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Metabonomic Phenotyping for the Gut Microbiota and Mammal Interactions

  • Huiru Tang
  • Yulan Wang
Part of the Advanced Topics in Science and Technology in China book series (ATSTC)

Abstract

All mammals consist of two distinct but integrated parts including hosts themselves and some symbiotic microorganisms [1–3]. Their symbiosis is established interactively through co-evolution and mutual selections [3–5]. Therefore, mammals are regarded as ‘superorganisms’ and their physiology and health in entirety have to be understood by taking into consideration hosts, symbiotic microbes and their interactions [1–4]. The symbiotic microorganisms are living mostly in the mammals’ gut and also known in different contexts as the gut microbiota, microparasites and microbiomes. It is now known that mammals harbor trillions of symbiotic microbes mainly in their gastrointestinal tract (GIT) with many different microbial species [2–7]. In normal adult human GIT, for instance, there is more than one kilogram of microbes with over ten times more cells than hosts and several thousands of species [2–7]. These symbiotic gut microbiota are co-developed with their hosts’ growth playing essential roles in many aspects of mammalian physiology and thus have profound effects on the hosts’ health [3–7]. For this reason, microbiomes are now considered collectively as an ‘essential organ’ or extended genomes, transcriptomes, proteomes and metabonomes [4, 7, 8] for their mammalian hosts. However, it is nontrivial at the moment to completely define the genomes of these microbiomes as has been done for human and rodent hosts. Neither can their composition, transcriptomes and proteomes be defined in detail, since many species cannot be cultured ex vivo.

Keywords

Nuclear Magnetic Resonance Bile Acid Nuclear Magnetic Resonance Spectroscopy Symbiotic Microbe Metabonomic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Lederberg J. Infectious history. Science, 2000, 288: 287–293.PubMedCrossRefGoogle Scholar
  2. [2]
    Qin J J, Li R Q, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 2010, 464: 59–65.PubMedCentralPubMedCrossRefGoogle Scholar
  3. [3]
    Xu J, Bjursell M K, Himrod J, et al. A genomic view of the human-bacteroides thetaiotaomicron symbiosis. Science, 2003, 299: 2074–2076.PubMedCrossRefGoogle Scholar
  4. [4]
    Nicholson J K, Holmes E, Wilson I D. Gut microorganisms, mammalian metabolism and personalized health care. Nat Rev Microbiol, 2005, 3: 431–438.PubMedCrossRefGoogle Scholar
  5. [5]
    Ley R E, Hamady M, Lozupone C, et al. Evolution of mammals and their gut microbes. Science, 2008, 320: 1647–1651.PubMedCentralPubMedCrossRefGoogle Scholar
  6. [6]
    Backhed F, Ley R E, Sonnenburg J L, et al. Host-bacterial mutualism in the human intestine. Science, 2005, 307: 1915–1920.PubMedCrossRefGoogle Scholar
  7. [7]
    Yatsunenko T, Rey F E, Manary M J, et al. Human gut microbiome viewed across age and geography. Nature, 2012, 486: 222–227.PubMedCentralPubMedGoogle Scholar
  8. [8]
    Nicholson J K, Holmes E, Kinross J, et al. Host-gut microbiota metabolic interactions. Science, 2012, 336: 1262–1267.PubMedCrossRefGoogle Scholar
  9. [9]
    Xu J, Chiang H C, Bjursell M K, et al. Message from a human gut symbiont: sensitivity is a prerequisite for sharing. Trends Microbiol, 2004, 12: 21–28.PubMedCrossRefGoogle Scholar
  10. [10]
    Hooper L V, Gordon J I. Commensal host-bacterial relationships in the gut. Science, 2001, 292: 1115–1118.PubMedCrossRefGoogle Scholar
  11. [11]
    Hooper L V, Littman D R, Macpherson A J. Interactions between the microbiota and the immune system. Science, 2012, 336: 1268–1273.PubMedCentralPubMedCrossRefGoogle Scholar
  12. [12]
    Macpherson A, Khoo U Y, Forgacs I, et al. Mucosal antibodies in inflammatory bowel disease are directed against intestinal bacteria. Gut, 1996, 38: 365–375.PubMedCentralPubMedCrossRefGoogle Scholar
  13. [13]
    Peterson D A, McNulty N P, Guruge J L, et al. Iga response to symbiotic bacteria as a mediator of gut homeostasis. Cell Host & Microbe, 2007, 2: 328–339.CrossRefGoogle Scholar
  14. [14]
    Kau A L, Ahern P P, Griffin N W, et al. Human nutrition, the gut microbiome and the immune system. Nature, 2011, 474: 327–336.PubMedCentralPubMedCrossRefGoogle Scholar
  15. [15]
    Garrett W S, Gordon J I, Glimcher L H. Homeostasis and inflammation in the intestine. Cell, 2010, 140: 859–870.PubMedCentralPubMedCrossRefGoogle Scholar
  16. [16]
    Sanz Y, Santacruz A, Gauffin P. Gut microbiota in obesity and metabolic disorders. Proc Nutri Soc, 2010, 69: 434–441.CrossRefGoogle Scholar
  17. [17]
    Turnbaugh P J, Hamady M, Yatsunenko T, et al. A core gut microbiome in obese and lean twins. Nature, 2009, 457: 480–487.PubMedCentralPubMedCrossRefGoogle Scholar
  18. [18]
    Ley R E, Backhed F, Turnbaugh P, et al. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA, 2005, 102: 11070–11075.PubMedCentralPubMedCrossRefGoogle Scholar
  19. [19]
    Ley R E, Tumbaugh P J, Klein S, et al. Microbial ecology-human gut microbes associated with obesity. Nature, 2006, 444: 1022–1023.PubMedCrossRefGoogle Scholar
  20. [20]
    Turnbaugh P J, Ley R E, Mahowald M A, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature, 2006, 444: 1027–1031.PubMedCrossRefGoogle Scholar
  21. [21]
    Marchesi J R, Holmes E, Khan F, et al. Rapid and non-invasive metabonomic characterisation of inflammatory bowel disease. J Proteome Res, 2007, 6: 546–552.PubMedCrossRefGoogle Scholar
  22. [22]
    Zhang X Y, Wang Y L, Hao F H, et al. Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J Proteome Res, 2009, 8: 5188–5195.PubMedCrossRefGoogle Scholar
  23. [23]
    Backhed F, Ding H, Wang T, et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA, 2004, 101: 15718–15723.PubMedCentralPubMedCrossRefGoogle Scholar
  24. [24]
    Tian Y, Zhang L M, Wang Y L, et al. Age-related topographical metabolic signatures for the rat gastrointestinal contents. J Proteome Res, 2012, 11: 1397–1411.PubMedCrossRefGoogle Scholar
  25. [25]
    Martin F P J, Dumas M E, Wang Y L, et al. A top-down systems biology view of microbiome- mammalian metabolic interactions in a mouse model. Mol Systems Biol, 2007, 3: article 112,CrossRefGoogle Scholar
  26. [26]
    Swann J R, Want E J, Geier F M, et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc Natl Acad Sci USA, 2011, 108: 4523–4530.PubMedCentralPubMedCrossRefGoogle Scholar
  27. [27]
    Li J V, Ashrafian H, Bueter M, et al. Metabolic surgery profoundly influences gut microbial-host metabolic cross-talk. Gut, 2011, 60: 1214–1223.PubMedCentralPubMedCrossRefGoogle Scholar
  28. [28]
    Ridlon J M, Kang D J, Hylemon P B. Bile salt biotransformations by human intestinal bacteria. J Lipid Res, 2006, 47: 241–259.PubMedCrossRefGoogle Scholar
  29. [29]
    Jones B V, Begley M, Hill C, et al. Functional and comparative metagenomic analysis of bile salt hydrolase activity in the human gut microbiome. Proc Natl Acad Sci USA, 2008, 105: 13580–13585.PubMedCentralPubMedCrossRefGoogle Scholar
  30. [30]
    O’Keefe S J D, Ou J H, Aufreiter S, et al. Products of the colonic microbiota mediate the effects of diet on colon cancer risk. J Nutr, 2009, 139: 2044–2048.PubMedCrossRefGoogle Scholar
  31. [31]
    Hope M E, Hold G L, Kain R, et al. Sporadic colorectal cancer — Role of the commensal microbiota. FEMS Microbiol Lett, 2005, 244: 1–7.PubMedCrossRefGoogle Scholar
  32. [32]
    Stepankova R, Tonar Z, Bartova J, et al. Absence of microbiota (germ-free conditions) accelerates the atherosclerosis in apoe-deficient mice fed standard low cholesterol diet. J Atheroscl Thromb, 2010, 17: 796–804.CrossRefGoogle Scholar
  33. [33]
    Rhee S H, Pothoulakis C, Mayer E A. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol, 2009, 6: 306–314.PubMedCrossRefGoogle Scholar
  34. [34]
    Parracho HMRT, Bingham M O, Gibson G R, et al. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol, 2005, 54: 987–991.PubMedCrossRefGoogle Scholar
  35. [35]
    Xu W X, Wu J F, An Y P, et al. Streptozotocin-induced dynamic metabonomic changes in rat biofluids. J Proteome Res, 2012, 11: 3423–3435.PubMedCrossRefGoogle Scholar
  36. [36]
    Dumas M E, Barton R H, Toye A, et al. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc Natl Acad Sci USA, 2006, 103: 12511–12516.PubMedCentralPubMedCrossRefGoogle Scholar
  37. [37]
    Clayton T A, Lindon J C, Cloarec O, et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 2006, 440: 1073–1077.PubMedCrossRefGoogle Scholar
  38. [38]
    Clayton T A, Baker D, Lindon J C, et al. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci USA, 2009, 106: 14728–14733.PubMedCentralPubMedCrossRefGoogle Scholar
  39. [39]
    Wilson I D, Nicholson J K. The role of gut microbiota in drug response. Curr Pharmaceut Design, 2009, 15: 1519–1523.CrossRefGoogle Scholar
  40. [40]
    Li M, Wang B H, Zhang M H, et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci USA, 2008, 105: 2117–2122.PubMedCentralPubMedCrossRefGoogle Scholar
  41. [41]
    Hooper L V, Wong M H, Thelin A, et al. Molecular analysis of commensal host-microbial relations hips in the intestine. Science, 2001, 291: 881–884.PubMedCrossRefGoogle Scholar
  42. [42]
    Zheng X J, Xie G X, Zhao A H, et al. The footprints of gut microbial-mammalian co-metabolism. J Proteome Res, 2011, 10: 5512–5522.PubMedCrossRefGoogle Scholar
  43. [43]
    Nicholson J K, Wilson I D. Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism. Nat Rev Drug Discov, 2003, 2: 668–676.PubMedCrossRefGoogle Scholar
  44. [44]
    Wang Y L, Holmes E, Nicholson J K, et al. Metabonomic investigations in mice infected with schistosoma mansoni: An approach for biomarker identification. Proc Natl Acad Sci USA, 2004, 101: 12676–12681.PubMedCentralPubMedCrossRefGoogle Scholar
  45. [45]
    Wang Y L, Utzinger J, Saric J, et al. Global metabolic responses of mice to trypanosoma brucei brucei infection. Proc Natl Acad Sci USA, 2008, 105: 6127–6132.PubMedCentralPubMedCrossRefGoogle Scholar
  46. [46]
    Zhang L M, Ye Y F, An Y P, et al. Systems responses of rats to aflatoxin b1 exposure revealed with metabonomic changes in multiple biological matrices. J Proteome Res, 2011, 10: 614–623.PubMedCrossRefGoogle Scholar
  47. [47]
    Martin F P J, Wang Y L, Sprenger N, et al. Top-down systems biology integration of conditional prebiotic modulated transgenomic interactions in a humanized microbiome mouse model. Mol Systems Biol, 2008, 4:article 205.Google Scholar
  48. [48]
    Wang Z N, Klipfell E, Bennett B J, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature, 2011, 472: 57–82.PubMedCentralPubMedCrossRefGoogle Scholar
  49. [49]
    Fukuda S, Toh H, Hase K, et al. Bifidobacteria can protect from enteropathogenic infection through production of acetate. Nature, 2011, 469: 543–791.PubMedCrossRefGoogle Scholar
  50. [50]
    Scheppach W. Effects of short-chain fatty-acids on gut morphology and function. Gut, 1994, 35: S35-S38.CrossRefGoogle Scholar
  51. [51]
    Wong J M W, de Souza R, Kendall C W C, et al. Colonic health: Fermentation and short chain fatty acids. J Clin Gastroenterol, 2006, 40: 235–243.PubMedCrossRefGoogle Scholar
  52. [52]
    Nicholson J K, Connelly J, Lindon J C, et al. Metabonomics: A platform for studying drug toxicity and gene function. Nat Rev Drug Discov, 2002, 1: 153–161.PubMedCrossRefGoogle Scholar
  53. [53]
    Wijeyesekera A, Selman C, Barton R H, et al. Metabotyping of long-lived mice using h-1 nmr spectroscopy. J Proteome Res, 2012, 11: 2224–2235.PubMedCentralPubMedCrossRefGoogle Scholar
  54. [54]
    Kinross J M, Holmes E, Darzi A W, et al. Metabolic phenotyping for monitoring surgical patients. Lancet, 2011, 377: 1817–1819.PubMedCrossRefGoogle Scholar
  55. [55]
    Holmes E, Loo R L, Stamler J, et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 2008, 453: 396–400.PubMedCrossRefGoogle Scholar
  56. [56]
    Nicholson J K, Lindon J C. Systems biology-metabonomics. Nature, 2008, 455: 1054–1056.PubMedCrossRefGoogle Scholar
  57. [57]
    Holmes E, Wilson I D, Nicholson J K. Metabolic phenotyping in health and disease. Cell, 2008, 134: 714–717.PubMedCrossRefGoogle Scholar
  58. [58]
    Martin FPJ, Collino S, Rezzi S. 1H NMR-based metabonomic applications to decipher gut microbial metabolic influence on mammalian health. Magn Reson Chem, 2011, 49: S47-S54.CrossRefGoogle Scholar
  59. [59]
    Martin F P J, Sprenger N, Montoliu I, et al. Dietary modulation of gut functional ecology studied by fecal metabonomics. J Proteome Res, 2010, 9: 5284–5295.PubMedCrossRefGoogle Scholar
  60. [60]
    Martin F P J, Wang Y, Yap I K S, et al. Topographical variation in murine intestinal metabolic profiles in relation to microbiome speciation and functional ecological activity. J Proteome Res, 2009, 8: 3464–3474.PubMedCrossRefGoogle Scholar
  61. [61]
    Nicholson J K, Holmes E, Lindon J C, et al. The challenges of modeling mammalian biocomplexity. Nat Biotechnol, 2004, 22: 1268–1274.PubMedCrossRefGoogle Scholar
  62. [62]
    Tang H R,Wang Y L. Metabonomics: A revolution in progress. Prog Biochem Biophys, 2006, 33: 401–417.Google Scholar
  63. [63]
    Tian J, Shi C Y, Gao P, et al. Phenotype differentiation of three e-coli strains by gc-fid and gc-ms based metabolomics. J Chromat Anal Technol Biomed Life Sci, 2008, 871: 220–226.CrossRefGoogle Scholar
  64. [64]
    Wilson I D, Plumb R, Granger J, et al. HPLC-MS-based methods for the study of metabonomics. J Chromat Anal Technol Biomed Life Sci, 2005, 817: 67–76.CrossRefGoogle Scholar
  65. [65]
    Lenz E M, Wilson I D. Analytical strategies in metabonomics. J Proteome Res, 2007, 6: 443–458.PubMedCrossRefGoogle Scholar
  66. [66]
    Humpfer E, Spraul M, Nicholls A W, et al. Direct observation of resolved intracellular and extracellular water signals in intact human red blood cells using 1H MAS NMR spectroscopy. Magn Reson Med, 1997, 38: 334–336.PubMedCrossRefGoogle Scholar
  67. [67]
    Cheng L L, Ma M J, Becerra L, et al. Quantitative neuropathology by high resolution magic angle spinning proton magnetic resonance spectroscopy. Proc Natl Acad Sci USA, 1997, 94: 6408–6413.PubMedCentralPubMedCrossRefGoogle Scholar
  68. [68]
    Cheng L L, Chang I W, Louis D N, et al. Correlation of high-resolution magic angle spinning proton magnetic resonance spectroscopy with histopathology of intact human brain tumor specimens. Cancer Res, 1998, 58: 1825–1832.PubMedGoogle Scholar
  69. [69]
    Ding L N, Hao F H, Shi Z M, et al. Systems biological responses to chronic perfluorododecanoic acid exposure by integrated metabonomic and transcriptomic studies. J Proteome Res, 2009, 8: 2882–2891.PubMedCrossRefGoogle Scholar
  70. [70]
    Yang Y X, Li C L, Nie X, et al. Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. J Proteome Res, 2007, 6: 2605–2614.PubMedCrossRefGoogle Scholar
  71. [71]
    Beckmann M, Parker D, Enot D P, et al. High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry. Nature Protocols, 2008, 3: 486–504.PubMedCrossRefGoogle Scholar
  72. [72]
    Fonville J M, Carter C, Cloarec O, et al. Robust data processing and normalization strategy for maldi mass spectrometric imaging. Anal Chem, 2012, 84: 1310–1319.PubMedCrossRefGoogle Scholar
  73. [73]
    Nemes P, Woods A S, Vertes A. Simultaneous imaging of small metabolites and lipids in rat brain tissues at atmospheric pressure by laser ablation electrospray ionization mass spectrometry. Anal Chem, 2010, 82: 982–988.PubMedCentralPubMedCrossRefGoogle Scholar
  74. [74]
    Koizumi S, Yamamoto S, Hayasaka T, et al. Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience, 2010, 168: 219–225PubMedCrossRefGoogle Scholar
  75. [75]
    Cooks R G, Ouyang Z, Takats Z, et al. Ambient mass spectrometry. Science, 2006, 311: 1566–1570.PubMedCrossRefGoogle Scholar
  76. [76]
    Dai H, Xiao C N, Liu H B, et al. Combined NMR and LC-MS analysis reveals the metabonomic changes in salvia miltiorrhiza bunge induced by water depletion. J Proteome Res, 2010, 9: 1460–1475.PubMedCrossRefGoogle Scholar
  77. [77]
    Dai H, Xiao C N, Liu H B, et al. Combined NMR and LC-DAD-MS analysis reveals comprehensive metabonomic variations for three phenotypic cultivars of salvia miltiorrhiza bunge. J Proteome Res, 2010, 9: 1565–1578.PubMedCrossRefGoogle Scholar
  78. [78]
    Holmes E, Loo R L, Cloarec O, et al. Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal Chem, 2007, 79: 2629–2640.PubMedCrossRefGoogle Scholar
  79. [79]
    Cloarec O, Campbell A, Tseng L H, et al. Virtual chromatographic resolution enhancement in cryoflow LC-NMR experiments via statistical total correlation spectroscopy. Anal Chem, 2007, 79: 3304–3311.PubMedCrossRefGoogle Scholar
  80. [80]
    Smith L M, Maher A D, Cloarec O ,et al. statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples. Anal Chem, 2007, 79: 5682–5689.PubMedCrossRefGoogle Scholar
  81. [81]
    Wang Y L, Cloarec O, Tang H R, et al. Magic angle spinning NMR and 1H-31P heteronuclear statistical total correlation spectroscopy of intact human gut biopsies. Anal Chem, 2008, 80: 1058–1066.PubMedCrossRefGoogle Scholar
  82. [82]
    Maher A D, Fonville J M, Coen M, et al. Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra. Anal Chem, 2012, 84: 1083–1091.PubMedCrossRefGoogle Scholar
  83. [83]
    Cloarec O, Dumas M E, Craig A, et al. Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal Chem, 2005, 77: 1282–1289.PubMedCrossRefGoogle Scholar
  84. [84]
    Crockford D J, Lindon J C, Cloarec O, et al. Statistical search space reduction and two-dimensional data display approaches for UPLC-MS in biomarker discovery and pathway analysis. Anal Chem, 2006, 78: 4398–4408.PubMedCrossRefGoogle Scholar
  85. [85]
    Lommen A, Godejohann M, Venema D P, et al. Application of directly coupled HPLC-NMR-MS to the identification and confirmation of quercetin glycosides and phloretin glycosides in apple peel. Anal Chem, 2000, 72: 1793–1797.PubMedCrossRefGoogle Scholar
  86. [86]
    Duarte I F, Godejohann M, Braumann U, et al. Application of NMR spectroscopy and LC-NMR/MS to the identification of carbohydrates in beer. J Agri Food Chem, 2003, 51: 4847–4852.CrossRefGoogle Scholar
  87. [87]
    Corcoran O, Spraul M. LC-NMR-MS in drug discovery. DDT, 2003, 8: 624–631.PubMedCrossRefGoogle Scholar
  88. [88]
    Spraul M, Freund A S, Nast R E, et al. Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe: Application to the analysis of acetaminophen metabolites in urine. Anal Chem, 2003, 75: 1536–1541.PubMedCrossRefGoogle Scholar
  89. [89]
    Holmes E, Tang H R, Wang Y L, et al. The assessment of plant metabolite profiles by NMR-based methodologies. Plant Med, 2006, 72: 771–785.CrossRefGoogle Scholar
  90. [90]
    Tang H R, Xiao C N, Wang Y L. Important roles of the hyphenated HPLC-DAD-SPE-MS/NMR technique in metabonomics. Magn Reson Chem, 2009, 47: S157–S162. CrossRefGoogle Scholar
  91. [91]
    Duarte I F, Legido-Quigley C, Parker D A, et al. Identification of metabolites in human hepatic bile using 800 MHz 1H NMR spectroscopy, HPLC-NMR/MS and UPLC-MS. Mol Biosys, 2009, 5: 180–190.CrossRefGoogle Scholar
  92. [92]
    Holmes E, Bonner F W, Sweatman B C, et al. Nuclear-magnetic-resonance spectroscopy and pattern-recognition analysis of the biochemical processes associated with the progression of and recovery from nephrotoxic lesions in the rat induced by mercury(II) chloride and 2-bromoethanamine. Mol Pharmacol, 1992, 42: 922–930.PubMedGoogle Scholar
  93. [93]
    Ghauri F Y K, Nicholson J K, Sweatman B C, et al. NMR spectroscopy of human postmortem cerebrospinal-fluid-distinction of alzheimers-disease from control using pattern- recognition and statistics. NMR Biomed, 1993, 6: 163–167.PubMedCrossRefGoogle Scholar
  94. [94]
    Gartland K P R, Beddell C R, Lindon J C, et al. A pattern-recognition approach to the comparison of PMR and clinical chemical-data for classification of nephrotoxicity. J Pharm Biomed Anal, 1990, 8: 963–968.PubMedCrossRefGoogle Scholar
  95. [95]
    Lindon J C, Nicholson J K, Holmes E, et al. Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol, 2005, 23: 833–838.PubMedCrossRefGoogle Scholar
  96. [96]
    Eriksson L, Trygg J, Wold S. CV-ANOVA for significance testing of PLS and OPLS (r) models. J Chemometr, 2008, 22: 594–600.CrossRefGoogle Scholar
  97. [97]
    Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemometr, 2002, 16: 119–128.CrossRefGoogle Scholar
  98. [98]
    Wang Y L, Tang H R, Holmes E, et al. Biochemical characterization of rat intestine development using high-resolution magic-angle-spinning 1H NMR spectroscopy and multivariate data analysis. J Proteome Res, 2005, 4: 1324–1329.PubMedCrossRefGoogle Scholar
  99. [99]
    Wang Y L, Holmes E, Comelli E M, et al. Topographical variation in metabolic signatures of human gastrointestinal biopsies revealed by high-resolution magic-angle spinning 1H NMR spectroscopy. J Proteome Res, 2007, 6: 3944–3951.PubMedCrossRefGoogle Scholar
  100. [100]
    Hopkins M J, Sharp R, Macfarlane G T. Age and disease related changes in intestinal bacterial popular-ions assessed by cell culture, 16S rRNA abundance, and community cellular fatty acid profiles. Gut, 2001, 48: 198–205.PubMedCentralPubMedCrossRefGoogle Scholar
  101. [101]
    Wu J F, An Y P, Yao J W, et al. An optimised sample preparation method for NMR-based faecal metabonomic analysis. Analyst, 2010, 135: 1023–1030.PubMedCrossRefGoogle Scholar
  102. [102]
    Saric J, Wang Y, Li J, et al. Species variation in the fecal metabolome gives insight into differential gastrointestinal function. J Proteome Res, 2008, 7: 352–360.PubMedCrossRefGoogle Scholar
  103. [103]
    Le Gall G, Noor S O, Ridgway K, et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J Proteome Res. 2011. 10: 4208–4218.PubMedCrossRefGoogle Scholar
  104. [104]
    Cao H C, Huang H J, Xu W, et al. Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry. Anal Chim Acta, 2011, 691: 68–75.PubMedCrossRefGoogle Scholar
  105. [105]
    Naruse S, Ishiguro H, Ko S B H, et al. Fecal pancreatic elastase: A reproducible marker for severe exocrine pancreatic insufficiency. J Gastroenterol, 2006, 41: 901–908.PubMedCrossRefGoogle Scholar
  106. [106]
    Hu S, Dong T S, Dalal S R, et al. The microbe-derived short chain fatty acid butyrate targets miRNA-dependent p21 gene expression in human colon cancer. Plos One, 2011, 6: e16221.Google Scholar
  107. [107]
    Jacobs D MDeltimple N, van Velzen E, et al., 1H NMR metabolite profiling of feces as a tool to assess the impact of nutrition on the human microbiome. NMR Biomed, 2008, 21: 615–626.PubMedCrossRefGoogle Scholar
  108. [108]
    Holmes E, Kinross J, Gibson G R, et al. Therapeutic modulation of microbiota-host metabolic interactions. Sci Transl Med, 2012, 4: 137–142.CrossRefGoogle Scholar
  109. [109]
    Wang X N, Wang X Y, Xie G X, et al. Urinary metabolite variation is associated with pathological progression of the post-hepatitis B cirrhosis patients. J Proteome Res, 2012, 11: 3838–3847.PubMedCrossRefGoogle Scholar
  110. [110]
    Cheng Y, Xie G X, Chen T L, et al. Distinct urinary metabolic profile of human colorectal cancer. J Proteome Res, 2012, 11: 1354–1363.PubMedCrossRefGoogle Scholar
  111. [111]
    Wang Y L, Tang H R, Nicholson J K, et al. A metabonomic strategy for the detection of the metabolic effects of chamomile (matricaria recutita L.) ingestion. J Agri Food Chem, 2005, 53: 191–196.CrossRefGoogle Scholar
  112. [112]
    Wu J F, Holmes E, Xue J, et al. Metabolic alterations in the hamster co-infected with schistosoma japonicum and necator americanus. Int J Parasitol, 2010, 40: 695–703.Google Scholar
  113. [113]
    Wu J F, Xu W X, Ming Z P, et al. Metabolic changes reveal the development of schistosomiasis in mice. PLOS Negl Trop Dis, 2010, 4: e807.Google Scholar
  114. [114]
    Dunn WB, Broadhurst D, Begley P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 2011, 6: 1060–1083.PubMedCrossRefGoogle Scholar
  115. [115]
    Chan E C Y, Pasikanti K K, Nicholson J K. Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nature Protocols, 2011, 6: 1483–1499.PubMedCrossRefGoogle Scholar
  116. [116]
    Beckonert O, Coen M, Keun H C, et al. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nature Protocols, 2010, 5: 1019–1032.PubMedCrossRefGoogle Scholar
  117. [117]
    Beckonert O, Keun H C, Ebbels T M D, et al. Metabolic profiling, metabolomic and metabonomic procedures for nmr spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols, 2007, 2: 2692–2703.PubMedCrossRefGoogle Scholar

Copyright information

© Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Huiru Tang
    • 1
  • Yulan Wang
    • 1
  1. 1.State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Center for Biospectroscopy and Metabonomics, Wuhan Institute of Physics and MathematicsChinese Academy of SciencesWuhanChina

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