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Metabolomics in rheumatic diseases: desperately seeking biomarkers

Nature Reviews Rheumatology volume 12, pages 269–281 (2016)Cite this article

Subjects

Key Points

  • Along with other 'omics' approaches, metabolomics — the comprehensive analysis of all metabolites in a system — represents a change from the traditional analysis of single genes, transcripts, proteins or metabolites

  • Improvements in analytical techniques and pattern-recognition methods have led to a rise in the numbers of untargeted and targeted metabolic studies that are being performed

  • Understanding metabolic changes that are specifically associated with the pathogenesis of autoimmune diseases should lead to novel insights into disease mechanisms and to new strategies for treatment of rheumatic diseases

  • The feasibility of metabolomics for biomarker discovery in rheumatology is supported by the assumption that metabolites are important players in biological systems and that diseases cause disruption of metabolic pathways

Abstract

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

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Figure 1: Overview of major metabolic pathways.
Figure 2: Metabolic alterations and signalling pathways involved in activated cells.

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References

  1. Dalgliesh, C. E., Horning, E. C., Horning, M. G., Knox, K. L. & Yarger, K. A gas-liquid-chromatographic procedure for separating a wide range of metabolites occuring in urine or tissue extracts. Biochem. J. 101, 792–810 (1966).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Efron, M. L., Young, D., Moser, H. W. & MacCready, R. A. A simple chromatographic screening test for the detection of disorders of amino acid metabolism: a technic using whole blood or urine collected on filter paper. N. Engl. J. Med. 270, 1378–1383 (1964).

    Article  CAS  PubMed  Google Scholar 

  3. Henneman, D. H. & Henneman, P. H. Effects of human growth hormone on levels of blood urinary carbohydrate and fat metabolites in man. J. Clin. Invest. 39, 1239–1245 (1960).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fiehn, O. Metabolomics — the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. 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 

  6. Lane, A. N., Fan, T. W. M. & Higashi, R. M. Stable isotope-assisted metabolomics in cancer research. IUBMB Life 60, 124–129 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Mueller, D. & Heinzle, E. Stable isotope-assisted metabolomics to detect metabolic flux changes in mammalian cell cultures. Curr. Opin. Biotechnol. 24, 54–59 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Creek, D. J. et al. Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation. Anal. Chem. 84, 8442–8447 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hiller, K., Metallo, C. & Stephanopoulos, G. Elucidation of cellular metabolism via metabolomics and stable-isotope assisted metabolomics. Curr. Pharm. Biotechnol. 12, 1075–1086 (2011).

    Article  CAS  PubMed  Google Scholar 

  10. Quehenberger, O. & Dennis, E. A. The human plasma lipidome. N. Engl. J. Med. 365, 1812–1823 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wymann, M. P. & Schneiter, R. Lipid signalling in disease. Nat. Rev. Mol. Cell. Biol. 9, 162–176 (2008).

    Article  CAS  PubMed  Google Scholar 

  12. Anastasiou, D. & Cantley, L. C. Breathless cancer cells get fat on glutamine. Cell Res. 22, 443–446 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang, T., Liu, G. & Wang, R. The intercellular metabolic interplay between tumor and immune cells. Front. Immunol. 5, 358 (2014).

    PubMed  PubMed Central  Google Scholar 

  14. Ruan, G. X. & Kazlauskas, A. Lactate engages receptor tyrosine kinases Axl, Tie2, and vascular endothelial growth factor receptor 2 to activate phosphoinositide 3-kinase/Akt and promote angiogenesis. J. Biol. Chem. 288, 21161–21172 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wei, X. et al. De novo lipogenesis maintains vascular homeostasis through endothelial nitric-oxide synthase (eNOS) palmitoylation. J. Biol. Chem. 286, 2933–2945 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mills, E. & O'Neill, L. A. Succinate: a metabolic signal in inflammation. Trends Cell Biol. 24, 313–320 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Kaelin, W. G. Jr & McKnight, S. L. Influence of metabolism on epigenetics and disease. Cell 153, 56–69 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Xiao, F. et al. Effects of individual branched-chain amino acids deprivation on insulin sensitivity and glucose metabolism in mice. Metabolism 63, 841–850 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Becker, J. C., Andersen, M. H., Schrama, D. & Thor Straten, P. Immune-suppressive properties of the tumor microenvironment. Cancer Immunol. Immunother. 62, 1137–1148 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Yan, Z., Garg, S. K., Kipnis, J. & Banerjee, R. Extracellular redox modulation by regulatory T cells. Nat. Chem. Biol. 5, 721–723 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bunney, T. D. & Katan, M. Phosphoinositide signalling in cancer: beyond PI3K and PTEN. Nat. Rev. Cancer 10, 342–352 (2010).

    Article  CAS  PubMed  Google Scholar 

  23. Huang, C. & Freter, C. Lipid metabolism, apoptosis and cancer therapy. Int. J. Mol. Sci. 16, 924–949 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gonzalez-Cabrera, P. J., Brown, S., Studer, S. M. & Rosen, H. S1P signaling: new therapies and opportunities. F1000Prime Rep. 6, 109 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Korotkova, M. & Jakobsson, P. J. Persisting eicosanoid pathways in rheumatic diseases. Nat. Rev. Rheumatol. 10, 229–241 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Madhavan, S., Gauba, R., Clarke, R. & Gusev, Y. Integrative analysis workflow for untargeted metabolomics in translational research. Metabolomics 4, 130 (2014).

    Google Scholar 

  27. 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 

  28. 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 

  29. Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50, S9–S14 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Wheeler, D. L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 35, D5–D12 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Degtyarenko, K. et al. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. 36, D344–D350 (2008).

    Article  CAS  PubMed  Google Scholar 

  32. Cui, Q. et al. Metabolite identification via the Madison Metabolomics Consortium Database. Nat. Biotechnol. 26, 162–164 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Smith, C. A. et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747–751 (2005).

    Article  CAS  PubMed  Google Scholar 

  34. Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).

    Article  CAS  PubMed  Google Scholar 

  35. Fiehn, O. et al. The metabolomics standards initiative (MSI). Metabolomics 3, 175–178 (2007).

    Article  CAS  Google Scholar 

  36. Oliver, S. G., Winson, M. K., Kell, D. B. & Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373–378 (1998).

    Article  CAS  PubMed  Google Scholar 

  37. Raamsdonk, L. M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. Tweeddale, H., Notley-McRobb, L. & Ferenci, T. Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool ('metabolome') analysis. J. Bacteriol. 180, 5109–5116 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kuehnbaum, N. L. & Britz-McKibbin, P. New advances in separation science for metabolomics: resolving chemical diversity in a post-genomic era. Chem. Rev. 113, 2437–2468 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Goodacre, R., Vaidyanathan, S., Dunn, W. B., Harrigan, G. G. & Kell, D. B. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245–252 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Moco, S., Bino, R. J., De Vos, R. C. & Vervoort, J. Metabolomics technologies and metabolite identification. Trends Anal. Chem. 26, 855–866 (2007).

    Article  CAS  Google Scholar 

  42. Dumas, M. E. et al. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal. Chem. 78, 2199–2208 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Viant, M. R. et al. International NMR-based environmental metabolomics intercomparison exercise. Environ. Sci. Technol. 43, 219–225 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Whitfield, P. D., German, A. J. & Noble, P. J. Metabolomics: an emerging post-genomic tool for nutrition. Br. J. Nutr. 92, 549–555 (2004).

    Article  CAS  PubMed  Google Scholar 

  45. Wishart, D. S. Quantitative metabolomics using NMR. Trends Anal. Chem. 27, 228–237 (2008).

    Article  CAS  Google Scholar 

  46. Zhang, S., Nagana Gowda, G. A., Ye, T. & Raftery, D. Advances in NMR-based biofluid analysis and metabolite profiling. Analyst 135, 1490–1498 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Swanson, M. G. et al. Proton HR-MAS spectroscopy and quantitative pathologic analysis of MRI/3D-MRSI-targeted postsurgical prostate tissues. Magn. Reson. Med. 50, 944–954 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Bhattacharya, A. Chemistry: breaking the billion-hertz barrier. Nature 463, 605–606 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Kovacs, H., Moskau, D. & Spraul, M. Cryogenically cooled probes — a leap in NMR technology. Prog. Nucl. Magn. Reson. Spectrosc. 46, 131–155 (2005).

    Article  CAS  Google Scholar 

  50. Molinski, T. F. NMR of natural products at the 'nanomole-scale'. Nat. Prod. Rep. 27, 321–329 (2010).

    Article  CAS  PubMed  Google Scholar 

  51. Styles, P. et al. A high-resolution NMR probe in which the coil and preamplifier are cooled with liquid helium. J. Magn. Reson. 213, 347–354 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Tiziani, S., Kang, Y., Choi, J. S., Roberts, W. & Paternostro, G. Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat. Commun. 2, 545 (2011).

    Article  PubMed  CAS  Google Scholar 

  53. Birkemeyer, C., Luedemann, A., Wagner, C., Erban, A. & Kopka, J. Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling. Trends Biotechnol. 23, 28–33 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Guennec, A. L., Giraudeau, P. & Caldarelli, S. Evaluation of fast 2D NMR for metabolomics. Anal. Chem. 86, 5946–5954 (2014).

    Article  CAS  PubMed  Google Scholar 

  55. Hyberts, S. G. et al. Ultrahigh-resolution 1H-13C HSQC spectra of metabolite mixtures using nonlinear sampling and forward maximum entropy reconstruction. J. Am. Chem. Soc. 129, 5108–5116 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Atherton, H. J. et al. A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of the PPAR-α null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome. Physiol. Genomics 27, 178–186 (2006).

    Article  CAS  PubMed  Google Scholar 

  58. Chan, E. C. et al. Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J. Proteome Res. 8, 352–361 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Pan, Z. & Raftery, D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal. Bioanal. Chem. 387, 525–527 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Psychogios, N. et al. The human serum metabolome. PLoS ONE 6, e16957 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Tiziani, S. et al. Metabolomics of the tumor microenvironment in pediatric acute lymphoblastic leukemia. PLoS ONE 8, e82859 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Holc˘apek, M., Jirásko, R. & Lísa, M. Recent developments in liquid chromatography–mass spectrometry and related techniques. J. Chromatogr. A 1259, 3–15 (2012).

    Article  CAS  Google Scholar 

  63. Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Forcisi, S. et al. Liquid chromatography–mass spectrometry in metabolomics research: mass analyzers in ultra high pressure liquid chromatography coupling. J. Chromatogr. A 1292, 51–65 (2013).

    Article  CAS  PubMed  Google Scholar 

  65. Koulman, A. et al. High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics. Rapid Commun. Mass Spectrom. 21, 421–428 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Han, X. & Gross, R. W. Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrom. Rev. 24, 367–412 (2005).

    Article  CAS  PubMed  Google Scholar 

  67. Han, X., Yang, K. & Gross, R. W. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom. Rev. 31, 134–178 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Kvitvang, H. F., Andreassen, T., Adam, T., Villas-Bôas, S. G. & Bruheim, P. Highly sensitive GC/MS/MS method for quantitation of amino and nonamino organic acids. Anal. Chem. 83, 2705–2711 (2011).

    Article  CAS  PubMed  Google Scholar 

  69. Quehenberger, O., Armando, A. M. & Dennis, E. A. High sensitivity quantitative lipidomics analysis of fatty acids in biological samples by gas chromatography–mass spectrometry. Biochim. Biophys. Acta 1811, 648–656 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Contrepois, K., Jiang, L. & Snyder, M. Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry. Mol. Cell. Proteomics 14, 1684–1695 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Sturm, S. & Seger, C. Liquid chromatography–nuclear magnetic resonance coupling as alternative to liquid chromatography–mass spectrometry hyphenations: curious option or powerful and complementary routine tool? J. Chromatogr. A 1259, 50–61 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. Rodrigues, T. B. et al. Magnetic resonance imaging of tumor glycolysis using hyperpolarized 13C-labeled glucose. Nat. Med. 20, 93–97 (2014).

    Article  CAS  PubMed  Google Scholar 

  74. Fan, T. W. et al. Altered regulation of metabolic pathways in human lung cancer discerned by 13C stable isotope-resolved metabolomics (SIRM). Mol. Cancer 8, 41 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Günther, U. L. et al. in Tumor Cell Metabolism (eds Mazurek, S. & Shosha, M.) 315–348 (Springer, 2015).

    Book  Google Scholar 

  76. Priori, R. et al. Metabolomics in rheumatic diseases: the potential of an emerging methodology for improved patient diagnosis, prognosis, and treatment efficacy. Autoimmun. Rev. 12, 1022–1030 (2013).

    Article  CAS  PubMed  Google Scholar 

  77. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Romick-Rosendale, L. E. et al. Identification of urinary metabolites that distinguish membranous lupus nephritis from proliferative lupus nephritis and focal segmental glomerulosclerosis. Arthritis Res. Ther. 13, R199 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Ding, X. et al. Rapid resolution liquid chromatography coupled with quadrupole time-of-flight mass spectrometry-based metabolomics approach to study the effects of jieduquyuziyin prescription on systemic lupus erythematosus. PLoS ONE 9, e88223 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Kang, S. W. et al. 12-lipoxygenase is increased in glucose-stimulated mesangial cells and in experimental diabetic nephropathy. Kidney Int. 59, 1354–1362 (2001).

    Article  CAS  PubMed  Google Scholar 

  82. Adams, S. B. Jr et al. Global metabolic profiling of human osteoarthritic synovium. Osteoarthritis Cartilage 20, 64–67 (2012).

    Article  PubMed  Google Scholar 

  83. Zhang, W. et al. Classification of osteoarthritis phenotypes by metabolomics analysis. BMJ Open 4, e006286 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  84. 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 

  85. Damyanovich, A. Z., Staples, J. R., Chan, A. D. & Marshall, K. W. Comparative study of normal and osteoarthritic canine synovial fluid using 500 MHz 1H magnetic resonance spectroscopy. J. Orthop. Res. 17, 223–231 (1999).

    Article  CAS  PubMed  Google Scholar 

  86. Damyanovich, A. Z., Staples, J. R. & Marshall, K. W. 1H NMR investigation of changes in the metabolic profile of synovial fluid in bilateral canine osteoarthritis with unilateral joint denervation. Osteoarthritis Cartilage 7, 165–172 (1999).

    Article  CAS  PubMed  Google Scholar 

  87. Hügle, T. et al. Synovial fluid metabolomics in different forms of arthritis assessed by nuclear magnetic resonance spectroscopy. Clin. Exp. Rheumatol. 30, 240–245 (2012).

    PubMed  Google Scholar 

  88. Lacitignola, L., Fanizzi, F. P., Francioso, E. & Crovace, A. 1H NMR investigation of normal and osteo-arthritic synovial fluid in the horse. Vet. Comp. Orthop. Traumatol. 21, 85–88 (2008).

    Article  CAS  PubMed  Google Scholar 

  89. 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 

  90. 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 

  91. Zhang, W. et al. Relationship between blood plasma and synovial fluid metabolite concentrations in patients with osteoarthritis. J. Rheumatol. 42, 859–865 (2015).

    Article  CAS  PubMed  Google Scholar 

  92. Kim, S. et al. Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis. PLoS ONE 9, e97501 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Giera, M. et al. Lipid and lipid mediator profiling of human synovial fluid in rheumatoid arthritis patients by means of LC-MS/MS. Biochim. Biophys. Acta 1821, 1415–1424 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Kosinska, M. K. et al. A lipidomic study of phospholipid classes and species in human synovial fluid. Arthritis Rheum. 65, 2323–2333 (2013).

    Article  CAS  PubMed  Google Scholar 

  95. 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 

  96. 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 

  97. 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 

  98. van Wietmarschen, H. A. et al. Characterization of rheumatoid arthritis subtypes using symptom profiles, clinical chemistry and metabolomics measurements. PLoS ONE 7, e44331 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 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 

  100. Fischer, R. et al. Discovery of candidate serum proteomic and metabolomic biomarkers in ankylosing spondylitis. Mol. Cell. Proteomics 11, M111.013904 (2012).

    Article  PubMed  CAS  Google Scholar 

  101. Gao, P. et al. Integrated GC-MS and LC-MS plasma metabonomics analysis of ankylosing spondylitis. Analyst 133, 1214–1220 (2008).

    Article  CAS  PubMed  Google Scholar 

  102. Ding, X. et al. Metabolomics analysis of collagen-induced arthritis in rats and interventional effects of oral tolerance. Anal. Biochem. 458, 49–57 (2014).

    Article  CAS  PubMed  Google Scholar 

  103. Wang, Z. et al. 1H NMR-based metabolomic analysis for identifying serum biomarkers to evaluate methotrexate treatment in patients with early rheumatoid arthritis. Exp. Ther. Med. 4, 165–171 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Ghesquière, B., Wong, B. W., Kuchnio, A. & Carmeliet, P. Metabolism of stromal and immune cells in health and disease. Nature 511, 167–176 (2014).

    Article  PubMed  CAS  Google Scholar 

  106. Maciolek, J. A., Pasternak, J. A. & Wilson, H. L. Metabolism of activated T lymphocytes. Curr. Opin. Immunol. 27, 60–74 (2014).

    Article  CAS  PubMed  Google Scholar 

  107. MacIver, N. J., Michalek, R. D. & Rathmell, J. C. Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259–283 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Palmer, C. S., Ostrowski, M., Balderson, B., Christian, N. & Crowe, S. M. Glucose metabolism regulates T cell activation, differentiation, and functions. Front. Immunol. 6, 1 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Pearce, E. L., Poffenberger, M. C., Chang, C. H. & Jones, R. G. Fueling immunity: insights into metabolism and lymphocyte function. Science 342, 1242454 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Wang, R. & Green, D. R. Metabolic reprogramming and metabolic dependency in T cells. Immunol. Rev. 249, 14–26 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Galván-Peña, S. & O'Neill, L. A. Metabolic reprograming in macrophage polarization. Front. Immunol. 5, 420 (2014).

    PubMed  PubMed Central  Google Scholar 

  112. Everts, B. & Pearce, E. J. Metabolic control of dendritic cell activation and function: recent advances and clinical implications. Front. Immunol. 5, 203 (2014).

    PubMed  PubMed Central  Google Scholar 

  113. Pearce, E. J. & Everts, B. Dendritic cell metabolism. Nat. Rev. Immunol. 15, 18–29 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Yin, Y. et al. Normalization of CD4+ T cell metabolism reverses lupus. Sci. Transl. Med. 7, 274ra218 (2015).

    Article  CAS  Google Scholar 

  115. McDonald, G. et al. Normalizing glycosphingolipids restores function in CD4+ T cells from lupus patients. J. Clin. Invest. 124, 712–724 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Yang, Z., Fujii, H., Mohan, S. V., Goronzy, J. J. & Weyand, C. M. Phosphofructokinase deficiency impairs ATP generation, autophagy, and redox balance in rheumatoid arthritis T cells. J. Exp. Med. 210, 2119–2134 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Bottini, N. & Firestein, G. S. Duality of fibroblast-like synoviocytes in RA: passive responders and imprinted aggressors. Nat. Rev. Rheumatol. 9, 24–33 (2013).

    Article  CAS  PubMed  Google Scholar 

  118. Garcia-Carbonell, R. et al. Critical role of fibroblast-like synoviocytes glycolytic metabolism in rheumatoid arthritis. Arthritis Rheumatol. http://dx.doi.org/10.1002/art.39608 (2016).

  119. Guma, M. et al. Choline kinase inhibition in rheumatoid arthritis. Ann. Rheum. Dis. 74, 1399–1407 (2014).

    Article  PubMed  CAS  Google Scholar 

  120. Glunde, K., Bhujwalla, Z. M. & Ronen, S. M. Choline metabolism in malignant transformation. Nat. Rev. Cancer 11, 835–848 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Jiménez, B., del Peso, L., Montaner, S., Esteve, P. & Lacal, J. C. Generation of phosphorylcholine as an essential event in the activation of Raf-1 and MAP-kinases in growth factors-induced mitogenic stimulation. J. Cell. Biochem. 57, 141–149 (1995).

    Article  PubMed  Google Scholar 

  122. Ramírez de Molina, A. et al. Choline kinase activation is a critical requirement for the proliferation of primary human mammary epithelial cells and breast tumor progression. Cancer Res. 64, 6732–6739 (2004).

    Article  PubMed  Google Scholar 

  123. Ramírez de Molina, A. et al. Choline kinase as a link connecting phospholipid metabolism and cell cycle regulation: implications in cancer therapy. Int. J. Biochem. Cell Biol. 40, 1753–1763 (2008).

    Article  PubMed  CAS  Google Scholar 

  124. Ramírez de Molina, A. et al. Expression of choline kinase alpha to predict outcome in patients with early-stage non-small-cell lung cancer: a retrospective study. Lancet Oncol. 8, 889–897 (2007).

    Article  PubMed  CAS  Google Scholar 

  125. Baker, D. A., Eudaly, J., Smith, C. D., Obeid, L. M. & Gilkeson, G. S. Impact of sphingosine kinase 2 deficiency on the development of TNF-alpha-induced inflammatory arthritis. Rheumatol. Int. 33, 2677–2681 (2013).

    Article  CAS  PubMed  Google Scholar 

  126. Hu, P. F., Chen, Y., Cai, P. F., Jiang, L. F. & Wu, L. D. Sphingosine-1-phosphate: a potential therapeutic target for rheumatoid arthritis. Mol. Biol. Rep. 38, 4225–4230 (2011).

    Article  CAS  PubMed  Google Scholar 

  127. Zhao, C. et al. Specific and overlapping sphingosine-1-phosphate receptor functions in human synoviocytes: impact of TNF-α. J. Lipid Res. 49, 2323–2337 (2008).

    Article  CAS  PubMed  Google Scholar 

  128. Bourgoin, S. G. & Zhao, C. Autotaxin and lysophospholipids in rheumatoid arthritis. Curr. Opin. Investig. Drugs 11, 515–526 (2010).

    CAS  PubMed  Google Scholar 

  129. Leah, E. Experimental arthritis: pathogenic role of autotaxin and LPA. Nat. Rev. Rheumatol. 8, 307 (2012).

    Article  PubMed  Google Scholar 

  130. Nikitopoulou, I. et al. Autotaxin expression from synovial fibroblasts is essential for the pathogenesis of modeled arthritis. J. Exp. Med. 209, 925–933 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Fuchs, B., Schiller, J., Wagner, U., Häntzschel, H. & Arnold, K. The phosphatidylcholine/lysophosphatidylcholine ratio in human plasma is an indicator of the severity of rheumatoid arthritis: investigations by 31P NMR and MALDI-TOF MS. Clin. Biochem. 38, 925–933 (2005).

    Article  CAS  PubMed  Google Scholar 

  132. Dang, C. V., Hamaker, M., Sun, P., Le, A. & Gao, P. Therapeutic targeting of cancer cell metabolism. J. Mol. Med. (Berl.) 89, 205–212 (2011).

    Article  CAS  Google Scholar 

  133. Ramsay, E. E., Hogg, P. J. & Dilda, P. J. Mitochondrial metabolism inhibitors for cancer therapy. Pharm. Res. 28, 2731–2744 (2011).

    Article  CAS  PubMed  Google Scholar 

  134. Vander Heiden, M. G. Targeting cancer metabolism: a therapeutic window opens. Nat. Rev. Drug Discov. 10, 671–684 (2011).

    Article  CAS  PubMed  Google Scholar 

  135. Wenger, J. B., Chun, S. Y., Dang, D. T., Luesch, H. & Dang, L. H. Combination therapy targeting cancer metabolism. Med. Hypotheses 76, 169–172 (2011).

    Article  CAS  PubMed  Google Scholar 

  136. Zhao, Y., Butler, E. B. & Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 4, e532 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Galluzzi, L., Kepp, O., Vander Heiden, M. G. & Kroemer, G. Metabolic targets for cancer therapy. Nat. Rev. Drug Discov. 12, 829–846 (2013).

    Article  CAS  PubMed  Google Scholar 

  138. Schulze, A. & Harris, A. L. How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature 491, 364–373 (2012).

    Article  CAS  PubMed  Google Scholar 

  139. Meier, F. M., Frerix, M., Hermann, W. & Müller-Ladner, U. Current immunotherapy in rheumatoid arthritis. Immunotherapy 5, 955–974 (2013).

    Article  CAS  PubMed  Google Scholar 

  140. Cardaci, S., Desideri, E. & Ciriolo, M. R. Targeting aerobic glycolysis: 3-bromopyruvate as a promising anticancer drug. J. Bioenerg. Biomembr. 44, 17–29 (2012).

    Article  CAS  PubMed  Google Scholar 

  141. Pedersen, P. L. 3-bromopyruvate (3BP) a fast acting, promising, powerful, specific, and effective 'small molecule' anti-cancer agent taken from labside to bedside: introduction to a special issue. J. Bioenerg. Biomembr. 44, 1–6 (2012).

    Article  CAS  PubMed  Google Scholar 

  142. Le, A. et al. Inhibition of lactate dehydrogenase A induces oxidative stress and inhibits tumor progression. Proc. Natl Acad. Sci. USA 107, 2037–2042 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Liu, Y. et al. A small-molecule inhibitor of glucose transporter 1 downregulates glycolysis, induces cell-cycle arrest, and inhibits cancer cell growth in vitro and in vivo. Mol. Cancer Ther. 11, 1672–1682 (2012).

    Article  CAS  PubMed  Google Scholar 

  144. Schneiderhan, W. et al. CD147 silencing inhibits lactate transport and reduces malignant potential of pancreatic cancer cells in in vivo and in vitro models. Gut 58, 1391–1398 (2009).

    Article  CAS  PubMed  Google Scholar 

  145. Patra, K. C. et al. Hexokinase 2 is required for tumor initiation and maintenance and its systemic deletion is therapeutic in mouse models of cancer. Cancer Cell 24, 213–228 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Robey, R. B. & Hay, N. Mitochondrial hexokinases, novel mediators of the antiapoptotic effects of growth factors and Akt. Oncogene 25, 4683–4696 (2006).

    Article  CAS  PubMed  Google Scholar 

  147. Wilson, J. E. Isozymes of mammalian hexokinase: structure, subcellular localization and metabolic function. J. Exp. Biol. 206, 2049–2057 (2003).

    Article  CAS  PubMed  Google Scholar 

  148. Stark, A. K., Sriskantharajah, S., Hessel, E. M. & Okkenhaug, K. PI3K inhibitors in inflammation, autoimmunity and cancer. Curr. Opin. Pharmacol. 23, 82–91 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Banham-Hall, E., Clatworthy, M. R. & Okkenhaug, K. The therapeutic potential for PI3K inhibitors in autoimmune rheumatic diseases. Open Rheumatol. J. 6, 245–258 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Roberts, E., Guerrero, M., Urbano, M. & Rosen, H. Sphingosine 1-phosphate receptor agonists: a patent review. Expert Opin. Ther. Pat. 23, 817–841 (2013).

    Article  CAS  PubMed  Google Scholar 

  151. Menendez, J. A. & Lupu, R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat. Rev. Cancer 7, 763–777 (2007).

    Article  CAS  PubMed  Google Scholar 

  152. Wise, D. R. & Thompson, C. B. Glutamine addiction: a new therapeutic target in cancer. Trends Biochem. Sci. 35, 427–433 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Seguin, F. et al. The fatty acid synthase inhibitor orlistat reduces experimental metastases and angiogenesis in B16-F10 melanomas. Br. J. Cancer 107, 977–987 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Hatzivassiliou, G. et al. ATP citrate lyase inhibition can suppress tumor cell growth. Cancer Cell 8, 311–321 (2005).

    Article  CAS  PubMed  Google Scholar 

  155. Beckers, A. et al. Chemical inhibition of acetyl-CoA carboxylase induces growth arrest and cytotoxicity selectively in cancer cells. Cancer Res. 67, 8180–8187 (2007).

    Article  CAS  PubMed  Google Scholar 

  156. Hernández-Alcoceba, R., Fernández, F. & Lacal, J. C. In vivo antitumor activity of choline kinase inhibitors: a novel target for anticancer drug discovery. Cancer Res. 59, 3112–3118 (1999).

    PubMed  Google Scholar 

  157. Lacal, J. C. Choline kinase: a novel target for antitumor drugs. IDrugs 4, 419–426 (2001).

    CAS  PubMed  Google Scholar 

  158. Nomura, D. K. et al. Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 140, 49–61 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Ghosh-Choudhury, N., Mandal, C. C., Ghosh-Choudhury, N. & Ghosh-Choudhury, G. Simvastatin induces derepression of PTEN expression via NFκB to inhibit breast cancer cell growth. Cell. Signal. 22, 749–758 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Kaddurah-Daouk, R. & Weinshilboum, R. M. & Pharmacometabolomics Research Network. Pharmacometabolomics: implications for clinical pharmacology and systems pharmacology. Clin. Pharmacol. Ther. 95, 154–167 (2014).

    Article  CAS  PubMed  Google Scholar 

  161. Bordbar, A., Monk, J. M., King, Z. A. & Palsson, B. O. Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107–120 (2014).

    Article  CAS  PubMed  Google Scholar 

  162. Saha, R., Chowdhury, A. & Maranas, C. D. Recent advances in the reconstruction of metabolic models and integration of omics data. Curr. Opin. Biotechnol. 29, 39–45 (2014).

    Article  CAS  PubMed  Google Scholar 

  163. Adamski, J. & Suhre, K. Metabolomics platforms for genome wide association studies — linking the genome to the metabolome. Curr. Opin. Biotechnol. 24, 39–47 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Holmes, E., Li, J. V., Marchesi, J. R. & Nicholson, J. K. Gut microbiota composition and activity in relation to host metabolic phenotype and disease risk. Cell. Metab. 16, 559–564 (2012).

    Article  CAS  PubMed  Google Scholar 

  166. El Aidy, S. et al. The gut microbiota elicits a profound metabolic reorientation in the mouse jejunal mucosa during conventionalisation. Gut 62, 1306–1314 (2013).

    Article  CAS  PubMed  Google Scholar 

  167. Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).

    Article  CAS  PubMed  Google Scholar 

  168. Arpaia, N. et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451–455 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  170. Lagarde, M., Géloën, A., Record, M., Vance, D. & Spener, F. Lipidomics is emerging. Biochim. Biophys. Acta 1634, 61 (2003).

    Article  CAS  PubMed  Google Scholar 

  171. Seeger, K. Metabolic changes in autoimmune diseases. Curr. Drug Discov. Technol. 6, 256–261 (2009).

    Article  CAS  PubMed  Google Scholar 

  172. Cairns, R. A., Harris, I. S. & Mak, T. W. Regulation of cancer cell metabolism. Nat. Rev. Cancer 11, 85–95 (2011).

    Article  CAS  PubMed  Google Scholar 

  173. Courtnay, R. et al. Cancer metabolism and the Warburg effect: the role of HIF-1 and PI3K. Mol. Biol. Rep. 42, 841–851 (2015).

    Article  CAS  PubMed  Google Scholar 

  174. Elstrom, R. L. et al. Akt stimulates aerobic glycolysis in cancer cells. Cancer Res. 64, 3892–3899 (2004).

    Article  CAS  PubMed  Google Scholar 

  175. Hsu, P. P. & Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 134, 703–707 (2008).

    Article  CAS  PubMed  Google Scholar 

  176. Chang, C. H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239–1251 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Patra, K. C. & Hay, N. The pentose phosphate pathway and cancer. Trends Biochem. Sci. 39, 347–354 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Nagy, C. & Haschemi, A. Time and demand are two critical dimensions of immunometabolism: the process of macrophage activation and the pentose phosphate pathway. Front. Immunol. 6, 164 (2015).

    PubMed  PubMed Central  Google Scholar 

  179. Lochner, M., Berod, L. & Sparwasser, T. Fatty acid metabolism in the regulation of T cell function. Trends Immunol. 36, 81–91 (2015).

    Article  CAS  PubMed  Google Scholar 

  180. Schriever, S. C., Deutsch, M. J., Adamski, J., Roscher, A. A. & Ensenauer, R. Cellular signaling of amino acids towards mTORC1 activation in impaired human leucine catabolism. J. Nutr. Biochem. 24, 824–831 (2013).

    Article  CAS  PubMed  Google Scholar 

  181. DeBerardinis, R. J. et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc. Natl Acad. Sci. USA 104, 19345–19350 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Pollizzi, K. N. & Powell, J. D. Integrating canonical and metabolic signalling programmes in the regulation of T cell responses. Nat. Rev. Immunol. 14, 435–446 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Agathocleous, M. & Harris, W. A. Metabolism in physiological cell proliferation and differentiation. Trends Cell Biol. 23, 484–492 (2013).

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Division of Rheumatology, Allergy and Immunology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, 92093–0656, California, USA

    Monica Guma & Gary S. Firestein

  2. Department of Nutritional Sciences, University of Texas at Austin, 1400 Barbara Jordan Boulevard, Austin, 78723, Texas, USA

    Stefano Tiziani

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M.G. and S.T. researched data for the article. All authors contributed to discussion of content, writing the article and reviewing and editing the manuscript before submission.

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Correspondence to Monica Guma.

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Biomechanistic eludication of metabolic pathways by metabolic flux analysis with stable-isotope labelling. (PDF 131 kb)

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Classification of the main metabolite categories. (PDF 119 kb)

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Guma, M., Tiziani, S. & Firestein, G. Metabolomics in rheumatic diseases: desperately seeking biomarkers. Nat Rev Rheumatol 12, 269–281 (2016). https://doi.org/10.1038/nrrheum.2016.1

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