Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut

Abstract

The gut microbiota regulates levels of serotonin (5-hydroxytryptamine (5-HT)) in the intestinal epithelium and lumen1,2,3,4,5. However, whether 5-HT plays a functional role in bacteria from the gut microbiota remains unknown. We demonstrate that elevating levels of intestinal lumenal 5-HT by oral supplementation or genetic deficiency in the host 5-HT transporter (SERT) increases the relative abundance of spore-forming members of the gut microbiota, which were previously reported to promote host 5-HT biosynthesis. Within this microbial community, we identify Turicibacter sanguinis as a gut bacterium that expresses a neurotransmitter sodium symporter-related protein with sequence and structural homology to mammalian SERT. T. sanguinis imports 5-HT through a mechanism that is inhibited by the selective 5-HT reuptake inhibitor fluoxetine. 5-HT reduces the expression of sporulation factors and membrane transporters in T. sanguinis, which is reversed by fluoxetine exposure. Treating T. sanguinis with 5-HT or fluoxetine modulates its competitive colonization in the gastrointestinal tract of antibiotic-treated mice. In addition, fluoxetine reduces the membership of T. sanguinis in the gut microbiota of conventionally colonized mice. Host association with T. sanguinis alters intestinal expression of multiple gene pathways, including those important for lipid and steroid metabolism, with corresponding reductions in host systemic triglyceride levels and inguinal adipocyte size. Together, these findings support the notion that select bacteria indigenous to the gut microbiota signal bidirectionally with the host serotonergic system to promote their fitness in the intestine.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Elevating intestinal 5-HT enriches spore-forming bacteria in the gut.
Fig. 2: T. sanguinis takes up 5-HT, which is inhibited by the SSRI fluoxetine.
Fig. 3: 5-HT and the SSRI fluoxetine regulate gene expression and intestinal colonization of T. sanguinis.
Fig. 4: T. sanguinis colonization regulates host lipid metabolism.

Data availability

Data generated or analysed during this study are included in this published article and its Supplementary Information files. Structural modelling files that support the findings of this study are available from Zenodo with the identifier 10.5281/zenodo.3266444. The 16S rDNA sequencing data that support the findings of this study are available from the Qiita database with study IDs 12585, 12596 and 12597, and are also available in the Supplementary Tables. Bacterial transcriptomic data that support the findings of this study are available in Gene Expression Omnibus repository with the accession number GSE133810 and are also available in the Supplementary Tables. Intestinal transcriptomic data that support the findings of this study are available in the Gene Expression Omnibus repository with the accession number GSE133809 and are also available in the Supplementary Tables.

References

  1. 1.

    Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Gershon, M. D. & Tack, J. The serotonin signaling system: from basic understanding to drug development for functional GI disorders. Gastroenterology 132, 397–414 (2007).

    CAS  PubMed  Google Scholar 

  3. 3.

    Reigstad, C. S. et al. Gut microbes promote colonic serotonin production through an effect of short-chain fatty acids on enterochromaffin cells. FASEB J. 29, 1395–1403 (2015).

    CAS  PubMed  Google Scholar 

  4. 4.

    Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. PLoS ONE 106, 3698–3703 (2009).

    CAS  Google Scholar 

  5. 5.

    Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Sjogren, K. et al. The gut microbiota regulates bone mass in mice. J. Bone Min. Res. 27, 1357–1367 (2012).

    Google Scholar 

  7. 7.

    Hata, T. et al. Regulation of gut luminal serotonin by commensal microbiota in mice. PLoS ONE 12, e0180745 (2017).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Fujimiya, M., Okumiya, K. & Kuwahara, A. Immunoelectron microscopic study of the luminal release of serotonin from rat enterochromaffin cells induced by high intraluminal pressure. Histochem. Cell Biol. 108, 105–113 (1997).

    CAS  PubMed  Google Scholar 

  9. 9.

    Mawe, G. M. & Hoffman, J. M. Serotonin signalling in the gut—functions, dysfunctions and therapeutic targets. Nat. Rev. Gastroenterol. Hepatol. 10, 473–486 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Chen, J. J. et al. Maintenance of serotonin in the intestinal mucosa and ganglia of mice that lack the high-affinity serotonin transporter: abnormal intestinal motility and the expression of cation transporters. J. Neurosci. 21, 6348–6361 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Olivella, M., Gonzalez, A., Pardo, L. & Deupi, X. Relation between sequence and structure in membrane proteins. Bioinformatics 29, 1589–1592 (2013).

    CAS  PubMed  Google Scholar 

  12. 12.

    Coleman, J. A. & Gouaux, E. Structural basis for recognition of diverse antidepressants by the human serotonin transporter. Nat. Struct. Mol. Biol. 25, 170–175 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Forrest, L. R., Tavoulari, S., Zhang, Y. W., Rudnick, G. & Honig, B. Identification of a chloride ion binding site in Na+/Cl-dependent transporters. Proc. Natl Acad. Sci. USA 104, 12761–12766 (2007).

    CAS  PubMed  Google Scholar 

  14. 14.

    Barker, E. L., Moore, K. R., Rakhshan, F. & Blakely, R. D. Transmembrane domain I contributes to the permeation pathway for serotonin and ions in the serotonin transporter. J. Neurosci. 19, 4705–4717 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Solis, E. Jr. et al. 4-(4-(dimethylamino)phenyl)-1-methylpyridinium (APP+) is a fluorescent substrate for the human serotonin transporter. J. Biol. Chem. 287, 8852–8863 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Chanrion, B. et al. Physical interaction between the serotonin transporter and neuronal nitric oxide synthase underlies reciprocal modulation of their activity. Proc. Natl Acad. Sci. USA 104, 8119–8124 (2007).

    CAS  PubMed  Google Scholar 

  17. 17.

    Seimandi, M. et al. Calcineurin interacts with the serotonin transporter C-terminus to modulate its plasma membrane expression and serotonin uptake. J. Neurosci. 33, 16189–16199 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ramijan, K. et al. Stress-induced formation of cell wall-deficient cells in filamentous actinomycetes. Nat. Commun. 9, 5164 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Browne, H. P. et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Goodrich, J. K., Davenport, E. R., Waters, J. L., Clark, A. G. & Ley, R. E. Cross-species comparisons of host genetic associations with the microbiome. Science 352, 532–535 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Geva-Zatorsky, N. et al. Mining the human gut microbiota for immunomodulatory organisms. Cell 168, 928–943 (2017).

    CAS  PubMed  Google Scholar 

  22. 22.

    Miyazaki, M., Dobrzyn, A., Elias, P. M. & Ntambi, J. M. Stearoyl-CoA desaturase-2 gene expression is required for lipid synthesis during early skin and liver development. Proc. Natl Acad. Sci. USA 102, 12501–12506 (2005).

    CAS  PubMed  Google Scholar 

  23. 23.

    Porter, N. T., Canales, P., Peterson, D. A. & Martens, E. C. A subset of polysaccharide capsules in the human symbiont Bacteroides thetaiotaomicron promote increased competitive fitness in the mouse gut. Cell Host Microbe 22, 494–506 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Lyte, M., Villageliu, D. N., Crooker, B. A. & Brown, D. R. Symposium review: microbial endocrinology—why the integration of microbes, epithelial cells, and neurochemical signals in the digestive tract matters to ruminant health. J. Dairy Sci. 101, 5619–5628 (2018).

    CAS  PubMed  Google Scholar 

  25. 25.

    Jackson, M. A. et al. Gut microbiota associations with common diseases and prescription medications in a population-based cohort. Nat. Commun. 9, 2655 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Bernstein, C. N. & Forbes, J. D. Gut microbiome in inflammatory bowel disease and other chronic immune-mediated inflammatory diseases. Inflamm. Intest. Dis. 2, 116–123 (2017).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Guo, X. et al. High fat diet alters gut microbiota and the expression of Paneth cell-antimicrobial peptides preceding changes of circulating inflammatory cytokines. Mediat. Inflamm. 2017, 9474896 (2017).

    Google Scholar 

  29. 29.

    Lee, S. H., Paz-Filho, G., Mastronardi, C., Licinio, J. & Wong, M. L. Is increased antidepressant exposure a contributory factor to the obesity pandemic? Transl. Psychiatry 6, e759 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Beyazyuz, M., Albayrak, Y., Egilmez, O. B., Albayrak, N. & Beyazyuz, E. Relationship between SSRIs and metabolic syndrome abnormalities in patients with generalized anxiety disorder: a prospective study. Psychiatry Invest. 10, 148–154 (2013).

    Google Scholar 

  31. 31.

    Abdala-Valencia, H. et al. Inhibition of allergic inflammation by supplementation with 5-hydroxytryptophan. Am. J. Physiol. Lung Cell Mol. Physiol. 303, L642–L660 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

    CAS  PubMed  Google Scholar 

  33. 33.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. https://doi.org/10.1093/bib/bbx108 (2017).

    PubMed Central  Google Scholar 

  36. 36.

    Robinson, O., Dylus, D. & Dessimoz, C. Phylo.io: interactive viewing and comparison of large phylogenetic trees on the web. Mol. Biol. Evol. 33, 2163–2166 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Protein Sci. 86, 2.9.1–2.9.37 (2016).

    Google Scholar 

  39. 39.

    Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Stamm, M., Staritzbichler, R., Khafizov, K. & Forrest, L. R. AlignMe—a membrane protein sequence alignment web server. Nucleic Acids Res. 42, W246–W251 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Wallner, B. ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling. Bioinformatics 30, 2221–2223 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Laskowski, R. A., Rullmannn, J. A., MacArthur, M. W., Kaptein, R. & Thornton, J. M. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR 8, 477–486 (1996).

    CAS  PubMed  Google Scholar 

  43. 43.

    Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).

    CAS  PubMed  Google Scholar 

  46. 46.

    Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    CAS  PubMed  Google Scholar 

  48. 48.

    Coleman, J. A., Green, E. M. & Gouaux, E. X-ray structures and mechanism of the human serotonin transporter. Nature 532, 334–339 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Jeong, J. Y. et al. One-step sequence- and ligation-independent cloning as a rapid and versatile cloning method for functional genomics studies. Appl. Environ. Microbiol. 78, 5440–5443 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Hiemke, C. & Hartter, S. Pharmacokinetics of selective serotonin reuptake inhibitors. Pharm. Ther. 85, 11–28 (2000).

    CAS  Google Scholar 

  51. 51.

    McClure, R. et al. Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res. 41, e140 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Google Scholar 

  53. 53.

    Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).

    CAS  PubMed  Google Scholar 

  54. 54.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  Google Scholar 

  55. 55.

    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).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank members of the Hsiao Laboratory and G. Donaldson for critical review of the manuscript; R. Kaback (UCLA), D. Yang and E. Gouaux (Vollum Institute, OHSU), K. C. Huang (Stanford), M. Quick and J. Javitch (Columbia University) for helpful advice; J. Murowski, D. Nusbaum and J. Yano (UCLA) for assistance with the initial pilot experiments; Y. Wang and J. F. Miller (UCLA) for providing Bacteroides strains and expression constructs; K. Williams (UCLA Lipidomic Core Facility) for performing lipidomic measurements; P. Bradley and R. Gunsalus (UCLA) for facilitating the radioisotope experiments; and G. Karsenty (Columbia), Y. Tintut (UCLA) and F. Bäckhed (University of Gothernburg) for providing the Tph1 mice and tissue samples. Support for this research was provided by the NIH Director’s Early Independence Award (5DP5OD017924) to E.Y.H., Klingenstein-Simons Award to E.Y.H., Packard Fellowship in Science and Engineering to E.Y.H., UCLA Postdocs’ Longitudinal Investment in Faculty Training Award (K12 GM106996) to H.E.V., Ruth L. Kirschstein National Research Service Award (AI007323) to G.N.P. and Division of Intramural Research of the NIH (National Institute of Neurological Disorders and Stroke) to L.R.F. All data and materials used to understand and assess the conclusions of this research are available in the main text and Supplementary Materials.

Author information

Affiliations

Authors

Contributions

T.C.F. performed the bacteriology, sequencing experiments and data analysis. C.D.G.L. and A.V. assisted with the bacteriology experiments. T.C.F., H.E.V. and G.N.P. performed the mouse experiments. A.A.A., N.G.R. and L.R.F. performed the structural modelling. J.M. and T.R. generated the gnotobiotic mice. L.R.F. and E.Y.H. contributed to the data analysis. T.C.F., L.R.F. and E.Y.H. supervised the study. T.C.F. and E.Y.H. wrote the manuscript. All authors reviewed and edited the final version of the text.

Corresponding authors

Correspondence to Thomas C. Fung or Elaine Y. Hsiao.

Ethics declarations

Competing interests

The authors declare no competing interests. Findings regarding the host effect of T. sanguinis reported in the manuscript are the subject of a provisional patent application (US 62/815,760), owned by UCLA.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–19.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–11.

Supplementary Dataset 1

Protein structural model of CUW_0748 from T. sanguinis MOL361, based on the available structure of human SERT (Protein Data Bank entry 5i6x chain A).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fung, T.C., Vuong, H.E., Luna, C.D.G. et al. Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat Microbiol 4, 2064–2073 (2019). https://doi.org/10.1038/s41564-019-0540-4

Download citation

Further reading

Search

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