Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota
Kristoffer Forslund, Falk Hildebrand, Trine Nielsen, Gwen Falony, Emmanuelle Le Chatelier, Shinichi Sunagawa, Edi Prifti, Sara Vieira-Silva, Valborg Gudmundsdottir, Helle K Pedersen, Manimozhiyan Arumugam, Karsten Kristiansen, Anita Yvonne Voigt, Henrik Vestergaard, Rajna Hercog, Paul Igor Costea, Jens Roat Kultima, Junhua Li, Torben Jørgensen, Florence Levenez, Joël Dore, MetaHIT consortium, H Bjørn Nielsen, Søren Brunak, Jeroen Raes, Torben Hansen, Jun Wang, S Dusko Ehrlich, Peer Bork, Oluf Pedersen, Mathieu Almeida, Maria Antolin, François Artiguenave, Jean-Michel Batto, Marcelo Bertalan, Hervé Blottière, Natalia Boruel, Christian Brechot, Thomas Bruls, Kristoffer Burgdorf, Francesc Casellas, Antonella Cultrone, Willem M de Vos, Christine Delorme, Gérard Denariaz, Muriel Derrien, Rozenn Dervyn, Qiang Feng, Niels Grarup, Francisco Guarner, Eric Guedon, Florence Haimet, Alexandre Jamet, Agnieska Juncker, Catherine Juste, Sean Kennedy, Ghalia Khaci, Michiel Kleerebezem, Jan Knoll, Séverine Layec, Marion Leclerc, Pierre Leonard, Denis LePaslier, Christine M'Rini, Emmanuelle Maguin, Chaysavanh Manichanh, Daniel Mende, Alexandre Mérieux, Raish Oozeer, Julian Parkhill, Eric Pelletier, Nicolas Pons, Junjie Qin, Simon Rasmussen, Pierre Renault, Maria Rescigno, Nicolas Sanchez, Thomas Sicheritz-Ponten, Julien Tap, Sebastian Tims, Antonio Torrejon, Keith Turner, Maarten van de Guchte, Johan E T van Hylckama Vlieg, Gaetana Vandemeulebrouck, Encarna Varela, Patrick Viega, Jean Weissenbach, Yohanan Winogradski, Takuji Yamada, Erwin G Zoetendal, Kristoffer Forslund, Falk Hildebrand, Trine Nielsen, Gwen Falony, Emmanuelle Le Chatelier, Shinichi Sunagawa, Edi Prifti, Sara Vieira-Silva, Valborg Gudmundsdottir, Helle K Pedersen, Manimozhiyan Arumugam, Karsten Kristiansen, Anita Yvonne Voigt, Henrik Vestergaard, Rajna Hercog, Paul Igor Costea, Jens Roat Kultima, Junhua Li, Torben Jørgensen, Florence Levenez, Joël Dore, MetaHIT consortium, H Bjørn Nielsen, Søren Brunak, Jeroen Raes, Torben Hansen, Jun Wang, S Dusko Ehrlich, Peer Bork, Oluf Pedersen, Mathieu Almeida, Maria Antolin, François Artiguenave, Jean-Michel Batto, Marcelo Bertalan, Hervé Blottière, Natalia Boruel, Christian Brechot, Thomas Bruls, Kristoffer Burgdorf, Francesc Casellas, Antonella Cultrone, Willem M de Vos, Christine Delorme, Gérard Denariaz, Muriel Derrien, Rozenn Dervyn, Qiang Feng, Niels Grarup, Francisco Guarner, Eric Guedon, Florence Haimet, Alexandre Jamet, Agnieska Juncker, Catherine Juste, Sean Kennedy, Ghalia Khaci, Michiel Kleerebezem, Jan Knoll, Séverine Layec, Marion Leclerc, Pierre Leonard, Denis LePaslier, Christine M'Rini, Emmanuelle Maguin, Chaysavanh Manichanh, Daniel Mende, Alexandre Mérieux, Raish Oozeer, Julian Parkhill, Eric Pelletier, Nicolas Pons, Junjie Qin, Simon Rasmussen, Pierre Renault, Maria Rescigno, Nicolas Sanchez, Thomas Sicheritz-Ponten, Julien Tap, Sebastian Tims, Antonio Torrejon, Keith Turner, Maarten van de Guchte, Johan E T van Hylckama Vlieg, Gaetana Vandemeulebrouck, Encarna Varela, Patrick Viega, Jean Weissenbach, Yohanan Winogradski, Takuji Yamada, Erwin G Zoetendal
Abstract
In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.
Figures
References
- Shreiner AB, Kao JY, Young VB. The gut microbiome in health and in disease. Curr Opin Gastroenterol. 2015;31:69–75. doi:10.1097/MOG.0000000000000139.
- Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nature reviews. Genetics. 2012;13:260–270. doi:10.1038/nrg3182.
- Qin J, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. doi:10.1038/nature11450.
- Karlsson FH, et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature. 2013;498:99–103. doi:10.1038/nature12198.
- Schellenberg ES, Dryden DM, Vandermeer B, Ha C, Korownyk C. Lifestyle interventions for patients with and at risk for type 2 diabetes: a systematic review and meta-analysis. Ann Intern Med. 2013;159:543–551. doi:10.7326/0003-4819-159-8-201310150-00007.
- Larsen N, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PloS one. 2010;5:e9085. doi:10.1371/journal.pone.0009085.
- Zhang X, et al. Human gut microbiota changes reveal the progression of glucose intolerance. PloS one. 2013;8:e71108. doi:10.1371/journal.pone.0071108.
- de Vos WM, Nieuwdorp M. Genomics: A gut prediction. Nature. 2013;498:48–49. doi:10.1038/nature12251.
- Pernicova I, Korbonits M. Metformin--mode of action and clinical implications for diabetes and cancer. Nat Rev Endocrinol. 2014;10:143–156. doi:10.1038/nrendo.2013.256.
- Shin NR, et al. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut. 2013;63:727–735. doi:10.1136/gutjnl-2012-303839.
- Napolitano A, et al. Novel gut-based pharmacology of metformin in patients with type 2 diabetes mellitus. PloS one. 2014;9:e100778. doi:10.1371/journal.pone.0100778.
- Le Chatelier E, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500:541–546. doi:10.1038/nature12506.
- Sato J, et al. Gut dysbiosis and detection of “live gut bacteria” in blood of Japanese patients with type 2 diabetes. Diabetes Care. 2014;37:2343–2350. doi: 10.2337/dc13-2817.
- Cabreiro F, et al. Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism. Cell. 2013;153:228–239. doi:10.1016/j.cell.2013.02.035.
- Gerritsen J, et al. Characterization of Romboutsia ilealis gen. nov., sp. nov., isolated from the gastro-intestinal tract of a rat, and proposal for the reclassification of five closely related members of the genus Clostridium into the genera Romboutsia gen. nov., Intestinibacter gen. nov., Terrisporobacter gen. nov. and Asaccharospora gen. nov. Int J Syst Evol Microbiol. 2014;64:1600–1616. doi:10.1099/ijs.0.059543-0.
- Song YL, Liu CX, McTeague M, Summanen P, Finegold SM. Clostridium bartlettii sp. nov., isolated from human faeces. Anaerobe. 2004;10:179–184.
- Messori S, Trevisi P, Simongiovanni A, Priori D, Bosi P. Effect of susceptibility to enterotoxigenic Escherichia coli F4 and of dietary tryptophan on gut microbiota diversity observed in healthy young pigs. Vet. Microbiol. 2013;162:173–179.
- Czyzyk A, Tawecki J, Sadowski J, Ponikowska I, Szczepanik Z. Effect of biguanides on intestinal absorption of glucose. Diabetes. 1968;17:492–498.
- Winter SE, et al. Host-derived nitrate boosts growth of E. coli in the inflamed gut. Science. 2013;339:708–711. doi:10.1126/science.1232467.
- Everard A, et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proceedings of the National Academy of Sciences of the United States of America. 2013;110:9066–9071. doi:10.1073/pnas.1219451110.
- Lee H, Ko G. Effect of metformin on metabolic improvement and gut microbiota. Appl Environ Microbiol. 2014;80:5935–5943. doi: 10.1128/AEM.01357-14.
- De Vadder F, et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell. 2014;156:84–96. doi:10.1016/j.cell.2013.12.016.
- Croset M, et al. Rat small intestine is an insulin-sensitive gluconeogenic organ. Diabetes. 2001;50:740–746.
- Jorgensen T, et al. A randomized non-pharmacological intervention study for prevention of ischaemic heart disease: baseline results Inter99. Eur J Cardiovasc Prev Rehabil. 2003;10:377–386. doi:10.1097/01.hjr.0000096541.30533.82.
- WHO . Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus. World Health Organization; Geneva: 1999. ( Tech. Rep. Ser. WHO/NCD/NCS/99.2 ed ).
- Li J, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32:834–841. doi:10.1038/nbt.2942.
- Kultima JR, et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PloS one. 2012;7:e47656. doi:10.1371/journal.pone.0047656.
- Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–1659. doi:10.1093/bioinformatics/btl158.
- Arumugam M, Harrington ED, Foerstner KU, Raes J, Bork P. SmashCommunity: a metagenomic annotation and analysis tool. Bioinformatics. 2010;26:2977–2978. doi:10.1093/bioinformatics/btq536.
- Kanehisa M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36:D480–484. doi:10.1093/nar/gkm882.
- Powell S, et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 2012;40:D284–289. doi:10.1093/nar/gkr1060.
- Sunagawa S, et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods. 2013;10:1196–1199. doi:10.1038/nmeth.2693.
- Sunagawa S, et al. Structure and function of the global ocean microbiome. Science. 2015;348(6237) DOI: 10.1126/science.1261359.
- Nielsen HB, et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol. 2014;32:822–828. doi:10.1038/nbt.2939.
- Hildebrand F, et al. LotuS: an efficient and user-friendly OTU processing pipeline. Microbiome. 2014;2:30. .
- Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–998. .
- Edgar RC, et al. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–2200. .
- Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics (Oxford, England) 2011;27:2957–2963. .
- Quast C, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–596. .
- Madden T. The BLAST Sequence Analysis Tool. 2002 Oct 9 [Updated 2003 Aug 13] In: McEntyre J, Ostell J, editors. The NCBI Handbook [Internet] National Center for Biotechnology Information (US); Bethesda (MD): 2002. Chapter 16. Available from:
- Benjamini YH, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testning. Journal of the Royal Statistical Society. 1995;57:289–300.
- Hothorn T, Hornik K, Van de Wiel MA, Zeileis A. A Lego system for conditional inference. Am Stat. 2006;60:257–263. doi:Doi 10.1198/000313006x118430.
- Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–930. doi:DOI 10.1111/j.1654-1103.2003.tb02228.x.
- Wickham J. ggplot2: elegant graphics for data analysis. Springer; New York: 2009.
- Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46. doi:DOI 10.1111/j.1442-9993.2001.01070.pp.x.
- Friedman J, et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 2010;33:1–22.
- Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics. 2010;26:392–398. doi:DOI 10.1093/bioinformatics/btp630.
- Hildebrand F, et al. A comparative analysis of the intestinal metagenomes present in guinea pigs (Cavia porcellus) and humans (Homo sapiens) BMC Genomics. 2012;13(1):514. .
- Hildebrand F, et al. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biology. 2013;14(1):R4. .
- Haraldsdóttir J, et al. Portionsstorleker - Nordiska standardportioner av mat och livsmedel (Portion sizes-Nordic standard portions of food and foodstuffs) Nordic Council & Council of Ministers; Sweden: 1998. pp. 1–41. 1998.
- Biltoft-Jensen A, et al. Danskernes kostvaner 2000-2002. Teknisk rapport. The National Dietary Survey of Denmark 2000-2002; 2005. Technical Report.
- Møller A, et al. Fødevaredatabanken version 5.0. Food databank version 5.0. Fødevareinformatik, Institut for Fødevaresikkerhed og Ernæring, Fødevaredirektoratet (Danish Ministry of Food, Agriculture and Fisheries); Denmark: 2002. Available from: URL: .
- Lauritsen J. FoodCalc. 2004 Feb. Available from: URL: .
Source: PubMed