A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols

International Journal of Epidemiology

Volumn 37, Issue SUPPL. 1, 2008, Pages

  Dunn, Warwick B ^a   Broadhurst, David ^a   Ellis, David I ^a   Brown, Marie ^a   Halsall, Anthony ^a   O'Hagan, Steven ^a   Spasic, Irena ^a   Tseng, Andrew ^a   Kell, Douglas B ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Biofluid; GC MS; Metabolic profiling; Metabolomics; Multivariate analysis; Serum; Univariate analysis; Urine

Indexed keywords

MAMMALIA;

ARTICLE; BLOOD ANALYSIS; CHEMISTRY; CRYOPRESERVATION; HEALTH CARE FACILITY; HUMAN; LABORATORY DIAGNOSIS; MASS FRAGMENTOGRAPHY; METHODOLOGY; PILOT STUDY; PRACTICE GUIDELINE; SERUM; STANDARD; TIME; UNITED KINGDOM; URINALYSIS; URINE; VALIDATION STUDY;

BIOLOGICAL SPECIMEN BANKS; BLOOD CHEMICAL ANALYSIS; CRYOPRESERVATION; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; GREAT BRITAIN; HUMANS; PILOT PROJECTS; PRACTICE GUIDELINES AS TOPIC; SERUM; SPECIMEN HANDLING; TIME FACTORS; URINALYSIS; URINE;

EID: 41849111818     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dym281     Document Type: Article

References (37)

1. Kell DB. Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. FEBS J 2006;273:873-94.
2. Castrillo JO, Oliver SG. Yeast as a touchstone in postgenomic research: strategies for integrative analysis in functional genomics. J Biochem Mol Biol 2004;37:93-106.
3. Beecher CWW. The human metabolome. In: Harrigan GG and Goodacre R (ed.). Metabolic Profiling:Its Role in Biomarker Discovery and Gene Function Analysis. London: Kluwer Academic Publishers, 2003. pp. 311-19.
4. Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol 1998;16:373-78.
5. Kell DB. Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 2004;7:296-307.
6. Kell DB, Westerhoff HV. Metabolic control theory: its role in microbiology and biotechnology. FEMS Microbiol Rev 1986;39:305-20.
7. Kell DB, Mendes P. Snapshots of systems - metabolic control analysis and biotechnology in the post-genomic era. In: Cornish-Bowden A and Cárdenas ML (ed.). Technological and Medical Implications of Metabolic Control Analysis. Dordrecht: Kluwer Academic Publishers, 1999. pp. 3-25.
8. Raamsdonk LM, Teusink B, Broadhurst D et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 2001;19:45-50.
9. Catchpole GS, Beckmann M, Enot DP et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci 2005;102:14458-62.
10. Brown M, Dunn WB, Ellis DI et al. A metabolome pipeline: from concept to data to knowledge. Metabolomics 2005;1:39-51.
11. Lindon JC, Nicholson JK, Holmes E et al. Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol 2005;23:833-38.
12. Spasic I, Dunn WB, Velarde G et al. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 2006;7:281.
13. Kell DB. Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Mol Biol Rep 2002;29:37-41.
14. Broadhurst D, Kell DB. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006;2:171-96.
15. Kell DB, Oliver SG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 2004;26:99-105.
16. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 2004;22:245-252.
17. Dunn WB, Ellis DI. Metabolomics: current analytical platforms and methodologies. Trends Analyt Chem 2005;24:285-94.
18. Duarte NC, Becker SA, Jamshidi N et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Nat Acad Sci USA 2007;104:1777-82.
19. Nicholson JK, Holmes E, Wilson ID. Gut microorganisms, mammalian metabolism and personalized health care. Nat Rev Microbiol 2005;3:431-38.
20. Kenny LC, Dunn WB, Ellis DI et al. Novel biomarkers for preeclampsia detected using metabolomics and machine learning. Metabolomics 2005;1:227-34.
21. Welthagen W, Shellie RA, Spranger J, Ristow M, Zimmermann R, Fiehn O. Comprehensive twodimensional gas chromatography-time-of-flight mass spectrometry (GC x GC-TOF) for high resolution metabolomics: biomarker discovery on spleen tissue extracts of obses NZO compared to lean C57BL/6 mice. Metabolomics 2005;1:65-73.
22. Wilson ID, Plumb R, Granger J, Major H, Williams R, Lenz EA. HPLC-MS-based methods for the study of metabonomics. J Chromatogr B Analyt Technol Biomed Life Sci 2005;817:67-76.
23. Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2003;2:488-94.
24. O'Hagan S, Dunn WB, Brown M, Knowles JD, Kell DB. Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal Chem 2005;77:290-303.
25. O'Hagan S, Dunn WB, Knowles JD et al. Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics. Anal Chem 2007;79:464-76.
26. Horning EC, Horning MG. Human metabolic profiles obtained by Gc and Gc/Ms. J Chromatogr Sci 1971;9:129.
27. Jellum E, Bjornson I, Nesbakken R, Johansson E, Wold S. Classification of human cancer cells by means of capillary gas chromatography and pattern recognition analysis. J Chromatogr 1981;217:231-37.
28. Kuhara T. Diagnosis and monitoring of inborn errors of metabolism using urease-pretreatment of urine, isotope dilution, and gas chromatography-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2002; 781:497-517.
29. Underwood BR, Broadhurst D, Dunn WB et al. Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles. Brain 2006;129:877-86.
30. Kell DB, Brown M, Davey HM, Dunn WB, Spasic I, Oliver SG. Metabolic footprinting and systems biology: the medium is the message. Nat Rev Microbiol 2005;3:557-65.
31. Peakman TC, Elliott P. The UK Biobank sample handling and storage validation studies. Int J Epidemiol 2008; 37(suppl 1):i2-i6.
32. Graham RC. Data Analysis for the Chemical Sciences: A Guide to Statistical Techniques. New York: VCH Publishers Inc, 1993.
33. Lenz EM, Bright J, Wilson ID et al. Metabonomics, dietary influences and cultural differences: a H-1 NMR-based study of urine samples obtained from healthy British and Swedish subjects. J Pharm Biomed Anal 2004;36:841-49.
34. Bollard ME, Holmes E, Lindon JC et al. Investigations into biochemical changes due to diurnal variation and estrus cycle in female rats using high-resolution H-1 NMR spectroscopy of urine and pattern recognition. Anal Biochem 2001;295:194-202.
35. Lenz EM, Bright J, Knight R, Wilson ID, Major H. A metabonomic investigation of the biochemical effects of mercuric chloride in the rat using H-1 NMR and HPLCTOF/MS: time dependant changes in the urinary profile of endogenous metabolites as a result of nephrotoxicity. Analyst 2004;129:535-41.
36. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 2004; 96:434-42.
37. Ioannidis JP. Why most published research findings are false. PLoS Med 2005;2:e124