Metabolomics in rheumatic diseases: Desperately seeking biomarkers

Nature Reviews Rheumatology

Volumn 12, Issue 5, 2016, Pages 269-281

  Guma, Monica ^a   Tiziani, Stefano ^b   Firestein, Gary S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; BIOLOGICAL MARKER; CARBOHYDRATE; LIPID; STABLE ISOTOPE; ANTIRHEUMATIC AGENT;

AEROBIC METABOLISM; DATA BASE; GLYCOLYSIS; HUMAN; LIPID METABOLISM; MASS SPECTROMETRY; METABOLITE; METABOLOMICS; MITOCHONDRIAL RESPIRATION; MOLECULAR INTERACTION; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; OSTEOARTHRITIS; PATHOPHYSIOLOGY; PRIORITY JOURNAL; REVIEW; RHEUMATIC DISEASE; SYSTEMIC LUPUS ERYTHEMATOSUS; METABOLISM;

ANTIRHEUMATIC AGENTS; BIOMARKERS; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; MASS SPECTROMETRY; METABOLIC NETWORKS AND PATHWAYS; METABOLOMICS; RHEUMATIC DISEASES;

EID: 84959474414     PISSN: 17594790     EISSN: 17594804     Source Type: Journal    
DOI: 10.1038/nrrheum.2016.1     Document Type: Review

References (183)

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).
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).
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).
4. Fiehn, O. Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155-171 (2002).
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).
6. Lane, A. N., Fan, T. W. M. & Higashi, R. M. Stable isotope-assisted metabolomics in cancer research. IUBMB Life 60, 124-129 (2008).
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).
8. Creek, D. J. et al. Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation. Anal. Chem. 84, 8442-8447 (2012).
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).
10. Quehenberger, O. & Dennis, E. A. The human plasma lipidome. N. Engl. J. Med. 365, 1812-1823 (2011).
11. Wymann, M. P. & Schneiter, R. Lipid signalling in disease. Nat. Rev. Mol. Cell. Biol. 9, 162-176 (2008).
12. Anastasiou, D. & Cantley, L. C. Breathless cancer cells get fat on glutamine. Cell Res. 22, 443-446 (2012).
13. Wang, T., Liu, G. & Wang, R. The intercellular metabolic interplay between tumor and immune cells. Front. Immunol. 5, 358 (2014).
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).
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).
16. Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238-242 (2013).
17. Mills, E. & O'Neill, L. A. Succinate: a metabolic signal in inflammation. Trends Cell Biol. 24, 313-320 (2014).
18. Kaelin, W. G. Jr & McKnight, S. L. Influence of metabolism on epigenetics and disease. Cell 153, 56-69 (2013).
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).
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).
21. Yan, Z., Garg, S. K., Kipnis, J. & Banerjee, R. Extracellular redox modulation by regulatory T cells. Nat. Chem. Biol. 5, 721-723 (2009).
22. Bunney, T. D. & Katan, M. Phosphoinositide signalling in cancer: beyond PI3K and PTEN. Nat. Rev. Cancer 10, 342-352 (2010).
23. Huang, C. & Freter, C. Lipid metabolism, apoptosis and cancer therapy. Int. J. Mol. Sci. 16, 924-949 (2015).
24. Gonzalez-Cabrera, P. J., Brown, S., Studer, S. M. & Rosen, H. S1P signaling: new therapies and opportunities. F1000Prime Rep. 6, 109 (2014).
25. Korotkova, M. & Jakobsson, P. J. Persisting eicosanoid pathways in rheumatic diseases. Nat. Rev. Rheumatol. 10, 229-241 (2014).
26. Madhavan, S., Gauba, R., Clarke, R. & Gusev, Y. Integrative analysis workflow for untargeted metabolomics in translational research. Metabolomics 4, 130 (2014).
27. Wishart, D. S. et al. HMDB 3.0 - the human metabolome database in 2013. Nucleic Acids Res. 41, D801-D807 (2013).
28. Kanehisa, M. et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354-D357 (2006).
29. Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50, S9-S14 (2009).
30. Wheeler, D. L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 35, D5-D12 (2007).
31. Degtyarenko, K. et al. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. 36, D344-D350 (2008).
32. Cui, Q. et al. Metabolite identification via the Madison Metabolomics Consortium Database. Nat. Biotechnol. 26, 162-164 (2008).
33. Smith, C. A. et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747-751 (2005).
34. Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703-714 (2010).
35. Fiehn, O. et al. The metabolomics standards initiative (MSI). Metabolomics 3, 175-178 (2007).
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).
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).
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).
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).
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).
41. Moco, S., Bino, R. J., De Vos, R. C. & Vervoort, J. Metabolomics technologies and metabolite identification. Trends Anal. Chem. 26, 855-866 (2007).
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).
43. Viant, M. R. et al. International NMR-based environmental metabolomics intercomparison exercise. Environ. Sci. Technol. 43, 219-225 (2009).
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).
45. Wishart, D. S. Quantitative metabolomics using NMR. Trends Anal. Chem. 27, 228-237 (2008).
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).
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).
48. Bhattacharya, A. Chemistry: breaking the billion-hertz barrier. Nature 463, 605-606 (2010).
49. Kovacs, H., Moskau, D. & Spraul, M. Cryogenically cooled probes - a leap in NMR technology. Prog. Nucl. Magn. Reson. Spectrosc. 46, 131-155 (2005).
50. Molinski, T. F. NMR of natural products at the 'nanomole-scale'. Nat. Prod. Rep. 27, 321-329 (2010).
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).
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).
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).
54. Guennec, A. L., Giraudeau, P. & Caldarelli, S. Evaluation of fast 2D NMR for metabolomics. Anal. Chem. 86, 5946-5954 (2014).
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).
56. Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).
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).
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).
59. Pan, Z. & Raftery, D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal. Bioanal. Chem. 387, 525-527 (2007).
60. Psychogios, N. et al. The human serum metabolome. PLoS ONE 6, e16957 (2011).
61. Tiziani, S. et al. Metabolomics of the tumor microenvironment in pediatric acute lymphoblastic leukemia. PLoS ONE 8, e82859 (2013).
62. Holčapek, M., Jirasko, R. & Lvsa, M. Recent developments in liquid chromatography-mass spectrometry and related techniques. J. Chromatogr. A 1259, 3-15 (2012).
63. Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51-78 (2007).
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).
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).
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).
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).
68. Kvitvang, H. F., Andreassen, T., Adam, T., Villas-Bias, S. G. & Bruheim, P. Highly sensitive GC/MS/MS method for quantitation of amino and nonamino organic acids. Anal. Chem. 83, 2705-2711 (2011).
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).
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).
71. Tautenhahn, R., Bøttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008).
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).
73. Rodrigues, T. B. et al. Magnetic resonance imaging of tumor glycolysis using hyperpolarized 13C-labeled glucose. Nat. Med. 20, 93-97 (2014).
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).
75. Gónther, U. L. et al. in Tumor Cell Metabolism (eds Mazurek, S. & Shosha, M.) 315-348 (Springer, 2015).
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).
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).
78. Wu, T. et al. Metabolic disturbances associated with systemic lupus erythematosus. PLoS ONE 7, e37210 (2012).
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).
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).
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).
82. Adams, S. B. Jr et al. Global metabolic profiling of human osteoarthritic synovium. Osteoarthritis Cartilage 20, 64-67 (2012).
83. Zhang, W. et al. Classification of osteoarthritis phenotypes by metabolomics analysis. BMJ Open 4, e006286 (2014).
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).
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).
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).
87. Hógle, T. et al. Synovial fluid metabolomics in different forms of arthritis assessed by nuclear magnetic resonance spectroscopy. Clin. Exp. Rheumatol. 30, 240-245 (2012).
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).
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).
90. Lamers, R. J. et al. Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis Cartilage 13, 762-768 (2005).
91. Zhang, W. et al. Relationship between blood plasma and synovial fluid metabolite concentrations in patients with osteoarthritis. J. Rheumatol. 42, 859-865 (2015).
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).
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).
94. Kosinska, M. K. et al. A lipidomic study of phospholipid classes and species in human synovial fluid. Arthritis Rheum. 65, 2323-2333 (2013).
95. Jiang, M. et al. Serum metabolic signatures of four types of human arthritis. J. Proteome Res. 12, 3769-3779 (2013).
96. Young, S. P. et al. The impact of inflammation on metabolomic profiles in patients with arthritis. Arthritis Rheum. 65, 2015-2023 (2013).
97. Madsen, R. K. et al. Diagnostic properties of metabolic perturbations in rheumatoid arthritis. Arthritis Res. Ther. 13, R19 (2011).
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).
99. Armstrong, A. W. et al. Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis. F1000Res. 3, 248 (2014).
100. Fischer, R. et al. Discovery of candidate serum proteomic and metabolomic biomarkers in ankylosing spondylitis. Mol. Cell. Proteomics 11, M111.013904 (2012).
101. Gao, P. et al. Integrated GC-MS and LC-MS plasma metabonomics analysis of ankylosing spondylitis. Analyst 133, 1214-1220 (2008).
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).
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).
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).
105. Ghesquiore, B., Wong, B. W., Kuchnio, A. & Carmeliet, P. Metabolism of stromal and immune cells in health and disease. Nature 511, 167-176 (2014).
106. Maciolek, J. A., Pasternak, J. A. & Wilson, H. L. Metabolism of activated T lymphocytes. Curr. Opin. Immunol. 27, 60-74 (2014).
107. MacIver, N. J., Michalek, R. D. & Rathmell, J. C. Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259-283 (2013).
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).
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).
110. Wang, R. & Green, D. R. Metabolic reprogramming and metabolic dependency in T cells. Immunol. Rev. 249, 14-26 (2012).
111. Galvan-Pepa, S. & O'Neill, L. A. Metabolic reprograming in macrophage polarization. Front. Immunol. 5, 420 (2014).
112. Everts, B. & Pearce, E. J. Metabolic control of dendritic cell activation and function: recent advances and clinical implications. Front. Immunol. 5, 203 (2014).
113. Pearce, E. J. & Everts, B. Dendritic cell metabolism. Nat. Rev. Immunol. 15, 18-29 (2015).
114. Yin, Y. et al. Normalization of CD4+ T cell metabolism reverses lupus. Sci. Transl. Med. 7, 274ra218 (2015).
115. McDonald, G. et al. Normalizing glycosphingolipids restores function in CD4+ T cells from lupus patients. J. Clin. Invest. 124, 712-724 (2014).
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).
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).
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).
120. Glunde, K., Bhujwalla, Z. M. & Ronen, S. M. Choline metabolism in malignant transformation. Nat. Rev. Cancer 11, 835-848 (2011).
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).
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).
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).
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).
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).
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).
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).
128. Bourgoin, S. G. & Zhao, C. Autotaxin and lysophospholipids in rheumatoid arthritis. Curr. Opin. Investig. Drugs 11, 515-526 (2010).
129. Leah, E. Experimental arthritis: pathogenic role of autotaxin and LPA. Nat. Rev. Rheumatol. 8, 307 (2012).
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).
131. Fuchs, B., Schiller, J., Wagner, U., Hntzschel, 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).
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).
133. Ramsay, E. E., Hogg, P. J. & Dilda, P. J. Mitochondrial metabolism inhibitors for cancer therapy. Pharm. Res. 28, 2731-2744 (2011).
134. Vander Heiden, M. G. Targeting cancer metabolism: a therapeutic window opens. Nat. Rev. Drug Discov. 10, 671-684 (2011).
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).
136. Zhao, Y., Butler, E. B. & Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 4, e532 (2013).
137. Galluzzi, L., Kepp, O., Vander Heiden, M. G. & Kroemer, G. Metabolic targets for cancer therapy. Nat. Rev. Drug Discov. 12, 829-846 (2013).
138. Schulze, A. & Harris, A. L. How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature 491, 364-373 (2012).
139. Meier, F. M., Frerix, M., Hermann, W. & Müller-Ladner, U. Current immunotherapy in rheumatoid arthritis. Immunotherapy 5, 955-974 (2013).
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).
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).
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).
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).
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).
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).
146. Robey, R. B. & Hay, N. Mitochondrial hexokinases, novel mediators of the antiapoptotic effects of growth factors and Akt. Oncogene 25, 4683-4696 (2006).
147. Wilson, J. E. Isozymes of mammalian hexokinase: structure, subcellular localization and metabolic function. J. Exp. Biol. 206, 2049-2057 (2003).
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).
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).
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).
151. Menendez, J. A. & Lupu, R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat. Rev. Cancer 7, 763-777 (2007).
152. Wise, D. R. & Thompson, C. B. Glutamine addiction: a new therapeutic target in cancer. Trends Biochem. Sci. 35, 427-433 (2010).
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).
154. Hatzivassiliou, G. et al. ATP citrate lyase inhibition can suppress tumor cell growth. Cancer Cell 8, 311-321 (2005).
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).
156. Hernandez-Alcoceba, R., Fernandez, 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).
157. Lacal, J. C. Choline kinase: a novel target for antitumor drugs. IDrugs 4, 419-426 (2001).
158. Nomura, D. K. et al. Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 140, 49-61 (2010).
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).
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).
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).
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).
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).
164. Adamski, J. Genome-wide association studies with metabolomics. Genome Med. 4, 34 (2012).
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).
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).
167. Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262-1267 (2012).
168. Arpaia, N. et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451-455 (2013).
169. Griffiths, W. J. et al. Targeted metabolomics for biomarker discovery. Angew. Chem. Int. Ed. Engl. 49, 5426-5445 (2010).
170. Lagarde, M., Gilon, A., Record, M., Vance, D. & Spener, F. Lipidomics is emerging. Biochim. Biophys. Acta 1634, 61 (2003).
171. Seeger, K. Metabolic changes in autoimmune diseases. Curr. Drug Discov. Technol. 6, 256-261 (2009).
172. Cairns, R. A., Harris, I. S. & Mak, T. W. Regulation of cancer cell metabolism. Nat. Rev. Cancer 11, 85-95 (2011).
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).
174. Elstrom, R. L. et al. Akt stimulates aerobic glycolysis in cancer cells. Cancer Res. 64, 3892-3899 (2004).
175. Hsu, P. P. & Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 134, 703-707 (2008).
176. Chang, C. H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239-1251 (2013).
177. Patra, K. C. & Hay, N. The pentose phosphate pathway and cancer. Trends Biochem. Sci. 39, 347-354 (2014).
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).
179. Lochner, M., Berod, L. & Sparwasser, T. Fatty acid metabolism in the regulation of T cell function. Trends Immunol. 36, 81-91 (2015).
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).
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).
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).
183. Agathocleous, M. & Harris, W. A. Metabolism in physiological cell proliferation and differentiation. Trends Cell Biol. 23, 484-492 (2013).