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Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women

Nature Medicine volume 24, pages 1070–1080 (2018)Cite this article

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Abstract

Hepatic steatosis is a multifactorial condition that is often observed in obese patients and is a prelude to non-alcoholic fatty liver disease. Here, we combine shotgun sequencing of fecal metagenomes with molecular phenomics (hepatic transcriptome and plasma and urine metabolomes) in two well-characterized cohorts of morbidly obese women recruited to the FLORINASH study. We reveal molecular networks linking the gut microbiome and the host phenome to hepatic steatosis. Patients with steatosis have low microbial gene richness and increased genetic potential for the processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid metabolism. We demonstrated that fecal microbiota transplants and chronic treatment with phenylacetic acid, a microbial product of aromatic amino acid metabolism, successfully trigger steatosis and branched-chain amino acid metabolism. Molecular phenomic signatures were predictive (area under the curve = 87%) and consistent with the gut microbiome having an effect on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies.

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Fig. 1: Flowchart showing the approach used for the integration of clinical, molecular phenomics and metagenomic information and biological validations.
Fig. 2: Association between liver steatosis, MGR and metagenomic data in obese women.
Fig. 3: Association of metabolomic and transcriptomic data with liver steatosis and MGR.
Fig. 4: Transfer of steatotic and metabolic phenotypes to mice through FMT of material from patients with liver steatosis grade 3.
Fig. 5: Microbial PAA induces liver steatosis and BCAA use in primary human hepatocytes and mice.
Fig. 6: Phenome-wide crosstalk and predictive modeling.

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  • 09 August 2018

    In the version of this article originally published, the received date was missing. It should have been listed as 2 January 2018. The error has been corrected in the HTML and PDF versions of this article.

References

  1. Saltiel, A. R. & Kahn, C. R. Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799–806 (2001).

    Article  PubMed  CAS  Google Scholar 

  2. Kahn, S. E., Hull, R. L. & Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 (2006).

    Article  PubMed  CAS  Google Scholar 

  3. Meex, R. C. R. & Watt, M. J. Hepatokines: linking nonalcoholic fatty liver disease and insulin resistance. Nat. Rev. Endocrinol. 13, 509–520 (2017).

    Article  PubMed  CAS  Google Scholar 

  4. Petersen, M. C., Vatner, D. F. & Shulman, G. I. Regulation of hepatic glucose metabolism in health and disease. Nat. Rev. Endocrinol. 13, 572–587 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Adams, L. A., Anstee, Q. M., Tilg, H. & Targher, G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut. 66, 1138–1153 (2017).

    Article  PubMed  Google Scholar 

  6. Dumas, M.-E. et al. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl Acad. Sci. USA 103, 12511–12516 (2006).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Le Roy, T. et al. Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice. Gut. 62, 1787–1794 (2013).

    Article  PubMed  CAS  Google Scholar 

  8. Qin, N. et al. Alterations of the human gut microbiome in liver cirrhosis. Nature 513, 59–64 (2014).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  11. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).

    Article  PubMed  CAS  Google Scholar 

  12. Craciun, S. & Balskus, E. P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl Acad. Sci. USA 109, 21307–21312 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hoyles, L. et al. Metabolic retroconversion of trimethylamine N-oxide and the gut microbiota. Microbiome. 6, 73 (2018).

  14. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Dumas, M.-E. et al. Microbial–host co-metabolites are prodromal markers predicting phenotypic heterogeneity in behavior, obesity, and impaired glucose tolerance. Cell Rep. 20, 136–148 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Lonardo, A. et al. Fatty liver is associated with an increased risk of diabetes and cardiovascular disease—evidence from three different disease models: NAFLD, HCV and HIV. World J. Gastroenterol. 22, 9674–9693 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855–866 (2010).

    Article  PubMed  CAS  Google Scholar 

  18. Dumas, M.-E., Kinross, J. & Nicholson, J. K. Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease. Gastroenterology 146, 46–62 (2014).

    Article  PubMed  Google Scholar 

  19. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

    Article  PubMed  CAS  Google Scholar 

  20. Newgard, C. B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Jang, C. et al. A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance. Nat. Med. 22, 421–426 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

    Article  PubMed  CAS  Google Scholar 

  23. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Wu, H. et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med. 23, 850–858 (2017).

    Article  PubMed  CAS  Google Scholar 

  25. European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), European Association for the Study of Obesity (EASO). EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. Diabetologia 59, 1121–1140 (2016).

  26. European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), European Association for the Study of Obesity (EASO). EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. J. Hepatol. 64, 1388–1402 (2016).

  27. Kleiner, D. E. et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41, 1313–1321 (2005).

    Article  PubMed  Google Scholar 

  28. Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).

    Article  PubMed  CAS  Google Scholar 

  29. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    Article  PubMed  CAS  Google Scholar 

  30. Holmes, E. et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453, 396–400 (2008).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  31. Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).

    Article  PubMed  CAS  Google Scholar 

  32. Besemer, J. Heuristic approach to deriving models for gene finding. Nucleic Acids Res. 27, 3911–3920 (1999).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    Article  PubMed  CAS  Google Scholar 

  35. Cani, P. D. et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56, 1761–1772 (2007).

    Article  PubMed  CAS  Google Scholar 

  36. Shoaie, S. et al. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320–331 (2015).

    Article  PubMed  CAS  Google Scholar 

  37. Spencer, M. D. et al. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology 140, 976–986 (2011).

    Article  PubMed  CAS  Google Scholar 

  38. Pallister, T. et al. Hippurate as a metabolomic marker of gut microbiome diversity: modulation by diet and relationship to metabolic syndrome. Sci. Rep. 7, 13670 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Kadar, H. et al. A multiplexed targeted assay for high-throughput quantitative analysis of serum methylamines by ultra performance liquid chromatography coupled to high resolution mass spectrometry. Arch. Biochem. Biophys. 597, 12–20 (2016).

    Article  PubMed  CAS  Google Scholar 

  40. Plovier, H. et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med. 23, 107–113 (2017).

    Article  PubMed  CAS  Google Scholar 

  41. Schugar, R. C. et al. The TMAO-producing enzyme flavin-containing monooxygenase 3 regulates obesity and the beiging of white adipose tissue. Cell Rep. 19, 2451–2461 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Tarca, A. L. et al. A novel signaling pathway impact analysis. Bioinformatics 25, 75–82 (2009).

    Article  PubMed  CAS  Google Scholar 

  43. Davidovic, L. et al. A metabolomic and systems biology perspective on the brain of the fragile X syndrome mouse model. Genome Res. 21, 2190–2202 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Rodriguez-Martinez, A. et al. MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways. Bioinformatics 33, 773–775 (2017).

    PubMed  CAS  Google Scholar 

  45. Biddinger, S. B. et al. Hepatic insulin resistance directly promotes formation of cholesterol gallstones. Nat. Med. 14, 778–782 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Michael, M. D. et al. Loss of insulin signaling in hepatocytes leads to severe insulin resistance and progressive hepatic dysfunction. Mol. Cell 6, 87–97 (2000).

    Article  PubMed  CAS  Google Scholar 

  47. Fujisaka, S. et al. Antibiotic effects on gut microbiota and metabolism are host dependent. J. Clin. Invest. 126, 4430–4443 (2016).

  48. Nicolas, S. et al. Transfer of dysbiotic gut microbiota has beneficial effects on host liver metabolism. Mol. Syst. Biol. 13, 921 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Grasset, E. et al. A specific gut microbiota dysbiosis of type 2 diabetic mice induces GLP-1 resistance through an enteric NO-dependent and gut–brain axis mechanism. Cell Metab. 25, 1075–1090.e5 (2017).

    Article  PubMed  CAS  Google Scholar 

  50. Latorre, J. et al. Decreased lipid metabolism but increased FA biosynthesis are coupled with changes in liver microRNAs in obese subjects with NAFLD. Int. J. Obes. (Lond.) 41, 620–630 (2017).

    Article  CAS  Google Scholar 

  51. Cotillard, A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).

    Article  PubMed  CAS  Google Scholar 

  52. Sharifnia, T. et al. Hepatic TLR4 signaling in obese NAFLD. Am. J. Physiol. Gastrointest. Liver Physiol. 309, G270–G278 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Thaiss, C. A. et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540, 540–551 (2016).

    Article  CAS  Google Scholar 

  54. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    Article  PubMed  CAS  Google Scholar 

  55. Boursier, J. et al. The severity of nonalcoholic fatty liver disease is associated with gut dysbiosis and shift in the metabolic function of the gut microbiota. Hepatology 63, 764–775 (2016).

    Article  PubMed  CAS  Google Scholar 

  56. Loomba, R. et al. Gut microbiome-based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease. Cell Metab. 25, 1054–1062.e5 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Serino, M. et al. Metabolic adaptation to a high-fat diet is associated with a change in the gut microbiota. Gut. 61, 543–553 (2012).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  59. Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    Article  PubMed  CAS  Google Scholar 

  60. Grasa, M. D. M. et al. Modulation of SHBG binding to testosterone and estradiol by sex and morbid obesity. Eur. J. Endocrinol. 176, 393–404 (2017).

    Article  PubMed  CAS  Google Scholar 

  61. American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2018. Diabetes Care 41, S13–S27 (2018).

    Article  Google Scholar 

  62. Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006).

    Article  PubMed  CAS  Google Scholar 

  63. Fleige, S. & Pfaffl, M. W. RNA integrity and the effect on the real-time qRT–PCR performance. Mol. Aspects Med. 27, 126–139 (2006).

    Article  PubMed  CAS  Google Scholar 

  64. Smyth, G. K. "limma: Linear Models for Microarray Data" in Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Series in Statistics for Biology and Health (eds Gentleman, R., Carey, V., Huber, W., Irizarry, R., Dudoit, S.) 397–420 (Springer, New York, NY, 2005).

  65. Zhang, J. D. & Wiemann, S. KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics 25, 1470–1471 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Lluch, J. et al. The characterization of novel tissue microbiota using an optimized 16S metagenomic sequencing pipeline. PLoS ONE 10, e0142334 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Escudié, F. et al. FROGS: find, rapidly, OTUs with galaxy solution. Bioinformatics 34, 1287–1294 (2018).

    Article  PubMed  Google Scholar 

  70. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    Article  PubMed  CAS  Google Scholar 

  72. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  PubMed  CAS  Google Scholar 

  73. Dumas, M.-E. et al. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal. Chem. 78, 2199–2208 (2006).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  74. Smilde, A. K., Kiers, H. A. L., Bijlsma, S., Rubingh, C. M. & van Erk, M. J. Matrix correlations for high-dimensional data: the modified RV-coefficient. Bioinformatics 25, 401–405 (2009).

    Article  PubMed  CAS  Google Scholar 

  75. Cloarec, O. et al. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 77, 517–526 (2005).

    Article  PubMed  CAS  Google Scholar 

  76. Blaise, B. J. et al. Metabotyping of Caenorhabditis elegans reveals latent phenotypes. Proc. Natl Acad. Sci. USA 104, 19808–19812 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank N. J. Gooderham for critical reading of the manuscript. This work was supported by EU-FP7 FLORINASH (Health-F2-2009-241913) to R.B., M.F., J.-M.F.-R., F.F., C.P., E.H. and J.K.N. This work used the computing resources of the UK MEDical BIOinformatics partnership—aggregation, integration, visualization and analysis of large, complex data (UK MED-BIO), which is supported by the Medical Research Council (grant number MR/L01632X/1). L.H. is in receipt of an MRC Intermediate Research Fellowship in Data Science (MR/L01632X/1, UK MED-BIO). This work was also supported by funding to R.B. ((Région Midi-Pyrénées 2009-2014 RPV09003BBA), Agence Nationale de la Recherche ANR 09-GEN TRANSFLORAP and GAD 08-2_378258), to M.F. (Ministry of University (MIUR) Progetti di Ricerca di Interesse Nazionale (PRIN) protocol number 2015MPESJS_004, Ministry of Health Ricerca Finalizzata RF-2011-02349921, Fondazione Roma call for Non-Communicable Diseases NCD 2014), to J.-M.F.-R. (Ministry of Health FIS project 15/01934, CIBERobn Pathophysiology of Obesity and Nutrition and FEDER funds) and to M.-E.D. (EU METACARDIS under agreement HEALTH-F4-2012-305312, Neuron II under agreement 291840 and the MRC MR/M501797/1). We acknowledge the support of the Imperial College National Institute of Biomedical Research andthe Clinical Phenotyping Centre for support.

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Author notes

  1. These authors contributed equally: Lesley Hoyles, José-Manuel Fernández-Real, Massimo Federici.

Authors and Affiliations

  1. Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK

    Lesley Hoyles, James Abbott, Richard H. Barton, Julien Chilloux, Antonis Myridakis, Laura Martinez-Gili, Christopher Tomlinson, Mark Woodbridge, Sarah A. Butcher, Elaine Holmes, Jeremy K. Nicholson & Marc-Emmanuel Dumas

  2. Department of Endocrinology, Diabetes and Nutrition and Department of Medicine, Hospital of Girona “Dr Josep Trueta”, Universitat of Girona and CIBERobn Pathophysiology of Obesity and Nutrition, Instituto de Salud Carlos III, Madrid, Spain

    José-Manuel Fernández-Real, Jèssica Latorre Luque, José Maria Moreno-Navarrete, Josep Puig, Gemma Xifra & Wifredo Ricart

  3. Department of Systems Medicine, University of Rome Tor Vergata, Via Montpellier, Rome, Italy

    Massimo Federici, Marina Cardellini, Francesca Davato & Iris Cardolini

  4. Institut National de la Santé et de la Recherche Médicale (INSERM), Toulouse, France

    Matteo Serino, Julie Charpentier, Christophe Heymes, Vincent Azalbert, Vincent Blasco-Baque, Frédéric Lopez & Rémy Burcelin

  5. Université Paul Sabatier (UPS), Unité Mixte de Recherche (UMR) 1048, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Team 2: ‘Intestinal Risk Factors, Diabetes, Dyslipidemia, and Heart Failure’, Toulouse, France

    Matteo Serino, Julie Charpentier, Christophe Heymes, Vincent Azalbert, Vincent Blasco-Baque, Frédéric Lopez & Rémy Burcelin

  6. Institut Cochin Inserm U1016 CNRS UMR 8104, Université Paris Descartes, Paris, France

    Elodie Anthony, Fadila Benhamed & Catherine Postic

  7. Department of Experimental Medicine and Surgery, University of Rome Tor Vergata, Rome, Italy

    Ottavia Porzio & Paolo Gentileschi

  8. Department of Laboratory Medicine, Bambino Gesù Children’s Hospital, Rome, Italy

    Ottavia Porzio

  9. Sorbonne Universités, UPMC Univ Paris 06, UMRS 1138, Centre de Recherche des Cordeliers, Paris, France

    Fabienne Foufelle

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  1. Lesley Hoyles
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  2. José-Manuel Fernández-Real
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Contributions

R.B., J.-M.F.-R., M.F., F.L., F.F., C.P., E.H. and J.K.N. designed the study and supervised all parts of the project. R.B. is the project leader and chaired the consortium. M.-E.D. led data integration and elaborated the primary interpretation of analytical outcomes with L.H., in close collaboration with M.F., J.-M.F.-R. and R.B. L.H. implemented the microarray data analysis workflow. C.T. and M.W. developed the data repository. J.A. developed the metagenomic data analysis pipeline in collaboration with L.H. S.A.B. supervised the development of the data repository and the pipeline. L.H., J.A., R.H.B. and M.-E.D. performed the data analyses. J.-M.F.-R. and M.F. designed the clinical protocol and oversaw the clinical activities. M.C., F.D., I.C., O.P., P.G., J.P., G.X. and W.R. recruited and phenotyped patients and collected biological samples and physiological data. M.S., V.A. and V.B.-B. performed the RNA and DNA extractions. R.B. and M.S. supervised the DNA sequencing and gene profiling. J.L.L. and J.M.M.-N. performed the cell culture experiments. F.B., E.A., J.Charpentier and C.H. performed the animal work. R.H.B., J.Chilloux and L.M.-G. performed the metabolic profiling of plasma and urine by 1H-NMR. E.H. and J.K.N. supervised the metabolic profiling. A.M. performed the methylamine quantifications. L.H. and M.-E.D. drafted the first versions of the paper with critical and substantial contributions from M.F., J.-M.F.-R. and R.B. All authors provided support and constructive criticism throughout the project and approved the final version of the paper.

Corresponding authors

Correspondence to José-Manuel Fernández-Real, Massimo Federici, Rémy Burcelin or Marc-Emmanuel Dumas.

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Competing interests

L.H., J.-M.F.-R., M.F., R.H.B., J.L.L., E.H., J.K.N., C.P., R.B. and M.-E.D. are named as co-inventors on pending patents held by INSERM Transfert, INSERM, University of Rome Tor Vergata, University of Girona and Imperial College on NAFLD diagnostics and have the right to receive royalty payments for inventions or discoveries related to NAFLD diagnostics.

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Hoyles, L., Fernández-Real, JM., Federici, M. et al. Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women. Nat Med 24, 1070–1080 (2018). https://doi.org/10.1038/s41591-018-0061-3

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  • DOI: https://doi.org/10.1038/s41591-018-0061-3

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