Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics
| Autor: | Erica M. Forsberg, Ellen Kuang, Matthew W. Marney, Robert Edwards, Daniel A. Cuevas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Endocrinology Diabetes and Metabolism Metabolite Microbial metabolism flux balance analysis lcsh:QR1-502 microbiome Genomics Computational biology Mass spectrometry 01 natural sciences Biochemistry Article lcsh:Microbiology 03 medical and health sciences chemistry.chemical_compound Metabolomics Molecular Biology mass spectrometry 010401 analytical chemistry bioinformatics metabolomics 0104 chemical sciences Flux balance analysis Mass 030104 developmental biology chemistry Flux (metabolism) multiomics |
| Zdroj: | Metabolites, Vol 10, Iss 156, p 156 (2020) Metabolites Volume 10 Issue 4 |
| ISSN: | 2218-1989 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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