Metabolomics in epidemiology: From metabolite concentrations to integrative reaction networks

International Journal of Epidemiology

Volumn 45, Issue 5, 2016, Pages 1319-1328

  Fearnley, Liam G ^a   Inouye, Michael ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Epidemiology; Genomics; Metabolomics; Systems biology; Transcriptomics

Indexed keywords

BIOLOGICAL MARKER; TRANSCRIPTOME;

CHEMICAL REACTION; GENOMICS; HUMAN; METABOLITE; METABOLOME; METABOLOMICS; PRIORITY JOURNAL; REVIEW; EPIDEMIOLOGY; MEDICAL RESEARCH; PROCEDURES; SYSTEMS BIOLOGY;

BIOMARKERS; BIOMEDICAL RESEARCH; EPIDEMIOLOGIC STUDIES; HUMANS; METABOLOMICS; SYSTEMS BIOLOGY;

EID: 84994568365     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyw046     Document Type: Review

References (107)

1. Boogerd FC, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV. Towards philosophical foundations of systems biology: Introduction. In: Boogerd FC, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV (eds) Systems Biology. Amsterdam: Elsevier, 2007.
2. Marx V. Biology: The big challenges of big data. Nature 2013;498:255–60.
3. Aderem A. Systems biology: its practice and challenges. Cell 2005;121:511–13.
4. Westerhoff HV, Palsson BO. The evolution of molecular biology into systems biology. Nat Biotechnol 2004;22:1249–52.
5. James AT, Martin AJ. Gas-liquid partition chromatography; the separation and micro-estimation of volatile fatty acids from formic acid to dodecanoic acid. Biochem J 1952;50:679–90.
6. Hoult DI, Busby SJW, Gadian DG, Radda GK, Richards RE, Seeley PJ. Observation of tissue metabolites using 31P nuclear magnetic resonance. Nature 1974;252:285–87.
7. Jonsson P, Johansson AI, Gullberg J et al. High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Anal Chem 2005;77:5635–42.
8. Barton RH, Nicholson JK, Elliott P, Holmes E. High-throughput 1H NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study. Int J Epidemiol 2008;37(Suppl 1):i31–40.
9. Junot C, Fenaille F, Colsch B, Becher F. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass Spectrom Rev 2014;33:471–500.
10. Fuhrer T, Zamboni N. High-throughput discovery metabolomics. Curr Opin Biotechnol 2015;31:73–78.
11. Sevin DC, Sauer U. Ubiquinone accumulation improves osmotic-stress tolerance in Escherichia coli. Nat Chem Biol 2014;10:266–72.
12. Soininen P, Kangas AJ, Wurtz P, Suna T, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet 2015;8:192–206.
13. Lander ES, Linton LM, Birren B et al. Initial sequencing and analysis of the human genome. Nature 2001;409:860–921.
14. Emes RD, Farrell WE. Make way for the ‘next generation’: application and prospects for genome-wide, epigenome-specific technologies in endocrine research. J Mol Endocrinol 2012;49:R19–27.
15. Tang F, Barbacioru C, Wang Y et al. mRNA-Seq whole-tran-scriptome analysis of a single cell. Nat Methods 2009;6: 377–82.
16. Nilsson T, Mann M, Aebersold R, Yates JR 3rd, Bairoch A, Bergeron JJ. Mass spectrometry in high-throughput prote-omics: ready for the big time. Nat Methods 2010;7:681–85.
17. Wilhelm M, Schlegl J, Hahne H et al. Mass-spectrometry-based draft of the human proteome. Nature 2014;509:582–87.
18. Zoldos V, Horvat T, Lauc G. Glycomics meets genomics, epige-nomics and other high throughput omics for system biology studies. Curr Opin Chem Biol 2013;17:34–40.
19. Han X, Yang K, Gross RW. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipido-mic analyses. Mass Spectrom Rev 2012;31:134–78.
20. Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature 2012;490:61–70.
21. Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000;406:747–52.
22. Soutar AK, Naoumova RP. Mechanisms of disease: genetic causes of familial hypercholesterolemia. Nat Clin Pract Cardiovasc Med 2007;4:214–25.
23. Nicholson JK, Holmes E, Elliott P. The metabolome-wide association study: a new look at human disease risk factors. J Proteome Res 2008;7:3637–38.
24. Chadeau-Hyam M, Ebbels TM, Brown IJ et al. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. J Proteome Res 2010;9:4620–27.
25. Wang TJ, Larson MG, Vasan RS et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448–53.
26. Sampson JN, Boca SM, Shu XO et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev 2013; 22:631–40.
27. Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol 2011;5:21.
28. Jourdan C, Petersen AK, Gieger C et al. Body fat free mass is associated with the serum metabolite profile in a population-based study. PLoS One 2012; 7:e40009.
29. Xia J, Wishart DS. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 2010;38:W71–77.
30. Subramanian A, Tamayo P, Mootha VK et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545–50.
31. Ashburner M, Ball CA, Blake JA et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25–29.
32. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 2014;42:D199–205.
33. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 2012;8:e1002375.
34. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet 2012;90:7–24.
35. Dumas ME, Wilder SP, Bihoreau MT et al. Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat Genet 2007;39:666–72.
36. Dumas M-E,Gauguier D. Mapping metabolomic quantitative trait loci (mQTL): a link between metabolome-wide association studies and systems biology. In: Suhre K (ed). Genetics Meets Metabolomics. New York, NY: Springer, 2012.
37. Tukiainen T, Kettunen J, Kangas AJ et al. Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci. Hum Mol Genet 2012;21:1444–55.
38. Adamski J. Genome-wide association studies with metabolomics. Genome Med 2012;4:34.
39. Adamski J, Suhre K. Metabolomics platforms for genome wide association studies - linking the genome to the metabolome. Curr Opin Biotechnol 2013;24:39–47.
40. Demirkan A, van Duijn CM, Ugocsai P et al. Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS Genet 2012;8:e1002490.
41. Kettunen J, Tukiainen T, Sarin AP et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 2012;44:269–76.
42. Inouye M, Ripatti S, Kettunen J et al. Novel loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genet 2012;8: e1002907.
43. Holle R, Happich M, Lowel H, Wichmann HE; Group MKS. KORA - a research platform for population based health research. Gesundheitswesen 2005;67(Suppl 1):S19–25.
44. Illig T, Gieger C, Zhai G et al. A genome-wide perspective of genetic variation in human metabolism. Nat Genet 2010;42:137–41.
45. Suhre K, Shin SY, Petersen AK et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 2011;477:54–60.
46. Gieger C, Geistlinger L, Altmaier E et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 2008;4:e1000282.
47. Welter D, MacArthur J, Morales J et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014;42:D1001–06.
48. Leung E, Hong J, Fraser AG, Merriman TR, Vishnu P, Krissansen GW. Polymorphisms in the organic cation transporter genes SLC22A4 and SLC22A5 and Crohn’s disease in a New Zealand Caucasian cohort. Immunol Cell Biol 2006;84:233–36.
49. Peltekova VD, Wintle RF, Rubin LA et al. Functional variants of OCTN cation transporter genes are associated with Crohn disease. Nat Genet 2004;36:471–75.
50. Davey Smith G, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol 2004;33:30–42.
51. Ogbuanu IU, Zhang H, Karmaus W. Can we apply the Mendelian randomization methodology without considering epigenetic effects? Emerg Themes Epidemiol 2009;6:3.
52. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014;23:R89–98.
53. Grummt I, Ladurner AG. A metabolic throttle regulates the epigenetic state of rDNA. Cell 2008;133:577–80.
54. Kaelin WG Jr, McKnight SL. Influence of metabolism on epi-genetics and disease. Cell 2013;153:56–69.
55. Haase CL, Tybjaerg-Hansen A, Qayyum AA, Schou J, Nordestgaard BG, Frikke-Schmidt R. LCAT, HDL cholesterol and ischemic cardiovascular disease: a Mendelian randomization study of HDL cholesterol in 54,500 individuals. J Clin Endocrinol Metab 2012;97:E248–56.
56. Voight BF, Peloso GM, Orho-Melander M et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian ran-domisation study. Lancet 2012;380:572–80.
57. Ference BA, Yoo W, Alesh I et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis. J Am Coll Cardiol 2012;60:2631–39.
58. Wurtz P, Wang Q, Kangas AJ et al. Metabolic signatures of adi-posity in young adults: Mendelian randomization analysis and effects of weight change. PLoS Med 2014;11:e1001765.
59. Varbo A, Benn M, Davey Smith G, Timpson NJ, Tybjaerg-Hansen A, Nordestgaard BG. Remnant cholesterol, low-density lipoprotein cholesterol, and blood pressure as mediators from obesity to ischemic heart disease. Circ Res 2015;116:665–73.
60. Burgess S, Daniel RM, Butterworth AS, Thompson SG; Consortium EP-I. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol 2015;44:484–95.
61. Brion M-J, Benyamin B, Visscher P, Smith G. Beyond the single SNP: emerging developments in Mendelian randomization in the “omics” era. Curr Epidemiol Rep 2014;1:228–36. Doi: 10.1007/s40471-014-0024-2.
62. Nicholson JK, Holmes E, Kinross J et al. Host-gut microbiota metabolic interactions. Science 2012;336:1262–67.
63. Tremaroli V, Backhed F. Functional interactions between the gut microbiota and host metabolism. Nature 2012;489:242–49.
64. Turnbaugh PJ, Hamady M, Yatsunenko T et al. A core gut microbiome in obese and lean twins. Nature 2009; 57: 80–84.
65. Teo SM, Mok D, Pham K et al. The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe 2015;17:704–15.
66. Kostic AD, Gevers D, Siljander H et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 2015;17:260–73.
67. Greenblum S, Turnbaugh PJ, Borenstein E. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci U S A 2012;109:594–99.
68. Wang Z, Klipfell E, Bennett BJ et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011;472:57–63.
69. Schadt EE, Bjorkegren JL. NEW: network-enabled wisdom in biology, medicine, and health care. Sci Transl Med 2012;4:115rv1.
70. Dobrin R, Zhu J, Molony C et al. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol 2009;10:R55.
71. Chen Y, Zhu J, Lum PY et al. Variations in DNA elucidate molecular networks that cause disease. Nature 2008;452:429–35.
72. Oldham MC, Langfelder P, Horvath S. Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst Biol 2012;6:63.
73. Dewey FE, Perez MV, Wheeler MT et al. Gene coexpression network topology of cardiac development, hypertrophy, and failure. Circ Cardiovasc Genet 2011;4:26–35.
74. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.
75. Ala-Korpela M, Kangas AJ, Inouye M. Genome-wide association studies and systems biology: together at last. Trends Genet 2011;27:493–98.
76. Inouye M, Silander K, Hamalainen E et al. An immune response network associated with blood lipid levels. PLoS Genet 2010;6:e1001113.
77. Inouye M, Kettunen J, Soininen P et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol 2010;6:441.
78. Bartel J, Krumsiek J, Schramm K et al. The Human Blood Metabolome-Transcriptome Interface. PLoS Genet 2015;11:e1005274.
79. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89–95.
80. Suhre K, Gieger C. Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet 2012;13: 759–69.
81. Bottomley PA. Noninvasive study of high-energy phosphate metabolism in human heart by depth-resolved 31P NMR spectroscopy. Science 1985;229:769–72.
82. Nelson SJ, Kurhanewicz J, Vigneron DB et al. Metabolic imaging of patients with prostate cancer using hyperpolarized [1-(1)(3)C]pyruvate. Sci Transl Med 2013;5:198ra08.
83. Moreno KX, Sabelhaus SM, Merritt ME, Sherry AD, Malloy CR. Competition of pyruvate with physiological substrates for oxidation by the heart: implications for studies with hyperpolarized [1-13C]pyruvate. Am J Physiol Heart Circ Physiol 2010;298:H1556–64.
84. Schroeder MA, Cochlin LE, Heather LC, Clarke K, Radda GK, Tyler DJ. In vivo assessment of pyruvate dehydrogenase flux in the heart using hyperpolarized carbon-13 magnetic resonance. Proc Natl Acad Sci U S A 2008;105:12051–56.
85. Bottomley PA, Panjrath GS, Lai S et al. Metabolic rates of ATP transfer through creatine kinase (CK Flux) predict clinical heart failure events and death. Sci Transl Med 2013;5:215re3.
86. Lygate CA, Neubauer S. Metabolic flux as a predictor of heart failure prognosis. Circ Res 2014;114:1228–30.
87. MacArthur DG, Balasubramanian S, Frankish A et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 2012;335:823–28.
88. Kaufman S, Berlow S, Summer GK et al. Hyperphenylalaninemia due to a deficiency of biopterin. A variant form of phenylketonuria. N Engl J Med 1978;299:673–79.
89. Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech 2009;2:231–37.
90. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646–74.
91. Papa S, Bubici C, Zazzeroni F, Franzoso G. Mechanisms of liver disease: cross-talk between the NF-kappaB and JNK pathways. Biol Chem 2009;390:965–76.
92. Campbell ME, Spielberg SP, Kalow W. A urinary metabolite ratio that reflects systemic caffeine clearance. Clin Pharmacol Ther 1987;42:157–65.
93. Dempsey D, Tutka P, Jacob P 3rd et al. Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity. Clin Pharmacol Ther 2004;76:64–72.
94. Thiele I, Swainston N, Fleming RM et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 2013;31:419–25.
95. Petersen AK, Krumsiek J, Wagele B et al. On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. BMC Bioinformatics 2012;13:120.
96. Shin SY, Fauman EB, Petersen AK et al. An atlas of genetic influences on human blood metabolites. Nat Genet 2014;46:543–50.
97. Palsson B. Systems Biology: Properties of Reconstructed Networks. New York, NY: Cambridge University Press, 2006.
98. Croft D, Mundo AF, Haw R et al. The Reactome pathway knowledgebase. Nucleic Acids Res 2014;42:D472–77.
99. Palsson B. Metabolic systems biology. FEBS Lett 2009;583:3900–04.
100. Chaouiya C. Petri net modelling of biological networks. Brief Bioinform 2007;8:210–19.
101. Ryll A, Bucher J, Bonin A et al. A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models. Biosystems 2014;124:26–38.
102. Goryanin I, Hodgman TC, Selkov E. Mathematical simulation and analysis of cellular metabolism and regulation. Bioinformatics 1999;15:749–58.
103. Veenstra TD. Metabolomics: the final frontier? Genome Med 2012;4:40.
104. Looser R, Krotzky AJ, Trethewey RN. Metabolite Profiling with GC-MS and LC-MS. Boston, MA: Springer US, 2005.
105. Koulman A, Tapper BA, Fraser K, Cao M, Lane GA, Rasmussen S. High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics. Rapid Commun Mass Spectrom 2007;21:421–28.
106. Fischer K, Kettunen J, Wurtz P et al. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons. PLoS Med 2014;11:e1001606.
107. Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol 2012;41:161–76.