Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Mirhakkak MH

PROVIDER: S-EPMC8115155 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Mirhakkak Mohammad H MH Schäuble Sascha S Klassert Tilman E TE Brunke Sascha S Brandt Philipp P Loos Daniel D Uribe Ruben V RV Senne de Oliveira Lino Felipe F Ni Yueqiong Y Vylkova Slavena S Slevogt Hortense H Hube Bernhard B Weiss Glen J GJ Sommer Morten O A MOA Panagiotou Gianni G

The ISME journal 20201215 5

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 analysi ...[more]

PMID: 33323978

Similar Datasets

Project description:The commensal and opportunistic pathogen Candida albicans is an important cause of fungal diseases in humans, with the gastrointestinal tract being an important reservoir for its infections. The study of the mechanisms promoting the C. albicans commensal state has attracted considerable attention over the last few years, and several studies have focused on the identification of the intestinal human mycobiota and the characterization of Candida genes involved in its establishment as a commensal. In this work, we have barcoded 114 clinical C. albicans isolates to identify strains with an enhanced fitness in a murine gastrointestinal commensalism model. The 114 barcoded clinical isolates were pooled in four groups of 28 to 30 strains that were inoculated by gavage in mice previously treated with antibacterial therapy. Eight strains that either exhibited higher colonization load and/or remained in the gut after antibiotic removal were selected. The phenotypic analysis of these strains compared to an RFP-tagged SC5314 wild type strain did not reveal any specific trait associated with its increased colonization; all strains were able to filament and six of the eight strains displayed invasive growth on Spider medium. Analysis of one of these strains, CaORAL3, revealed that although mice required previous bacterial microbiota reduction with antibiotics to be able to be colonized, removal of this procedure could take place the same day (or even before) Candida inoculation. This strain was able to colonize the intestine of mice already colonized with Candida without antibiotic treatment in co-housing experiments. CaORAL3 was also able to be established as a commensal in mice previously colonized by another (CaHG43) or the same (CaORAL3) C. albicans strain. Therefore, we have identified C. albicans isolates that display higher colonization load than the standard strain SC5314 which will surely facilitate the analysis of the factors that regulate fungal colonization.

Project description:Oropharyngeal candidiasis (OPC) is the most prevalent oral infection in immunocompromised patients, primarily associated with Candida albicans. Increasing evidence points to a significant role of mucosal bacteria on the transition of C. albicans from commensal to pathogenic. In this work, we hypothesized that changes in the abundance or composition of the mucosal bacterial microbiota induced by dietary sucrose during the development of OPC can modulate C. albicans virulence. C. albicans burdens and mucosal lesions were evaluated in a mouse cortisone immunosuppression model amended with sucrose. We also analyzed the mucosal bacterial composition using 16S rRNA gene sequencing and culture methods. In immunocompetent mice, sucrose significantly increased total bacterial burdens and reduced alpha diversity, by increasing the relative abundance of mitis group streptococci. In immunocompromised mice, C. albicans infection was associated with a significantly reduced bacterial alpha diversity due to an increase in the relative abundance of enterococci. When exposed to dietary sucrose, these mice had reduced C. albicans burdens and reduced bacterial alpha diversity, associated with an increase in the relative abundance of Lactobacillus. SparCC correlation networks showed a significant negative correlation between Lactobacillus and Enterococcus in all Candida-infected mice. Depletion of lactobacilli with antibiotic treatment partially restored C. albicans burdens in mice receiving sucrose. In coculture in vitro experiments, mouse oral Lactobacillus johnsonii isolates inhibited growth of Enterococcus faecalis isolates and C. albicans. These results support the hypothesis that the sucrose-induced attenuation of C. albicans virulence was a result of changes in the mucosal bacterial microbiome characterized by a reduction in enterococci and an increase in lactobacilli. IMPORTANCE By comparing Candida albicans virulence and the mucosal bacterial composition in a mouse oral infection model, we were able to dissect the effects of the host environment (immunosuppression), infection with C. albicans, and local modulating factors (availability of sucrose as a carbon source) on the mucosal bacterial microbiome and its role on fungal virulence. We showed that changes in endogenous microbial communities in response to sucrose can lead to attenuation of fungal disease. We also showed that Lactobacillus johnsonii may curtail Candida virulence both by inhibiting its growth and by inhibiting the growth of potentially synergistic bacteria such as enterococci. Our results support the concept that Candida pathogenesis should be viewed in the contexts of both a susceptible host and a mucosal bacterial microbiota conducive to virulence.

Project description:The gut microbiome plays a key role in bile acid (BA) metabolism, where a diversity of metabolic products contribute to human health and disease. In particular, Inflammatory Bowel Disease (IBD) is characterized by a low concentration of secondary bile acids (SBAs), whose transformation from primary bile acids (PBAs) is an essential function performed solely by gut bacteria. BA-transformation activity mediated by the bile acid inducible (bai) operon has been functionally characterized in the genus Clostridium, and homologous bai gene sequences have been found in metagenome-assembled genomes (MAGs) belonging to other taxa in the human gut, but it is unclear which species of bai-carrying bacteria perform physiologically significant amounts of bile acid transformation in healthy and sick individuals. Here, we analyzed hundreds of stool samples with paired metagenomic and metabolomic data from IBD patients and controls and found that the abundance of the bai operon in metagenomic samples was highly predictive of that sample's high- or low-SBA metabolic state. We further found that bai genes from the Clostridium species best characterized as BA transformers were more prevalent in IBD patients than in non-IBD controls, while bai genes from uncharacterized taxa known only from MAGs were much more physiologically relevant in non-IBD samples. These un-isolated clades of BA-transforming bacteria merit further research; as beyond their prevalence in the human population, we found some cases in which they engrafted in IBD patients who had undergone fecal microbiota transplantation and experienced a clinical response.IMPORTANCEIn this paper, we identify specific bacteria that perform an important metabolic function in the human gut and demonstrate that in the guts of a large subset of patients with IBD, these bacteria are missing and the function is defective. This is a rare example where the correlation between the absence of specific bacteria and the dysfunction of metabolism is directly observed, not in mice nor in the lab, but in physiologic microbial communities in the human gut. Our results point to a path for studying how a small but important set of bacteria is affected by conditions in the IBD gut and perhaps to the development of interventions to mitigate the loss of these bacteria in IBD.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets