GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

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.

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GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

10.1101/819508 ◽

2019 ◽

Author(s):

Khushboo Borah ◽

Jacque-Lucca Kearney ◽

Ruma Banerjee ◽

Pankaj Vats ◽

Huihai Wu ◽

...

Keyword(s):

Amino Acids ◽

Metabolic Network ◽

In Silico ◽

Metabolic Pathways ◽

Mycobacterium Leprae ◽

Human Pathogen ◽

Intracellular Growth ◽

A Genome ◽

Genome Scale

AbstractLeprosy, caused by Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen’s obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis (DPA) to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.Author SummaryMycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth.

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A genome-scale metabolic network reconstruction of extremely halophilic bacterium Salinibacter ruber

PLoS ONE ◽

10.1371/journal.pone.0216336 ◽

2019 ◽

Vol 14 (5) ◽

pp. e0216336 ◽

Cited By ~ 1

Author(s):

Maryam Bagheri ◽

Sayed-Amir Marashi ◽

Mohammad Ali Amoozegar

Keyword(s):

Metabolic Network ◽

Halophilic Bacterium ◽

Network Reconstruction ◽

Metabolic Network Reconstruction ◽

A Genome ◽

Genome Scale

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True and Illusory Benefits of Modeling: Comment on “Genome-Scale Metabolic Network Reconstruction and In Silico Analysis of Hexanoic Acid Producing Megasphaera elsdenii. Microorganisms 2020, 8, 539”

Microorganisms ◽

10.3390/microorganisms8111742 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1742

Author(s):

Nicolai S. Panikov

Keyword(s):

Metabolic Network ◽

In Silico ◽

In Silico Analysis ◽

Network Reconstruction ◽

Hexanoic Acid ◽

Metabolic Network Reconstruction ◽

Megasphaera Elsdenii ◽

Genome Scale ◽

Silico Analysis

Lee et al [...]

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The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of N-fixing versus C-fixing cells in a diazotrophic cyanobacterium, Trichodesmium erythraeum

BMC Systems Biology ◽

10.1186/s12918-016-0383-z ◽

2017 ◽

Vol 11 (1) ◽

Cited By ~ 5

Author(s):

Joseph J. Gardner ◽

Nanette R. Boyle

Keyword(s):

Metabolic Network ◽

Equilibrium Composition ◽

Network Reconstruction ◽

Metabolic Network Reconstruction ◽

Trichodesmium Erythraeum ◽

Genome Scale

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

npj Systems Biology and Applications ◽

10.1038/s41540-021-00198-2 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Dawson D. Payne ◽

Alina Renz ◽

Laura J. Dunphy ◽

Taylor Lewis ◽

Andreas Dräger ◽

...

Keyword(s):

Pseudomonas Aeruginosa ◽

Metabolic Network ◽

Infection Prevention ◽

Network Reconstruction ◽

The Body ◽

Carbon Source Utilization ◽

Metabolic Network Reconstruction ◽

Propionate Metabolism ◽

Context Specific ◽

Genome Scale

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.

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