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  • Review Article
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Mixing omics: combining genetics and metabolomics to study rheumatic diseases

Nature Reviews Rheumatology volume 13, pages 174–181 (2017)Cite this article

Subjects

Key Points

  • Although large-scale genome-wide association studies have identified many rheumatic disease-associated genetic variants, effect sizes are small (with the exception of HLA loci)

  • Metabolomics is a promising field for investigating the molecular pathogenesis of rheumatic diseases and for monitoring response to therapy, but only a few modest-sized studies have been conducted to date

  • The contribution made by genetic factors to the production of metabolites and to disease processes in general needs to be investigated if metabolomics studies are to reach their full potential

  • Integrating data from different omics studies, including metagenomics and proteomics, will help to increase our knowledge of pathways and diseases

Abstract

Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

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Figure 1: Metabolic profiling as a tool for studying rheumatic diseases.
Figure 2: Targeted versus untargeted metabolomics approaches.
Figure 3: Statistical approaches for the analysis of metabolomic data.

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References

  1. Nicholson, J. K. & Lindon, J. C. Systems biology: metabonomics. Nature 455, 1054–1056 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Tektonidou, M. G. & Ward, M. M. Validity of clinical associations of biomarkers in translational research studies: the case of systemic autoimmune diseases. Arthritis Res. Ther. 12, R179 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kapoor, S. R. et al. Metabolic profiling predicts response to anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis. Arthritis Rheum. 65, 1448–1456 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Suhre, K. et al. A genome-wide association study of metabolic traits in human urine. Nat. Genet. 43, 565–569 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Illig, T. et al. A genome-wide perspective of genetic variation in human metabolism. Nat. Genet. 42, 137–141 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rhee, E. P. et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 18, 130–143 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269–276 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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 

  11. Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872–881 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Larive, C. K., Barding, G. A. Jr & Dinges, M. M. NMR spectroscopy for metabolomics and metabolic profiling. Anal. Chem. 87, 133–146 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Patti, G. J., Yanes, O. & Siuzdak, G. Innovation: metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell. Biol. 13, 263–269 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lewis, G. D., Asnani, A. & Gerszten, R. E. Application of metabolomics to cardiovascular biomarker and pathway discovery. J. Am. Coll. Cardiol. 52, 117–123 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Armstrong, A. W. et al. Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis. F1000Res. 3, 248 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zabek, A. et al. Application of 1H NMR-based serum metabolomic studies for monitoring female patients with rheumatoid arthritis. J. Pharm. Biomed. Anal. 117, 544–550 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Loeser, R. F. et al. Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: an exploratory study. Osteoarthritis Cartilage 24, 1479–1486 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang, W. et al. Metabolomic analysis of human plasma reveals that arginine is depleted in knee osteoarthritis patients. Osteoarthritis Cartilage 24, 827–834 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Menni, C. et al. Metabolomic identification of a novel pathway of blood pressure regulation involving hexadecanedioate. Hypertension 66, 422–429 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Menni, C. et al. Circulating levels of antioxidant vitamins correlate with better lung function and reduced exposure to ambient pollution. Am. J. Respir. Crit. Care Med. 191, 1203–1207 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Krumsiek, J. et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8, e1003005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Malet-Martino, M. & Holzgrabe, U. NMR techniques in biomedical and pharmaceutical analysis. J. Pharm. Biomed. Anal. 55, 1–15 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Griffiths, W. J. et al. Targeted metabolomics for biomarker discovery. Angew. Chem. Int. Ed. 49, 5426–5445 (2010).

    Article  CAS  Google Scholar 

  24. Koal, T. & Deigner, H. P. Challenges in mass spectrometry based targeted metabolomics. Curr. Mol. Med. 10, 216–226 (2010).

    Article  CAS  PubMed  Google Scholar 

  25. Guma, M., Tiziani, S. & Firestein, G. S. Metabolomics in rheumatic diseases: desperately seeking biomarkers. Nat. Rev. Rheumatol. 12, 269–281 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Marion, D. An introduction to biological NMR spectroscopy. Mol. Cell. Proteomics 12, 3006–3025 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Soininen, P., Kangas, A. J., Wurtz, P., Suna, T. & Ala-Korpela, M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ. Cardiovasc. Genet. 8, 192–206 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Markley, J. L. et al. The future of NMR-based metabolomics. Curr. Opin. Biotechnol. 43, 34–40 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fan, T. W. & Lane, A. N. Applications of NMR spectroscopy to systems biochemistry. Prog. Nucl. Magn. Reson. Spectrosc. 92–93, 18–53 (2016).

  30. Lei, Z., Huhman, D. V. & Sumner, L. W. Mass spectrometry strategies in metabolomics. J. Biol. Chem. 286, 25435–25442 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Feng, X., Liu, X., Luo, Q. & Liu, B. F. Mass spectrometry in systems biology: an overview. Mass Spectrom. Rev. 27, 635–660 (2008).

    Article  PubMed  Google Scholar 

  32. Kind, T. et al. FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81, 10038–10048 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sellick, C. A. et al. Evaluation of extraction processes for intracellular metabolite profiling of mammalian cells: matching extraction approaches to cell type and metabolite targets. Metabolomics 6, 427–438 (2010).

    Article  CAS  Google Scholar 

  34. Xi, B., Gu, H., Baniasadi, H. & Raftery, D. Statistical analysis and modeling of mass spectrometry-based metabolomics data. Methods Mol. Biol. 1198, 333–353 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Madsen, R., Lundstedt, T. & Trygg, J. Chemometrics in metabolomics — a review in human disease diagnosis. Anal. Chim. Acta 659, 23–33 (2010).

    Article  CAS  PubMed  Google Scholar 

  36. Nikolic, S. B., Sharman, J. E., Adams, M. J. & Edwards, L. M. Metabolomics in hypertension. J. Hypertens. 32, 1159–1169 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Manoli, T. et al. Group testing for pathway analysis improves comparability of different microarray datasets. Bioinformatics 22, 2500–2506 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Draghici, S., Khatri, P., Martins, R. P., Ostermeier, G. C. & Krawetz, S. A. Global functional profiling of gene expression. Genomics 81, 98–104 (2003).

    Article  CAS  PubMed  Google Scholar 

  39. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Kramer, A., Green, J., Pollard, J. Jr & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Wheeler, D. L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 36, D13–D21 (2008).

    Article  CAS  PubMed  Google Scholar 

  42. Kanehisa, M. et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354–D357 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 44, D471–D480 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Hastings, J. et al. ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res. 44, D1214–D1219 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Kale, N. S. et al. MetaboLights: an open-access database repository for metabolomics data. Curr. Protoc. Bioinformatics http://dx.doi.org/10.1002/0471250953.bi1413s53 (2016).

  46. Yan, B. et al. Urinary metabolomic study of systemic lupus erythematosus based on gas chromatography/mass spectrometry. Biomed. Chromatogr. 30, 1877–1881 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Madsen, R. K. et al. Diagnostic properties of metabolic perturbations in rheumatoid arthritis. Arthritis Res. Ther. 13, R19 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zhai, G. et al. Serum branched-chain amino acid to histidine ratio: a novel metabolomic biomarker of knee osteoarthritis. Ann. Rheum. Dis. 69, 1227–1231 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Young, S. P. et al. The impact of inflammation on metabolomic profiles in patients with arthritis. Arthritis Rheum. 65, 2015–2023 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wu, T. et al. Metabolic disturbances associated with systemic lupus erythematosus. PLoS ONE 7, e37210 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Perl, A. et al. Comprehensive metabolome analyses reveal -acetylcysteine-responsive accumulation of kynurenine in systemic lupus erythematosus: implications for activation of the mechanistic target of rapamycin. Metabolomics 11, 1157–1174 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kamleh, M. A. et al. LC-MS metabolomics of psoriasis patients reveals disease severity-dependent increases in circulating amino acids that are ameliorated by anti-TNFalpha treatment. J. Proteome Res. 14, 557–566 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Priori, R. et al. 1H-NMR-based metabolomic study for identifying serum profiles associated with the response to etanercept in patients with rheumatoid arthritis. PLoS ONE 10, e0138537 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Schmidt, M. et al. Estrone/17beta-estradiol conversion to, and tumor necrosis factor inhibition by, estrogen metabolites in synovial cells of patients with rheumatoid arthritis and patients with osteoarthritis. Arthritis Rheum. 60, 2913–2922 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Jiang, M. et al. Serum metabolic signatures of four types of human arthritis. J. Proteome Res. 12, 3769–3779 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Zeggini, E. et al. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study. Lancet 380, 815–823 (2012).

    Article  CAS  PubMed  Google Scholar 

  57. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Yin, X. et al. Genome-wide meta-analysis identifies multiple novel associations and ethnic heterogeneity of psoriasis susceptibility. Nat. Commun. 6, 6916 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Yang, W. et al. Meta-analysis followed by replication identifies loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with systemic lupus erythematosus in Asians. Am. J. Hum. Genet. 92, 41–51 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Michaud, K. & Wolfe, F. Comorbidities in rheumatoid arthritis. Best Pract. Res. Clin. Rheumatol. 21, 885–906 (2007).

    Article  PubMed  Google Scholar 

  61. Vande Walle, L. et al. Negative regulation of the NLRP3 inflammasome by A20 protects against arthritis. Nature 512, 69–73 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Lee, H. S. et al. Regulation of apoptosis and inflammatory responses by insulin-like growth factor binding protein 3 in fibroblast-like synoviocytes and experimental animal models of rheumatoid arthritis. Arthritis Rheumatol. 66, 863–873 (2014).

    Article  CAS  PubMed  Google Scholar 

  63. Kamekura, S. et al. Contribution of runt-related transcription factor 2 to the pathogenesis of osteoarthritis in mice after induction of knee joint instability. Arthritis Rheum. 54, 2462–2470 (2006).

    Article  CAS  PubMed  Google Scholar 

  64. Schminke, B., Trautmann, S., Mai, B., Miosge, N. & Blaschke, S. Interleukin 17 inhibits progenitor cells in rheumatoid arthritis cartilage. Eur. J. Immunol. 46, 440–445 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Adamski, J. Genome-wide association studies with metabolomics. Genome Med. 4, 34 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Conigliaro, P. et al. Autoantibodies in inflammatory arthritis. Autoimmun. Rev. 15, 673–683 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Smith, G. D. & Ebrahim, S. Mendelian randomization: prospects, potentials, and limitations. Int. J. Epidemiol. 33, 30–42 (2004).

    Article  PubMed  Google Scholar 

  69. Vuckovic, F. et al. Association of systemic lupus erythematosus with decreased immunosuppressive potential of the IgG glycome. Arthritis Rheumatol. 67, 2978–2989 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Evangelou, E. et al. A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip. Ann. Rheum. Dis. 73, 2130–2136 (2014).

    Article  CAS  PubMed  Google Scholar 

  72. Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lin, P. et al. HLA-B27 and human beta2-microglobulin affect the gut microbiota of transgenic rats. PLoS ONE 9, e105684 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Vellucci, R. Heterogeneity of chronic pain. Clin. Drug Invest. 32 (Suppl. 1), 3–10 (2012).

    Article  CAS  Google Scholar 

  75. Lamers, R. J. et al. Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis Cartilage 13, 762–768 (2005).

    Article  CAS  PubMed  Google Scholar 

  76. Mickiewicz, B. et al. Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis. J. Orthop. Res. 33, 1631–1638 (2015).

    Article  PubMed  Google Scholar 

  77. Zhou, J. et al. Exploration of the serum metabolite signature in patients with rheumatoid arthritis using gas chromatography-mass spectrometry. J. Pharm. Biomed. Anal. 127, 60–67 (2016).

    Article  CAS  PubMed  Google Scholar 

  78. Ouyang, X., Dai, Y., Wen, J. L. & Wang, L. X. 1H NMR-based metabolomic study of metabolic profiling for systemic lupus erythematosus. Lupus 20, 1411–1420 (2011).

    Article  CAS  PubMed  Google Scholar 

  79. Yan, B. et al. Serum metabolomic profiling in patients with systemic lupus erythematosus by GC/MS. Mod. Rheumatol. 26, 914–922 (2016).

    Article  CAS  PubMed  Google Scholar 

  80. Sitter, B., Johnsson, M. K., Halgunset, J. & Bathen, T. F. Metabolic changes in psoriatic skin under topical corticosteroid treatment. BMC Dermatol. 13, 8 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Liu, Y. et al. A metabolic profiling analysis of symptomatic gout in human serum and urine using high performance liquid chromatography-diode array detector technique. Clin. Chim. Acta 412, 2132–2140 (2011).

    Article  CAS  PubMed  Google Scholar 

  82. Albrecht, E. et al. Metabolite profiling reveals new insights into the regulation of serum urate in humans. Metabolomics 10, 141–151 (2014).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

TwinsUK was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) Clinical Research Facility at Guy's & St Thomas' NHS Foundation Trust and NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. T.D.S. is an NIHR Senior Investigator and is holder of a European Research Council Advanced Principal Investigator award. The work of C.M., A.M.V. and T.D.S. is supported by a grant from the Medical Research Council AimHY programme (MR/M016560/1). The work of A.M.V. was supported by a grant from the Arthritis Research UK Pain Centre (18769).

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Authors and Affiliations

  1. The Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Lambeth Palace Road, London, SE1 7EH, UK

    Cristina Menni, Jonas Zierer, Ana M. Valdes & Tim D. Spector

  2. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany

    Jonas Zierer

  3. Academic Rheumatology, The University of Nottingham, Clinical Sciences Building, Nottingham City Hospital, Hucknall Road, Nottingham, NG5 1PB, UK

    Ana M. Valdes

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Contributions

C.M., J.Z. and A.M.V. researched the data for the article. C.M. and A.M.V. wrote the article. All authors provided a substantial contribution to discussions of the content and contributed equally to review and/or editing of the manuscript before submission.

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Correspondence to Tim D. Spector.

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Examples of metabolomics studies investigating rheumatic diseases. (PDF 231 kb)

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Genetic basis of rheumatic diseases. (PDF 849 kb)

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Menni, C., Zierer, J., Valdes, A. et al. Mixing omics: combining genetics and metabolomics to study rheumatic diseases. Nat Rev Rheumatol 13, 174–181 (2017). https://doi.org/10.1038/nrrheum.2017.5

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