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A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links

Nature Protocols volume 13, pages 2781–2800 (2018)Cite this article

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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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Fig. 1: Overview of the protocol workflow integrating human phenotype, serum metabolome and gut microbiome data.
Fig. 2: Schematic illustration of the leave-one-MGS-out approach in the driver-species analysis.
Fig. 3: Sample plot produced by the Procedure in Step 15.
Fig. 4: Sample plot produced by the Procedure in Step 19 for the leave-one-MGS-out analysis, here shown for the combined BCAA–biosynthesis module (KEGG modules M00019, M00570, M00535 and M00432; together constituting 13 KOs).

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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.

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Author notes

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

Authors and 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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  1. Helle Krogh Pedersen
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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.

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Correspondence to Oluf Pedersen.

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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

Integrated supplementary information

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

Supplementary Figure 1

Supplementary Figure 1 and Supplementary Methods

Supplementary Table 1

Supplementary Data

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Pedersen, H.K., Forslund, S.K., Gudmundsdottir, V. et al. A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nat Protoc 13, 2781–2800 (2018). https://doi.org/10.1038/s41596-018-0064-z

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