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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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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.

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  1. Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Marcobal, A. et al. A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice. ISME J. 7, 1933–1943 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: A phylogenetic perspective. Science 350, aac9323 (2015).

    Article  PubMed  Google Scholar 

  4. Tjalsma, H., Boleij, A., Marchesi, J. R. & Dutilh, B. E. A bacterial driver-passenger model for colorectal cancer: beyond the usual suspects. Nat. Rev. Microbiol. 10, 575–582 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Smith, M. B. Natural bacterial communities serve as quantitative geochemical biosensors. mBio 6, e00326-15 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Merrifield, C. A. et al. Neonatal environment exerts a sustained influence on the development of the intestinal microbiota and metabolic phenotype. ISME J. 10, 145–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Adams, R. I., Bateman, A. C., Bik, H. M. & Meadow, J. F. Microbiota of the indoor environment: a meta-analysis. Microbiome 3, 49 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Garza, D. R. & Dutilh, B. E. From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cell. Mol. Life Sci. 72, 4287–4308 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Larsen, P. E. et al. Predicted Relative Metabolomic Turnover aPRMTa: determining metabolic turnover from a coastal marine metagenomic dataset. Microb. Inform. Exp. 1, 4 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Levy, R. & Borenstein, E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl Acad. Sci. USA 110, 12804–12809 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hanson, N. W. et al. Metabolic pathways for the whole community. BMC Genom. 15, 619 (2014).

    Article  Google Scholar 

  13. Silva, G. G. Z., Green, K. T., Dutilh, B. E. & Edwards, R. A. SUPER-FOCUS: a tool for agile functional analysis of shotgun metagenomic data. Bioinformatics 32, 354–361 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  Google Scholar 

  16. Martin, J. et al. Optimizing read mapping to reference genomes to determine composition and species prevalence in microbial communities. PLoS ONE 7, e36427 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kortman, G. A. M. et al. Microbial metabolism shifts towards an adverse profile with supplementary iron in the TIM-2 in vitro model of the human colon. Microb. Physiol. Metab. 6, 1481 (2015).

    Google Scholar 

  18. Vitali, B. Vaginal microbiome and metabolome highlight specific signatures of bacterial vaginosis. Eur. J. Clin. Microbiol. 34, 2367–2376 (2015).

    Article  CAS  Google Scholar 

  19. Wishart, D. S. et al. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Gao, X., Pujos-Guillot, E. & Sébédio, J.-L. Development of a quantitative metabolomic approach to study clinical human fecal water metabolome based on trimethylsilylation derivatization and GC/MS analysis. Anal. Chem. 82, 6447–6456 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Tsuruoka, M. et al. Capillary electrophoresis-mass spectrometry-based metabolome analysis of serum and saliva from neurodegenerative dementia patients. Electrophoresis 34, 2865–2872 (2013).

    CAS  PubMed  Google Scholar 

  22. Sugimoto, M. et al. Physiological and environmental parameters associated with mass spectrometry-based salivary metabolomic profiles. Metabolomics 9, 454–463 (2013).

    Article  CAS  Google Scholar 

  23. Dame, Z. T. et al. The human saliva metabolome. Metabolomics 11, 1864–1883 (2015).

    Article  CAS  Google Scholar 

  24. Bouslimani, A. et al. Molecular cartography of the human skin surface in 3D. Proc. Natl Acad. Sci. USA 112, 2120–2129 (2015).

    Article  Google Scholar 

  25. Larsen, P. E. & Dai, Y. Metabolome of human gut microbiome is predictive of host dysbiosis. GigaScience 4, 42 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).

    Article  PubMed  Google Scholar 

  27. Reed, J. L. in The Chemistry of Microbiomes (National Academies of Sciences, Engineering and Medicine) Chap. 12 (National Academies Press, Washington, DC, 2017);

  28. Rodriguez-Valera, F. et al. Explaining microbial population genomics through phage predation. Nat. Rev. Microbiol. 7, 828–836 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wishart, D. S. Advances in metabolite identification. Bioanalysis 3, 1769–1782 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 11, 395 (2010).

    Article  Google Scholar 

  33. Zhou, J. & Yin, Y. Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry. Analyst 141, 6362–6373 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Ebrahim, A., Lerman, J. A., Palsson, B. O. & Hyduke, D. R. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst. Biol. 7, 74 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hastings, W. K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109 (1970).

    Article  Google Scholar 

  36. Barbu, V. S. & Limnios, N. Semi-Markov Chains and Hidden Semi-Markov Models toward Applications 1st edn, Vol 191 (Springer-Verlag, New York, 2008).

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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.

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

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Correspondence to Bas E. Dutilh.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

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

Life Sciences Reporting Summary

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.

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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Garza, D.R., van Verk, M.C., Huynen, M.A. et al. Towards predicting the environmental metabolome from metagenomics with a mechanistic model. Nat Microbiol 3, 456–460 (2018).

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