Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.

Autor: Nev, Olga A., Zamaraeva, Elena, De Oliveira, Romain, Ryzhkov, Ilia, Duvenage, Lucian, Abou-Jaoudé, Wassim, Ouattara, Djomangan Adama, Hoving, Jennifer Claire, Gudelj, Ivana, Brown, Alistair J. P.
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Zdroj: PLoS Computational Biology; 10/28/2024, Vol. 20 Issue 10, p1-27, 27p
Abstrakt: Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro. Author summary: Pneumocystis jirovecii is a human pathogen that causes life-threatening pneumonia in hundreds of thousands of immunocompromised individuals each year. Neither this fungus nor its close relative, the mouse pathogen Pneumocystis murina, can be cultured in vitro, and this is significantly hindering scientific progress. Therefore, we developed a comprehensive genome-scale metabolic model for P. murina using bioinformatics, software tools, and recently published genome data, and we used this metabolic model to predict critical components required for growth of the fungus. Our findings suggest that a subset of amino acids and specific lipids are essential for Pneumocystis survival. Additionally, we employed non-classical Flux Balance Analysis and Reinforcement Learning approaches to optimise ingredients for a Pneumocystis growth medium. This novel methodology has provided new insights into Pneumocystis metabolism and offers a potential approach to overcoming the challenge of culturing this pathogen in vitro, which would accelerate progress towards the development of improved diagnostics and therapies. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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