computational structural biology that allow us to predict high quality protein structure at scale and across species barriers using Alphafold2 (Jumper et al, 2021). To test how the divergent metabolic properties between these species, their metabolic pathways, and the different enzyme reaction mechanisms represented in the metabolic network acted on protein structural evolution, we used AlphaFold2 to assemble structures of 11,269 enzymes from a selection of 27 species, which evolved over 400 million years (Opulente et al, 2024; Shen et al, 2018). These are clustered into 424 groups of orthologs (orthogroups) covering 361 different metabolic reactions as represented by different Enzyme Commission (EC) classifiers, participating in 224 metabolic pathways.…”