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Volume 10
Issue 4
10.3390/metabo10040156
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Article

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

by
Ellen Kuang
1,
Matthew Marney
2,
Daniel Cuevas
3,
Robert A. Edwards
2,3,4 and
Erica M. Forsberg
1,2,3,*
1
Department of Chemistry and Biochemistry, San Diego State University, San Diego, CA 92182, USA
2
Department of Biomedical Informatics, San Diego State University, San Diego, CA 92182, USA
3
Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
4
Department of Biology, San Diego State University, San Diego, CA 92182, USA
*
Author to whom correspondence should be addressed.
Metabolites 2020, 10(4), 156; https://doi.org/10.3390/metabo10040156
Submission received: 1 March 2020 / Revised: 13 April 2020 / Accepted: 13 April 2020 / Published: 17 April 2020
(This article belongs to the Special Issue Metabolomics and Microbiota Metabolism)
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Abstract

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.
Keywords: metabolomics; flux balance analysis; multiomics; bioinformatics; mass spectrometry; microbiome metabolomics; flux balance analysis; multiomics; bioinformatics; mass spectrometry; microbiome
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MDPI and ACS Style

Kuang, E.; Marney, M.; Cuevas, D.; Edwards, R.A.; Forsberg, E.M. Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics. Metabolites 2020, 10, 156. https://doi.org/10.3390/metabo10040156

AMA Style

Kuang E, Marney M, Cuevas D, Edwards RA, Forsberg EM. Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics. Metabolites. 2020; 10(4):156. https://doi.org/10.3390/metabo10040156

Chicago/Turabian Style

Kuang, Ellen, Matthew Marney, Daniel Cuevas, Robert A. Edwards, and Erica M. Forsberg. 2020. "Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics" Metabolites 10, no. 4: 156. https://doi.org/10.3390/metabo10040156

APA Style

Kuang, E., Marney, M., Cuevas, D., Edwards, R. A., & Forsberg, E. M. (2020). Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics. Metabolites, 10(4), 156. https://doi.org/10.3390/metabo10040156

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