Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.
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. jir...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012545 |
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| author | Olga A Nev Elena Zamaraeva Romain De Oliveira Ilia Ryzhkov Lucian Duvenage Wassim Abou-Jaoudé Djomangan Adama Ouattara Jennifer Claire Hoving Ivana Gudelj Alistair J P Brown |
| author_facet | Olga A Nev Elena Zamaraeva Romain De Oliveira Ilia Ryzhkov Lucian Duvenage Wassim Abou-Jaoudé Djomangan Adama Ouattara Jennifer Claire Hoving Ivana Gudelj Alistair J P Brown |
| author_sort | Olga A Nev |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-98034779b1d34ab98454f66a8b7ecf45 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-98034779b1d34ab98454f66a8b7ecf452025-08-20T02:22:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-10-012010e101254510.1371/journal.pcbi.1012545Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.Olga A NevElena ZamaraevaRomain De OliveiraIlia RyzhkovLucian DuvenageWassim Abou-JaoudéDjomangan Adama OuattaraJennifer Claire HovingIvana GudeljAlistair J P BrownEstablishing 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.https://doi.org/10.1371/journal.pcbi.1012545 |
| spellingShingle | Olga A Nev Elena Zamaraeva Romain De Oliveira Ilia Ryzhkov Lucian Duvenage Wassim Abou-Jaoudé Djomangan Adama Ouattara Jennifer Claire Hoving Ivana Gudelj Alistair J P Brown Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium. PLoS Computational Biology |
| title | Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium. |
| title_full | Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium. |
| title_fullStr | Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium. |
| title_full_unstemmed | Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium. |
| title_short | Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium. |
| title_sort | metabolic modelling as a powerful tool to identify critical components of pneumocystis growth medium |
| url | https://doi.org/10.1371/journal.pcbi.1012545 |
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