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Cognitive analysis of metabolomics data for systems biology

Nature Protocols volume 16, pages 1376–1418 (2021)Cite this article

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Abstract

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

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Fig. 1: Framework of computational literature search tool components for cognitive and conventional searches.
Fig. 2: Overview of workflows incorporating AI-based natural language processing into metabolomics data analysis and interpretation pipelines.
Fig. 3: Literature-assisted metabolite identification.
Fig. 4: Metabolome-level disease comparisons for biomarker discovery.
Fig. 5: Metabolome-level disease comparisons for drug repurposing.
Fig. 6: Literature contextualization of metabolites using Microsoft Academic.
Fig. 7: Literature contextualization of metabolites using IBM WDD Explore an Entity.
Fig. 8: Literature contextualization of metabolites using SciFinder.
Fig. 9: Literature contextualization of metabolites using Semantic Scholar.
Fig. 10: Ranking top related metabolites for diseases with HUPO B/D-HPP.
Fig. 11: Ranking top related metabolites for diseases with IBM WDD.
Fig. 12: Co-occurrence of metabolites in literature with IBM WDD.
Fig. 13: Metabolome-level comparison of diseases for drug repurposing with IBM WDD.
Fig. 14: Metabolite prioritization similarity tree.
Fig. 15: Determination of function of dysregulated metabolic pathway in disease state.

Data availability

The datasets analyzed during the current study that were not generated by the authors but mined from public sources are available in the MetaboLights repository (MTBLS298), the Human Metabolome Database (https://hmdb.ca/unearth/q?utf8=%E2%9C%93&query=NAFLD&searcher=diseases&button=), or the main text and Supplementary Information, or upon request of the author of the following publications: ‘Metabolomics identifies perturbations in human disorders of propionate metabolism’ (https://doi.org/10.1373/clinchem.2007.089011), ‘Metabolism links bacterial biofilms and colon carcinogenesis’ (https://doi.org/10.1016/j.cmet.2015.04.011) and ‘Systems biology guided by XCMS Online metabolomics’ (https://doi.org/10.1038/nmeth.4260). Additional data generated by the authors or analyzed during this study are included in this published article and its Supplementary Information files.

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Acknowledgements

We acknowledge the use of cloud computing credits from the National Institutes of Health. M.M.R. was supported by a fellowship from the Deutsche Forschungsgemeinschaft (DFG; RI2811/1-1). This research was partially funded by US National Institutes of Health grants R35 GM130385 (G.S.), P30 MH062261 (G.S.), P01 DA026146 (G.S.) and U01 CA235493 (G.S.) and by Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory for the US Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract number DE-AC02-05CH11231 (G.S.).

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Author notes

  1. These authors contributed equally: Erica L.-W. Majumder, Elizabeth M. Billings.

Authors and Affiliations

  1. Center for Mass Spectrometry and Metabolomics, The Scripps Research Institute, La Jolla, CA, USA

    Erica L.-W. Majumder, Elizabeth M. Billings, H. Paul Benton, Amelia Palermo, Carlos Guijas, Markus M. Rinschen, Xavier Domingo-Almenara, J. Rafael Montenegro-Burke & Gary Siuzdak

  2. IBM Watson Health, Cambridge, MA, USA

    Richard L. Martin & Bradley A. Tagtow

  3. Waters Corporation, Milford, MA, USA

    Robert S. Plumb

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Contributions

E.L.-W.M. and E.M.B. led the protocol development and wrote the manuscript. H.P.B., A.P., C.G., M.M.R., X.D.-A. and J.R.M.-B. contributed ideas and data, tested the protocol and edited the manuscript. R.L.M. and B.A.T. assisted in protocol development and data analysis. R.S.P. and G.S. contributed ideas and edited the manuscript.

Corresponding author

Correspondence to Gary Siuzdak.

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Competing interests

Our initial interactions with IBM motivated these efforts; however, the technologies described herein are largely (>99%) independent of IBM.

Additional information

Peer review information Nature Protocols thanks Jianxin Chen, Alisdair Fernie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Warth, B. et al. Anal. Chem. 89, 11505–11513 (2017): https://pubs.acs.org/doi/abs/10.1021/acs.analchem.7b02759

Rinschen, M. M. et al. Sci. Signal. 12, eaax9760 (2019): https://stke.sciencemag.org/content/12/611/eaax9760.abstract

Guijas, C. et al. Sci. Signal. 13, eabb2490 (2020): https://stke.sciencemag.org/content/13/648/eabb2490.full

Rinschen, M. M. et al. Nat. Rev. Mol. Cell Biol. 20, 353–367 (2019): https://www.nature.com/articles/s41580-019-0108-4

Domingo-Almenara, X. et al. Nat. Commun. 10, 5811 (2019): https://www.nature.com/articles/s41467-019-13680-7

Key data used in this protocol

Wikoff, W. R., Gangoiti, J. A., Barshop, B. A., & Siuzdak, G. Clin. Chem. 53, 2169–2176 (2007): https://academic.oup.com/clinchem/article/53/12/2169/5627367

Hyötyläinen, T. et al. Nat. Commun. 7, 8994 (2016): https://www.nature.com/articles/ncomms9994

Johnson, C. H. et al. Cell Metab. 21, 891–897 (2015): https://www.sciencedirect.com/science/article/pii/S1550413115001667

Huan, T. et al. Nat. Methods 14, 461–462 (2017): https://www.nature.com/articles/nmeth.4260

Supplementary information

Supplementary Information

Supplementary Procedures 1 and 2, Instructions for XCMS Systems Biology, Supplementary Tables 1–12 and Supplementary Figs. 1–14

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Majumder, E.LW., Billings, E.M., Benton, H.P. et al. Cognitive analysis of metabolomics data for systems biology. Nat Protoc 16, 1376–1418 (2021). https://doi.org/10.1038/s41596-020-00455-4

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