The Human Blood Metabolome-Transcriptome Interface

English version České info

Biological systems operate on multiple, intertwined organizational layers that can nowadays be accesses by high-throughput measurement methods, the so-called ‘omics’ technologies. A major aim in the field of systems biology is to understand the flow of biological information between the different layers at a systems level in both health and disease. To unravel the complex mechanisms underlying those molecular processes and to understand how the different functional levels interact with each other, an integrated analysis of multiple layers, i.e. a ‘multi-omics‘ approach is required. In our present study, we investigate the relationship between circulating metabolites in serum and whole-blood gene expression measured in the blood of individuals from a population-based cohort. To this end, we constructed a correlation network that displays which transcript and metabolite show the same trend of up- and down-regulation. We derived a functional characterization of the network by developing a novel computational analysis. The analysis revealed systematic signatures of signaling, transport and metabolic processes on both a regulatory and a pathway level. Moreover, integrating the network with associations to clinical markers such as HDL-cholesterol, LDL-cholesterol and TG identified coordinately activated pathways or modules which might help to assess the molecular machinery behind such an intermediate phenotype.

Vyšlo v časopise: The Human Blood Metabolome-Transcriptome Interface. PLoS Genet 11(6): e32767. doi:10.1371/journal.pgen.1005274
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005274

Souhrn

Zdroje

1. Liew C-C, Ma J, Tang H-C, Zheng R, Dempsey AA. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J Lab Clin Med. 2006;147: 126–132. doi: 10.1016/j.lab.2005.10.005 16503242

2. Herder C, Karakas M, Koenig W. Biomarkers for the Prediction of Type 2 Diabetes and Cardiovascular Disease. Clin Pharmacol Ther. 2011;90: 52–66. doi: 10.1038/clpt.2011.93 21654741

3. Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003;34: 166–176. doi: 10.1038/ng1165 12740579

4. Stuart JM, Segal E, Koller D, Kim SK. A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003;302: 249–255. doi: 10.1126/science.1087447 12934013

5. Bergmann S, Ihmels J, Barkai N. Similarities and Differences in Genome-Wide Expression Data of Six Organisms. PLoS Biol. 2003;2: e9. doi: 10.1371/journal.pbio.0020009 14737187

6. Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013;14: 632. doi: 10.1186/1471-2164-14-632 24053356

7. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37: 382–390. doi: 10.1038/ng1532 15778709

8. Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol. 2010;6: 377. doi: 10.1038/msb.2010.31 20531406

9. Nayak RR, Kearns M, Spielman RS, Cheung VG. Coexpression network based on natural variation in human gene expression reveals gene interactions and functions. Genome Res. 2009;19: 1953–1962. doi: 10.1101/gr.097600.109 19797678

10. Doering TA, Crawford A, Angelosanto JM, Paley MA, Ziegler CG, Wherry EJ. Network Analysis Reveals Centrally Connected Genes and Pathways Involved in CD8+ T Cell Exhaustion versus Memory. Immunity. 2012;37: 1130–1144. doi: 10.1016/j.immuni.2012.08.021 23159438

11. He F, Chen H, Probst-Kepper M, Geffers R, Eifes S, del Sol A, et al. PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells. Molecular Systems Biology. 2012;8. doi: 10.1038/msb.2012.56

12. Saris CG, Horvath S, Vught PW van, Es MA van, Blauw HM, Fuller TF, et al. Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients. BMC Genomics. 2009;10: 405. doi: 10.1186/1471-2164-10-405 19712483

13. Inouye M, Silander K, Hamalainen E, Salomaa V, Harald K, Jousilahti P, et al. An Immune Response Network Associated with Blood Lipid Levels. PLoS Genet. 2010;6: e1001113. doi: 10.1371/journal.pgen.1001113 20844574

14. Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol. 2014;15: 195–204. doi: 10.1038/ni.2789 24336226

15. Steuer R, Kurths J, Fiehn O, Weckwerth W. Observing and interpreting correlations in metabolomic networks. Bioinformatics. 2003;19: 1019–1026. doi: 10.1093/bioinformatics/btg120 12761066

16. Camacho D, de la Fuente A, Mendes P. The origin of correlations in metabolomics data. Metabolomics. 2005;1: 53–63. doi: 10.1007/s11306-005-1107-3

17. Morgenthal K, Weckwerth W, Steuer R. Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation. BioSystems. 2006;83: 108–117. doi: 10.1016/j.biosystems.2005.05.017 16303239

18. Orešič M, Tang J, Seppänen-Laakso T, Mattila I, Saarni SE, Saarni SI, et al. Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Medicine. 2011;3: 19. doi: 10.1186/gm233 21429189

19. Kujala UM, Mäkinen V-P, Heinonen I, Soininen P, Kangas AJ, Leskinen TH, et al. Long-term Leisure-time Physical Activity and Serum Metabolome. Circulation. 2013;127: 340–348. doi: 10.1161/CIRCULATIONAHA.112.105551 23258601

20. Valcarcel B, Ebbels TMD, Kangas AJ, Soininen P, Elliot P, Ala-Korpela M, et al. Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity. J R Soc Interface. 2014;11. doi: 10.1098/rsif.2013.0908

21. Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;advance online publication. doi: 10.1038/ng.2982

22. Krumsiek J, Suhre K, Illig T, Adamski J, Theis F. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Systems Biology. 2011;5: 21. doi: 10.1186/1752-0509-5-21 21281499

23. Krumsiek J, Suhre K, Evans AM, Mitchell MW, Mohney RP, Milburn MV, et al. Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information. PLoS Genet. 2012;8: e1003005. doi: 10.1371/journal.pgen.1003005 23093944

24. Mittelstrass K, Ried JS, Yu Z, Krumsiek J, Gieger C, Prehn C, et al. Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet. 2011;7: e1002215. doi: 10.1371/journal.pgen.1002215 21852955

25. Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, et al. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. PNAS. 2004;101: 10205–10210. doi: 10.1073/pnas.0403218101 15199185

26. Bylesjö M, Eriksson D, Kusano M, Moritz T, Trygg J. Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data. The Plant Journal. 2007;52: 1181–1191. doi: 10.1111/j.1365-313X.2007.03293.x 17931352

27. Ferrara CT, Wang P, Neto EC, Stevens RD, Bain JR, Wenner BR, et al. Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling. PLoS Genet. 2008;4: e1000034. doi: 10.1371/journal.pgen.1000034 18369453

28. Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, Vu H, et al. Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation. PLoS Biol. 2012;10: e1001301. doi: 10.1371/journal.pbio.1001301 22509135

29. Inouye M, Kettunen J, Soininen P, Silander K, Ripatti S, Kumpula LS, et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Molecular Systems Biology. 2010;6. doi: 10.1038/msb.2010.93

30. Homuth G, Teumer A, Völker U, Nauck M. A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling. J Endocrinol. 2012;215: 17–28. doi: 10.1530/JOE-12-0144 22782382

31. Petersen A-K, Zeilinger S, Kastenmüller G, Römisch-Margl W, Brugger M, Peters A, et al. Epigenetics meets metabolomics: An epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet. 2013; ddt430. doi: 10.1093/hmg/ddt430

32. Civelek M, Lusis AJ. Systems genetics approaches to understand complex traits. Nature Reviews Genetics. 2013;15: 34–48. doi: 10.1038/nrg3575 24296534

33. Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, et al. A community-driven global reconstruction of human metabolism. Nat Biotech. 2013;31: 419–425. doi: 10.1038/nbt.2488

34. Arsenault BJ, Boekholdt SM, Kastelein JJP. Lipid parameters for measuring risk of cardiovascular disease. Nat Rev Cardiol. 2011;8: 197–206. doi: 10.1038/nrcardio.2010.223 21283149

35. Aoki K, Ogata Y, Shibata D. Approaches for Extracting Practical Information from Gene Co-expression Networks in Plant Biology. Plant and Cell Physiology. 2007;48: 381–390. doi: 10.1093/pcp/pcm013 17251202

36. Palmer C, Diehn M, Alizadeh AA, Brown PO. Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics. 2006;7: 115. doi: 10.1186/1471-2164-7-115 16704732

37. Watkins NA, Gusnanto A, de Bono B, De S, Miranda-Saavedra D, Hardie DL, et al. A HaemAtlas: characterizing gene expression in differentiated human blood cells. Blood. 2009;113: e1–e9. doi: 10.1182/blood-2008-06-162958 19228925

38. Shoemaker JE, Lopes TJ, Ghosh S, Matsuoka Y, Kawaoka Y, Kitano H. CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data. BMC genomics. 2012;13: 460. doi: 10.1186/1471-2164-13-460 22953731

39. Sinclair D, Fillman SG, Webster MJ, Weickert CS. Dysregulation of glucocorticoid receptor co-factors FKBP5, BAG1 and PTGES3 in prefrontal cortex in psychotic illness. Sci Rep. 2013;3. doi: 10.1038/srep03539

40. Schoneveld OJLM, Gaemers IC, Lamers WH. Mechanisms of glucocorticoid signalling. Biochimica et Biophysica Acta (BBA)—Gene Structure and Expression. 2004;1680: 114–128. doi: 10.1016/j.bbaexp.2004.09.004

41. Wong S, Tan K, Carey KT, Fukushima A, Tiganis T, Cole TJ. Glucocorticoids stimulate hepatic and renal catecholamine inactivation by direct rapid induction of the dopamine sulfotransferase Sult1d1. Endocrinology. 2010;151: 185–194. doi: 10.1210/en.2009-0590 19966186

42. Polman JAE, Hunter RG, Speksnijder N, van den Oever JME, Korobko OB, McEwen BS, et al. Glucocorticoids modulate the mTOR pathway in the hippocampus: differential effects depending on stress history. Endocrinology. 2012;153: 4317–4327. doi: 10.1210/en.2012-1255 22778218

43. Schmidt S. Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia. Blood. 2006;107: 2061–2069. doi: 10.1182/blood-2005-07-2853 16293608

44. Philip AM, Daniel Kim S, Vijayan MM. Cortisol modulates the expression of cytokines and suppressors of cytokine signaling (SOCS) in rainbow trout hepatocytes. Dev Comp Immunol. 2012;38: 360–367. doi: 10.1016/j.dci.2012.07.005 22878426

45. Pei H, Yao Y, Yang Y, Liao K, Wu J-R. Krüppel-like factor KLF9 regulates PPARγ transactivation at the middle stage of adipogenesis. Cell Death Differ. 2011;18: 315–327. doi: 10.1038/cdd.2010.100 20725087

46. Liu Y-X, Wang J, Guo J, Wu J, Lieberman HB, Yin Y. DUSP1 Is Controlled by p53 during the Cellular Response to Oxidative Stress. Mol Cancer Res. 2008;6: 624–633. doi: 10.1158/1541-7786.MCR-07-2019 18403641

47. Spörl F, Korge S, Jürchott K, Wunderskirchner M, Schellenberg K, Heins S, et al. Krüppel-like factor 9 is a circadian transcription factor in human epidermis that controls proliferation of keratinocytes. PNAS. 2012;109: 10903–10908. doi: 10.1073/pnas.1118641109 22711835

48. Charmandari E, Chrousos GP, Lambrou GI, Pavlaki A, Koide H, Ng SSM, et al. Peripheral CLOCK regulates target-tissue glucocorticoid receptor transcriptional activity in a circadian fashion in man. PLoS ONE. 2011;6: e25612. doi: 10.1371/journal.pone.0025612 21980503

49. Zechner R, Zimmermann R, Eichmann TO, Kohlwein SD, Haemmerle G, Lass A, et al. FAT SIGNALS—Lipases and Lipolysis in Lipid Metabolism and Signaling. Cell Metabolism. 2012;15: 279–291. doi: 10.1016/j.cmet.2011.12.018 22405066

50. Fonseca BM, Costa MA, Almada M, Correia-da-Silva G, Teixeira NA. Endogenous cannabinoids revisited: A biochemistry perspective. Prostaglandins & Other Lipid Mediators. 2013;102–103: 13–30. doi: 10.1016/j.prostaglandins.2013.02.002

51. Kondo H, Hase T, Murase T, Tokimitsu I. Digestion and assimilation features of dietary DAG in the rat small intestine. Lipids. 2003;38: 25–30. 12669816

52. Schneider E, Leite-de-Moraes M, Dy M. Histamine, Immune Cells and Autoimmunity. In: Thurmond RL, editor. Histamine in Inflammation. Springer US; 2010. pp. 81–94. Available: http://link.springer.com/chapter/10.1007/978-1-4419-8056-4_9

53. Bresnick EH, Katsumura KR, Lee H-Y, Johnson KD, Perkins AS. Master regulatory GATA transcription factors: mechanistic principles and emerging links to hematologic malignancies. Nucl Acids Res. 2012; gks281. doi: 10.1093/nar/gks281

54. Zhang Y, Guo K, LeBlanc RE, Loh D, Schwartz GJ, Yu Y-H. Increasing Dietary Leucine Intake Reduces Diet-Induced Obesity and Improves Glucose and Cholesterol Metabolism in Mice via Multimechanisms. Diabetes. 2007;56: 1647–1654. doi: 10.2337/db07-0123 17360978

55. Kennedy MA, Barrera GC, Nakamura K, Baldán Á, Tarr P, Fishbein MC, et al. ABCG1 has a critical role in mediating cholesterol efflux to HDL and preventing cellular lipid accumulation. Cell Metabolism. 2005;1: 121–131. doi: 10.1016/j.cmet.2005.01.002 16054053

56. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125: 2222–2231. doi: 10.1161/CIRCULATIONAHA.111.067827 22496159

57. Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ. Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data. J Proteome Res. 2012;11: 4120–4131. doi: 10.1021/pr300231n 22713116

58. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics. 2014;23: R89–R98. doi: 10.1093/hmg/ddu328 25064373

59. Schramm K, Marzi C, Schurmann C, Carstensen M, Reinmaa E, Biffar R, et al. Mapping the Genetic Architecture of Gene Regulation in Whole Blood. PLoS ONE. 2014;9: e93844. doi: 10.1371/journal.pone.0093844 24740359

60. Walther D, Strassburg K, Durek P, Kopka J. Metabolic Pathway Relationships Revealed by an Integrative Analysis of the Transcriptional and Metabolic Temperature Stress-Response Dynamics in Yeast. OMICS: A Journal of Integrative Biology. 2010;14: 261–274. doi: 10.1089/omi.2010.0010 20455750

61. Klíma M, Broučková A, Koc M, Anděra L. T-cell activation triggers death receptor-6 expression in a NF-κB and NF-AT dependent manner. Molecular Immunology. 2011;48: 1439–1447. doi: 10.1016/j.molimm.2011.03.021 21501873

62. Kendall AC, Nicolaou A. Bioactive lipid mediators in skin inflammation and immunity. Progress in Lipid Research. 2013;52: 141–164. doi: 10.1016/j.plipres.2012.10.003 23124022

63. Alhouayek M, Masquelier J, Muccioli GG. Controlling 2-arachidonoylglycerol metabolism as an anti-inflammatory strategy. Drug Discovery Today. 2014;19: 295–304. doi: 10.1016/j.drudis.2013.07.009 23891880

64. Scortegagna M. The HIF family member EPAS1/HIF-2 is required for normal hematopoiesis in mice. Blood. 2003;102: 1634–1640. doi: 10.1182/blood-2003-02-0448 12750163

65. Radtke F, Fasnacht N, MacDonald HR. Notch Signaling in the Immune System. Immunity. 2010;32: 14–27. doi: 10.1016/j.immuni.2010.01.004 20152168

66. Bühring H-J, Streble A, Valent P. The Basophil-Specific Ectoenzyme E-NPP3 (CD203c) as a Marker for Cell Activation and Allergy Diagnosis. International Archives of Allergy and Immunology. 2004;133: 317–329. doi: 10.1159/000077351 15031605

67. Frateschi S, Camerer E, Crisante G, Rieser S, Membrez M, Charles R-P, et al. PAR2 absence completely rescues inflammation and ichthyosis caused by altered CAP1/Prss8 expression in mouse skin. Nat Commun. 2011; 161. doi: 10.1038/ncomms1162

68. Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucl Acids Res. 2013; gkt997. doi: 10.1093/nar/gkt997

69. Zeng L, Liao H, Liu Y, Lee T-S, Zhu M, Wang X, et al. Sterol-responsive element-binding protein (SREBP) 2 down-regulates ATP-binding cassette transporter A1 in vascular endothelial cells: a novel role of SREBP in regulating cholesterol metabolism. J Biol Chem. 2004;279: 48801–48807. doi: 10.1074/jbc.M407817200 15358760

70. Tontonoz P, Nagy L, Alvarez JG, Thomazy VA, Evans RM. PPARgamma promotes monocyte/macrophage differentiation and uptake of oxidized LDL. Cell. 1998;93: 241–252. 9568716

71. Cowell IG. E4BP4/NFIL3, a PAR-related bZIP factor with many roles. Bioessays. 2002;24: 1023–1029. doi: 10.1002/bies.10176 12386933

72. Everett L, Hansen M, Hannenhalli S. Regulating the regulators: modulators of transcription factor activity. Methods Mol Biol. 2010;674: 297–312. doi: 10.1007/978-1-60761-854-6_19 20827600

73. Tsuruoka N, Arima M, Arguni E, Saito T, Kitayama D, Sakamoto A, et al. Bcl6 is required for the IL-4-mediated rescue of the B cells from apoptosis induced by IL-21. Immunology Letters. 2007;110: 145–151. doi: 10.1016/j.imlet.2007.04.009 17532053

74. Floegel A, Wientzek A, Bachlechner U, Jacobs S, Drogan D, Prehn C, et al. Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study. Int J Obes (Lond). 2014; doi: 10.1038/ijo.2014.39

75. Mahaney MC, Blangero J, Comuzzie AG, VandeBerg JL, Stern MP, MacCluer JW. Plasma HDL Cholesterol, Triglycerides, and Adiposity A Quantitative Genetic Test of the Conjoint Trait Hypothesis in the San Antonio Family Heart Study. Circulation. 1995;92: 3240–3248. doi: 10.1161/01.CIR.92.11.3240 7586310

76. Li S, Ogawa W, Emi A, Hayashi K, Senga Y, Nomura K, et al. Role of S6K1 in regulation of SREBP1c expression in the liver. Biochem Biophys Res Commun. 2011;412: 197–202. doi: 10.1016/j.bbrc.2011.07.038 21806970

77. Perner S, Rupp NJ, Braun M, Rubin MA, Moch H, Dietel M, et al. Loss of SLC45A3 protein (prostein) expression in prostate cancer is associated with SLC45A3-ERG gene rearrangement and an unfavorable clinical course. Int J Cancer. 2013;132: 807–812. doi: 10.1002/ijc.27733 22821757

78. Hancock T, Wicker N, Takigawa I, Mamitsuka H. Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles. Schönbach C, editor. PLoS ONE. 2012;7: e31345. doi: 10.1371/journal.pone.0031345 22355360

79. Zelezniak A, Sheridan S, Patil KR. Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes. PLoS Comput Biol. 2014;10: e1003572. doi: 10.1371/journal.pcbi.1003572 24762675

80. Stobbe MD, Houten SM, Jansen GA, van Kampen AHC, Moerland PD. Critical assessment of human metabolic pathway databases: a stepping stone for future integration. BMC systems biology. 2011;5: 165. doi: 10.1186/1752-0509-5-165 21999653

81. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, et al. The Human Serum Metabolome. PLoS ONE. 2011;6: e16957. doi: 10.1371/journal.pone.0016957 21359215

82. Greene MW, Burrington CM, Lynch DT, Davenport SK, Johnson AK, Horsman MJ, et al. Lipid Metabolism, Oxidative Stress and Cell Death Are Regulated by PKC Delta in a Dietary Model of Nonalcoholic Steatohepatitis. Alisi A, editor. PLoS ONE. 2014;9: e85848. doi: 10.1371/journal.pone.0085848 24454937

83. The ENCODE Project Consortium TEP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489: 57–74. doi: 10.1038/nature11247 22955616

84. Calkin AC, Tontonoz P. Transcriptional integration of metabolism by the nuclear sterol-activated receptors LXR and FXR. Nat Rev Mol Cell Biol. 2012;13: 213–224. doi: 10.1038/nrm3312 22414897

85. Yvan-Charvet L, Wang N, Tall AR. The role of HDL, ABCA1 and ABCG1 transporters in cholesterol efflux and immune responses. Arterioscler Thromb Vasc Biol. 2010;30: 139–143. doi: 10.1161/ATVBAHA.108.179283 19797709

86. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A Branched-Chain Amino Acid-Related Metabolic Signature that Differentiates Obese and Lean Humans and Contributes to Insulin Resistance. Cell Metabolism. 2009;9: 311–326. doi: 10.1016/j.cmet.2009.02.002 19356713

87. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17: 448–453. doi: 10.1038/nm.2307 21423183

88. O’Connell TM. The Complex Role of Branched Chain Amino Acids in Diabetes and Cancer. Metabolites. 2013;3: 931–945. doi: 10.3390/metabo3040931 24958258

89. Holle R, Happich M, Löwel H, Wichmann HE, MONICA/KORA Study Group. KORA—a research platform for population based health research. Gesundheitswesen. 2005;67 Suppl 1: S19–25. doi: 10.1055/s-2005-858235 16032513

90. Wichmann H-E, Gieger C, Illig T, MONICA/KORA Study Group. KORA-gen—resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen. 2005;67 Suppl 1: S26–30. doi: 10.1055/s-2005-858226 16032514

91. Rathmann W, Strassburger K, Heier M, Holle R, Thorand B, Giani G, et al. Incidence of Type 2 diabetes in the elderly German population and the effect of clinical and lifestyle risk factors: KORA S4/F4 cohort study. Diabetic Medicine. 2009;26: 1212–1219. doi: 10.1111/j.1464-5491.2009.02863.x 20002472

92. Suhre K, Shin S-Y, Petersen A-K, Mohney RP, Meredith D, Wägele B, et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature. 2011;477: 54–60. doi: 10.1038/nature10354 21886157

93. Mehta D, Heim K, Herder C, Carstensen M, Eckstein G, Schurmann C, et al. Impact of common regulatory single-nucleotide variants on gene expression profiles in whole blood. European Journal of Human Genetics. 2012; Available: http://www.nature.com/ejhg/journal/vaop/ncurrent/full/ejhg2012106a.html

94. Schurmann C, Heim K, Schillert A, Blankenberg S, Carstensen M, Dörr M, et al. Analyzing Illumina Gene Expression Microarray Data from Different Tissues: Methodological Aspects of Data Analysis in the MetaXpress Consortium. Shomron N, editor. PLoS ONE. 2012;7: e50938. doi: 10.1371/journal.pone.0050938 23236413

95. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24: 1547–1548. doi: 10.1093/bioinformatics/btn224 18467348

96. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995;57: 289–300. doi: 10.2307/2346101

97. Arnold M, Raffler J, Pfeufer A, Suhre K, Kastenmuller G. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics. 2014; doi: 10.1093/bioinformatics/btu779

98. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine. 2008;27: 1133–1163. doi: 10.1002/sim.3034 17886233

99. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25: 25–29. doi: 10.1038/75556 10802651

100. Tonon L, Touzet H, Varré J-S. TFM-Explorer: mining cis-regulatory regions in genomes. Nucl Acids Res. 2010;38: W286–W292. doi: 10.1093/nar/gkq473 20522509

101. Defrance M, Touzet H. Predicting transcription factor binding sites using local over-representation and comparative genomics. BMC Bioinformatics. 2006;7: 396. doi: 10.1186/1471-2105-7-396 16945132