scholarly journals An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

2021 ◽  
Author(s):  
Dawson D. Payne ◽  
Alina Renz ◽  
Laura J. Dunphy ◽  
Taylor Lewis ◽  
Andreas Dräger ◽  
...  

AbstractMucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also providing a novel insight about an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing novel biological insights.

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Dawson D. Payne ◽  
Alina Renz ◽  
Laura J. Dunphy ◽  
Taylor Lewis ◽  
Andreas Dräger ◽  
...  

AbstractMucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.


2016 ◽  
Vol 85 (2) ◽  
pp. 289-304 ◽  
Author(s):  
Huili Yuan ◽  
C.Y. Maurice Cheung ◽  
Mark G. Poolman ◽  
Peter A. J. Hilbers ◽  
Natal A. W. Riel

2013 ◽  
Vol 46 (31) ◽  
pp. 131-136
Author(s):  
Carla Portela ◽  
Silas Villas-Bôas ◽  
Isabel Rocha ◽  
Eugénio C. Ferreira

2020 ◽  
Vol 14 (7) ◽  
pp. e0007871
Author(s):  
Khushboo Borah ◽  
Jacque-Lucca Kearney ◽  
Ruma Banerjee ◽  
Pankaj Vats ◽  
Huihai Wu ◽  
...  

2019 ◽  
Author(s):  
Thomas J. Moutinho ◽  
Benjamin C. Neubert ◽  
Matthew L. Jenior ◽  
Maureen A. Carey ◽  
Gregory L. Medlock ◽  
...  

AbstractMembers of the Lactobacillus genus are frequently utilized in the probiotic industry with many species conferring demonstrated health benefits; however, these effects are largely strain-dependent. We designed a method called PROTEAN (Probabilistic Reconstruction Of constituent Anabolic Networks) to computationally analyze the genomic annotations and predicted metabolic production capabilities of 144 strains across 16 species of Lactobacillus isolated from human intestinal, oral, and vaginal body sites. Using PROTEAN we conducted a genome-scale metabolic network comparison between strains, revealing that metabolic capabilities differ by isolation site. Notably, PROTEAN does not require a well-curated genome-scale metabolic network reconstruction to provide biological insights. We found that predicted metabolic capabilities of lactobacilli isolated from the vaginal microbiota cluster separately from intestinal and oral isolates, and we also uncovered an overlap in the predicted metabolic production capabilities of intestinal and oral isolates. Using machine learning, we determined the most informative metabolic products driving the difference between predicted metabolic capabilities of intestinal, oral, and vaginal isolates. Notably, intestinal and oral isolates were predicted to have a higher likelihood of producing D-alanine, D/L-serine, and L-proline, while the vaginal isolates were distinguished by a higher predicted likelihood of producing L-arginine, citrulline, and D/L-lactate. We found the distinguishing products to be consistent with published experimental literature. This study showcases a systematic technique, PROTEAN, for comparing the predicted functional metabolic output of microbes using genome-scale metabolic network analysis and computational modeling and provides unique insight into human-associated Lactobacillus biology.ImportanceThe Lactobacillus genus has been shown to be important for human health. Lactobacilli have been isolated from human intestinal, oral, and vaginal sites. Members of the genus contribute significantly to the maintenance of vaginal health by providing colonization resistance to invading pathogens. A wide variety of clinical studies have indicated that Lactobacillus-based probiotics confer health benefits for several gut- and immune-associated diseases. Microbes interact with the human body in several ways, including the production of metabolites that influence physiology or other surrounding microbes. We have conducted a strain-level genome-scale metabolic network reconstruction analysis of human-associated Lactobacillus strains, revealing that predicted metabolic capabilities differ when comparing intestinal/oral isolate to vaginal isolates. The technique we present here allows for direct interpretation of discriminating features between the experimental groups.


2011 ◽  
Vol 5 (1) ◽  
pp. 163 ◽  
Author(s):  
Pep Charusanti ◽  
Sadhana Chauhan ◽  
Kathleen McAteer ◽  
Joshua A Lerman ◽  
Daniel R Hyduke ◽  
...  

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