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

Mohammad H Mirhakkak, Sascha Schäuble, Tilman E Klassert, Sascha Brunke, Philipp Brandt, Daniel Loos, Ruben V Uribe, Felipe Senne de Oliveira Lino, Yueqiong Ni, Slavena Vylkova, Hortense Slevogt, Bernhard Hube, Glen J Weiss, Morten Otto Alexander Sommer, Gianni Panagiotou*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
JournalISME Journal
Number of pages14
ISSN1751-7362
DOIs
Publication statusAccepted/In press - 2021

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