Human metabolic individuality in biomedical and pharmaceutical research (original) (raw)

References

1. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009)
   Article ADS CAS Google Scholar
2. Newgard, C. B. & Attie, A. D. Getting biological about the genetics of diabetes. Nature Med. 16, 388–391 (2010)
   Article CAS Google Scholar
3. Illig, T. et al. A genome-wide perspective of genetic variation in human metabolism. Nature Genet. 42, 137–141 (2010)
   Article CAS Google Scholar
4. Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008)
   Article Google Scholar
5. Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81, 6656–6667 (2009)
   Article CAS Google Scholar
6. Ohta, T. et al. Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats. Toxicol. Pathol. 37, 521–535 (2009)
   Article CAS Google Scholar
7. Suhre, K. et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE 5, e13953 (2010)
   Article ADS Google Scholar
8. Altmaier, E. et al. Bioinformatics analysis of targeted metabolomics—uncovering old and new tales of diabetic mice under medication. Endocrinology 149, 3478–3489 (2008)
   Article CAS Google Scholar
9. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009)
   Article Google Scholar
10. Chambers, J. C. et al. Genetic loci influencing kidney function and chronic kidney disease. Nature Genet. 42, 373–375 (2010)
    Article CAS Google Scholar
11. Köttgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nature Genet. 42, 376–384 (2010)
    Article Google Scholar
12. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature Genet. 42, 105–116 (2010)
    Article CAS Google Scholar
13. Aulchenko, Y. S. et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nature Genet. 41, 47–55 (2009)
    Article CAS Google Scholar
14. Panneerselvam, K. & Freeze, H. H. Mannose enters mammalian cells using a specific transporter that is insensitive to glucose. J. Biol. Chem. 271, 9417–9421 (1996)
    Article CAS Google Scholar
15. Taguchi, T. et al. Hepatic glycogen breakdown is implicated in the maintenance of plasma mannose concentration. Am. J. Physiol. Endocrinol. Metab. 288, E534–E540 (2005)
    Article CAS Google Scholar
16. Blombaeck, B., Blombaeck, M., Edman, P. & Hessel, B. Amino-acid sequence and the occurrence of phosphorus in human fibrinopeptides. Nature 193, 833–834 (1962)
    Article ADS CAS Google Scholar
17. Martin, S. C., Ekman, P., Forsberg, P. O. & Ersmark, H. Increased phosphate content of fibrinogen in vivo correlates with alteration in fibrinogen behaviour. Thromb. Res. 68, 467–473 (1992)
    Article CAS Google Scholar
18. Yuan, X. et al. Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes. Am. J. Hum. Genet. 83, 520–528 (2008)
    Article CAS Google Scholar
19. Tregouet, D. A. et al. Common susceptibility alleles are unlikely to contribute as strongly as the FV and ABO loci to VTE risk: results from a GWAS approach. Blood 113, 5298–5303 (2009)
    Article CAS Google Scholar
20. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010)
    Article ADS CAS Google Scholar
21. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nature Genet. 43, 333–338 (2011)
    Article CAS Google Scholar
22. Döring, A. et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nature Genet. 40, 430–436 (2008)
    Article Google Scholar
23. Caulfield, M. J. et al. SLC2A9 is a high-capacity urate transporter in humans. PLoS Med. 5, e197 (2008)
    Article Google Scholar
24. Klein, T. E. et al. Integrating genotype and phenotype information: an overview of the PharmGKB project. Pharmacogenomics J. 1, 167–170 (2001)
    Article CAS Google Scholar
25. Deeken, J. F. et al. A pharmacogenetic study of docetaxel and thalidomide in patients with castration-resistant prostate cancer using the DMET genotyping platform. Pharmacogenomics J. 10, 191–199 (2010)
    Article CAS Google Scholar
26. Lankisch, T. O. et al. Gilbert’s Syndrome and irinotecan toxicity: combination with UDP-glucuronosyltransferase 1A7 variants increases risk. Cancer Epidemiol. Biomarkers Prev. 17, 695–701 (2008)
    Article CAS Google Scholar
27. Huang, R. S. et al. A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity. Proc. Natl Acad. Sci. USA 104, 9758–9763 (2007)
    Article ADS CAS Google Scholar
28. Chen, Y. et al. Effect of genetic variation in the organic cation transporter 2 on the renal elimination of metformin. Pharmacogenet. Genomics 19, 497–504 (2009)
    Article Google Scholar
29. Shu, Y. et al. Effect of genetic variation in the organic cation transporter 1, OCT1, on metformin pharmacokinetics. Clin. Pharmacol. Ther. 83, 273–280 (2008)
    Article CAS Google Scholar
30. The SEARCH Collaborative Group SLCO1B1 variants and statin-induced myopathy—a genomewide study. N. Engl. J. Med. 359, 789–799 (2008)
    Article Google Scholar
31. Davies, N. J. et al. AKR1C isoforms represent a novel cellular target for jasmonates alongside their mitochondrial-mediated effects. Cancer Res. 69, 4769–4775 (2009)
    Article CAS Google Scholar
32. Sanna, S. et al. Common variants in the SLCO1B3 locus are associated with bilirubin levels and unconjugated hyperbilirubinemia. Hum. Mol. Genet. 18, 2711–2718 (2009)
    Article CAS Google Scholar
33. Johnson, A. D. et al. Genome-wide association meta-analysis for total serum bilirubin levels. Hum. Mol. Genet. 18, 2700–2710 (2009)
    Article CAS Google Scholar
34. Kolz, M. et al. Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet. 5, e1000504 (2009)
    Article Google Scholar
35. Zhai, G. et al. Eight common genetic variants associated with serum DHEAS levels suggest a key role in ageing mechanisms. PLoS Genet. 7, e1002025 (2011)
    Article CAS Google Scholar
36. Mootha, V. K. & Hirschhorn, J. N. Inborn variation in metabolism. Nature Genet. 42, 97–98 (2010)
    Article CAS Google Scholar
37. Meredith, D. & Christian, H. C. The SLC16 monocaboxylate transporter family. Xenobiotica 38, 1072–1106 (2008)
    Article CAS Google Scholar
38. Koepsell, H. & Endou, H. The SLC22 drug transporter family. Pflugers Arch. 447, 666–676 (2004)
    Article CAS Google Scholar
39. Wichmann, H. E., Gieger, C. & Illig, T. KORA-gen—resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67 (Suppl 1). 26–30 (2005)
    Article Google Scholar
40. Andrew, T. et al. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res. 4, 464–477 (2001)
    Article CAS Google Scholar
41. Lawton, K. A. et al. Analysis of the adult human plasma metabolome. Pharmacogenomics 9, 383–397 (2008)
    Article CAS Google Scholar
42. Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009)
    Article ADS CAS Google Scholar
43. Soranzo, N. et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nature Genet. 41, 1182–1190 (2009)
    Article CAS Google Scholar
44. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009)
    Article Google Scholar
45. Richards, J. B. et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371, 1505–1512 (2008)
    Article CAS Google Scholar
46. Soranzo, N. et al. Meta-analysis of genome-wide scans for human adult stature identifies novel loci and associations with measures of skeletal frame size. PLoS Genet. 5, e1000445 (2009)
    Article Google Scholar
47. Teo, Y. Y. et al. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 23, 2741–2746 (2007)
    Article CAS Google Scholar
48. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007)
    Article CAS Google Scholar
49. Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nature Genet. 30, 97–101 (2002)
    Article CAS Google Scholar
50. Meredith, D. Site-directed mutation of arginine 282 to glutamate uncouples the movement of peptides and protons by the rabbit proton-peptide cotransporter PepT1. J. Biol. Chem. 279, 15795–15798 (2004)
    Article CAS Google Scholar

Download references

Acknowledgements

Acknowledgements We acknowledge the contributions of P. Lichtner, G. Eckstein, G. Fischer, T. Strom and all other members of the Helmholtz Zentrum München genotyping staff in generating the SNP data set, as well as all members of field staff who were involved in the planning and conduct of the MONICA (Monitoring trends and determinants on cardiovascular diseases) and KORA (Kooperative Gesundheitsforschung in der Region Augsburg) studies. The KORA group consists of H. E. Wichmann (speaker), A. Peters, R. Holle, J. John, C.M., T.I. and their co-workers, who are responsible for the design and conduct of the KORA studies. For TwinsUK, we thank the staff from the genotyping facilities at the Wellcome Trust Sanger Institute for sample preparation, quality control and genotyping. G. Fischer (KORA) and G. Surdulescu (TwinsUK) selected the samples; sample handling and shipment was organized by H. Chavez (KORA) and D. Hodgkiss (TwinsUK); and U. Goebel (Helmholtz) provided administrative support. Special thanks go to D. Garcia-West for his role in facilitating this study. We are grateful to the CARDIoGRAM investigators for access to their data set. Finally, we thank all study participants of the KORA and the TwinsUK studies for donating their blood and time. The KORA research platform and the MONICA studies were initiated and financed by the Helmholtz Zentrum München, National Research Center for Environmental Health, funded by the German Federal Ministry of Education, Science, Research and Technology and by the State of Bavaria. This study was supported by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). Part of this work was financed by the German National Genome Research Network (NGFNPlus: 01GS0823). Computing resources were made available by the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (HLRB project h1231) and the DEISA Extreme Computing Initiative (project MeMGenA). Part of this research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. The TwinsUK study was funded by the Wellcome Trust; the European Community’s Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F2-2008-201865-GEFOS and (FP7/2007-2013); and the FP-5 GenomEUtwin Project (QLG2-CT-2002-01254). The study also receives support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London. T.D.S. is an NIHR Senior Investigator. The project also received support from a Biotechnology and Biological Sciences Research Council (BBSRC) project grant (G20234). Both studies received support from ENGAGE project grant agreement HEALTH-F4-2007-201413. N.J.S. holds a British Heart Foundation Chair, is an NIHR Senior Investigator and is supported by the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease. The authors acknowledge the funding and support of the National Eye Institute via an NIH/CIDR genotyping project (PI: T. Young). Genotyping was also performed by CIDR as part of an NEI/NIH project grant. D.M. received support from the Early Career Researcher Scheme at Oxford Brookes University. J.R. is supported by DFG Graduiertenkolleg ‘GRK 1563, Regulation and Evolution of Cellular Systems’ (RECESS); E.A., by BMBF grant 0315494A (project SysMBo); W.R.-M., by BMBF grant 03IS2061B (project Gani_Med); and B.W., by Era-Net grant 0315442A (project PathoGenoMics). A.K. is supported by the Emmy Noether Programme of the German Research Foundation (DFG grant KO-3598/2-1) and F.K., by grants from the ‘Genomics of Lipid-associated Disorders (GOLD)’ of the Austrian Genome Research Programme (GEN-AU). N.S. is supported by the Wellcome Trust (core grant number 091746/Z/10/Z).

Author information

Author notes

1. So-Youn Shin, Ann-Kristin Petersen, Nicole Soranzo and Christian Gieger: These authors contributed equally to this work.

Authors and Affiliations

1. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
   Karsten Suhre, Brigitte Wägele, Elisabeth Altmaier, Gabi Kastenmüller, Hans-Werner Mewes, Johannes Raffler & Werner Römisch-Margl
2. Faculty of Biology, Ludwig-Maximilians-Universität, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany
   Karsten Suhre & Johannes Raffler
3. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, PO Box 24144, Doha, State of Qatar
   Karsten Suhre
4. The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK
   So-Youn Shin, Panos Deloukas, Elin Grundberg & Nicole Soranzo
5. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
   Ann-Kristin Petersen, Janina S. Ried & Christian Gieger
6. Metabolon Inc., Durham, PO Box 110407, Research Triangle Park, 27709, North Carolina, USA
   Robert P. Mohney & Michael V. Milburn
7. School of Life Sciences, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK
   David Meredith
8. Department of Genome-oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität München, Alte Akademie 1, 85354 Freising, Germany
   Brigitte Wägele & Hans-Werner Mewes
9. Universität zu Lübeck, Medizinische Klinik II, Ratzeburger Allee 160, 23538 Lübeck, Germany
   Jeanette Erdmann
10. Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas' Hospital Campus, 1st Floor South Wing Block, 4 Westminster Bridge Road, London SE1 7EH, UK
    Elin Grundberg, Christopher J. Hammond, Massimo Mangino, Kerrin S. Small, Guangju Zhai & Tim D. Spector
11. Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
    Martin Hrabé de Angelis, Cornelia Prehn & Jerzy Adamski
12. Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universität München, Alte Akademie 1, 85354 Freising, Germany
    Martin Hrabé de Angelis & Jerzy Adamski
13. Renal Division, University Hospital Freiburg, Breisacherstrasse 66, 79106 Freiburg, Germany
    Anna Köttgen
14. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Christoph Probst Platz 1, 6020 Innsbruck, Austria
    Florian Kronenberg
15. Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
    Christa Meisinger
16. Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
    Thomas Meitinger
17. Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 München, Germany
    Thomas Meitinger
18. Department of Cardiovascular Sciences, University of Leicester and Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, University Road, Leicester LE1 7RH, UK
    Nilesh J. Samani
19. Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
    H. -Erich Wichmann
20. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Geschwister-Scholl-Platz 1, 80539 München, Germany
    H. -Erich Wichmann
21. Klinikum Grosshadern, Marchioninistraße 15, 81377 München, Germany
    H. -Erich Wichmann
22. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
    Thomas Illig

Authors

1. Karsten Suhre
   You can also search for this author inPubMed Google Scholar
2. So-Youn Shin
   You can also search for this author inPubMed Google Scholar
3. Ann-Kristin Petersen
   You can also search for this author inPubMed Google Scholar
4. Robert P. Mohney
   You can also search for this author inPubMed Google Scholar
5. David Meredith
   You can also search for this author inPubMed Google Scholar
6. Brigitte Wägele
   You can also search for this author inPubMed Google Scholar
7. Elisabeth Altmaier
   You can also search for this author inPubMed Google Scholar
8. Panos Deloukas
   You can also search for this author inPubMed Google Scholar
9. Jeanette Erdmann
   You can also search for this author inPubMed Google Scholar
10. Elin Grundberg
    You can also search for this author inPubMed Google Scholar
11. Christopher J. Hammond
    You can also search for this author inPubMed Google Scholar
12. Martin Hrabé de Angelis
    You can also search for this author inPubMed Google Scholar
13. Gabi Kastenmüller
    You can also search for this author inPubMed Google Scholar
14. Anna Köttgen
    You can also search for this author inPubMed Google Scholar
15. Florian Kronenberg
    You can also search for this author inPubMed Google Scholar
16. Massimo Mangino
    You can also search for this author inPubMed Google Scholar
17. Christa Meisinger
    You can also search for this author inPubMed Google Scholar
18. Thomas Meitinger
    You can also search for this author inPubMed Google Scholar
19. Hans-Werner Mewes
    You can also search for this author inPubMed Google Scholar
20. Michael V. Milburn
    You can also search for this author inPubMed Google Scholar
21. Cornelia Prehn
    You can also search for this author inPubMed Google Scholar
22. Johannes Raffler
    You can also search for this author inPubMed Google Scholar
23. Janina S. Ried
    You can also search for this author inPubMed Google Scholar
24. Werner Römisch-Margl
    You can also search for this author inPubMed Google Scholar
25. Nilesh J. Samani
    You can also search for this author inPubMed Google Scholar
26. Kerrin S. Small
    You can also search for this author inPubMed Google Scholar
27. H. -Erich Wichmann
    You can also search for this author inPubMed Google Scholar
28. Guangju Zhai
    You can also search for this author inPubMed Google Scholar
29. Thomas Illig
    You can also search for this author inPubMed Google Scholar
30. Tim D. Spector
    You can also search for this author inPubMed Google Scholar
31. Jerzy Adamski
    You can also search for this author inPubMed Google Scholar
32. Nicole Soranzo
    You can also search for this author inPubMed Google Scholar
33. Christian Gieger
    You can also search for this author inPubMed Google Scholar

Consortia

CARDIoGRAM

Contributions

Designed the study: J.A., C.G., T.I., D.M., N.S. and K.S. Conducted the experiments: D.M., M.V.M. and R.P.M. Analysed the data: J.A., E.A., C.G., G.K., A.K., F.K., C.M., D.M., A.-K.P., C.P., J.R., J.S.R., W.R.-M., S.-Y.S., K.S. and B.W. Provided material, data or analysis tools: the CARDIoGRAM consortium, P.D., J.E., E.G., C.J.H., M.H.d.A., T.I., M.M., T.M., H.-W.M., N.J.S., K.S.S., T.D.S., H.-E.W. and G.Z. Wrote the paper: C.G., N.S. and K.S. All authors read the paper and contributed to its final form.

Corresponding authors

Correspondence toKarsten Suhre or Nicole Soranzo.

Ethics declarations

Competing interests

M.V.M. and R.P.M. are employees of Metabolon Inc.

Additional information

A list of authors and their affiliations appears in Supplementary Information.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-8 (see separate files for Supplementary Tables 2A and 2B), Supplementary References, a listing of the CARDIoGRAM consortium and funding and Supplementary Figures 1-4. (PDF 4723 kb)

Supplementary Data

This file contains Supplementary Table 2a, which contains the KORA.best.ratios data set and Supplementary Table 2b, which contains the TwinsUK.best.ratios data set. These file were replace on 12 September 2011 as the previous versions seen online had corrupted. (ZIP 47844 kb)

PowerPoint slides

Rights and permissions

About this article

Cite this article

Suhre, K., Shin, SY., Petersen, AK. et al. Human metabolic individuality in biomedical and pharmaceutical research.Nature 477, 54–60 (2011). https://doi.org/10.1038/nature10354

Download citation

• Received: 22 November 2010
• Accepted: 30 June 2011
• Published: 31 August 2011
• Issue Date: 01 September 2011
• DOI: https://doi.org/10.1038/nature10354