Optimization of metabolomics of defined in vitro gut microbial ecosystems.

Wissenbach DK., Oliphant K., Rolle-Kampczyk U., Yen S., Höke H., Baumann S., Haange SB., Verdu EF., Allen-Vercoe E., von Bergen M.

The metabolic functionality of a microbial community is a key to the understanding of its inherent ecological processes and the interaction with the host. However, the study of the human gut microbiota is hindered by the complexity of this ecosystem. One way to resolve this issue is to derive defined communities that may be cultured ex vivo in bioreactor systems and used to approximate the native ecosystem. Doing so has the advantage of experimental reproducibility and ease of sampling, and furthermore, in-depth analysis of metabolic processes becomes highly accessible. Here, we review the use of bioreactor systems for ex vivo modelling of the human gut microbiota with respect to analysis of the metabolic output of the microbial ecosystem, and discuss the possibility of mechanistic insights using these combined techniques. We summarize the different platforms currently used for metabolomics and suitable for analysis of gut microbiota samples from a bioreactor system. With the help of representative datasets obtained from a series of bioreactor runs, we compare the outputs of both NMR and mass spectrometry based approaches in terms of their coverage, sensitivity and quantification. We also discuss the use of untargeted and targeted analyses in mass spectroscopy and how these techniques can be combined for optimal biological interpretation. Potential solutions for linking metabolomic and phylogenetic datasets with regards to active, key species within the ecosystem will be presented.

DOI

10.1016/j.ijmm.2016.03.007

Type

Other

Publication Date

2016-08-01T00:00:00+00:00

Volume

306

Pages

280 - 289

Total pages

9

Keywords

(1)H NMR, Bioreactor, Human gut microbiota, LC–MS/MS, Metabolic profiling, Metabolomics, Bioreactors, Ecosystem, Gastrointestinal Microbiome, Gastrointestinal Tract, Humans, Metabolomics, Microbiota, Models, Theoretical

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