Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Cognitive analysis of metabolomics data for systems biology

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

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Kurczy, M. E. et al. Determining conserved metabolic biomarkers from a million database queries. Bioinformatics 31, 3721–3724 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Monteiro, M. S., Carvalho, M., Bastos, M. L. & Guedes de Pinho, P. Metabolomics analysis for biomarker discovery: advances and challenges. Curr. Med. Chem. 20, 257–271 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Zhang, A., Sun, H., Yan, G., Wang, P. & Wang, X. Metabolomics for biomarker discovery: moving to the clinic. Biomed. Res. Int. 2015, 354671–354671 (2015).

    PubMed  PubMed Central  Google Scholar 

  4. Zhang, F. et al. Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: a systematic review. Oncotarget 8, 35460–35472 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Taylor, J., King, R. D., Altmann, T. & Fiehn, O. Application of metabolomics to plant genotype discrimination using statistics and machine learning. Bioinformatics 18, S241–S248 (2002).

    Article  PubMed  Google Scholar 

  6. Guijas, C., Montenegro-Burke, J. R., Warth, B., Spilker, M. E. & Siuzdak, G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat. Biotechnol. 36, 316–320 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Paris, L. P. et al. Global metabolomics reveals metabolic dysregulation in ischemic retinopathy. Metabolomics 12, 15 (2016).

    Article  PubMed  Google Scholar 

  8. Goodacre, R. et al. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3, 231–241 (2007).

    Article  CAS  Google Scholar 

  9. Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Domingo-Almenara, X., Montenegro-Burke, J. R., Benton, H. P. & Siuzdak, G. Annotation: a computational solution for streamlining metabolomics analysis. Anal. Chem. 90, 480–489 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Smolinska, A. et al. Current breathomics—a review on data pre-processing techniques and machine learning in metabolomics breath analysis. J. Breath Res. 8, 027105 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Kell, D. B. Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7, 296–307 (2004).

    Article  CAS  PubMed  Google Scholar 

  13. Spasić, I. et al. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinf. 7, 281 (2006).

    Article  Google Scholar 

  14. Spasić, I. et al. Facilitating the development of controlled vocabularies for metabolomics technologies with text mining. BMC Bioinf. 9, S5 (2008).

    Article  Google Scholar 

  15. Tenopir, C., King, D. W., Christian, L. & Volentine, R. Scholarly article seeking, reading, and use: a continuing evolution from print to electronic in the sciences and social sciences. Learned Publ. 28, 93–105 (2015).

    Article  Google Scholar 

  16. Bornmann, L. & Mutz, R. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J. Assoc. Inf. Sci. Technol. 66, 2215–2222 (2015).

    Article  CAS  Google Scholar 

  17. de Solla Price, D. J. Networks of scientific papers. Science 149, 510–515 (1965).

    Article  Google Scholar 

  18. Yandell, M. D. & Majoros, W. H. Genomics and natural language processing. Nat. Rev. Genet. 3, 601–610 (2002).

    Article  CAS  PubMed  Google Scholar 

  19. Hirschberg, J. & Manning, C. D. Advances in natural language processing. Science 349, 261–266 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Chen, Y., Elenee Argentinis, J. D. & Weber, G. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Ther. 38, 688–701 (2016).

    Article  PubMed  Google Scholar 

  21. Choi, B.-K. et al. Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2. Proc. Natl. Acad. Sci. USA 115, 10666–10671 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bakkar, N. et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol. 135, 227–247 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Ivanisevic, J. et al. Toward ‘omic scale metabolite profiling: a dual separation–mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal. Chem. 85, 6876–6884 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Forsberg, E. M. et al. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online. Nat. Protoc. 13, 633–651 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhu, Z.-J. et al. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat. Protoc. 8, 451–460 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Patti, G. J., Tautenhahn, R. & Siuzdak, G. Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat. Protoc. 7, 508–516 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Domingo-Almenara, X. et al. XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules. Nat. Methods 15, 681–684 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Tautenhahn, R., Patti, G. J., Rinehart, D. & Siuzdak, G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bhinderwala, F. & Powers, R. NMR metabolomics protocols for drug discovery. Methods Mol. Biol. 2037, 265–311 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Bliziotis, N. G. et al. A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics. Metabolomics 16, 64 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Das, S., Edison, A. S. & Merz, K. M. Metabolite structure assignment using in silico NMR techniques. Anal. Chem. 92, 10412–10419 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Divaris, K. et al. The supragingival biofilm in early childhood caries: clinical and laboratory protocols and bioinformatics pipelines supporting metagenomics, metatranscriptomics, and metabolomics studies of the oral microbiome. Methods Mol. Biol. 1922, 525–548 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Erban, A. et al. Multiplexed profiling and data processing methods to identify temperature-regulated primary metabolites using gas chromatography coupled to mass spectrometry. Methods Mol. Biol. 2156, 203–239 (2020).

    Article  PubMed  Google Scholar 

  35. Palmas, F., Mussap, M. & Fattuoni, C. Urine metabolome analysis by gas chromatography-mass spectrometry (GC-MS): standardization and optimization of protocols for urea removal and short-term sample storage. Clin. Chim. Acta 485, 236–242 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Papadimitropoulos, M. P., Vasilopoulou, C. G., Maga-Nteve, C. & Klapa, M. I. Untargeted GC-MS metabolomics. Methods Mol. Biol. 1738, 133–147 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Zarate, E. et al. Fully automated trimethylsilyl (tms) derivatisation protocol for metabolite profiling by GC-MS. Metabolites 7, 1 (2016).

    Article  PubMed Central  Google Scholar 

  38. Huan, T. et al. Systems biology guided by XCMS Online metabolomics. Nat. Methods 14, 461–462 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gowda, H. et al. Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal. Chem. 86, 6931–6939 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chong, J. et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486–W494 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chong, J., Yamamoto, M. & Xia, J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites 9, 57 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  42. Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  44. O’Shea, K. & Misra, B. B. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics 16, 36 (2020).

    Article  PubMed  Google Scholar 

  45. Domingo-Almenara, X. et al. Autonomous METLIN-guided in-source fragment annotation for untargeted metabolomics. Anal. Chem. 91, 3246–3253 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).

    Article  PubMed  Google Scholar 

  47. Chong, J. & Xia, J. Using MetaboAnalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data. Methods Mol. Biol. 2104, 337–360 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Blaženović, I., Kind, T., Ji, J. & Fiehn, O. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 8, 31 (2018).

    Article  PubMed Central  Google Scholar 

  49. Gabrielson, S. W. SciFinder. JMLA 106, 588–590 (2018).

    Article  PubMed Central  Google Scholar 

  50. Yu, K.-H. et al. A cloud-based metabolite and chemical prioritization system for the biology/disease-driven Human Proteome Project. J. Proteome Res. 17, 4345–4357 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Johnston, T. H. et al. Repurposing drugs to treat l-DOPA-induced dyskinesia in Parkinson’s disease. Neuropharmacology 147, 11–27 (2018).

    Article  PubMed  Google Scholar 

  52. Warth, B. et al. Exposome-scale investigations guided by global metabolomics, pathway analysis, and cognitive computing. Anal. Chem. 89, 11505–11513 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Guijas, C. et al. Metabolic adaptation to calorie restriction. Sci. Signaling 13, eabb2490 (2020).

    Article  CAS  Google Scholar 

  54. Rinschen, M. M. et al. Metabolic rewiring of the hypertensive kidney. Sci. Signaling 12, eaax9760 (2019).

    Article  CAS  Google Scholar 

  55. Rey, F. E. et al. Metabolic niche of a prominent sulfate-reducing human gut bacterium. Proc. Natl. Acad. Sci. USA 110, 13582–13587 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Junping, Z. et al. N‐Acetyl‐cysteine alleviates gut dysbiosis and glucose metabolic disorder in high‐fat diet‐induced mice. J. Diabetes 11, 32–45 (2019).

    Article  Google Scholar 

  57. Hale, V. L. et al. Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. Methods 149, 59–68 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hyötyläinen, T. et al. Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat. Commun. 7, 8994 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Raman, M. et al. Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 11, 868–875.e863 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Scheller, R. et al. Toward mechanistic models for genotype–phenotype correlations in phenylketonuria using protein stability calculations. Hum. Mutat. 40, 444–457 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. Chen, T. et al. Mutational and phenotypic spectrum of phenylalanine hydroxylase deficiency in Zhejiang Province, China. Sci. Rep. 8, 17137 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Duan, H. et al. Non-invasive prenatal testing of pregnancies at risk for phenylketonuria. Arch. Dis. Child. Fetal Neonatal Ed. 104, F24–F29 (2019).

    Article  PubMed  Google Scholar 

  63. Zori, R. et al. Induction, titration, and maintenance dosing regimen in a phase 2 study of pegvaliase for control of blood phenylalanine in adults with phenylketonuria. Mol. Genet. Metab. 125, 217–227 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Brantley, K. D., Douglas, T. D. & Singh, R. H. One-year follow-up of B vitamin and iron status in patients with phenylketonuria provided tetrahydrobiopterin (BH4). Orphanet J. Rare Dis. 13, 192 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wikoff, W. R., Gangoiti, J. A., Barshop, B. A. & Siuzdak, G. Metabolomics identifies perturbations in human disorders of propionate metabolism. Clin. Chem. 53, 2169–2176 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Kenny, L. C. et al. Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics 1, 227 (2005).

    Article  Google Scholar 

  68. Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E. & Hsu, C.-Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N. Engl. J. Med. 351, 1296–1305 (2004).

    Article  CAS  PubMed  Google Scholar 

  69. Tuñón, J. et al. Design and rationale of a multicentre, randomised, double-blind, placebo-controlled clinical trial to evaluate the effect of vitamin D on ventricular remodelling in patients with anterior myocardial infarction: the VITamin D in Acute Myocardial Infarction (VITDAMI) trial. BMJ Open 6, e011287 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Fricke, S. Semantic Scholar. JMLA 106, 145–147 (2018).

    Article  PubMed Central  Google Scholar 

  71. Toonen, L. J. A. et al. Transcriptional profiling and biomarker identification reveal tissue specific effects of expanded ataxin-3 in a spinocerebellar ataxia type 3 mouse model. Mol. Neurodegener. 13, 31 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Roden, D. & Denny, J. Integrating electronic health record genotype and phenotype datasets to transform patient care. Clin. Pharmacol. Ther. 99, 298–305 (2016).

    Article  CAS  PubMed  Google Scholar 

  73. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M. & Kitai, T. Artificial intelligence in precision cardiovascular medicine. J. Am. Coll. Cardiol. 69, 2657–2664 (2017).

    Article  PubMed  Google Scholar 

  74. Palmblad, M. Visual and semantic enrichment of analytical chemistry literature searches by combining text mining and computational chemistry. Anal. Chem. 91, 4312–4316 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Venkatesan, A. et al. SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data [version 2; referees: 2 approved, 1 approved with reservations]. Wellcome Open Res. https://doi.org/10.12688/wellcomeopenres.10210.2 (2017).

  76. Soto, A. J., Przybyła, P. & Ananiadou, S. Thalia: semantic search engine for biomedical abstracts. Bioinformatics 35, 1799–1801 (2018).

    Article  PubMed Central  Google Scholar 

  77. Miwa, M., Thompson, P. & Ananiadou, S. Boosting automatic event extraction from the literature using domain adaptation and coreference resolution. Bioinformatics 28, 1759–1765 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Nobata, C. et al. Mining metabolites: extracting the yeast metabolome from the literature. Metabolomics 7, 94–101 (2011).

    Article  CAS  PubMed  Google Scholar 

  79. Wei, C. H., Kao, H. Y. & Lu, Z. PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res. 41, W518–W522 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Wei, C. H., Allot, A., Leaman, R. & Lu, Z. PubTator central: automated concept annotation for biomedical full text articles. Nucleic Acids Res. 47, W587–W593 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Mohimani, H. et al. Dereplication of microbial metabolites through database search of mass spectra. Nat. Commun. 9, 4035 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Lai, Z. et al. Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat. Methods 15, 53–56 (2018).

    Article  CAS  PubMed  Google Scholar 

  83. Tsugawa, H. et al. A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms. Nat. Methods 16, 295–298 (2019).

    Article  CAS  PubMed  Google Scholar 

  84. Pence, H. E. & Williams, A. ChemSpider: an online chemical information resource. J. Chem. Educ. 87, 1123–1124 (2010).

    Article  CAS  Google Scholar 

  85. Heinonen, M., Shen, H., Zamboni, N. & Rousu, J. Metabolite identification and molecular fingerprint prediction through machine learning. Bioinformatics 28, 2333–2341 (2012).

    Article  CAS  PubMed  Google Scholar 

  86. Kim, S. et al. Literature information in PubChem: associations between PubChem records and scientific articles. J. Cheminform 8, 32 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    Article  CAS  PubMed  Google Scholar 

  88. Ramirez-Gaona, M. et al. YMDB 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Res. 45, D440–D445 (2017).

    Article  CAS  PubMed  Google Scholar 

  89. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    Article  CAS  PubMed  Google Scholar 

  90. Kanehisa, M. KEGG bioinformatics resource for plant genomics and metabolomics. Methods Mol. Biol. 1374, 55–70 (2016).

    Article  CAS  PubMed  Google Scholar 

  91. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    Article  CAS  PubMed  Google Scholar 

  92. Szklarczyk, D. et al. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 44, D380–D384 (2016).

    Article  CAS  PubMed  Google Scholar 

  93. The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

    Article  Google Scholar 

  94. Adams, K. J. et al. Skyline for Small Molecules: a unifying software package for quantitative metabolomics. J. Proteome Res. 19, 1447–1458 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zukunft, S. et al. High-throughput extraction and quantification method for targeted metabolomics in murine tissues. Metabolomics 14, 18 (2018).

    Article  PubMed  Google Scholar 

  96. Yang, B., Tsui, T., Caprioli, R. M. & Norris, J. L. Sample preparation and analysis of single cells using high performance MALDI FTICR mass spectrometry. Methods Mol. Biol. 2064, 125–134 (2020).

    Article  CAS  PubMed  Google Scholar 

  97. Maia, M. et al. Metabolite extraction for high-throughput FTICR-MS-based metabolomics of grapevine leaves. EuPA Open Proteom. 12, 4–9 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Southam, A. D., Weber, R. J., Engel, J., Jones, M. R. & Viant, M. R. A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics. Nat. Protoc. 12, 310–328 (2016).

    Article  PubMed  Google Scholar 

  99. Snytnikova, O. A., Khlichkina, A. A., Sagdeev, R. Z. & Tsentalovich, Y. P. Evaluation of sample preparation protocols for quantitative NMR-based metabolomics. Metabolomics 15, 84 (2019).

    Article  PubMed  Google Scholar 

  100. Spicer, R., Salek, R. M., Moreno, P., Cañueto, D. & Steinbeck, C. Navigating freely-available software tools for metabolomics analysis. Metabolomics 13, 106 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Sansone, S.-A. et al. Metabolomics standards initiative: ontology working group work in progress. Metabolomics 3, 249–256 (2007).

    Article  CAS  Google Scholar 

  102. Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Eicher, T. et al. Metabolomics and multi-omics integration: a survey of computational methods and resources. Metabolites 10, 202 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  104. Misra, B. B. Open-source software tools, databases, and resources for single-cell and single-cell-type metabolomics. Methods Mol. Biol. 2064, 191–217 (2020).

    Article  CAS  PubMed  Google Scholar 

  105. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2017).

    Article  PubMed Central  Google Scholar 

  106. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    Article  CAS  PubMed  Google Scholar 

  107. Johnson, C. H. et al. Metabolism links bacterial biofilms and colon carcinogenesis. Cell Metab. 21, 891–897 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Beyer, B. A. et al. Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation. Nat. Chem. Biol. 14, 22 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Wolswijk, G. Oligodendrocyte precursor cells in the demyelinated multiple sclerosis spinal cord. Brain 125, 338–349 (2002).

    Article  PubMed  Google Scholar 

  110. Boelen, A. et al. Type 3 deiodinase expression in inflammatory spinal cord lesions in rat experimental autoimmune encephalomyelitis. Thyroid 19, 1401–1406 (2009).

    Article  CAS  PubMed  Google Scholar 

  111. Gallai, V. et al. Neuropeptide Y plasma levels and serum dopamine-beta-hydroxylase activity in MS patients with and without abnormal cardiovascular reflexes. Acta Neurol. Belg. 94, 44–52 (1994).

    CAS  PubMed  Google Scholar 

  112. Mann, M. B. et al. Association between the phenylethanolamine N-methyltransferase gene and multiple sclerosis. J. Neuroimmunol. 124, 101–105 (2002).

    Article  CAS  PubMed  Google Scholar 

  113. Cosentino, M. et al. Catecholamine production and tyrosine hydroxylase expression in peripheral blood mononuclear cells from multiple sclerosis patients: effect of cell stimulation and possible relevance for activation-induced apoptosis. J. Neuroimmunol. 133, 233–240 (2002).

    Article  CAS  PubMed  Google Scholar 

  114. Niland, B. et al. Cleavage of transaldolase by granzyme B causes the loss of enzymatic activity with retention of antigenicity for multiple sclerosis patients. J. Immunol. 184, 4025–4032 (2010).

    Article  CAS  PubMed  Google Scholar 

  115. Samland, A. K. & Sprenger, G. A. Transaldolase: from biochemistry to human disease. Int. J. Biochem. Cell Biol. 41, 1482–1494 (2009).

    Article  CAS  PubMed  Google Scholar 

  116. Esposito, M. et al. Human transaldolase and cross-reactive viral epitopes identified by autoantibodies of multiple sclerosis patients. J. Immunol. 163, 4027–4032 (1999).

    Article  CAS  PubMed  Google Scholar 

  117. Banki, K. et al. Oligodendrocyte-specific expression and autoantigenicity of transaldolase in multiple sclerosis. J. Exp. Med. 180, 1649–1663 (1994).

    Article  CAS  PubMed  Google Scholar 

  118. Dousset, J.-C., Trouilh, M. & Foglietti, M.-J. Plasma malonaldehyde levels during myocardial infarction. Clin. Chim. Acta 129, 319–322 (1983).

    Article  CAS  PubMed  Google Scholar 

  119. Loughrey, C. M. et al. Oxidative stress in haemodialysis. QJM 87, 679–683 (1994).

    CAS  PubMed  Google Scholar 

  120. Lim, C. S. & Vaziri, N. D. The effects of iron dextran on the oxidative stress in cardiovascular tissues of rats with chronic renal failure. Kidney Int. 65, 1802–1809 (2004).

    Article  CAS  PubMed  Google Scholar 

  121. Virella, G. & Lopes-Virella, M. F. The pathogenic role of the adaptive immune response to modified LDL in diabetes. Front. Endocrinol. (Lausanne) 3, 76 (2012).

    Article  Google Scholar 

  122. Vallejo, J., Duner, P., Fredrikson, G. N., Nilsson, J. & Bengtsson, E. Autoantibodies against aldehyde-modified collagen type IV are associated with risk of development of myocardial infarction. J. Intern. Med. 282, 496–507 (2017).

    Article  CAS  PubMed  Google Scholar 

  123. Hudson, B. G., Tryggvason, K., Sundaramoorthy, M. & Neilson, E. G. Alport’s syndrome, Goodpasture’s syndrome, and type IV collagen. N. Engl. J. Med 348, 2543–2556 (2003).

    Article  CAS  PubMed  Google Scholar 

  124. Wang, Y. et al. COL4A3 gene variants and diabetic kidney disease in MODY. Clin. J. Am. Soc. Nephrol. 13, 1162–1171 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.).

Author information

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

Authors
  1. Erica L.-W. Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth M. Billings
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Paul Benton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard L. Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amelia Palermo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Carlos Guijas
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Markus M. Rinschen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xavier Domingo-Almenara
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. J. Rafael Montenegro-Burke
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bradley A. Tagtow
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Robert S. Plumb
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gary Siuzdak
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

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

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-00455-4

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing