Recognition of galactose by a scaffold protein recruits a transcriptional activator for the GAL regulon induction in Candida albicans

CO2 potentiates echinocandin efficacy during invasive candidiasis therapy via dephosphorylation of Hsp90 by Ptc2 in condensates

Carbon dioxide is a signaling cue critical for fungal pathogenesis. Ptc2, a type 2C protein phosphatase (PP2C), serves as a conserved CO2 sensor in fungi. By combining phosphoproteomic and biochemical assays, we identified Hsp90 as a direct target of Ptc2 at host CO2 concentrations and Ssb1 as a Ptc2 target protein regardless of CO2 levels in Candida albicans, the most prevalent human fungal pathogen. Ptc2 forms reversible condensates at elevated CO2, which enables the recruitment of Hsp90, but not Ssb1, to condensates, allowing efficient dephosphorylation. This process confers an enhanced susceptibility to caspofungin in vitro and during in vivo infection therapy. Importantly, we demonstrate this phenomenon in non-albicans Candida species. Sequential passages of C. albicans in mice with caspofungin treatment readily induce in vivo drug tolerance, causing therapeutic failure. These evolved strains display increased resistance to caspofungin under host concentrations of CO2 but remain susceptible in air. Collectively, our study reveals a profound impact of host concentrations of CO2 on antifungal drug susceptibility and connects this phenotype to therapeutic outcomes and highlights condensate formation as an efficient means that enables selective recruitment of substrates for certain signaling events.

A unique metabolic gene cluster regulates lactose and galactose metabolism in the yeast Candida intermedia

Lactose assimilation is a relatively rare trait in yeasts, and Kluyveromyces yeast species have long served as model organisms for studying lactose metabolism. Meanwhile, the metabolic strategies of most other lactose-assimilating yeasts remain unknown. In this work, we have elucidated the genetic determinants of the superior lactose-growing yeast Candida intermedia. Through genomic and transcriptomic analyses, we identified three interdependent gene clusters responsible for the metabolism of lactose and its hydrolysis product galactose: the conserved LAC cluster (LAC12, LAC4) for lactose uptake and hydrolysis, the conserved GAL cluster (GAL1, GAL7, and GAL10) for galactose catabolism through the Leloir pathway, and a "GALLAC" cluster containing the transcriptional activator gene LAC9, second copies of GAL1 and GAL10, and a XYL1 gene encoding an aldose reductase involved in carbon overflow metabolism. Bioinformatic analysis suggests that the GALLAC cluster is unique to C. intermedia and has evolved through gene duplication and divergence, and deletion mutant phenotyping proved that the cluster is indispensable for C. intermedia's growth on lactose and galactose. We also show that the regulatory network in C. intermedia, governed by Lac9 and Gal1 from the GALLAC cluster, differs significantly from the galactose and lactose regulons in Saccharomyces cerevisiae, Kluyveromyces lactis, and Candida albicans. Moreover, although lactose and galactose metabolism are closely linked in C. intermedia, our results also point to important regulatory differences.IMPORTANCEThis study paves the way to a better understanding of lactose and galactose metabolism in the non-conventional yeast C. intermedia. Notably, the unique GALLAC cluster represents a new, interesting example of metabolic network rewiring and likely helps to explain how C. intermedia has evolved into an efficient lactose-assimilating yeast. With the Leloir pathway of budding yeasts acting like a model system for understanding the function, evolution, and regulation of eukaryotic metabolism, this work provides new evolutionary insights into yeast metabolic pathways and regulatory networks. In extension, the results will facilitate future development and use of C. intermedia as a cell-factory for conversion of lactose-rich whey into value-added products.

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.