High-Throughput Profiling of Candida auris Isolates Reveals Clade-Specific Metabolic Differences.

Candida auris, a multidrug-resistant human fungal pathogen that causes outbreaks of invasive infections, emerged as four distinct geographical clades. Previous studies identified genomic and proteomic differences in nutrient utilization on comparison to Candida albicans, suggesting that certain metabolic features may contribute to C. auris emergence. Since no high-throughput clade-specific metabolic characterization has been described yet, we performed a phenotypic screening of C. auris strains from all 4 clades on 664 nutrients, 120 chemicals, and 24 stressors. We identified common and clade- or strain-specific responses, including the preferred utilization of various dipeptides as nitrogen source and the inability of the clade II isolate AR 0381 to withstand chemical stress. Further analysis of the metabolic properties of C. auris isolates showed robust growth on intermediates of the tricarboxylic acid cycle, such as citrate and succinic and malic acids. However, there was reduced or no growth on pyruvate, lactic acid, or acetate, likely due to the lack of the monocarboxylic acid transporter Jen1, which is conserved in most pathogenic Candida species. Comparison of C. auris and C. albicans transcriptomes of cells grown on alternative carbon sources and dipeptides as a nitrogen source revealed common as well as species-unique responses. C. auris induced a significant number of genes with no ortholog in C. albicans, e.g., genes similar to the nicotinic acid transporter TNA1 (alternative carbon sources) and to the oligopeptide transporter (OPT) family (dipeptides). Thus, C. auris possesses unique metabolic features which could have contributed to its emergence as a pathogen. IMPORTANCE Four main clades of the emerging, multidrug-resistant human pathogen Candida auris have been identified, and they differ in their susceptibilities to antifungals and disinfectants. Moreover, clade- and strain-specific metabolic differences have been identified, but a comprehensive overview of nutritional characteristics and resistance to various stressors is missing. Here, we performed high-throughput phenotypic characterization of C. auris on various nutrients, stressors, and chemicals and obtained transcriptomes of cells grown on selected nutrients. The generated data sets identified multiple clade- and strain-specific phenotypes and induction of C. auris-specific metabolic genes, showing unique metabolic properties. The presented work provides a large amount of information for further investigations that could explain the role of metabolism in emergence and pathogenicity of this multidrug-resistant fungus.

Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome.

Aspergillus fumigatus, an opportunistic human pathogen, frequently infects the lungs of people with cystic fibrosis and is one of the most common causes of infectious-disease death in immunocompromised patients. Here, we construct 252 strain-specific, genome-scale metabolic models of this important fungal pathogen to study and better understand the metabolic component of its pathogenic versatility. The models show that 23.1% of A. fumigatus metabolic reactions are not conserved across strains and are mainly associated with amino acid, nucleotide, and nitrogen metabolism. Profiles of non-conserved reactions and growth-supporting reaction fluxes are sufficient to differentiate strains, for example by environmental or clinical origin. In addition, shotgun metagenomics analysis of sputum from 40 cystic fibrosis patients (15 females, 25 males) before and after diagnosis with an A. fumigatus colonization suggests that the fungus shapes the lung microbiome towards a more beneficial fungal growth environment associated with aromatic amino acid availability and the shikimate pathway. Our findings are starting points for the development of drugs or microbiome intervention strategies targeting fungal metabolic needs for survival and colonization in the non-native environment of the human lung.

Candida albicans SR-Like Protein Kinases Regulate Different Cellular Processes: Sky1 Is Involved in Control of Ion Homeostasis, While Sky2 Is Important for Dipeptide Utilization.

Protein kinases play a crucial role in regulating cellular processes such as growth, proliferation, environmental adaptation and stress responses. Serine-arginine (SR) protein kinases are highly conserved in eukaryotes and regulate fundamental processes such as constitutive and alternative splicing, mRNA processing and ion homeostasis. The Candida albicans genome encodes two (Sky1, Sky2) and the Candida glabrata genome has one homolog (Sky1) of the human SR protein kinase 1, but their functions have not yet been investigated. We used deletion strains of the corresponding genes in both fungi to study their cellular functions. C. glabrata and C. albicans strains lacking SKY1 exhibited higher resistance to osmotic stress and toxic polyamine concentrations, similar to Saccharomyces cerevisiae sky1Δ mutants. Deletion of SKY2 in C. albicans resulted in impaired utilization of various dipeptides as the sole nitrogen source. Subsequent phosphoproteomic analysis identified the di- and tripeptide transporter Ptr22 as a potential Sky2 substrate. Sky2 seems to be involved in Ptr22 regulation since overexpression of PTR22 in the sky2Δ mutant restored the ability to grow on dipeptides and made the cells more susceptible to the dipeptide antifungals Polyoxin D and Nikkomycin Z. Altogether, our results demonstrate that C. albicans and C. glabrata Sky1 protein kinases are functionally similar to Sky1 in S. cerevisiae, whereas C. albicans Sky2, a unique kinase of the CTG clade, likely regulates dipeptide uptake via Ptr22.

Lactobacillus rhamnosus colonisation antagonizes Candida albicans by forcing metabolic adaptations that compromise pathogenicity.

Intestinal microbiota dysbiosis can initiate overgrowth of commensal Candida species - a major predisposing factor for disseminated candidiasis. Commensal bacteria such as Lactobacillus rhamnosus can antagonize Candida albicans pathogenicity. Here, we investigate the interplay between C. albicans, L. rhamnosus, and intestinal epithelial cells by integrating transcriptional and metabolic profiling, and reverse genetics. Untargeted metabolomics and in silico modelling indicate that intestinal epithelial cells foster bacterial growth metabolically, leading to bacterial production of antivirulence compounds. In addition, bacterial growth modifies the metabolic environment, including removal of C. albicans' favoured nutrient sources. This is accompanied by transcriptional and metabolic changes in C. albicans, including altered expression of virulence-related genes. Our results indicate that intestinal colonization with bacteria can antagonize C. albicans by reshaping the metabolic environment, forcing metabolic adaptations that reduce fungal pathogenicity.

Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels.

Candida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal-bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans.