Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.
References
[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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:16303239
[18]
Ore？i？ M, Tang J, Sepp？nen-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. pmid:21429189
[19]
Kujala UM, M？kinen 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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:15199185
[26]
Bylesj？ 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. pmid: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. pmid: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. pmid: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, V？lker 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. pmid:22782382
[31]
Petersen A-K, Zeilinger S, Kastenmüller G, R？misch-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. pmid: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.
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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:18403641
[47]
Sp？rl 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. pmid: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. pmid: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. pmid: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. pmid:12669816 doi: 10.1007/s11745-003-1027-7
[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.
[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. pmid: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. pmid: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. pmid:22496159
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. pmid: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. pmid: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. pmid:20455750
[61]
Klíma M, Brou？ková 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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:9568716 doi: 10.1016/s0092-8674(00)81575-5
[71]
Cowell IG. E4BP4/NFIL3, a PAR-related bZIP factor with many roles. Bioessays. 2002;24: 1023–1029. doi: 10.1002/bies.10176. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:22821757
[78]
Hancock T, Wicker N, Takigawa I, Mamitsuka H. Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles. Sch？nbach C, editor. PLoS ONE. 2012;7: e31345. doi: 10.1371/journal.pone.0031345. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:24958258
[89]
Holle R, Happich M, L？wel 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. pmid: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. pmid: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. pmid:20002472
[92]
Suhre K, Shin S-Y, Petersen A-K, Mohney RP, Meredith D, W？gele B, et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature. 2011;477: 54–60. doi: 10.1038/nature10354. pmid: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;
[94]
Schurmann C, Heim K, Schillert A, Blankenberg S, Carstensen M, D？rr 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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:16945132
