Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut (original) (raw)

References

1. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
   Article Google Scholar
2. Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
   Article Google Scholar
3. Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2015).
   Article Google Scholar
4. Tigchelaar, E. F. et al. Gut microbiota composition associated with stool consistency. Gut 65, 540–542 (2016).
   Article Google Scholar
5. Wang, Y. T. et al. Regional gastrointestinal transit and pH studied in 21 healthy volunteers using the wireless motility capsule: influence of age, gender, study country and testing protocol. Aliment. Pharmacol. Ther. 42, 761–772 (2015).
   Article Google Scholar
6. Nyangale, E. P., Mottram, D. S. & Gibson, G. R. Gut microbial activity, implications for health and disease: the potential role of metabolite analysis. J. Proteome Res. 11, 5573–5585 (2012).
   Article Google Scholar
7. Tremaroli, V. & Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012).
   Article Google Scholar
8. Davila, A.-M. et al. Intestinal luminal nitrogen metabolism: role of the gut microbiota and consequences for the host. Pharmacol. Res. 68, 95–107 (2013).
   Article Google Scholar
9. Russell, W. R., Hoyles, L., Flint, H. J. & Dumas, M.-E. Colonic bacterial metabolites and human health. Curr. Opin. Microbiol. 16, 246–254 (2013).
   Article Google Scholar
10. Andriamihaja, M. et al. The deleterious metabolic and genotoxic effects of the bacterial metabolite p-cresol on colonic epithelial cells. Free Radic. Biol. Med. 85, 219–227 (2015).
    Article Google Scholar
11. Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nature Rev. Microbiol. 12, 661–672 (2014).
    Article Google Scholar
12. Shafi, T. et al. Free levels of selected organic solutes and cardiovascular morbidity and mortality in hemodialysis patients: results from the retained organic solutes and clinical outcomes (ROSCO) investigators. PLoS ONE 10, e0126048 (2015).
    Article Google Scholar
13. Gabriele, S. et al. Urinary p-cresol is elevated in young French children with autism spectrum disorder: a replication study. Biomarkers 19, 463–470 (2014).
    Article Google Scholar
14. Ibrügger, S. et al. Two randomized cross-over trials assessing the impact of dietary gluten or wholegrain on the gut microbiome and host metabolic health. J. Clin. Trials 4, 1000178 (2014).
    Article Google Scholar
15. Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
    Article Google Scholar
16. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).
    Article Google Scholar
17. Roager, H. M., Licht, T. R., Poulsen, S. K., Larsen, T. M. & Bahl, M. I. Microbial enterotypes, inferred by the prevotella-to-bacteroides ratio, remained stable during a 6-month randomized controlled diet intervention with the new nordic diet. Appl. Environ. Microbiol. 80, 1142–1149 (2014).
    Article Google Scholar
18. Claus, S. P. et al. Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes. Mol. Syst. Biol. 4, 219 (2008).
    Article Google Scholar
19. He, X. & Slupsky, C. M. Metabolic fingerprint of dimethyl sulfone (DMSO2) in microbial-mammalian co-metabolism. J. Proteome Res. 13, 5281–5292 (2014).
    Article Google Scholar
20. Kilkkinen, A. et al. Use of oral antimicrobials decreases serum enterolactone concentration. Am. J. Epidemiol. 155, 472–477 (2002).
    Article Google Scholar
21. Topp, H., Sander, G., Heller-Schöch, G. & Schöch, G. Determination of 7-methylguanine, _N_2,_N_2-dimethylguanosine, and pseudouridine in ultrafiltrated serum of healthy adults by high-performance liquid chromatography. Anal. Biochem. 161, 49–56 (1987).
    Article Google Scholar
22. Topp, H. & Schöch, G. Whole-body degradation rates of transfer-, ribosomal-, and messenger ribonucleic acids and resting metabolic rate in 3- to 18-year-old humans. Pediatr. Res. 47, 163–163 (2000).
    Article Google Scholar
23. Mirvish, S. S., Medalie, J., Linsell, C. A., Yousuf, E. & Reyad, S. 7-methylguanine and other minor urinary purines: values for normal subjects from Israel, Gaza, and Kenya, and for patients with cancer of various organs or cirrhosis of the liver. Cancer 27, 736–743 (1971).
    Article Google Scholar
24. Johansson, M. E. V., Larsson, J. M. H. & Hansson, G. C. The two mucus layers of colon are organized by the MUC2 mucin, whereas the outer layer is a legislator of host–microbial interactions. Proc. Natl Acad. Sci. USA 108(Suppl), 4659–4665 (2011).
    Article CAS Google Scholar
25. Barrios, C. et al. Gut-microbiota-metabolite axis in early renal function decline. PLoS ONE 10, e0134311 (2015).
    Article Google Scholar
26. Pimentel, M. et al. Methane, a gas produced by enteric bacteria, slows intestinal transit and augments small intestinal contractile activity. Am. J. Physiol. Gastrointest. Liver Physiol. 290, G1089–G1095 (2006).
    Article Google Scholar
27. Soret, R. et al. Short-chain fatty acids regulate the enteric neurons and control gastrointestinal motility in rats. Gastroenterology 138, 1772–1782 (2010).
    Article Google Scholar
28. Degen, L. P. & Phillips, S. F. How well does stool form reflect colonic transit? Gut 39, 109–113 (1996).
    Article Google Scholar
29. Macfarlane, G. T., Cummings, J. H., Macfarlane, S. & Gibson, G. R. Influence of retention time on degradation of pancreatic enzymes by human colonic bacteria grown in a 3-stage continuous culture system. J. Appl. Bacteriol. 67, 520–527 (1989).
    Article Google Scholar
30. Macfarlane, S., Quigley, M., Hopkins, M., Newton, D. F. & Macfarlane, G. Polysaccharide degradation by human intestinal bacteria during growth under multi-substrate limiting conditions in a three-stage continuous culture system. FEMS Microbiol. Ecol. 26, 231–243 (1998).
    Article Google Scholar
31. Cummings, J. H., Hill, M. J., Bone, E. S., Branch, W. J. & Jenkins, D. J. The effect of meat protein and dietary fiber on colonic function and metabolism. II: Bacterial metabolites in feces and urine. Am. J. Clin. Nutr. 32, 2094–2101 (1979).
    Article Google Scholar
32. Benus, R. F. J. et al. Association between Faecalibacterium prausnitzii and dietary fibre in colonic fermentation in healthy human subjects. Br. J. Nutr. 104, 693–700 (2010).
    Article Google Scholar
33. Manach, C., Scalbert, A., Morand, C., Remesy, C. & Jimenez, L. Polyphenols: food sources and bioavailability. Am. J. Clin. Nutr. 79, 727–747 (2004).
    Article Google Scholar
34. van Duynhoven, J. et al. Metabolic fate of polyphenols in the human superorganism. Proc. Natl Acad. Sci. USA 108(Suppl), 4531–4538 (2011).
    Article CAS Google Scholar
35. Gross, G. et al. In vitro bioconversion of polyphenols from black tea and red wine/grape juice by human intestinal microbiota displays strong interindividual variability. J. Agric. Food Chem. 58, 10236–10246 (2010).
    Article Google Scholar
36. Bode, L. M. et al. In vivo and in vitro metabolism of trans-resveratrol by human gut microbiota. Am. J. Clin. Nutr. 97, 295–309 (2013).
    Article Google Scholar
37. Shimotoyodome, A., Meguro, S., Hase, T., Tokimitsu, I. & Sakata, T. Decreased colonic mucus in rats with loperamide-induced constipation. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 126, 203–212 (2000).
    Article Google Scholar
38. Toden, S., Bird, A. R., Topping, D. L. & Conlon, M. A. Resistant starch attenuates colonic DNA damage induced by higher dietary protein in rats. Nutr. Cancer 51, 45–51 (2005).
    Article Google Scholar
39. Ten Bruggencate, S. J. M., Bovee-Oudenhoven, I. M. J., Lettink-Wissink, M. L. G., Katan, M. B. & Van Der Meer, R. Dietary fructo-oligosaccharides and inulin decrease resistance of rats to salmonella: protective role of calcium. Gut 53, 530–535 (2004).
    Article Google Scholar
40. Rao, J. N. & Wang, J.-Y. in Molecule to Function to Disease (eds Granger, N., Granger, J. & Princeton, N. ) 11–114 (Morgan & Claypool, 2011).
    Google Scholar
41. Sakata, T. Effects of indigestible dietary bulk and short chain fatty acids on the tissue weight and epithelial cell proliferation rate of the digestive tract in rats. J. Nutr. Sci. Vitaminol. (Tokyo) 32, 355–362 (1986).
    Article Google Scholar
42. Goodlad, R. A. et al. Effects of an elemental diet, inert bulk and different types of dietary fibre on the response of the intestinal epithelium to refeeding in the rat and relationship to plasma gastrin, enteroglucagon, and PYY concentrations. Gut 28, 171–180 (1987).
    Article Google Scholar
43. Lewis, S. J. & Heaton, K. W. Increasing butyrate concentration in the distal colon by accelerating intestinal transit. Gut 41, 245–251 (1997).
    Article Google Scholar
44. Timmons, J., Chang, E. T., Wang, J.-Y. & Rao, J. N. Polyamines and gut mucosal homeostasis. J. Gastrointest. Dig. Syst. 2(Suppl 7), 001 (2012).
    PubMed PubMed Central Google Scholar
45. Earle, K. A. et al. Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18, 478–488 (2015).
    Article Google Scholar
46. Guérin, A. et al. Risk of developing colorectal cancer and benign colorectal neoplasm in patients with chronic constipation. Aliment. Pharmacol. Ther. 40, 83–92 (2014).
    Article Google Scholar
47. Schadt, S., Chen, L.-Z. & Bischoff, D. Evaluation of relative LC/MS response of metabolites to parent drug in LC/nanospray ionization mass spectrometry: potential implications in MIST assessment. J. Mass Spectrom. 46, 1281–1286 (2011).
    Article Google Scholar
48. McKeown, C., Hisle-Gorman, E., Eide, M., Gorman, G. H. & Nylund, C. M. Association of constipation and fecal incontinence with attention-deficit/hyperactivity disorder. Pediatrics 132, e1210-5 (2013).
    Article Google Scholar
49. Pang, K. H. & Croaker, G. D. H. Constipation in children with autism and autistic spectrum disorder. Pediatr. Surg. Int. 27, 353–358 (2011).
    Article Google Scholar
50. Wu, M.-J. et al. Colonic transit time in long-term dialysis patients. Am. J. Kidney Dis. 44, 322–327 (2004).
    Article Google Scholar
51. Waller, P. A. et al. Dose–response effect of Bifidobacterium lactis HN019 on whole gut transit time and functional gastrointestinal symptoms in adults. Scand. J. Gastroenterol. 46, 1057–1064 (2011).
    Article Google Scholar
52. Abrahamsson, H. & Antov, S. Accuracy in assessment of colonic transit time with particles: how many markers should be used? Neurogastroenterol. Motil. 22, 1164–1169 (2010).
    Article Google Scholar
53. Wesolowska-Andersen, A. et al. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2, 19 (2014).
    Article Google Scholar
54. Godon, J. J., Zumstein, E., Dabert, P., Habouzit, F. & Moletta, R. Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997).
    Google Scholar
55. Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).
    Article Google Scholar
56. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10, 996–998 (2013).
    Article Google Scholar
57. Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504 (2011).
    Article Google Scholar
58. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010).
    Article Google Scholar
59. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
    Article Google Scholar
60. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).
    Article Google Scholar
61. Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).
    Article Google Scholar
62. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).
    Article Google Scholar
63. Chen, Y. et al. Combination of injection volume calibration by creatinine and MS signals’ normalization to overcome urine variability in LC-MS-based metabolomics studies. Anal. Chem. 85, 7659–7665 (2013).
    Article Google Scholar
64. Want, E. J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nature Protoc. 5, 1005–1018 (2010).
    Article Google Scholar
65. Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).
    Article Google Scholar
66. Wishart, D. S. et al. HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).
    Article Google Scholar
67. Kim, S. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Commun. Stat. Appl. Methods 22, 665–674 (2015).
    Google Scholar
68. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
    Google Scholar
69. Warnes, G. R. et al. gplots: various R programming tools for plotting data. R package version 2.16.0 (CRAN, 2015); http://cran.r-project.org/package=gplots
70. Oksanen, J. et al. vegan: community ecology package. R package version 2.3.1 (CRAN, 2015); https://cran.r-project.org/package=vegan
71. Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
    Article Google Scholar

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