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Dietary intervention impact on gut microbial gene richness

A Corrigendum to this article was published on 23 October 2013

Abstract

Complex gene–environment interactions are considered important in the development of obesity1. The composition of the gut microbiota can determine the efficacy of energy harvest from food2,3,4 and changes in dietary composition have been associated with changes in the composition of gut microbial populations5,6. The capacity to explore microbiota composition was markedly improved by the development of metagenomic approaches7,8, which have already allowed production of the first human gut microbial gene catalogue9 and stratifying individuals by their gut genomic profile into different enterotypes10, but the analyses were carried out mainly in non-intervention settings. To investigate the temporal relationships between food intake, gut microbiota and metabolic and inflammatory phenotypes, we conducted diet-induced weight-loss and weight-stabilization interventions in a study sample of 38 obese and 11 overweight individuals. Here we report that individuals with reduced microbial gene richness (40%) present more pronounced dys-metabolism and low-grade inflammation, as observed concomitantly in the accompanying paper11. Dietary intervention improves low gene richness and clinical phenotypes, but seems to be less efficient for inflammation variables in individuals with lower gene richness. Low gene richness may therefore have predictive potential for the efficacy of intervention.

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Figure 1: Gut microbial composition of LGC (n = 18) and HGC (n = 27) subjects.
Figure 2: Differences between LGC and HGC subjects in bioclinical variables.
Figure 3: Gene richness of LGC and HGC groups during the intervention.

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Accessions

European Nucleotide Archive

Data deposits

The raw solid read data for all samples has been deposited in the European Bioinformatics Institute (EBI) European Nucleotide Archive (ENA) under the accession number ERP003699.

References

  1. Mutch, D. M. & Clément, K. Unraveling the genetics of human obesity. PLoS Genet. 2, e188 (2006)

    Article  Google Scholar 

  2. Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004)

    ADS  Article  Google Scholar 

  3. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)

    ADS  Article  Google Scholar 

  4. Bäckhed, F., Manchester, J. K., Semenkovich, C. F. & Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl Acad. Sci. USA 104, 979–984 (2007)

    ADS  Article  Google Scholar 

  5. Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

    ADS  CAS  Article  Google Scholar 

  6. Duncan, S. H. et al. 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 (2007)

    CAS  Article  Google Scholar 

  7. Riesenfeld, C. S., Schloss, P. D. & Handelsman, J. Metagenomics: genomic analysis of microbial communities. Annu. Rev. Genet. 38, 525–552 (2004)

    CAS  Article  Google Scholar 

  8. National Research Council The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet (The National Academies Press, 2007)

    Google Scholar 

  9. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)

    CAS  Article  Google Scholar 

  10. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011)

    CAS  Article  Google Scholar 

  11. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature http://dx.doi.org/10.1038/nature12506. (this issue)

  12. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009)

    ADS  CAS  Article  Google Scholar 

  13. Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012)

    ADS  CAS  Article  Google Scholar 

  14. Wasserman, S. & Faust, K. Social Network Analysis: Methods and Applications. (Cambridge Univ. Press, 1994)

    Book  Google Scholar 

  15. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011)

    ADS  CAS  Article  Google Scholar 

  16. Ouchi, N., Parker, J. L., Lugus, J. J. & Walsh, K. Adipokines in inflammation and metabolic disease. Nature Rev. Immunol. 11, 85–97 (2011)

    CAS  Article  Google Scholar 

  17. Shoelson, S. E., Lee, J. & Goldfine, A. B. Inflammation and insulin resistance. J. Clin. Invest. 116, 1793–1801 (2006)

    CAS  Article  Google Scholar 

  18. Renehan, A. G., Tyson, M., Egger, M., Heller, R. F. & Zwahlen, M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 371, 569–578 (2008)

    Article  Google Scholar 

  19. Rizkalla, S. W. et al. Differential effects of macronutrient content in 2 energy-restricted diets on cardiovascular risk factors and adipose tissue cell size in moderately obese individuals: a randomized controlled trial. Am. J. Clin. Nutr. 95, 49–63 (2012)

    CAS  Article  Google Scholar 

  20. Bouché, C. et al. Five-week, low-glycemic index diet decreases total fat mass and improves plasma lipid profile in moderately overweight nondiabetic men. Diabetes Care 25, 822–828 (2002)

    Article  Google Scholar 

  21. Tordjman, J. et al. Structural and inflammatory heterogeneity in subcutaneous adipose tissue: Relation with liver histopathology in morbid obesity. J. Hepatol. 56, 1152–1158 (2012)

    Article  Google Scholar 

  22. Disse, E. et al. A lipid-parameter-based index for estimating insulin sensitivity and identifying insulin resistance in a healthy population. Diabetes Metab. 34, 457–463 (2008)

    CAS  Article  Google Scholar 

  23. Antuna-Puente, B. et al. Evaluation of insulin sensitivity with a new lipid-based index in non-diabetic postmenopausal overweight and obese women before and after a weight loss intervention. Eur. J. Endocrinol. 161, 51–56 (2009)

    CAS  Article  Google Scholar 

  24. Prat-Larquemin, L. et al. Adipose angiotensinogen secretion, blood pressure, and AGT M235T polymorphism in obese patients. Obes. Res. 12, 556–561 (2004)

    CAS  Article  Google Scholar 

  25. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B 57, 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  26. Pons, N. et al. METEOR, a platform for quantitative metagenomic profiling of complex ecosystems. Journées Ouvertes en Biologie, Informatique et Mathématiques http://www.jobim2010.fr/sites/default/files/presentations/27Pons.pdf (2010)

  27. Jiang, D., Huang, J. & Zhang, Y. The cross-validated AUC for MCP-logistic regression with high-dimensional data. Stat. Methods Med. Res http://dx.doi.org/10.1177/0962280211428385 (28 November 2011)

  28. Shannon, C. E. A mathematical theory of communication. Bell Sys. Tech. J. 27, 379–423 (1995) 623–656 (1948)

    MathSciNet  Article  Google Scholar 

  29. Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman and Hall, 1986)

    Book  Google Scholar 

  30. R Development Core Team. R: A Language and Environment for Statistical Computinghttp://www.R-project.org (R Foundation for Statistical Computing, 2011)

Download references

Acknowledgements

We are grateful to O. Pedersen (Univ. Copenhagen) for helpful comments on this manuscript and to the MetaHIT consortium for providing the gene profiles of the Danish subjects used to test the ROC models in advance of publication and the DNA samples sequenced on the SOLiD platform for comparison with the Illumina platform used in the accompanying manuscript. We thank C. Baudoin, P. Ancel and V. Pelloux who contributed to the clinical investigation study; S. Fellahi and J.-P. Bastard for analyses of inflammatory markers; D. Bonnefont-Rousselot and R. Bittar for help with the analysis of plasma lipid profile. This work was supported by Agence Nationale de la Recherche (ANR MICRO-Obes, ANR, Nutra2sens, ANR-10-IAHU-05), the Metagenopolis grant ANR-11-DPBS-0001, KOT-Ceprodi (Florence Massiera), Danone Research (Damien Paineau) and the associations Fondacoeur, and Louis-Bonduelle. Additional funding came from the European Commission FP7 grant HEALTH-F4-2007-201052 and METACARDIS.

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S.D.E., J.D. and K.C. designed the study; S.D.E., J.D., K.C. and P.R. managed the study; K.C. and S.R. designed the clinical research; S.R. and L.C.K. conducted the clinical research and clinical data management; A.C., S.R. and L.C.K. conducted clinical and dietary data analysis; S.G. gave dietary counselling to the patients and carried out analysis of dietary data; F.L. prepared the DNA for sequencing; S.K. managed DNA sequencing, which B.Q. and N.G. carried out; N.P. and J.-M.B. established the sequence analysis pipeline; A.C., J.-D.Z., E.P., N.P., E.L.C., M.A., J.-M.B., S.K. and S.D.E. carried out microbial data analysis; A.C., K.C., L.C.K. and S.D.E. wrote the manuscript.

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Correspondence to Karine Clément or Stanislav Dusko Ehrlich.

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Cotillard, A., Kennedy, S., Kong, L. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013). https://doi.org/10.1038/nature12480

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