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Towards predicting the environmental metabolome from metagenomics with a mechanistic model


The environmental metabolome and metabolic potential of microorganisms are dominant and essential factors shaping microbial community composition. Recent advances in genome annotation and systems biology now allow us to semiautomatically reconstruct genome-scale metabolic models (GSMMs) of microorganisms based on their genome sequence1. Next, growth of these models in a defined metabolic environment can be predicted in silico, mechanistically linking the metabolic fluxes of individual microbial populations to the community dynamics. A major advantage of GSMMs is that no training data is needed, besides information about the metabolic capacity of individual genes (genome annotation) and knowledge of the available environmental metabolites that allow the microorganism to grow. However, the composition of the environment is often not fully determined and remains difficult to measure2. We hypothesized that the relative abundance of different bacterial species, as measured by metagenomics, can be combined with GSMMs of individual bacteria to reveal the metabolic status of a given biome. Using a newly developed algorithm involving over 1,500 GSMMs of human-associated bacteria, we inferred distinct metabolomes for four human body sites that are consistent with experimental data. Together, we link the metagenome to the metabolome in a mechanistic framework towards predictive microbiome modelling.

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We thank M. Kooyman (SURFsara) for help implementing MAMBO on the Netherlands Life Science Grid, C.R. Berkers (Utrecht University) for insights regarding the annotation of untargeted metabolome datasets and the CMBI Comics Group for fruitful discussions. D.R.G. is supported by the Science Without Borders program of CNPQ/BRASIL. B.E.D. is supported by Netherlands Organization for Scientific Research (NWO) Vidi grant 864.14.004.

Author information

D.R.G. created the algorithm and performed the experiments. All authors devised the study and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Bas E. Dutilh.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–3, Supplementary Table 1, Supplementary References and Supplementary Table 2–3 legends.

  2. Life Sciences Reporting Summary

  3. Supplementary Table 2

    Metabolomic profiles predicted by MAMBO and genes-only approach based on 37 oral, 50 skin, 39 stool and 49 vaginal metagenomes, and 6 experimentally measured metabolomic profiles. Values are normalised predicted abundances.

  4. Supplementary Table 3

    Pearson correlations between 6 measured metabolomic profiles and 175 predicted metabolomic profiles by MAMBO and genes-only approach. Correlations are only shown if >5 metabolites of the predicted metabolites were measured and vice versa.

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Further reading

Fig. 1: Overview of the MAMBO algorithm.
Fig. 2: A typical optimization of a randomly chosen metagenome (buccal mucosa sample SRS058186).
Fig. 3: Principal component analysis of predicted metabolomes.
Fig. 4: Pearson correlations between 175 metabolomes from four body sites predicted by MAMBO and six experimentally measured metabolomes from literature.