Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Gut microbiota modulates weight gain in mice after discontinued smoke exposure

A Publisher Correction to this article was published on 15 March 2022

This article has been updated

Abstract

Cigarette smoking constitutes a leading global cause of morbidity and preventable death1, and most active smokers report a desire or recent attempt to quit2. Smoking-cessation-induced weight gain (SCWG; 4.5 kg reported to be gained on average per 6–12 months, >10 kg year–1 in 13% of those who stopped smoking3) constitutes a major obstacle to smoking abstinence4, even under stable5,6 or restricted7 caloric intake. Here we use a mouse model to demonstrate that smoking and cessation induce a dysbiotic state that is driven by an intestinal influx of cigarette-smoke-related metabolites. Microbiome depletion induced by treatment with antibiotics prevents SCWG. Conversely, fecal microbiome transplantation from mice previously exposed to cigarette smoke into germ-free mice naive to smoke exposure induces excessive weight gain across diets and mouse strains. Metabolically, microbiome-induced SCWG involves a concerted host and microbiome shunting of dietary choline to dimethylglycine driving increased gut energy harvest, coupled with the depletion of a cross-regulated weight-lowering metabolite, N-acetylglycine, and possibly by the effects of other differentially abundant cigarette-smoke-related metabolites. Dimethylglycine and N-acetylglycine may also modulate weight and associated adipose-tissue immunity under non-smoking conditions. Preliminary observations in a small cross-sectional human cohort support these findings, which calls for larger human trials to establish the relevance of this mechanism in active smokers. Collectively, we uncover a microbiome-dependent orchestration of SCWG that may be exploitable to improve smoking-cessation success and to correct metabolic perturbations even in non-smoking settings.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Microbiome depletion modulates SCWG.
Fig. 2: Cigarette-smoke-induced dysbiosis drives SCWG.
Fig. 3: DMG dynamics during smoke exposure and cessation.
Fig. 4: Cigarette-smoke-related metabolites affect SCWG.

Data availability

Raw metagenomic sequences and resolved MAGs are available at the European Nucleotide Archive under project accession number PRJEB40095. Single-cell RNA raw sequences are deposited in ArrayExpress with accession E-MTAB-10869. Metabolomic data are provided in Supplementary Table 4 and in the publicly accessible platform of Mendeley at https://doi.org/10.17632/539zh45tw2.1Source data are provided with this paper.

Change history

References

  1. Centers for Disease Control and Prevention (CDC). Vital signs: current cigarette smoking among adults aged ≥18 years with mental illness—United States, 2009–2011. MMWR Morb. Mortal Wkly Rep. 62, 81–87 (2013).

    PubMed  Google Scholar 

  2. Centers for Disease Control and Prevention (CDC). Quitting smoking among adults—United States, 2001–2010. MMWR Morb. Mortal Wkly Rep. 60, 1513–1519 (2011).

    PubMed  Google Scholar 

  3. Harris, K. K., Zopey, M. & Friedman, T. C. Metabolic effects of smoking cessation. Nat. Rev. Endocrinol. 12, 299–308 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Aubin, H. J., Farley, A., Lycett, D., Lahmek, P. & Aveyard, P. Weight gain in smokers after quitting cigarettes: meta-analysis. BMJ 345, e4439 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Biedermann, L. et al. Smoking cessation induces profound changes in the composition of the intestinal microbiota in humans. PLoS ONE 8, e59260 (2013).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Rodin, J. Weight change following smoking cessation: the role of food intake and exercise. Addict. Behav. 12, 303–317 (1987).

    CAS  Article  PubMed  Google Scholar 

  7. Hankey, C. & Leslie, W. Obesity: is weight gain after smoking cessation an important concern? Nat. Rev. Endocrinol. 8, 630–632 (2012).

    Article  PubMed  Google Scholar 

  8. Biedermann, L. et al. Smoking cessation alters intestinal microbiota: insights from quantitative investigations on human fecal samples using FISH. Inflamm. Bowel Dis. 20, 1496–1501 (2014).

    Article  PubMed  Google Scholar 

  9. Lee, S. H. et al. Association between cigarette smoking status and composition of gut microbiota: population-based cross-sectional study. J. Clin. Med. 7, 282 (2018).

    CAS  Article  PubMed Central  Google Scholar 

  10. Lim, M. Y. et al. Analysis of the association between host genetics, smoking, and sputum microbiota in healthy humans. Sci. Rep. 6, 23745 (2016).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Russell, M. A., Wilson, C., Patel, U. A., Feyerabend, C. & Cole, P. V. Plasma nicotine levels after smoking cigarettes with high, medium, and low nicotine yields. BMJ 2, 414–416 (1975).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Benowitz, N. L. & Jacob, P. 3rd Daily intake of nicotine during cigarette smoking. Clin. Pharmacol. Ther. 35, 499–504 (1984).

    CAS  Article  PubMed  Google Scholar 

  13. Bush, T., Lovejoy, J. C., Deprey, M. & Carpenter, K. M. The effect of tobacco cessation on weight gain, obesity, and diabetes risk. Obesity 24, 1834–1841 (2016).

    Article  PubMed  Google Scholar 

  14. Graff-Iversen, S., Hewitt, S., Forsen, L., Grotvedt, L. & Ariansen, I. Associations of tobacco smoking with body mass distribution; a population-based study of 65,875 men and women in midlife. BMC Public Health 19, 1439 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Crooks, P. A., Bardo, M. T. & Dwoskin, L. P. Nicotinic receptor antagonists as treatments for nicotine abuse. Adv. Pharmacol. 69, 513–551 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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

    ADS  Article  PubMed  Google Scholar 

  17. Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Teng, Y. W., Mehedint, M. G., Garrow, T. A. & Zeisel, S. H. Deletion of betaine-homocysteine S-methyltransferase in mice perturbs choline and 1-carbon metabolism, resulting in fatty liver and hepatocellular carcinomas. J. Biol. Chem. 286, 36258–36267 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Hill, D. A. et al. Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue. Proc. Natl Acad. Sci. USA 115, E5096–E5105 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698.e14 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Coats, B. R. et al. Metabolically activated adipose tissue macrophages perform detrimental and beneficial functions during diet-induced obesity. Cell Rep. 20, 3149–3161 (2017).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Crewe, C., An, Y. A. & Scherer, P. E. The ominous triad of adipose tissue dysfunction: inflammation, fibrosis, and impaired angiogenesis. J. Clin. Invest. 127, 74–82 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Flaherty, S. E. 3rd et al. A lipase-independent pathway of lipid release and immune modulation by adipocytes. Science 363, 989–993 (2019).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Witschi, H., Espiritu, I., Dance, S. T. & Miller, M. S. A mouse lung tumor model of tobacco smoke carcinogenesis. Toxicol. Sci. 68, 322–330 (2002).

    CAS  Article  PubMed  Google Scholar 

  26. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  Article  PubMed  Google Scholar 

  30. Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    CAS  Article  PubMed  Google Scholar 

  32. Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).

    ADS  MathSciNet  CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26, 1721–1729 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).

    CAS  Article  PubMed  Google Scholar 

  35. Jain, C., Rodriguez, R. L., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  36. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888-1902.e21 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Tabula Muris, C. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    ADS  Article  Google Scholar 

  38. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the members of the Elinav laboratory, Weizmann Institute of Science, and members of the DKFZ Cancer–Microbiome Division for insightful discussions; R. Alon, A. Bar-Shai and M. Hatzav for assistance with operation of the smoking machine; M. Eren for fruitful discussions and guidance on making the MAG reference catalogue; and C. Bar-Natan for care of the GF animals and animal work. A.A.K. is a recipient of EMBO Long Term Fellowship number 2016‐1088 and the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska–Curie grant agreement number 747114. M.T. is the Incumbent of the Carolito Stiftung Research Fellow Chair in Neurodegenerative Diseases. Y.K. is the incumbent of the Sarah and Rolando Uziel Research Associate Chair. H.S. is the incumbent of the Vera Rosenberg Schwartz Research Fellow Chair. E.E. is supported by the Leona M. and Harry B. Helmsley Charitable Trust, the Adelis Foundation, the Pearl Welinsky Merlo Scientific Progress Research Fund, the Park Avenue Charitable Fund, The Hanna and Dr. Ludwik Wallach Cancer Research Fund, the Daniel Morris Trust, The Wolfson Family Charitable Trust & The Wolfson Foundation, the Ben B. and Joyce E. Eisenberg Foundation, the White Rose International Foundation, the Estate of Malka Moskowitz, the Estate of Myron H. Ackerman, the Estate of Bernard Bishin for the WIS-Clalit Program, the Else Kroener Fresenius Foundation, the Jeanne and Joseph Nissim Center for Life Sciences Research, Aliza Moussaieff, Miel de Botton, the Vainboim Family, Alex Davidoff, the V. R. Schwartz Research Fellow Chair, the Swiss Society Institute for Cancer Prevention Research at the Weizmann Institute of Science, Rehovot, Israel and by grants funded by the European Research Council, the Israel Science Foundation, the Israel Ministry of Science and Technology, the Israel Ministry of Health, the Helmholtz Foundation, the Garvan Institute, the European Crohn’s and Colitis Organization, the Deutsch–Israelische Projektkooperation, the IDSA Foundation, the Sagol Institute for Longevity Research Program, the Charlie Teo Foundation, and the Wellcome Trust. E.E. is the incumbent of the Sir Marc and Lady Tania Feldmann Professorial Chair, is a senior fellow of the Canadian Institute of Advanced Research (CIFAR), and is an international scholar of The Bill & Melinda Gates Foundation and Howard Hughes Medical Institute (HHMI).

Author information

Authors and Affiliations

Authors

Contributions

L.F. and U.M. contributed equally to the study. L.F. designed, performed, interpreted the experiments and wrote the manuscript. U.M. designed and headed all computational and analytical aspects related to this study. A.A.K., performed and analysed the single-cell transcriptomics analysis. M.D.-B., A. Leshem, S.I., S.H., H.K., D.K., A. Livne, C.M., S.M., N.A. and H.S. helped with experiments. Y.C., R.V.-M. and N.Z. assisted with data analysis. A. Brandis and T.M. performed the metabolomic experiments. Y.K. assisted with the metabolic cage experiments. M.T. assisted with the smoking-chamber experiments. N.S. and A.H. supervised all the GF experiments. J.S., A. Bukimer, S.E.-M. and A.M. designed and conducted the human trial, and assisted in the analysis of its results. H.S. and E.E. conceived the study, jointly supervised the participants, interpreted the experiments and wrote the manuscript.

Corresponding authors

Correspondence to Hagit Shapiro or Eran Elinav.

Ethics declarations

Competing interests

E.E. is the scientific founder of Daytwo & BiomX, and a consultant to HELLO INSIDE in topics unrelated to the subject of this work. L.F., U.M., A.A.K., M.D.-B., A. Leshem, S.I., Y.C., J.S., N.Z., C.M., S.M., N.A., R.V.-M., S.H., H.K., D.K., A. Livne, A. Brandis, S.E.-M., A.M., A. Bukimer, T.M., Y.K., M.T., N.S., A.H. and H.S. do not have any financial or non-financial competing interests.  Weizmann Institute of Science is in the process of applying for a patent application covering weight modulatory effects of discovered microbes and metabolites that lists L.F., U.M. and E.E. as inventors.

Additional information

Peer review information Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 The smoking cessation mouse model.

(a) Experimental scheme, HFD-fed mice. Mice not exposed to cigarette smoke (Non-SMK), mice exposed to smoke and cessation (SMK) and mice continuously exposed to smoke (continuous SMK). SMK group was exposed to cigarette smoke for 3 weeks, 5 days/week. Continuously smoke exposed group was exposed to cigarette-smoke throughout the duration of the experiment. (b) Weight change of HFD-fed mice during smoke exposure  and following discontinuation of smoke exposure (cessation). Non-SMK, SMK and continuous SMK (n = 10), mean ± s.e.m.; day 63: one-way ANOVA and Tukey correction. Inset: iAUC of weight change at smoke exposure or cessation, one-way ANOVA and Tukey correction, mean ± s.e.m. (c–e) Ammonia (c), cholesterol (d) and HDL (e) levels during smoke exposure and cessation: Non-SMK (at smoke exposure n = 9 for ammonia and n = 10 for d-e; at cessation n = 8 for all groups), SMK and continuous SMK (at smoke exposure n = 10, at cessation n = 8), mean values. Smoke exposure phase: two-sided Mann-Whitney U-test (c, e) or unpaired two-sided t-test (d); cessation: Kruskal-Wallis and Dunn’s test. In the smoke exposure phase, SMK and continuous SMK were considered as one group. (f–g) Aspartate aminotransferase (AST, f) and alanine aminotransferase (ALT, g) levels during smoke exposure and cessation: Non-SMK, SMK, continuous SMK (n = 10 per group during smoke exposure; n = 8 per group during cessation); two-sided Mann-Whitney U- test for smoke exposure, Kruskal-Wallis and Dunn’s test for cessation, mean values. (h–j) non-fasting glucose (h), triglycerides (i) and albumin (ALB, j) levels during smoke exposure and cessation: Non-SMK, SMK and continuous SMK (n = 10 per group during smoke exposure, n = 8 per group during cessation). Glucose: unpaired two-sided t-test for smoke exposure and one-way ANOVA with Tukey correction for cessation; triglycerides and albumin: unpaired two-sided t-test for smoke exposure and Kruskal-Wallis and Dunn’s test for cessation. Plasma samples were collected 21 days during smoke exposure and on day 63 during cessation, mean values. Grey backgrounds represent cessation period. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001, exact P values presented in Supplementary Table 1.

Extended Data Fig. 2 Impact of microbiome depletion on SCWG.

(a) Lean (top) and fat (bottom) mass during smoke exposure (day 19) and cessation (day 35). Non-SMK (n = 30 at smoke exposure, n = 6 at cessation), SMK (n = 38 at smoke exposure, n = 5 at cessation), Non-SMK+abx (at smoke exposure n = 20, at cessation n = 5), SMK+abx (at smoke exposure n = 15, at cessation n = 5), HFD-fed mice, three-way during smoke exposure and cessation, mean values. (b) Weight change during smoke exposure and cessation. HFD-fed mice, Non-SMK and Non-SMK+abx (n = 10), SMK, SMK+abx and SMK+abx at cessation (n = 9), mean ± s.e.m. For (b–d): last day: one-way ANOVA and Sidak correction. Inset: iAUC, one-way ANOVA and Sidak correction, mean ± s.e.m. (c) Weight change during smoke exposure and cessation in mice exposed to low-nicotine cigarette smoke and fed with HFD. Non-SMK, SMK and Non-SMK+abx (n = 10), SMK+abx (n = 9), mean ± s.e.m. (d) Weight change during smoke exposure and cessation. HFD-fed mice were implanted with osmotic pumps containing vehicle (PBS) or 0.5 mg/kg/day varenicline (var). Non-SMK and Non-SMK+var (n = 5), SMK and SMK+var (n = 10), mean ± s.e.m. Grey background in graphs refers to cessation period. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001, exact P values presented in Supplementary Table 1.

Extended Data Fig. 3 Metabolic impacts of antibiotics treatment on SCWG.

(a–c) Metabolic cage analysis during smoke exposure cessation over a period of 260h, n = 4 for all mice groups, HFD-fed mice. Locomotion (a, ‘cnts’ refers to light beam break count), food intake (b, total kcal), respiratory exchange ratio (c, RER); inset: AUC (baseline = 0, baseline of RER = 0.7). One-way ANOVA and Sidak correction, mean ± s.e.m. (d) Energy expenditure analysis during cessation over a period of 260h. Non-SMK and Non-SMK+abx (n = 8), SMK (n = 5), SMK+abx (n = 6). Inset: AUC (baseline= minimum score, 14.1), One-way ANOVA and Sidak correction, mean ± s.e.m. (e) Pearson correlation between fecal calories and weight gain rate, smoke exposure (circles) and cessation (triangles). SMK (smoke exposure period n = 8, cessation period n = 9, red), SMK+abx (smoke exposure period n = 9, cessation period n = 9, yellow). Lines represent linear regression slopes and intercepts. Grey background in graphs refers to the dark cycle. *P<0.05; **P<0.01; exact P values presented in Supplementary Table 1.

Extended Data Fig. 4 Microbiome changes during smoke exposure and cessation.

(a–b) 16S rDNA-based principal coordinate analysis (PCoA) for (a) Bray-Curtis dissimilarity or (b) Jaccard similarity for HFD-fed Non-SMK and SMK mice at day 21 (smoke exposure). Non-SMK (n = 10), SMK (n = 8) mice. Inset: PERMANOVA. (c) KEGG modules fully covered by KEGG orthologs that significantly differed upon comparisons between day 0 and day 21. HFD-fed mice, Non-SMK (n = 10 for all times), SMK (n = 10 for day 0, n = 8 for day 21); asterisks denote significant differences between day 21 versus day 0 for each group (Q < 0.1); two-sided Mann-Whitney U-test with BKY correction. (d) Alpha diversity quantified as Shannon index in fecal samples at day 0 (baseline), 21 (smoke exposure) and 35 (cessation). HFD-fed mice, Non-SMK and SMK n = 10 mice per group; PERMANOVA, mean values. (e–f) PCA of species-level relative abundance at day 0 (e, baseline) and day 35 (f, cessation), HFD-fed mice n = 10 per group; inset: PERMANOVA. (g) Alpha diversity quantified as Shannon index in fecal samples at day 0 (baseline), 21 (smoke exposure) and 35 (cessation). HFD-fed mice, SMK (n = 10 for all times), SMK+abx (n = 10 at days 0 and 21, n = 9 at day 35); PERMANOVA, mean values. (h) Heatmap representing differentially abundant bacteria (genus level) in HFD-fed Non-SMK versus SMK mice at day 21 (smoke exposure). n = 10 mice per group; DESeq2; coloured bar represents z score. *P<0.05; ***P<0.001; ****P<0.0001, exact P values presented in Supplementary Table 1.

Extended Data Fig. 5 Nicotine impacts on the microbiome and SCWG.

(a) Nicotine and cotinine levels in peripheral plasma, portal vein plasma and stool in naive SPF and GF mice. SPF (n = 6), GF (n = 5 for plasma and portal vein, n = 6 for stool); two-sided Mann-Whitney U test, mean values. (b) Plasma nicotine and cotinine levels during smoke exposure. Non-SMK (n = 8), SMK and SMK+abx (n = 10), Non-SMK+abx (n = 9), HFD-fed mice, Kruskal-Wallis and Dunn’s test, mean values. (c) Fecal nicotine levels in HFD-fed mice exposed for 3 weeks to cigarettes smoke or to nicotine by intra-peritoneal (i.p.) injections; n = 10 mice per group; Kruskal-Wallis and Dunn’s test, mean values. (d) PCoA depicting Jaccard similarity in mice exposed to cigarette smoke or to i.p. nicotine; Naive (n = 9), SMK (n = 10), nicotine-administered (i.p., n = 8) mice; pairwise PERMANOVA. (e) Weight change in mice exposed to cigarette smoke or to i.p. nicotine; Naïve and nicotine (n = 9), SMK (n = 10); day 21: one-way ANOVA and Tukey correction, mean values. (f–g) 16S rDNA-based PCoA depicting (f) Bray-Curtis dissimilarity and (g) Jaccard distance; Naive and SMK (n = 10), nicotine-administered via drinking water (n = 9), pairwise PERMANOVA. (h) Weight change in mice exposed for 3 weeks to cigarette smoke or to nicotine via drinking water. Naive and SMK (n = 10) nicotine (n = 9); day 21: one-way ANOVA and Tukey correction, mean values. (i) Plasma levels of nicotine and cotinine in HFD-fed mice administered with PBS or nicotine by osmotic pumps for 3 weeks, n = 10 mice per group; two-sided Mann-Whitney U-test, mean values. (j) Weight change in HFD-fed mice administered with PBS or nicotine by osmotic pumps for 4 weeks. n = 10 mice per group; day 28: unpaired two-sided t-test, mean values. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001, P values presented in Supplementary Table 1.

Extended Data Fig. 6 Fecal microbiome transplantation (FMT) from mice exposed to smoke.

(a) Experimental scheme of FMT experiments performed using fecal donations from mice exposed to smoke. Fecal samples from Non-SMK and SMK mice at day 7 (early smoke exposure) or day 18 (during smoke exposure) were transferred into GF mice of the designated strains, fed with the designated diets. (b–d) Weight change of GF mice following FMT. Fecal donations were collected at day 7 from HFD-fed C57BL/6 mice. For b, c, d, f: day 28, unpaired two-sided t-test, mean ± s.e.m.; upper inset: shotgun metagenomic sequencing showing PCA of species composition (for b Non-SMK n = 5, SMK n = 7 mice, for c, d, f n = 6 mice per group), PERMANOVA. (b) Recipients: HFD-fed SW mice. Non-SMK (n = 11), and SMK (n = 13) mice; day 28: unpaired two-sided t-test, mean ± s.e.m. (c) Recipients: HFD-fed C57BL/6 mice. Non-SMK (n = 7) and SMK (n = 6) mice, mean ± s.e.m. (d) Recipients: NC-fed C57BL/6 mice, n = 6 mice per group, mean ± s.e.m. (e) Experimental scheme of FMT experiments performed during cessation. Fecal samples from Non-SMK and SMK mice at day 24 (cessation) were transferred into GF mice of the designated strains, fed the designated diets. (f) Weight change of GF mice receiving FMT from C57BL/6 NC-fed mice at day 4 of cessation (total day 24). Recipients: NC-fed C57BL/6 mice, n = 6 mice per group, mean ± s.e.m. (g) Heatmap presenting log2 fold change of bacteria that were significantly changed (Q < 0.05) at least in two conditions (presented in Extended Data Fig. 6e); DESeq2; ns.: non-significant; coloured bar represents log fold change. *P<0.05; **P<0.01, ****P<0.0001, exact P values presented in Supplementary Table 1.

Extended Data Fig. 7 Functional features of transplanted microbiomes from mice exposed to smoke and at cessation.

(a) Heatmaps describing the functional potential of metagenomic features (EC annotated) in shotgun metagenomics, corresponding to the conditions of Extended Data Fig. 6e. Features were grouped according to BRITE hierarchy; colored bar represents z score. (b–c) Weight change of GF C57BL/6 mice following FMT. Fecal donations were collected at day 24 of smoke exposure and cessation from abx-treated or untreated HFD-fed C57BL/6 mice (b) or NC (c); Day 28, unpaired two-sided t-test, mean ± s.e.m. (b) Recipients: HFD-fed C57BL/6 mice, SMK (n = 7) and SMK+abx (n = 6), mean ± s.e.m. (c) Recipients: NC-fed C57BL/6 mice, n = 6 per group, mean ± s.e.m. (d–e) Fecal calories of GF recipients after FMT from early smoke exposure (d, Non-SMK n = 5, SMK n = 6) or cessation (e, n = 5 mice per group); unpaired two-sided t-test, mean values. *P<0.05, **P<0.01, P values presented in Supplementary Table 1.

Extended Data Fig. 8 Candidate metabolites associated with SCWG.

(a) Heatmaps depicting plasma metabolites of HFD-fed mice during smoke exposure (day 15) and cessation (day 30). Non-SMK, SMK+abx (n = 7 smoke exposure, n = 6 cessation), SMK (n = 6 smoke exposure, n = 5 cessation), Non-SMK+abx (n = 8 smoke exposure, n = 6 cessation); heatmap intensities represent normalized metabolite levels (z-score). (b) Heatmaps depicting fecal metabolites during smoke exposure (day 21) and cessation (day 35). Non-SMK and Non-SMK+abx (n = 9 for smoke exposure and n = 6 for cessation), SMK (n = 8 for smoke exposure and n = 5 for cessation) and SMK+abx (n = 8 for smoke exposure and n = 6 for cessation) HFD-fed mice; heatmap intensities represent normalized metabolite levels (z-score). (c) LMM, utilizing both smoke and antibiotics alterations throughout time and quantifying their interactive impacts on plasma metabolite levels at smoke exposure. Venn diagram (left) represents significant (p<0.05) metabolites impacted by smoke exposure, antibiotics and their interactions. Post-hoc: Tukey correction for multiple comparisons. Venn diagram (right): metabolites fulfilling both mixed linear model and statistical hypothesis testing criteria. (d) Predicted gene copy numbers (PICRUSt2) of fecal choline sulfatase: Non-SMK (n = 10), SMK (n = 8) HFD-fed mice; unpaired two-sided t-test; mean values. (e-f) Fecal metabolites, untargeted mass spectrometry, HFD-fed mice, Non-SMK (n = 9), SMK (n = 8 at smoke exposure), unpaired two-sided t-test, mean ± s.e.m. values of (e) choline and (f) betaine; SmD- scaled imputed data, log scale values (methods). (g) qPCR quantification of liver betaine-homocysteine S-methyltransferase (BHMT) transcripts during smoke exposure. HFD-fed mice, Non-SMK (n = 4), SMK (n = 6); two-sided Mann-Whitney U-test, mean ±s.e.m. (h) Plasma DMG levels, assessed by targeted mass spectrometry. Non-SMK, Non-SMK+abx and SMK+abx (n = 10), SMK (n = 9), mice fed with HFD; Kruskal-Wallis and Dunn’s test, mean values. *P<0.05, **P<0.01, exact P values presented in Supplementary Table 1.

Extended Data Fig. 9 Metabolite impacts on SCWG.

(a–b) Weight change during smoke exposure and cessation upon addition of N-formylanthranilic acid (a, N-FAN acid) or trigonelline (b, TRI). a: n = 10 HFD-fed mice per group, mean ± s.e.m; day 42: unpaired 2-sided t test; iAUC: unpaired 2-sided t test, mean ± s.e.m. b: Non-SMK+abx+PBS, Non-SMK+abx+TRI and SMK+abx+TRI (n = 10), SMK+abx+PBS (n = 9), HFD-fed mice, mean ± s.e.m; day 35: one-way ANOVA Sidak correction; inset: iAUC; one-way ANOVA Sidak correction, mean ± s.e.m. (c) iAUC of weight change at smoke exposure or cessation of Fig 4a. NS+abx (n = 9), SMK+abx+PBS (n = 10 smoke exposure, n = 9 cessation), all other groups n = 10; one-way ANOVA Sidak correction; box plots represent max-min values. Rx- treatment. (d) Plasma levels of metabolites of the DMG biosynthesis pathway assessed by targeted mass spectrometry in mice consuming choline-deficient diet (CDD), n = 10; two-sided Mann-Whitney U-test, mean values. (e) Hepatic BHMT qPCR during smoke exposure in mice consuming CDD, n = 10; unpaired two-sided t-test, mean values. (f) Targeted mass spectrometry of plasma DMG. PBS (n = 9), DMG (n = 10) HFD-fed mice; two-sided Mann-Whitney U-test, mean values. (g–h) Weight change during DMG supplementation on day 14 (g, n = 10) or day 49 (h, PBS: n = 19, DMG: n = 18, pool of 2 independent repeats), mean ± s.e.m., HFD-fed mice; last time point: unpaired two-sided t-test. Inset: iAUC, unpaired two-sided t-test, mean ± s.e.m. (i) Plasma levels of HFD-fed mice treated with PBS (n = 8 for all paraments except n = 7 for NH3) or DMG (n = 10 for all parameters except n = 9 for ALT); unpaired two-sided t-test, except two-sided Mann Whitney U test for NH3 and HDL; mean values. (j–m) Metabolic cage analysis for 172h in DMG versus PBS supplemented mice. Locomotion (j, n = 8), respiratory exchange ratio (k, RER, n = 4), energy expenditure (l, n = 4) and food intake (Total kcal, m, n = 8). Inset: AUC; unpaired two-sided t-test, mean ± s.e.m. Grey background depicts the cessation period (a–c) or the dark cycle (j–i). *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001, P values presented in Supplementary Table 1.

Extended Data Fig. 10 ACG supplementation ameliorates weight gain.

(a) Plasma levels of ACG during smoke exposure (day 15) and cessation (day 30). Non-SMK (n = 7 for smoke exposure, n = 5 for cessation), SMK (n = 6 for smoke exposure, n = 5 for cessation), Non-SMK+abx (n = 8 for smoke exposure, n = 6 for cessation), SMK+abx (n = 7 for smoke exposure, n = 6 for cessation), HFD-fed mice; two-way ANOVA and BH correction (Q < 0.1), mean values. (b) Calories measured in rodent diet. n = 3 mice per group; one-way ANOVA and Tukey correction, mean values. (c) Plasma levels of ACG in HFD (Control, n = 10) or HFD+ACG (ACG, n = 9) mice, assessed by targeted mass spectrometry. Unpaired two-sided t-test, mean values. (d) Weight change during ACG supplementation. n = 5 HFD-fed mice per group, mean ± s.e.m.; day 14: unpaired two-sided t-test. Inset: iAUC, unpaired two-sided t-test, mean ± s.e.m. (e) GTT during ACG administration (day 49) n = 10 HFD-fed mice per group. Inset: AUC, Unpaired two-sided t-test, mean ± s.e.m. (f) Plasma biochemical levels Control (n = 19 except n = 17 for TG & urea, n = 18 for albumin). ACG (n = 19 except for n = 18 for NH3) HFD-fed mice. Two-sided Mann-Whitney U-test, except for unpaired two-sided t-test for glucose, total cholesterol, urea and total protein, mean values. (g–j) Metabolic cage analysis for 384h, n = 4 HFD-fed mice per group, dashed line indicates the starting day of the diet. (g) Food intake (total kcal); (h) locomotion (‘cnts’ refers to light beam break count); (i) respiratory exchange ratio (RER); and (j) energy expenditure; inset: AUC, unpaired two-sided t-test except two-sided Mann-Whitney U-test for energy expenditure, mean ± s.e.m. Grey background depicts the dark cycle. (k) Plasma DMG levels in Control (n = 7) or ACG (n = 6) mice, unpaired two-sided t-test, mean values. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001, exact P values presented in Supplementary Table 1.

Extended Data Fig. 11 Single cell transcriptomic analysis of adipose tissue immune cells of HFD-fed, DMG- and ACG-supplemented mice not exposed to smoke.

(a) UMAP representation of single cell data showing stepwise clustering of adipose immune cell populations. (b) Balloonplot showing the mean of normalized and scaled gene expression of key markers used to annotate clusters to cell types. (c) Balloonplot showing the mean of normalized and scaled gene expression of key markers of macrophage and monocyte populations. (d) Balloonplot showing the mean of normalized and scaled gene expression of key markers of chemokines and cytokines in macrophage and monocyte populations. A larger size of the dot in the balloonplot represent increased expression. See Supplementary Table 6 for single cell transcriptomic data.

Extended Data Fig. 12 Adipose tissue immune cell features of HFD-fed, DMG- and ACG-supplemented mice not exposed to smoke.

(a) Heatmap showing -log10 of BH adjusted p-value GO functional enrichment analysis of genes differentially expressed in macrophage subpopulations upon treatment with ACG and DMG. See Supplementary Table 6 for single cell transcriptomic data.

Extended Data Fig. 13 Potential microbiome and metabolite associations with human who smoke.

(a) Experimental outline of the human cohort. (b–f) human fecal microbiome analysis (Non-SMK n = 40, SMK n = 20). (b) PCA of metagenomically-assembled genomes (MAGs) relative abundances in human stool; inset: PERMANOVA. (c) Differentially abundant MAGs; asterisks denote significant differences (p<0.05); two-sided Mann-Whitney U-test. (d) PCA of KO annotated reads; inset: PERMANOVA. (e) KEGG orthologs of highest effect (feature with negative values enriched in SMK), two-sided Mann-Whitney U-test, highest (and lowest), 2-fold log change. (f) ROC curves for binary classifier (methods). (g–i) Targeted mass spectrometry of metabolites from the DMG biosynthesis pathway: choline (g), betaine (h) and DMG (i); Non-SMK (n = 62), SMK (n = 34); two-sided Mann-Whitney U-test, mean values. *P<0.05; **P<0.01, exact P values presented in Supplementary Table 1, raw data presented in Supplementary Table 7.

Supplementary information

Reporting Summary

Supplementary information 1

Absolute weight panels.

Supplementary information 2

Individual repeats of pooled main figure panels.

Supplementary Table 1

P values and mixed linear model.

Supplementary Table 2

16S rDNA.

Supplementary Table 3

Shotgun metagenomic.

Supplementary Table 4

Metabolomics.

Supplementary Table 5

Candidate metabolites identified by LMM.

Supplementary Table 6

Single-cell transcriptomics.

Supplementary Table 7

Human metadata and shotgun metagenomics.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fluhr, L., Mor, U., Kolodziejczyk, A.A. et al. Gut microbiota modulates weight gain in mice after discontinued smoke exposure. Nature 600, 713–719 (2021). https://doi.org/10.1038/s41586-021-04194-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-04194-8

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing