Inter-individual differences in response to dietary intervention: Integrating omics platforms towards personalised dietary recommendations

Proceedings of the Nutrition Society

Volumn 72, Issue 2, 2013, Pages 207-218

  Lampe, Johanna W ^a   Navarro, Sandi L ^a   Hullar, Meredith A J ^a   Shojaie, Ali ^b  

^a Fred Hutchinson Cancer Research Center   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Data integration; Gut microbiome; Human feeding study; Metabolomics

Indexed keywords

TAMSULOSIN;

ARTICLE; DIET; FEEDING; GENETIC DIFFERENCE; GENETIC POLYMORPHISM; HEALTH STATUS; HUMAN; OBSERVATIONAL STUDY; PHENOTYPIC VARIATION; POPULATION RESEARCH; RISK;

DIET; FOOD HABITS; GENETIC PREDISPOSITION TO DISEASE; HUMANS; INDIVIDUALITY; METABOLOMICS;

EID: 84876110893     PISSN: 00296651     EISSN: 14752719     Source Type: Journal    
DOI: 10.1017/S0029665113000025     Document Type: Article

References (77)

1. Jones DP, Park Y & Ziegler TR (2012) Nutritional metabolomics: progress in addressing complexity in diet and health. Annu Rev Nutr 32, 183-202.
2. Zeisel SH, Freake HC, Bauman DE et al. (2005) The nutritional phenotype in the age of metabolomics. J Nutr 135, 1613-1616.
3. Lampe JW & Potter JD (2006) Genetic variation, diet and disease susceptibility. In Gene-Environment Interactions: Fundamentals of Ecogenetics, pp. 321-350 [LG Costa and DL Eaton, editors]. Hoboken, NJ: John Wiley & Sons, Inc.
4. Bryan AD & Hutchison KE (2012) The role of genomics in health behavior change: challenges and opportunities. Public Health Genomics 15, 139-145.
5. Gieger C, Geistlinger L, Altmaier E et al. (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4, e1000282.
6. Suhre K, Wallaschofski H, Raffler J et al. (2011) A genomewide association study of metabolic traits in human urine. Nat Genet 43, 565-569.
7. Guarner F & Malagelada JR (2003) Gut flora in health and disease. Lancet 361, 512-519.
8. Qin J, Li R, Raes J et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59-65.
9. Gill SR, Pop M, Deboy RT et al. (2006) Metagenomic analysis of the human distal gut microbiome. Science 312, 1355-1359.
10. Arumugam M, Raes J, Pelletier E et al. (2011) Enterotypes of the human gut microbiome. Nature 473, 174-180.
11. Donohoe DR & Bultman SJ (2012) Metaboloepigenetics: interrelationships between energy metabolism and epigenetic control of gene expression. J Cell Physiol 227, 3169-3177.
12. Donohoe DR, Garge N, Zhang XX et al. (2011) The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab 13, 517-526.
13. Ordovas JM & Mooser V (2006) Metagenomics: the role of the microbiome in cardiovascular diseases. Curr Opin Lipidol 17, 157-161.
14. Qin JJ, Li YR, Cai ZM et al. (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55-60.
15. Marchesi JR, Dutilh BE, Hall N et al. (2011) Towards the human colorectal cancer microbiome. PLoS One 6, e20447.
16. Plottel CS & Blaser MJ (2011) Microbiome and malignancy. Cell Host Microbe 10, 324-335.
17. Willing BP, Dicksved J, Halfvarson J et al. (2010) A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844-1854.
18. Lampe JW (2004) Nutrition and cancer prevention: smallscale human studies for the 21st century. Cancer Epidemiol Biomarkers Prev 13, 1987-1988.
19. Navarro SL, Chang J, Peterson S et al. (2009) Modulation of human serum glutathione S-transferase-A1/2 concentration by cruciferous vegetables in a controlled feeding study is influenced by GSTM1 and GSTT1 genotypes. Cancer Epidemiol Biomarkers Prev 18, 2974-2978.
20. Cross AJ, Major JM, Sinha R (2011) Urinary biomarkers of meat consumption. Cancer Epidemiol Biomarkers Prev 20, 1107-1111.
21. Neuhouser ML, Schwarz Y, Wang C et al. (2012) A lowglycemic load diet reduces serum C-reactive protein and modestly increases adiponectin in overweight and obese adults. J Nutr 142, 369-374.
22. Navarro SL, Chen Y, Li L et al. (2011) UGT1A6 and UGT2B15 polymorphisms and acetaminophen conjugation in response to a randomized, controlled diet of select fruits and vegetables. Drug Metab Dispos 39, 1650-1657.
23. Russell WR, Gratz SW, Duncan SH et al. (2011) Highprotein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. Am J Clin Nutr 93, 1062-1072.
24. Li F, Hullar MA, Schwarz Y et al. (2009) Human gut bacterial communities are altered by addition of cruciferous vegetables to a controlled fruit- and vegetable-free diet. J Nutr 139, 1685-1691.
25. Tuohy KM, Kolida S, Lustenberger AM et al. (2001) The prebiotic effects of biscuits containing partially hydrolysed guar gum and fructo- oligosaccharides-a human volunteer study. Br J Nutr 86, 341-348.
26. Faust K & Raes J (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10, 538-550.
27. Faust K, Sathirapongsasuti JF, Izard J et al. (2012) Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 8, e1002606.
28. Lozupone C, Faust K, Raes J et al. (2012) Identifying genomic and metabolic features that can underline early successional and opportunistic lifestyles of human gut symbionts. Genome Res 22, 1974-1984.
29. Bolca S, Van de Wiele T & Possemiers S (2012) Gut metabotypes govern health effects of dietary polyphenols. Curr Opin Biotechnol (Epublication ahead of print version).
30. Niculescu MD, Pop EA, Fischer LM et al. (2007) Dietary isoflavones differentially induce gene expression changes in lymphocytes from postmenopausal women who form equol as compared with those who do not. J Nutr Biochem 18, 380-390.
31. Solanky KS, Bailey NJ, Beckwith-Hall BM et al. (2005) Biofluid 1H NMR-based metabonomic techniques in nutrition research - metabolic effects of dietary isoflavones in humans. J Nutr Biochem 16, 236-244.
32. Brauer HA, Libby TE, Mitchell BL et al. (2011) Cruciferous vegetable supplementation in a controlled diet study alters the serum peptidome in a GSTM1-genotype dependent manner. Nutr J 10, 11.
33. Rezzi S, Ramadan Z, Martin FP et al. (2007) Human metabolic phenotypes link directly to specific dietary preferences in healthy individuals. J Proteome Res 6, 4469-4477.
34. Heinzmann SS, Merrifield CA, Rezzi S et al. (2012) Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res 11, 643-655.
35. Hirai MY, Yano M, Goodenowe DB et al. (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA 101, 10205-10210.
36. Hirai MY, Klein M, Fujikawa Y et al. (2005) Elucidation of gene-to-gene and metabolite-to-gene networks in arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem 280, 25590-25595.
37. Lê Cao KA, Martin PG, Robert-Granié C et al. (2009) Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics 10, 34.
38. Gygi SP, Rochon Y, Franza BR et al. (1999) Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 19, 1720-1730.
39. Van Deun K, Smilde AK, van der Werf MJ et al. (2009) A structured overview of simultaneous component based data integration. BMC Bioinformatics 10, 246.
40. Putluri N, Shojaie A, Vasu VT et al. (2011) Metabolomic profiling reveals a role for androgen in activating amino acid metabolism and methylation in prostate cancer cells. PLoS One 6, e21417.
41. Putluri N, Shojaie A, Vasu VT et al. (2011) Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res 71, 7376-7386.
42. Imielinski M, Cha S, Rejtar T et al. (2012) Integrated proteomic, transcriptomic, and biological network analysis of breast carcinoma reveals molecular features of tumorigenesis and clinical relapse. Mol Cell Proteomics 11, M111 014910.
43. Poisson LM, Taylor JM & Ghosh D (2011) Integrative set enrichment testing for multiple omics platforms. BMC Bioinformatics 12, 459.
44. Jauhiainen A, Nerman O, Michailidis G et al. (2012) Transcriptional and metabolic data integration and modeling for identification of active pathways. Biostatistics 13, 748-761.
45. Shojaie A & Michailidis G (2010) Network enrichment analysis in complex experiments. Stat Appl Genet Mol Biol 9, Article22, 1-34.
46. Coviello AD, Haring R, Wellons M et al. (2012) A genomewide association meta-analysis of circulating sex hormonebinding globulin reveals multiple Loci implicated in sex steroid hormone regulation. PLoS Genet 8, e1002805.
47. Qayyum R, Snively BM, Ziv E et al. (2012) A meta-analysis and genome-wide association study of platelet count and mean platelet volume in african americans. PLoS Genet 8, e1002491.
48. Nyholt DR, Low SK, Anderson CA et al. (2012) Genomewide association meta-analysis identifies new endometriosis risk loci. Nat Genet 44, 1355-1359.
49. Houlston RS, Webb E, Broderick P et al. (2008) Metaanalysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer. Nat Genet 40, 1426- 1435.
50. Zeggini E, Scott LJ, Saxena R et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40, 638-645.
51. Cooper JD, Smyth DJ, Smiles AM et al. (2008) Metaanalysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat Genet 40, 1399-1401.
52. Wirapati P, Sotiriou C, Kunkel S et al. (2008) Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 10, R65.
53. Rhodes DR, Yu J, Shanker K et al. (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 101, 9309-9314.
54. Rhodes DR, Barrette TR, Rubin MA et al. (2002) Metaanalysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 62, 4427-4433.
55. Chiuve SE, Fung TT, Rimm EB et al. (2012) Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 142, 1009-1018.
56. Hooda S, Boler BM, Serao MC et al. (2012) 454 pyrosequencing reveals a shift in fecal microbiota of healthy adult men consuming polydextrose or soluble corn fiber. J Nutr 142, 1259-1265.
57. Ross AB, Bruce SJ, Blondel-Lubrano A et al. (2011) A whole-grain cereal-rich diet increases plasma betaine, and tends to decrease total and LDL-cholesterol compared with a refined-grain diet in healthy subjects. Br J Nutr 105, 1492- 1502.
58. Costabile A, Klinder A, Fava F et al. (2008) Whole-grain wheat breakfast cereal has a prebiotic effect on the human gut microbiota: a double-blind, placebo-controlled, crossover study. Br J Nutr 99, 110-120.
59. Finley JW, Burrell JB & Reeves PG (2007) Pinto bean consumption changes SCFA profiles in fecal fermentations, bacterial populations of the lower bowel, and lipid profiles in blood of humans. J Nutr 137, 2391-2398.
60. Smith SC, Choy R, Johnson SK et al. (2006) Lupin kernel fiber consumption modifies fecal microbiota in healthy men as determined by rRNA gene fluorescent in situ hybridization. Eur J Nutr 45, 335-341.
61. Johnson SK, Chua V, Hall RS et al. (2006) Lupin kernel fibre foods improve bowel function and beneficially modify some putative faecal risk factors for colon cancer in men. Br J Nutr 95, 372-378.
62. Hylla S, Gostner A, Dusel G et al. (1998) Effects of resistant starch on the colon in healthy volunteers: possible implications for cancer prevention. Am J Clin Nutr 67, 136-142.
63. Weickert MO, Arafat AM, Blaut M et al. (2011) Changes in dominant groups of the gut microbiota do not explain cerealfiber induced improvement of whole-body insulin sensitivity. Nutr Metab (Lond) 8, 90.
64. Duncan SH, Belenguer A, Holtrop G et al. (2007) Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol 73, 1073-1078.
65. Ley RE, Turnbaugh PJ, Klein S et al. (2006) Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022-1023.
66. van Dorsten FA, Peters S, Gross G et al. (2012) Gut microbial metabolism of polyphenols from black tea and red wine/ grape juice is source-specific and colon-region dependent. J Agric Food Chem 60, 11331-11342.
67. Heinzmann SS, Brown IJ, Chan Q et al. (2010) Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr 92, 436-443.
68. Walsh MC, Brennan L, Pujos-Guillot E et al. (2007) Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr 86, 1687- 1693.
69. Tulipani S, Llorach R, Jauregui O et al. (2011) Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption. J Proteome Res 10, 5047-5058.
70. Llorach R, Garrido I, Monagas M et al. (2010) Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) D.A. Webb) skin polyphenols. J Proteome Res 9, 5859-5867.
71. Van Dorsten FA, Daykin CA, Mulder TP et al. (2006) Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J Agric Food Chem 54, 6929-6938.
72. Rasmussen LG, Winning H, Savorani F et al. (2012) Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR. Nutrients 4, 112-131.
73. Moazzami AA, Zhang JX, Kamal-Eldin A et al. (2011) Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients. J Nutr 141, 2126-2132.
74. Zivkovic AM, Wiest MM, Nguyen U et al. (2009) Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach. Metabolomics 5, 209-218.
75. Llorach R, Urpi-Sarda M, Jauregui O et al. (2009) An LCMS- based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. J Proteome Res 8, 5060-5068.
76. Bertram HC, Hoppe C, Petersen BO et al. (2007) An NMRbased metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys. Br J Nutr 97, 758-763.
77. Stella C, Beckwith-Hall B, Cloarec O et al. (2006) Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res 5, 2780-2788.