Abstract

We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various ‘-omics’ readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome–microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.

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Key references using this protocol

Pedersen, H. K. et al. Nature 535, 376–381 (2016): https://doi.org/10.1038/nature18646

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Acknowledgements

This research received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013): MetaHIT, grant agreement HEALTH-F4-2007-201052 and MetaCardis, grant agreement HEALTH-2012-305312. The Department of Bio and Health Informatics, Technical University of Denmark, and the Novo Nordisk Foundation Center for Basic Metabolic Research have in addition received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), the resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. The Novo Nordisk Foundation Center for Protein Research received funding from the Novo Nordisk Foundation (grant agreement NNF14CC0001). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (http://www.metabol.ku.dk). A.Ø.P. received funding from the Lundbeck Foundation (grant R218-2016-1367) and S.D.E. received funding from Agence Nationale de la Recherche MetaGenoPolis grant ‘Investissements d’avenir’ ANR-11-DPBS-0001.

Author information

Author notes

  1. These authors contributed equally: Helle Krogh Pedersen, Sofia K. Forslund

Affiliations

  1. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Helle Krogh Pedersen
    • , Trine Nielsen
    • , Torben Hansen
    •  & Oluf Pedersen
  2. Experimental and Clinical Research Centre, a joint center of Max Delbrück Centre for Molecular Medicine & Charité University Hospital, Berlin, Germany

    • Sofia K. Forslund
  3. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    • Sofia K. Forslund
    • , Falk Hildebrand
    •  & Peer Bork
  4. Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark

    • Valborg Gudmundsdottir
    • , Anders Østergaard Petersen
    •  & Søren Brunak
  5. MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden

    • Tuulia Hyötyläinen
  6. MetaGénoPolis (MGP), INRA, Université Paris-Saclay, Jouy-en-Josas, France

    • S. Dusko Ehrlich
  7. Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, King’s College London, London, UK

    • S. Dusko Ehrlich
  8. Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Søren Brunak
  9. School of Medical Sciences, Örebro University, Örebro, Sweden

    • Matej Oresic
  10. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland

    • Matej Oresic
  11. Clinical Microbiomics A/S, Copenhagen, Denmark

    • Henrik Bjørn Nielsen

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Contributions

The protocol was written by S.K.F., H.K.P., V.G., A.Ø.P., F.H., T. Hyötyläinen., T.N. and H.B.N., together with T. Hansen, S.D.E., S.B., M.O., P.B. and O.P., with reusable code and example data compiled and tested by H.K.P. and V.G.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Oluf Pedersen.

Integrated supplementary information

  1. Supplementary Figure 1 Composition and concentration range of lipids and polar metabolites detected in the human serum samples in the MetaHIT cohort.

    The polar metabolites include both fully identified metabolites as well as metabolites identified only at the compound class level (n=778) while the lipids include only fully identified such (n=287).

Supplementary information

  1. Supplementary Figure 1

    Supplementary Figure 1 and Supplementary Methods

  2. Supplementary Table 1

  3. Supplementary Data

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DOI

https://doi.org/10.1038/s41596-018-0064-z

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