Omics: Potential Role in Early-Phase Drug Development

  • Harald Grallert
  • Carola S. Marzi
  • Stefanie M. Hauck
  • Christian Gieger


The development of high-throughput omics technologies has nourished the hope to improve our understanding and treatment of the pathophysiology of globally increasing diseases such as type 2 diabetes and obesity. These technologies provide innovative tools that have the potential to truly revolutionize patient care. Technologies continue to propel the omics fields forward. However, translating research discovery into routine clinical applications use is a complex process not only from scientific prospective but also from ethical, political, and logistic points of view. Particularly the implementation of omics-based tests requires changes in fundamental processes of regulation, reimbursement, and clinical practice. Altogether, developments in the field of omics technologies hold great promise to optimize patient care and improve outcomes and eventually lead to new tests that are well integrated in routine medical care.


Genomics Epigenomics Transcriptomics Proteomics Metabolomics Microarray Sequencing Disease prediction Pharmacogenomics Personalized medicine 


  1. 1.
    International Diabetes Federation (IDF) Diabetes atlas. Brussels: International Diabetes Federation; 2013. Available from:
  2. 2.
    Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev. 2008;17(11):2954–7. Epub 2008/11/08.PubMedGoogle Scholar
  3. 3.
    Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem. 2011;57(9):1224–6. Epub 2011/06/22.PubMedGoogle Scholar
  4. 4.
    McCarthy MI. Genomics, type 2 diabetes, and obesity. N Engl J Med. 2010;363(24):2339–50. Epub 2010/12/15.PubMedGoogle Scholar
  5. 5.
    Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615. Epub 2012/09/27.PubMedCentralPubMedGoogle Scholar
  6. 6.
    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(4):448–53. Epub 2011/03/23.PubMedCentralPubMedGoogle Scholar
  7. 7.
    Mannino GC, Sesti G. Individualized therapy for type 2 diabetes: clinical implications of pharmacogenetic data. Mol Diagn Ther. 2012;16(5):285–302. Epub 2012/09/29.PubMedGoogle Scholar
  8. 8.
    Wheeler E, Barroso I. Genome-wide association studies and type 2 diabetes. Brief Funct Genomics. 2011;10(2):52–60.PubMedGoogle Scholar
  9. 9.
    Mahajan A, Go MJ, Zhang W, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46(3):234–44. Epub 2014/02/11.PubMedGoogle Scholar
  10. 10.
    Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–90. Epub 2012/08/14.PubMedCentralPubMedGoogle Scholar
  11. 11.
    Locke AE, Kahali B, Berndt S, Justice AE, Pers TH, Day FR, et al. Large-scale genetic studies of body mass index provide insight into the biological basis of obesity. 2014 (in press).Google Scholar
  12. 12.
    Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, et al. New genetic loci link adipocyte and insulin biology to body 1 fat distribution. 2014 (in press).Google Scholar
  13. 13.
    Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: does genomic profiling to assess type 2 diabetes risk improve health outcomes? Genet Med. 2013;15(8):612–7. Epub 2013/03/16.Google Scholar
  14. 14.
    Burke W, Psaty BM. Personalized medicine in the era of genomics. JAMA. 2007;298(14):1682–4. Epub 2007/10/11.PubMedGoogle Scholar
  15. 15.
    Maxam AM, Gilbert W. A new method for sequencing DNA. Proc Natl Acad Sci U S A. 1977;74(2):560–4. Epub 1977/02/01.PubMedCentralPubMedGoogle Scholar
  16. 16.
    Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74(12):5463–7. Epub 1977/12/01.PubMedCentralPubMedGoogle Scholar
  17. 17.
    Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308(5720):385–9. Epub 2005/03/12.PubMedCentralPubMedGoogle Scholar
  18. 18.
    McCarthy JJ, McLeod HL, Ginsburg GS. Genomic medicine: a decade of successes, challenges, and opportunities. Sci Transl Med. 2013;5(189):189sr4. Epub 2013/06/14.PubMedGoogle Scholar
  19. 19.
    Lander ES. Initial impact of the sequencing of the human genome. Nature. 2011;470(7333):187–97. Epub 2011/02/11.PubMedGoogle Scholar
  20. 20.
    Boyd SD. Diagnostic applications of high-throughput DNA sequencing. Annu Rev Pathol. 2013;8:381–410. Epub 2012/11/06.PubMedGoogle Scholar
  21. 21.
    Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65. Epub 2012/11/07.PubMedGoogle Scholar
  22. 22.
    Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011;3(96):96ra76. Epub 2011/08/19.PubMedCentralPubMedGoogle Scholar
  23. 23.
    Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;30(4):317–20. Epub 2012/04/12.PubMedGoogle Scholar
  24. 24.
    Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011;3(96):96ra77. Epub 2011/08/19.PubMedCentralPubMedGoogle Scholar
  25. 25.
    Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057–68. Epub 2010/10/15.PubMedGoogle Scholar
  26. 26.
    Feil R, Fraga MF. Epigenetics and the environment: emerging patterns and implications. Nat Rev Genet. 2011;13(2):97–109. Epub 2012/01/05.Google Scholar
  27. 27.
    Herceg Z, Vaissiere T. Epigenetic mechanisms and cancer: an interface between the environment and the genome. Epigenetics: Off J DNA Methylation Soc. 2011;6(7):804–19. Epub 2011/07/16.Google Scholar
  28. 28.
    Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics: Off J DNA Methylation Soc. 2011;6(6):692–702. Epub 2011/05/20.Google Scholar
  29. 29.
    Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98(4):288–95. Epub 2011/08/16.PubMedGoogle Scholar
  30. 30.
    Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL, et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol. 2010;28(10):1097–105. Epub 2010/09/21.PubMedCentralPubMedGoogle Scholar
  31. 31.
    Baylin SB, Jones PA. A decade of exploring the cancer epigenome – biological and translational implications. Nat Rev Cancer. 2011;11(10):726–34. Epub 2011/09/24.PubMedCentralPubMedGoogle Scholar
  32. 32.
    Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature. 2008;452(7184):215–9. Epub 2008/02/19.PubMedCentralPubMedGoogle Scholar
  33. 33.
    Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008;133(3):523–36. Epub 2008/04/22.PubMedCentralPubMedGoogle Scholar
  34. 34.
    Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–22. Epub 2009/10/16.PubMedCentralPubMedGoogle Scholar
  35. 35.
    Schweighoffer F, Ait-Ikhlef A, Resink AL, Brinkman B, Melle-Milovanovic D, Laurent-Puig P, et al. Qualitative gene profiling: a novel tool in genomics and in pharmacogenomics that deciphers messenger RNA isoforms diversity. Pharmacogenomics. 2000;1(2):187–97. Epub 2001/03/21.PubMedGoogle Scholar
  36. 36.
    Yamada K, Lim J, Dale JM, Chen H, Shinn P, Palm CJ, et al. Empirical analysis of transcriptional activity in the Arabidopsis genome. Science. 2003;302(5646):842–6. Epub 2003/11/01.PubMedGoogle Scholar
  37. 37.
    Cheng J, Kapranov P, Drenkow J, Dike S, Brubaker S, Patel S, et al. Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science. 2005;308(5725):1149–54. Epub 2005/03/26.PubMedGoogle Scholar
  38. 38.
    Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, et al. Global identification of human transcribed sequences with genome tiling arrays. Science. 2004;306(5705):2242–6. Epub 2004/11/13.PubMedGoogle Scholar
  39. 39.
    David L, Huber W, Granovskaia M, Toedling J, Palm CJ, Bofkin L, et al. A high-resolution map of transcription in the yeast genome. Proc Natl Acad Sci U S A. 2006;103(14):5320–5. Epub 2006/03/30.PubMedCentralPubMedGoogle Scholar
  40. 40.
    Okoniewski MJ, Miller CJ. Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC Bioinformatics. 2006;7:276. Epub 2006/06/06.PubMedCentralPubMedGoogle Scholar
  41. 41.
    Royce TE, Rozowsky JS, Gerstein MB. Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification. Nucleic Acids Res. 2007;35(15):e99. Epub 2007/08/10.PubMedCentralPubMedGoogle Scholar
  42. 42.
    Wolf-Yadlin A, Sevecka M, MacBeath G. Dissecting protein function and signaling using protein microarrays. Curr Opin Chem Biol. 2009;13(4):398–405. Epub 2009/08/08.PubMedCentralPubMedGoogle Scholar
  43. 43.
    Ahrens CH, Brunner E, Qeli E, Basler K, Aebersold R. Generating and navigating proteome maps using mass spectrometry. Nat Rev Mol Cell Biol. 2010;11(11):789–801. Epub 2010/10/15.PubMedGoogle Scholar
  44. 44.
    Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, Domon B, et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat Methods. 2010;7(1):43–6. Epub 2009/12/08.PubMedGoogle Scholar
  45. 45.
    Omenn GS, Baker MS, Aebersold R. Recent Workshops of the HUPO Human Plasma Proteome Project (HPPP): a bridge with the HUPO CardioVascular Initiative and the emergence of SRM targeted proteomics. Proteomics. 2011;11(17):3439–43. Epub 2011/08/19.PubMedGoogle Scholar
  46. 46.
    Farrah T, Deutsch EW, Omenn GS, Campbell DS, Sun Z, Bletz JA, et al. A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas. Mol Cell Proteomics. 2011;10(9), M110 006353. Epub 2011/06/03.PubMedCentralPubMedGoogle Scholar
  47. 47.
    Fagerberg L, Stromberg S, El-Obeid A, Gry M, Nilsson K, Uhlen M, et al. Large-scale protein profiling in human cell lines using antibody-based proteomics. J Proteome Res. 2011;10(9):4066–75. Epub 2011/07/06.PubMedGoogle Scholar
  48. 48.
    Ayoglu B, Haggmark A, Neiman M, Igel U, Uhlen M, Schwenk JM, et al. Systematic antibody and antigen-based proteomic profiling with microarrays. Expert Rev Mol Diagn. 2011;11(2):219–34. Epub 2011/03/17.PubMedGoogle Scholar
  49. 49.
    Legrain P, Aebersold R, Archakov A, Bairoch A, Bala K, Beretta L, et al. The human proteome project: current state and future direction. Mol Cell Proteomics. 2011;10(7):M111 009993. Epub 2011/07/12.PubMedCentralPubMedGoogle Scholar
  50. 50.
    Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010;5(12):e15004. Epub 2010/12/18.PubMedCentralPubMedGoogle Scholar
  51. 51.
    Service RF. Chemistry. Click chemistry clicks along. Science. 2008;320(5878):868–9. Epub 2008/05/20.PubMedGoogle Scholar
  52. 52.
    Weckwerth W. Metabolomics in systems biology. Annu Rev Plant Biol. 2003;54:669–89. Epub 2003/09/25.PubMedGoogle Scholar
  53. 53.
    Zhang GF, Sadhukhan S, Tochtrop GP, Brunengraber H. Metabolomics, pathway regulation, and pathway discovery. J Biol Chem. 2011;286(27):23631–5. Epub 2011/05/14.PubMedCentralPubMedGoogle Scholar
  54. 54.
    Seppanen-Laakso T, Oresic M. How to study lipidomes. J Mol Endocrinol. 2009;42(3):185–90. Epub 2008/12/09.PubMedGoogle Scholar
  55. 55.
    Masoodi M, Eiden M, Koulman A, Spaner D, Volmer DA. Comprehensive lipidomics analysis of bioactive lipids in complex regulatory networks. Anal Chem. 2010;82(19):8176–85. Epub 2010/09/11.PubMedGoogle Scholar
  56. 56.
    Drexler DM, Reily MD, Shipkova PA. Advances in mass spectrometry applied to pharmaceutical metabolomics. Anal Bioanal Chem. 2011;399(8):2645–53. Epub 2010/11/26.PubMedGoogle Scholar
  57. 57.
    Lewis GD, Gerszten RE. Toward metabolomic signatures of cardiovascular disease. Circ Cardiovasc Genet. 2010;3(2):119–21. Epub 2010/04/22.PubMedCentralPubMedGoogle Scholar
  58. 58.
    Arrell DK, Zlatkovic Lindor J, Yamada S, Terzic A. K(ATP) channel-dependent metaboproteome decoded: systems approaches to heart failure prediction, diagnosis, and therapy. Cardiovasc Res. 2011;90(2):258–66. Epub 2011/02/16.PubMedCentralPubMedGoogle Scholar
  59. 59.
    Millis MP. Medium-throughput SNP, genotyping using mass spectrometry: multiplex SNP genotyping using the iPLEX(R) Gold assay. Methods Mol Biol. 2011;700:61–76. Epub 2011/01/05.PubMedGoogle Scholar
  60. 60.
    Johnson JA, Burkley BM, Langaee TY, Clare-Salzler MJ, Klein TE, Altman RB. Implementing personalized medicine: development of a cost-effective customized pharmacogenetics genotyping array. Clin Pharmacol Ther. 2012;92(4):437–9. Epub 2012/08/23.PubMedCentralPubMedGoogle Scholar
  61. 61.
    Kim KK, Won HH, Cho SS, Park JH, Kim MJ, Kim S, et al. Comparison of identical single nucleotide polymorphisms genotyped by the GeneChip Targeted Genotyping 25K, Affymetrix 500K and Illumina 550K platforms. Genomics. 2009;94(2):89–93. Epub 2009/04/28.PubMedGoogle Scholar
  62. 62.
    Zagursky RJ, McCormick RM. DNA sequencing separations in capillary gels on a modified commercial DNA sequencing instrument. Biotechniques. 1990;9(1):74–9. Epub 1990/07/01.PubMedGoogle Scholar
  63. 63.
    Kircher M, Kelso J. High-throughput DNA sequencing – concepts and limitations. BioEssays. 2010;32(6):524–36. Epub 2010/05/21.PubMedGoogle Scholar
  64. 64.
    Kircher M, Stenzel U, Kelso J. Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Genome Biol. 2009;10(8):R83. Epub 2009/08/18.PubMedCentralPubMedGoogle Scholar
  65. 65.
    Emond MJ, Louie T, Emerson J, Zhao W, Mathias RA, Knowles MR, et al. Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis. Nat Genet. 2012;44(8):886–9. Epub 2012/07/10.PubMedCentralPubMedGoogle Scholar
  66. 66.
    Schnabel RB, Baccarelli A, Lin H, Ellinor PT, Benjamin EJ. Next steps in cardiovascular disease genomic research – sequencing, epigenetics, and transcriptomics. Clin Chem. 2012;58(1):113–26. Epub 2011/11/22.PubMedCentralPubMedGoogle Scholar
  67. 67.
    Umer M, Herceg Z. Deciphering the epigenetic code: an overview of DNA methylation analysis methods. Antioxid Redox Signal. 2013;18(15):1972–86. Epub 2012/11/06.PubMedCentralPubMedGoogle Scholar
  68. 68.
    Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A. 1996;93(18):9821–6. Epub 1996/09/03.PubMedCentralPubMedGoogle Scholar
  69. 69.
    Eads CA, Danenberg KD, Kawakami K, Saltz LB, Blake C, Shibata D, et al. MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res. 2000;28(8):E32. Epub 2000/03/29.PubMedCentralPubMedGoogle Scholar
  70. 70.
    Warnecke PM, Stirzaker C, Melki JR, Millar DS, Paul CL, Clark SJ. Detection and measurement of PCR bias in quantitative methylation analysis of bisulphite-treated DNA. Nucleic Acids Res. 1997;25(21):4422–6. Epub 1997/10/23.PubMedCentralPubMedGoogle Scholar
  71. 71.
    Uhlmann K, Brinckmann A, Toliat MR, Ritter H, Nurnberg P. Evaluation of a potential epigenetic biomarker by quantitative methyl-single nucleotide polymorphism analysis. Electrophoresis. 2002;23(24):4072–9. Epub 2002/12/14.PubMedGoogle Scholar
  72. 72.
    Dejeux E, El Abdalaoui H, Gut IG, Tost J. Identification and quantification of differentially methylated loci by the pyrosequencing technology. Methods Mol Biol. 2009;507:189–205. Epub 2008/11/07.PubMedGoogle Scholar
  73. 73.
    Tost J, El Abdalaoui H, Gut IG. Serial pyrosequencing for quantitative DNA methylation analysis. Biotechniques. 2006;40(6):721–2. 4, 6. Epub 2006/06/16.PubMedGoogle Scholar
  74. 74.
    Ehrich M, Nelson MR, Stanssens P, Zabeau M, Liloglou T, Xinarianos G, et al. Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry. Proc Natl Acad Sci U S A. 2005;102(44):15785–90. Epub 2005/10/26.PubMedCentralPubMedGoogle Scholar
  75. 75.
    Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet. 2005;37(8):853–62. Epub 2005/07/12.PubMedGoogle Scholar
  76. 76.
    Rauch TA, Wu X, Zhong X, Riggs AD, Pfeifer GP. A human B cell methylome at 100-base pair resolution. Proc Natl Acad Sci U S A. 2009;106(3):671–8. Epub 2009/01/14.PubMedCentralPubMedGoogle Scholar
  77. 77.
    Laird PW. Principles and challenges of genomewide DNA methylation analysis. Nat Rev Genet. 2010;11(3):191–203. Epub 2010/02/04.PubMedGoogle Scholar
  78. 78.
    Bibikova M, Fan JB. GoldenGate assay for DNA methylation profiling. Methods Mol Biol. 2009;507:149–63. Epub 2008/11/07.PubMedGoogle Scholar
  79. 79.
    Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, et al. Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics. 2009;1(1):177–200. Epub 2009/10/01.PubMedGoogle Scholar
  80. 80.
    Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A. 1992;89(5):1827–31. Epub 1992/03/01.PubMedCentralPubMedGoogle Scholar
  81. 81.
    Lister R, Ecker JR. Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 2009;19(6):959–66. Epub 2009/03/11.PubMedCentralPubMedGoogle Scholar
  82. 82.
    Oda M, Glass JL, Thompson RF, Mo Y, Olivier EN, Figueroa ME, et al. High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 2009;37(12):3829–39. Epub 2009/04/24.PubMedCentralPubMedGoogle Scholar
  83. 83.
    Brunner AL, Johnson DS, Kim SW, Valouev A, Reddy TE, Neff NF, et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res. 2009;19(6):1044–56. Epub 2009/03/11.PubMedCentralPubMedGoogle Scholar
  84. 84.
    Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature. 2008;454(7205):766–70. Epub 2008/07/05.PubMedCentralPubMedGoogle Scholar
  85. 85.
    Park JH, Park J, Choi JK, Lyu J, Bae MG, Lee YG, et al. Identification of DNA methylation changes associated with human gastric cancer. BMC Med Genomics. 2011;4:82. Epub 2011/12/03.PubMedCentralPubMedGoogle Scholar
  86. 86.
    Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E, et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol. 2008;26(7):779–85. Epub 2008/07/10.PubMedCentralPubMedGoogle Scholar
  87. 87.
    Brinkman AB, Simmer F, Ma K, Kaan A, Zhu J, Stunnenberg HG. Whole-genome DNA methylation profiling using MethylCap-seq. Methods. 2010;52(3):232–6. Epub 2010/06/15.PubMedGoogle Scholar
  88. 88.
    Serre D, Lee BH, Ting AH. MBD-isolated genome sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 2010;38(2):391–9. Epub 2009/11/13.PubMedCentralPubMedGoogle Scholar
  89. 89.
    Heyn H, Esteller M. DNA methylation profiling in the clinic: applications and challenges. Nat Rev Genet. 2012;13(10):679–92. Epub 2012/09/05.PubMedGoogle Scholar
  90. 90.
    Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol. 2010;28(10):1045–8. Epub 2010/10/15.PubMedCentralPubMedGoogle Scholar
  91. 91.
    Adams D, Altucci L, Antonarakis SE, Ballesteros J, Beck S, Bird A, et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat Biotechnol. 2012;30(3):224–6. Epub 2012/03/09.PubMedGoogle Scholar
  92. 92.
    Holloway AJ, van Laar RK, Tothill RW, Bowtell DD. Options available – from start to finish–for obtaining data from DNA microarrays II. Nat Genet. 2002;32(Suppl):481–9. Epub 2002/11/28.PubMedGoogle Scholar
  93. 93.
    Schulze A, Downward J. Navigating gene expression using microarrays–a technology review. Nat Cell Biol. 2001;3(8):E190–5. Epub 2001/08/03.PubMedGoogle Scholar
  94. 94.
    Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ. Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res. 2000;28(22):4552–7. Epub 2000/11/10.PubMedCentralPubMedGoogle Scholar
  95. 95.
    Gerhard DS, Wagner L, Feingold EA, Shenmen CM, Grouse LH, Schuler G, et al. The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC). Genome Res. 2004;14(10B):2121–7. Epub 2004/10/19.PubMedGoogle Scholar
  96. 96.
    Boguski MS, Tolstoshev CM, Bassett Jr DE. Gene discovery in dbEST. Science. 1994;265(5181):1993–4. Epub 1994/09/30.PubMedGoogle Scholar
  97. 97.
    Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science. 1995;270(5235):484–7. Epub 1995/10/20.PubMedGoogle Scholar
  98. 98.
    Harbers M, Carninci P. Tag-based approaches for transcriptome research and genome annotation. Nat Methods. 2005;2(7):495–502. Epub 2005/06/24.PubMedGoogle Scholar
  99. 99.
    Shiraki T, Kondo S, Katayama S, Waki K, Kasukawa T, Kawaji H, et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc Natl Acad Sci U S A. 2003;100(26):15776–81. Epub 2003/12/10.PubMedCentralPubMedGoogle Scholar
  100. 100.
    Kodzius R, Kojima M, Nishiyori H, Nakamura M, Fukuda S, Tagami M, et al. CAGE: cap analysis of gene expression. Nat Methods. 2006;3(3):211–22. Epub 2006/02/21.PubMedGoogle Scholar
  101. 101.
    Reinartz J, Bruyns E, Lin JZ, Burcham T, Brenner S, Bowen B, et al. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Brief Funct Genomic Proteomic. 2002;1(1):95–104. Epub 2004/07/15.PubMedGoogle Scholar
  102. 102.
    Peiffer JA, Kaushik S, Sakai H, Arteaga-Vazquez M, Sanchez-Leon N, Ghazal H, et al. A spatial dissection of the Arabidopsis floral transcriptome by MPSS. BMC Plant Biol. 2008;8:43. Epub 2008/04/23.PubMedCentralPubMedGoogle Scholar
  103. 103.
    Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat Biotechnol. 2000;18(6):630–4. Epub 2000/06/03.PubMedGoogle Scholar
  104. 104.
    Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008;453(7199):1239–43. Epub 2008/05/20.PubMedGoogle Scholar
  105. 105.
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320(5881):1344–9. Epub 2008/05/03.PubMedCentralPubMedGoogle Scholar
  106. 106.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8. Epub 2008/06/03.PubMedGoogle Scholar
  107. 107.
    Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, et al. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques. 2008;45(1):81–94. Epub 2008/07/10.PubMedGoogle Scholar
  108. 108.
    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18(9):1509–17. Epub 2008/06/14.PubMedCentralPubMedGoogle Scholar
  109. 109.
    Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008;5(7):613–9. Epub 2008/06/03.PubMedGoogle Scholar
  110. 110.
    Holt RA, Jones SJ. The new paradigm of flow cell sequencing. Genome Res. 2008;18(6):839–46. Epub 2008/06/04.PubMedGoogle Scholar
  111. 111.
    Vera JC, Wheat CW, Fescemyer HW, Frilander MJ, Crawford DL, Hanski I, et al. Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Mol Ecol. 2008;17(7):1636–47. Epub 2008/02/13.PubMedGoogle Scholar
  112. 112.
    Emrich SJ, Barbazuk WB, Li L, Schnable PS. Gene discovery and annotation using LCM-454 transcriptome sequencing. Genome Res. 2007;17(1):69–73. Epub 2006/11/11.PubMedCentralPubMedGoogle Scholar
  113. 113.
    Dutrow N, Nix DA, Holt D, Milash B, Dalley B, Westbroek E, et al. Dynamic transcriptome of Schizosaccharomyces pombe shown by RNA-DNA hybrid mapping. Nat Genet. 2008;40(8):977–86. Epub 2008/07/22.PubMedCentralPubMedGoogle Scholar
  114. 114.
    Wu JQ, Du J, Rozowsky J, Zhang Z, Urban AE, Euskirchen G, et al. Systematic analysis of transcribed loci in ENCODE regions using RACE sequencing reveals extensive transcription in the human genome. Genome Biol. 2008;9(1):R3. Epub 2008/01/05.PubMedCentralPubMedGoogle Scholar
  115. 115.
    Scott EM, Carter AM, Findlay JB. The application of proteomics to diabetes. Diab Vasc Dis Res. 2005;2(2):54–60. Epub 2005/11/25.PubMedGoogle Scholar
  116. 116.
    Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem. 2007;389(4):1017–31. Epub 2007/08/02.PubMedGoogle Scholar
  117. 117.
    Paul FE, Hosp F, Selbach M. Analyzing protein-protein interactions by quantitative mass spectrometry. Methods. 2011;54(4):387–95. Epub 2011/03/09.PubMedGoogle Scholar
  118. 118.
    Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell. 2009;138(4):795–806. Epub 2009/08/12.PubMedCentralPubMedGoogle Scholar
  119. 119.
    Schafer A, von Toerne C, Becker S, Sarioglu H, Neschen S, Kahle M, et al. Two-dimensional peptide separation improving sensitivity of selected reaction monitoring-based quantitative proteomics in mouse liver tissue: comparing off-gel electrophoresis and strong cation exchange chromatography. Anal Chem. 2012;84(20):8853–62. Epub 2012/09/22.PubMedGoogle Scholar
  120. 120.
    Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol. 2007;25(1):125–31. Epub 2007/01/02.PubMedGoogle Scholar
  121. 121.
    Huttenhain R, Malmstrom J, Picotti P, Aebersold R. Perspectives of targeted mass spectrometry for protein biomarker verification. Curr Opin Chem Biol. 2009;13(5–6):518–25. Epub 2009/10/13.PubMedCentralPubMedGoogle Scholar
  122. 122.
    Edvardsson U, von Lowenhielm HB, Panfilov O, Nystrom AC, Nilsson F, Dahllof B. Hepatic protein expression of lean mice and obese diabetic mice treated with peroxisome proliferator-activated receptor activators. Proteomics. 2003;3(4):468–78. Epub 2003/04/11.PubMedGoogle Scholar
  123. 123.
    Sanchez JC, Converset V, Nolan A, Schmid G, Wang S, Heller M, et al. Effect of rosiglitazone on the differential expression of obesity and insulin resistance associated proteins in lep/lep mice. Proteomics. 2003;3(8):1500–20. Epub 2003/08/19.PubMedGoogle Scholar
  124. 124.
    Kim GH, Park EC, Yun SH, Hong Y, Lee DG, Shin EY, et al. Proteomic and bioinformatic analysis of membrane proteome in type 2 diabetic mouse liver. Proteomics. 2013;13(7):1164–79. Epub 2013/01/26.PubMedGoogle Scholar
  125. 125.
    von Toerne C, Kahle M, Schafer A, Ispiryan R, Blindert M, Hrabe De Angelis M, et al. Apoe, Mbl2, and Psp plasma protein levels correlate with diabetic phenotype in NZO mice–an optimized rapid workflow for SRM-based quantification. J Proteome Res. 2013;12(3):1331–43.Google Scholar
  126. 126.
    Schmid GM, Converset V, Walter N, Sennitt MV, Leung KY, Byers H, et al. Effect of high-fat diet on the expression of proteins in muscle, adipose tissues, and liver of C57BL/6 mice. Proteomics. 2004;4(8):2270–82. Epub 2004/07/27.PubMedGoogle Scholar
  127. 127.
    Sabido E, Wu Y, Bautista L, Porstmann T, Chang CY, Vitek O, et al. Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet. Mol Syst Biol. 2013;9:681. Epub 2013/07/19.PubMedCentralPubMedGoogle Scholar
  128. 128.
    Qiu L, List EO, Kopchick JJ. Differentially expressed proteins in the pancreas of diet-induced diabetic mice. Mol Cell Proteomics. 2005;4(9):1311–8. Epub 2005/06/18.PubMedGoogle Scholar
  129. 129.
    Kirpich IA, Gobejishvili LN, Bon Homme M, Waigel S, Cave M, Arteel G, et al. Integrated hepatic transcriptome and proteome analysis of mice with high-fat diet-induced nonalcoholic fatty liver disease. J Nutr Biochem. 2011;22(1):38–45. Epub 2010/03/23.PubMedGoogle Scholar
  130. 130.
    Guo Y, Darshi M, Ma Y, Perkins GA, Shen Z, Haushalter KJ, et al. Quantitative proteomic and functional analysis of liver mitochondria from high fat diet (HFD) diabetic mice. Mol Cell Proteomics. 2013;12(12):3744–58. Epub 2013/09/14.PubMedGoogle Scholar
  131. 131.
    Hwang H, Bowen BP, Lefort N, Flynn CR, De Filippis EA, Roberts C, et al. Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes. Diabetes. 2010;59(1):33–42. Epub 2009/10/17.PubMedCentralPubMedGoogle Scholar
  132. 132.
    Thingholm TE, Bak S, Beck-Nielsen H, Jensen ON, Gaster M. Characterization of human myotubes from type 2 diabetic and nondiabetic subjects using complementary quantitative mass spectrometric methods. Mol Cell Proteomics. 2011;10(9):M110 006650. Epub 2011/06/24.PubMedCentralPubMedGoogle Scholar
  133. 133.
    Hojlund K, Wrzesinski K, Larsen PM, Fey SJ, Roepstorff P, Handberg A, et al. Proteome analysis reveals phosphorylation of ATP synthase beta -subunit in human skeletal muscle and proteins with potential roles in type 2 diabetes. J Biol Chem. 2003;278(12):10436–42. Epub 2003/01/18.PubMedGoogle Scholar
  134. 134.
    Giebelstein J, Poschmann G, Hojlund K, Schechinger W, Dietrich JW, Levin K, et al. The proteomic signature of insulin-resistant human skeletal muscle reveals increased glycolytic and decreased mitochondrial enzymes. Diabetologia. 2012;55(4):1114–27. Epub 2012/01/28.PubMedGoogle Scholar
  135. 135.
    Topf F, Schvartz D, Gaudet P, Priego-Capote F, Zufferey A, Turck N, et al. The Human Diabetes Proteome Project (HDPP): from network biology to targets for therapies and prevention. Trans Proteomics. 2013;1(1):3–11.Google Scholar
  136. 136.
    Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, et al. The human serum metabolome. PLoS One. 2011;6(2):e16957. Epub 2011/03/02.PubMedCentralPubMedGoogle Scholar
  137. 137.
    Suhre K, Gieger C. Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet. 2012;13(11):759–69. Epub 2012/10/04.PubMedGoogle Scholar
  138. 138.
    Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5(11):e13953. Epub 2010/11/19.PubMedCentralPubMedGoogle Scholar
  139. 139.
    Gieger C, Geistlinger L, Altmaier E, Hrabe de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008;4(11):e1000282.PubMedCentralPubMedGoogle Scholar
  140. 140.
    Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F, et al. SLCO1B1 variants and statin-induced myopathy – a genomewide study. N Engl J Med. 2008;359(8):789–99. Epub 2008/07/25.PubMedGoogle Scholar
  141. 141.
    Lu Y, Feskens EJ, Dolle ME, Imholz S, Verschuren WM, Muller M, et al. Dietary n-3 and n-6 polyunsaturated fatty acid intake interacts with FADS1 genetic variation to affect total and HDL-cholesterol concentrations in the Doetinchem Cohort Study. Am J Clin Nutr. 2010;92(1):258–65. Epub 2010/05/21.PubMedGoogle Scholar
  142. 142.
    Dumont J, Huybrechts I, Spinneker A, Gottrand F, Grammatikaki E, Bevilacqua N, et al. FADS1 genetic variability interacts with dietary alpha-linolenic acid intake to affect serum non-HDL-cholesterol concentrations in European adolescents. J Nutr. 2011;141(7):1247–53. Epub 2011/05/20.PubMedGoogle Scholar
  143. 143.
    Nicholson G, Rantalainen M, Li JV, Maher AD, Malmodin D, Ahmadi KR, et al. A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet. 2011;7(9):e1002270. Epub 2011/09/21.PubMedCentralPubMedGoogle Scholar
  144. 144.
    Suhre K, Wallaschofski H, Raffler J, Friedrich N, Haring R, Michael K, et al. A genome-wide association study of metabolic traits in human urine. Nat Genet. 2011;43(6):565–9. Epub 2011/05/17.PubMedGoogle Scholar
  145. 145.
    Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009;37(Database issue):D603–10. Epub 2008/10/28.PubMedCentralPubMedGoogle Scholar
  146. 146.
    Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–14. Epub 2011/11/15.PubMedCentralPubMedGoogle Scholar
  147. 147.
    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(10):e1003005. Epub 2012/10/25.PubMedCentralPubMedGoogle Scholar
  148. 148.
    Krug S, Kastenmuller G, Stuckler F, Rist MJ, Skurk T, Sailer M, et al. The dynamic range of the human metabolome revealed by challenges. FASEB J. 2012;26(6):2607–19. Epub 2012/03/20.PubMedGoogle Scholar
  149. 149.
    Zhang A, Sun H, Wang P, Han Y, Wang X. Recent and potential developments of biofluid analyses in metabolomics. J Proteomics. 2012;75(4):1079–88. Epub 2011/11/15.PubMedGoogle Scholar
  150. 150.
    Tukiainen T, Kettunen J, Kangas AJ, Lyytikainen LP, Soininen P, Sarin AP, et al. Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci. Hum Mol Genet. 2012;21(6):1444–55. Epub 2011/12/14.PubMedGoogle Scholar
  151. 151.
    Inouye M, Kettunen J, Soininen P, Silander K, Ripatti S, Kumpula LS, et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol. 2010;6:441. Epub 2010/12/24.PubMedCentralPubMedGoogle Scholar
  152. 152.
    Petersen AK, Zeilinger S, Kastenmuller G, Romisch-Margl W, Brugger M, Peters A, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet. 2014;23(2):534–45. Epub 2013/09/10.PubMedCentralPubMedGoogle Scholar
  153. 153.
    Matafora V, Bachi A, Capasso G. Genomics and proteomics: how long do we need to reach clinical results? Blood Purif. 2013;36(1):7–11. Epub 2013/06/06.PubMedGoogle Scholar
  154. 154.
    Vassy JL, Meigs JB. Is genetic testing useful to predict type 2 diabetes? Best Pract Res Clin Endocrinol Metab. 2012;26(2):189–201. Epub 2012/04/14.PubMedCentralPubMedGoogle Scholar
  155. 155.
    Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208–19. Epub 2008/11/21.PubMedCentralPubMedGoogle Scholar
  156. 156.
    Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359(21):2220–32. Epub 2008/11/21.PubMedGoogle Scholar
  157. 157.
    de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, Grant RW, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 \ using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care. 2011;34(1):121–5. Epub 2010/10/05.PubMedCentralPubMedGoogle Scholar
  158. 158.
    Muhlenbruch K, Jeppesen C, Joost HG, Boeing H, Schulze MB. The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity. PLoS One. 2013;8(5):e64307. Epub 2013/05/24.PubMedCentralPubMedGoogle Scholar
  159. 159.
    Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol. 2014;2(1):65–75.PubMedGoogle Scholar
  160. 160.
    Bonnefond A, Clement N, Fawcett K, Yengo L, Vaillant E, Guillaume JL, et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat Genet. 2012;44(3):297–301. Epub 2012/01/31.PubMedCentralPubMedGoogle Scholar
  161. 161.
    Hardeland R. Antioxidative protection by melatonin: multiplicity of mechanisms from radical detoxification to radical avoidance. Endocrine. 2005;27(2):119–30. Epub 2005/10/12.PubMedGoogle Scholar
  162. 162.
    Korkmaz A, Ma S, Topal T, Rosales-Corral S, Tan DX, Reiter RJ. Glucose: a vital toxin and potential utility of melatonin in protecting against the diabetic state. Mol Cell Endocrinol. 2012;349(2):128–37. Epub 2011/11/15.PubMedGoogle Scholar
  163. 163.
    Kirchheiner J, Brockmoller J, Meineke I, Bauer S, Rohde W, Meisel C, et al. Impact of CYP2C9 amino acid polymorphisms on glyburide kinetics and on the insulin and glucose response in healthy volunteers. Clin Pharmacol Ther. 2002;71(4):286–96. Epub 2002/04/17.PubMedGoogle Scholar
  164. 164.
    Zhou K, Donnelly L, Burch L, Tavendale R, Doney AS, Leese G, et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: a Go-DARTS study. Clin Pharmacol Ther. 2010;87(1):52–6. Epub 2009/10/02.PubMedGoogle Scholar
  165. 165.
    Sesti G, Laratta E, Cardellini M, Andreozzi F, Del Guerra S, Irace C, et al. The E23K variant of KCNJ11 encoding the pancreatic beta-cell adenosine 5′-triphosphate-sensitive potassium channel subunit Kir6.2 is associated with an increased risk of secondary failure to sulfonylurea in patients with type 2 diabetes. J Clin Endocrinol Metab. 2006;91(6):2334–9.PubMedGoogle Scholar
  166. 166.
    Feng Y, Mao G, Ren X, Xing H, Tang G, Li Q, et al. Ser1369Ala variant in sulfonylurea receptor gene ABCC8 is associated with antidiabetic efficacy of gliclazide in Chinese type 2 diabetic patients. Diabetes Care. 2008;31(10):1939–44. Epub 2008/07/05.PubMedCentralPubMedGoogle Scholar
  167. 167.
    Wang DS, Jonker JW, Kato Y, Kusuhara H, Schinkel AH, Sugiyama Y. Involvement of organic cation transporter 1 in hepatic and intestinal distribution of metformin. J Pharmacol Exp Ther. 2002;302(2):510–5. Epub 2002/07/20.PubMedGoogle Scholar
  168. 168.
    Tanihara Y, Masuda S, Sato T, Katsura T, Ogawa O, Inui K. Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H(+)-organic cation antiporters. Biochem Pharmacol. 2007;74(2):359–71. Epub 2007/05/19.PubMedGoogle Scholar
  169. 169.
    Shu Y, Sheardown SA, Brown C, Owen RP, Zhang S, Castro RA, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest. 2007;117(5):1422–31. Epub 2007/05/04.PubMedCentralPubMedGoogle Scholar
  170. 170.
    Becker ML, Visser LE, van Schaik RH, Hofman A, Uitterlinden AG, Stricker BH. Genetic variation in the multidrug and toxin extrusion 1 transporter protein influences the glucose-lowering effect of metformin in patients with diabetes: a preliminary study. Diabetes. 2009;58(3):745–9. Epub 2009/02/21.PubMedCentralPubMedGoogle Scholar
  171. 171.
    Zhou K, Bellenguez C, Sutherland C, Hardie G, Palmer C, Donnelly P, et al. The role of ATM in response to metformin treatment and activation of AMPK. Nat Genet. 2012;44(4):361–2. Epub 2012/03/30.PubMedGoogle Scholar
  172. 172.
    Ly A, Scheerer MF, Zukunft S, Muschet C, Merl J, Adamski J, et al. Retinal proteome alterations in a mouse model of type 2 diabetes. Diabetologia. 2014;57(1):192–203. Epub 2013/10/01.PubMedCentralPubMedGoogle Scholar
  173. 173.
    Kim SJ, Nian C, McIntosh CH. Glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 modulate beta-cell chromatin structure. J Biol Chem. 2009;284(19):12896–904. Epub 2009/03/13.PubMedCentralPubMedGoogle Scholar
  174. 174.
    Pinney SE, Simmons RA. Epigenetic mechanisms in the development of type 2 diabetes. Trends Endocrinol Metab. 2010;21(4):223–9. Epub 2009/10/30.PubMedCentralPubMedGoogle Scholar
  175. 175.
    Christensen DP, Dahllof M, Lundh M, Rasmussen DN, Nielsen MD, Billestrup N, et al. Histone deacetylase (HDAC) inhibition as a novel treatment for diabetes mellitus. Mol Med. 2011;17(5–6):378–90. Epub 2011/01/29.PubMedCentralPubMedGoogle Scholar
  176. 176.
    Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375(9725):1525–35. Epub 2010/05/04.PubMedCentralPubMedGoogle Scholar
  177. 177.
    Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Miriami E, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–307. Epub 2012/03/20.PubMedCentralPubMedGoogle Scholar
  178. 178.
    Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92(4):414–7. Epub 2012/09/21.PubMedCentralPubMedGoogle Scholar
  179. 179.
    Collino S, Martin FP, Rezzi S. Clinical metabolomics paves the way towards future healthcare strategies. Br J Clin Pharmacol. 2013;75(3):619–29. Epub 2012/02/22.PubMedCentralPubMedGoogle Scholar
  180. 180.
    Trupp M, Zhu H, Wikoff WR, Baillie RA, Zeng ZB, Karp PD, et al. Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS One. 2012;7(7):e38386. Epub 2012/07/19.PubMedCentralPubMedGoogle Scholar
  181. 181.
    Ji Y, Hebbring S, Zhu H, Jenkins GD, Biernacka J, Snyder K, et al. Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther. 2011;89(1):97–104. Epub 2010/11/26.PubMedCentralPubMedGoogle Scholar
  182. 182.
    Koulman A, Lane GA, Harrison SJ, Volmer DA. From differentiating metabolites to biomarkers. Anal Bioanal Chem. 2009;394(3):663–70. Epub 2009/03/12.PubMedCentralPubMedGoogle Scholar
  183. 183.
    Teague B, Waterman MS, Goldstein S, Potamousis K, Zhou S, Reslewic S, et al. High-resolution human genome structure by single-molecule analysis. Proc Natl Acad Sci U S A. 2010;107(24):10848–53. Epub 2010/06/11.PubMedCentralPubMedGoogle Scholar
  184. 184.
    Tang F, Lao K, Surani MA. Development and applications of single-cell transcriptome analysis. Nat Methods. 2011;8(4 Suppl):S6–11. Epub 2011/04/01.PubMedCentralPubMedGoogle Scholar
  185. 185.
    Ma C, Fan R, Ahmad H, Shi Q, Comin-Anduix B, Chodon T, et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat Med. 2011;17(6):738–43. Epub 2011/05/24.PubMedCentralPubMedGoogle Scholar
  186. 186.
    Ideker T, Dutkowski J, Hood L. Boosting signal-to-noise in complex biology: prior knowledge is power. Cell. 2011;144(6):860–3. Epub 2011/03/19.PubMedCentralPubMedGoogle Scholar
  187. 187.
    Chang RL, Xie L, Bourne PE, Palsson BO. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol. 2010;6(9):e1000938. Epub 2010/10/20.PubMedCentralPubMedGoogle Scholar
  188. 188.
    Bailey RC, Kwong GA, Radu CG, Witte ON, Heath JR. DNA-encoded antibody libraries: a unified platform for multiplexed cell sorting and detection of genes and proteins. J Am Chem Soc. 2007;129(7):1959–67. Epub 2007/01/31.PubMedCentralPubMedGoogle Scholar
  189. 189.
    Roach JC, Glusman G, Smit AF, Huff CD, Hubley R, Shannon PT, et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. 2010;328(5978):636–9. Epub 2010/03/12.PubMedCentralPubMedGoogle Scholar
  190. 190.
    Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, Gildersleeve HI, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet. 2010;42(9):790–3. Epub 2010/08/17.PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Harald Grallert
    • 1
  • Carola S. Marzi
    • 1
  • Stefanie M. Hauck
    • 2
  • Christian Gieger
    • 3
  1. 1.Research Unit of Molecular Epidemiology, Institute of Epidemiology IIHelmholtz Zentrum München, German Research Center for Evironmental Health (GmbH)NeuherbergGermany
  2. 2.Research Unit Protein ScienceHelmholtz Zentrum München – German Research Center for Evironmental Health (GmbH)NeuherbergGermany
  3. 3.Research Unit of Molecular Epidemiology, Institute of Genetic EpidemiologyHelmholtz Zentrum München, German Research Center for Environmental Health (GmbH)NeuherbergGermany

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