Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

ABSTRACT Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF l...

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Autores principales:	Michael A. Henson, Giulia Orazi, Poonam Phalak, George A. O’Toole
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	community metabolism cystic fibrosis metabolic modeling metabolite cross-feeding Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/431ff31f70774e2f88f392ed549e98b4
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spelling	oai:doaj.org-article:431ff31f70774e2f88f392ed549e98b42021-12-02T19:46:17ZMetabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance10.1128/mSystems.00026-192379-5077https://doaj.org/article/431ff31f70774e2f88f392ed549e98b42019-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00026-19https://doaj.org/toc/2379-5077ABSTRACT Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy. IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.Michael A. HensonGiulia OraziPoonam PhalakGeorge A. O’TooleAmerican Society for Microbiologyarticlecommunity metabolismcystic fibrosismetabolic modelingmetabolite cross-feedingMicrobiologyQR1-502ENmSystems, Vol 4, Iss 2 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	community metabolism cystic fibrosis metabolic modeling metabolite cross-feeding Microbiology QR1-502
spellingShingle	community metabolism cystic fibrosis metabolic modeling metabolite cross-feeding Microbiology QR1-502 Michael A. Henson Giulia Orazi Poonam Phalak George A. O’Toole Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
description	ABSTRACT Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy. IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.
format	article
author	Michael A. Henson Giulia Orazi Poonam Phalak George A. O’Toole
author_facet	Michael A. Henson Giulia Orazi Poonam Phalak George A. O’Toole
author_sort	Michael A. Henson
title	Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
title_short	Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
title_full	Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
title_fullStr	Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
title_full_unstemmed	Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
title_sort	metabolic modeling of cystic fibrosis airway communities predicts mechanisms of pathogen dominance
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/431ff31f70774e2f88f392ed549e98b4
work_keys_str_mv	AT michaelahenson metabolicmodelingofcysticfibrosisairwaycommunitiespredictsmechanismsofpathogendominance AT giuliaorazi metabolicmodelingofcysticfibrosisairwaycommunitiespredictsmechanismsofpathogendominance AT poonamphalak metabolicmodelingofcysticfibrosisairwaycommunitiespredictsmechanismsofpathogendominance AT georgeaotoole metabolicmodelingofcysticfibrosisairwaycommunitiespredictsmechanismsofpathogendominance
_version_	1718376021417263104

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

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