The neuroactive potential of the human gut microbiota in quality of life and depression

Abstract

The relationship between gut microbial metabolism and mental health is one of the most intriguing and controversial topics in microbiome research. Bidirectional microbiota–gut–brain communication has mostly been explored in animal models, with human research lagging behind. Large-scale metagenomics studies could facilitate the translational process, but their interpretation is hampered by a lack of dedicated reference databases and tools to study the microbial neuroactive potential. Surveying a large microbiome population cohort (Flemish Gut Flora Project, n = 1,054) with validation in independent data sets (ntotal = 1,070), we studied how microbiome features correlate with host quality of life and depression. Butyrate-producing Faecalibacterium and Coprococcus bacteria were consistently associated with higher quality of life indicators. Together with Dialister, Coprococcus spp. were also depleted in depression, even after correcting for the confounding effects of antidepressants. Using a module-based analytical framework, we assembled a catalogue of neuroactive potential of sequenced gut prokaryotes. Gut–brain module analysis of faecal metagenomes identified the microbial synthesis potential of the dopamine metabolite 3,4-dihydroxyphenylacetic acid as correlating positively with mental quality of life and indicated a potential role of microbial γ-aminobutyric acid production in depression. Our results provide population-scale evidence for microbiome links to mental health, while emphasizing confounder importance.

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Fig. 1: Ecosystem-wide and specific effects of QoL variables on microbiome variation.
Fig. 2: Bacteroides enterotype 2 association with lower QoL and depression status in the FGFP cohort.
Fig. 3: GBM distribution in microbial genomes.
Fig. 4: Association of the DOPAC synthesis GBM with mental QoL.

Code availability

A custom Biopython script to fix broken pairs in metagenomic sequences is publicly available at https://github.com/raeslab/raeslab-utils/. The code to compute GBM abundances from an ortholog abundance table is freely available at: https://github.com/raeslab/omixer-rpm, and a web application is also available at http://www.raeslab.org/gomixer/.

Data availability

FGFP 16S sequencing data and metadata on the microbiota covariates used in this study are available at the European Genome-phenome Archive (EGA, https://www.ebi.ac.uk/ega/), accession no. EGAS00001003296. The LLD sequence data and age and sex information per sample are also available at the EGA with accession no. EGAS00001001704; the rest of the microbiota covariates can be requested from the Lifelines cohort study (https://lifelines.nl/lifelines-research/access-to-lifelines) following the standard protocol for data access. FGFP and TR-MDD shotgun sequencing data and metadata are available at the EGA (accession no. EGAS00001003298).

References

  1. 1.

    Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    O’Mahony, S. M., Clarke, G., Borre, Y. E., Dinan, T. G. & Cryan, J. F. Serotonin, tryptophan metabolism and the brain–gut–microbiome axis. Behav. Brain. Res. 277, 32–48 (2015).

    PubMed  Google Scholar 

  3. 3.

    Braniste, V. et al. The gut microbiota influences blood–brain barrier permeability in mice. Sci. Transl. Med. 6, 263ra158 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Lyte, M. & Brown, D. R. Evidence for PMAT- and OCT-like biogenic amine transporters in a probiotic strain of Lactobacillus: implications for interkingdom communication within the microbiota–gut–brain axis. PLoS ONE 13, e0191037 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Mcdonald, D. et al. American Gut: an open platform for citizen science. mSystems 3, e00031-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Naseribafrouei, A. et al. Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil. 26, 1155–1162 (2014).

    CAS  PubMed  Google Scholar 

  7. 7.

    Jiang, H. et al. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav. Immun. 48, 186–194 (2015).

    PubMed  Google Scholar 

  8. 8.

    Zheng, P. et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. Mol. Psychiatry 21, 786–796 (2016).

    CAS  PubMed  Google Scholar 

  9. 9.

    Kelly, J. R. et al. Transferring the blues: depression-associated gut microbiota induces neurobehavioural changes in the rat. J. Psychiatr. Res. 82, 109–118 (2016).

    PubMed  Google Scholar 

  10. 10.

    Hill, J. M., Clement, C., Pogue, A. I., Bhattacharjee, S. & Zhao, Y. et al. Pathogenic microbes, the microbiome, and Alzheimer’s disease (AD). Front. Aging Neurosci. 6, 127 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Burokas, A., Moloney, R. D., Dinan, T. G. & Cryan, J. F. Microbiota regulation of the mammalian gut–brain axis. 91, 1–62 (2015).

  12. 12.

    Falony, G., Vieira-Silva, S. & Raes, J. Richness and ecosystem development across faecal snapshots of the gut microbiota. Nat. Microbiol. 3, 526–528 (2018).

    CAS  PubMed  Google Scholar 

  13. 13.

    Kelly, J. R. et al. Lost in translation? The potential psychobiotic Lactobacillus rhamnosus (JB-1) fails to modulate stress or cognitive performance in healthy male subjects. Brain Behav. Immun. 61, 50–59 (2017).

    CAS  PubMed  Google Scholar 

  14. 14.

    Bedarf, J. R. et al. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients. Genome Med. 9, 39 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

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

    CAS  PubMed  Google Scholar 

  16. 16.

    Zhernakova, A. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Hays, R. D., Sherbourne, C. D. & Mazel, R. M. The RAND 36-Item Health Survey 1.0. Health Econ. 2, 217–227 (1993).

    CAS  PubMed  Google Scholar 

  19. 19.

    Hays, R. D. & Morales, L. S. The RAND-36 measure of health-related quality of life. Ann. Med. 33, 350–357 (2001).

    CAS  PubMed  Google Scholar 

  20. 20.

    Ware, J. E., Keller, S. D. & Kosinski, M. SF-36: Physical and Mental Health Summary Scales (Health Institute, New England Medical Center, Boston, 1994).

  21. 21.

    Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997).

    CAS  PubMed  Google Scholar 

  22. 22.

    Rivière, A., Selak, M., Lantin, D., Leroy, F. & De Vuyst, L. Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut. Front. Microbiol. 7, 979 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661–672 (2014).

    CAS  PubMed  Google Scholar 

  24. 24.

    Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Li, L. et al. Gut microbes in correlation with mood: case study in a closed experimental human life support system. Neurogastroenterol. Motil. 28, 1233–1240 (2016).

    CAS  PubMed  Google Scholar 

  26. 26.

    Watten, R. G., Syversen, J. L. & Myhrer, T. Quality of life, intelligence and mood. Soc. Indic. Res. 36, 287–299 (1995).

    Google Scholar 

  27. 27.

    National Collaborating Centre for Mental Health, National Institute for Health and Clinical Excellence, Royal College of Psychiatrists, British Psychological Society Depression: the Treatment and Management of Depression in Adults (British Psychological Society and Royal College of Psychiatrists, London, 2010).

  28. 28.

    Bruffaerts, R., Bonnewyn, A. & Demyttenaere, K. The epidemiology of depression in Belgium. A review and some reflections for the future [Article in Dutch]. Tijdschr. Psychiatr. 50, 655–665 (2008).

    CAS  PubMed  Google Scholar 

  29. 29.

    Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Cussotto, S. et al. Differential effects of psychotropic drugs on microbiome composition and gastrointestinal function. Psychopharmacology https://doi.org/10.1007/s00213-018-5006-5 (2018).

    PubMed  Google Scholar 

  31. 31.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Dinan, T. G., Stanton, C. & Cryan, J. F. Psychobiotics: a novel class of psychotropic. Biol. Psychiatry 74, 720–726 (2013).

    CAS  PubMed  Google Scholar 

  33. 33.

    Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbialmetagenomics. PLoS ONE 7, e30126 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Markowitz, V. M. et al. IMG 4 version of the integrated microbial genomes comparative analysis system. Nucleic Acids Res. 42, D560–D567 (2014).

    CAS  PubMed  Google Scholar 

  36. 36.

    Lyte, M. & Ernst, S. Catecholamine induced growth of gram negative bacteria. Life Sci. 50, 203–212 (1992).

    CAS  PubMed  Google Scholar 

  37. 37.

    McLean, P. G., Borman, R. A. & Lee, K. 5-HT in the enteric nervous system: gut function and neuropharmacology. Trends Neurosci. 30, 9–13 (2007).

    CAS  PubMed  Google Scholar 

  38. 38.

    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 

  39. 39.

    Tsavkelova, E. A., Klimova, S. Y., Cherdyntseva, T. A. & Netrusov, A. I. Hormones and hormone-like substances of microorganisms: a review [Article in Russian]. Prikl. Biokhim. Mikrobiol. 42, 261–268 (2006).

    CAS  PubMed  Google Scholar 

  40. 40.

    Lyte, M. & Cryan, J. F. (eds) Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health (Springer, New York, 2014).

  41. 41.

    Mazzoli, R. & Pessione, E. The neuro-endocrinological role of microbial glutamate and GABA signaling. Front. Microbiol. 7, 1934 (2016).

    PubMed Central  Google Scholar 

  42. 42.

    Feehily, C., O’Byrne, C. P. & Karatzas, K. A. G. Functional γ-aminobutyrate shunt in Listeria monocytogenes: role in acid tolerance and succinate biosynthesis. Appl. Environ. Microbiol. 79, 74–80 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Nogueira, T. et al. Horizontal gene transfer of the secretome drives the evolution of bacterial cooperation and virulence. Curr. Biol. 19, 1683–1691 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Gao, K. et al. Of the major phenolic acids formed during human microbial fermentation of tea, citrus, and soy flavonoid supplements, only 3,4-dihydroxyphenylacetic acid has antiproliferative activity. J. Nutr. 136, 52–57 (2006).

    CAS  PubMed  Google Scholar 

  45. 45.

    Goldstein, D. S., Holmes, C., Lopez, G. J., Wu, T. & Sharabi, Y. Cerebrospinal fluid biomarkers of central dopamine deficiency predict Parkinson’s disease. Parkinsonism Relat. Disord. 50, 108–112 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Bienenstock, J., Forsythe, P., Karimi, K. & Kunze, W. Neuroimmune aspects of food intake. Int. Dairy J. 20, 253–258 (2010).

    CAS  Google Scholar 

  47. 47.

    Petty, F. Plasma concentrations of gamma-aminobutyric acid (GABA) and mood disorders: a blood test for manic depressive disease? Clin. Chem. 40, 296–302 (1994).

    CAS  PubMed  Google Scholar 

  48. 48.

    Inoshita, M. et al. Elevated peripheral blood glutamate levels in major depressive disorder. Neuropsychiatr. Dis. Treat. 14, 945–953 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Chow, S., Shao, J. & Wang, H. Sample Size Calculations in Clinical Trial Research (Chapman and Hall, Boca Raton, 2008).

  50. 50.

    Aaronson, N. K. et al. Translation, validation, and norming of the Dutch language version of the SF-36 Health Survey in community and chronic disease populations. J. Clin. Epidemiol. 51, 1055–1068 (1998).

    CAS  PubMed  Google Scholar 

  51. 51.

    WHO Collaborating Centre for Drug Statistics Methodology ATC Classification Index with DDDs (WHO, 2017).

  52. 52.

    American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders: Text Revision 4th edn (American Psychiatric Association, Washington, 2002).

  53. 53.

    Mujagic, Z. et al. A novel biomarker panel for irritable bowel syndrome and the application in the general population. Sci. Rep. 6, 26420 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Tito, R. Y. et al. Brief report: Dialister as a microbial marker of disease activity in spondyloarthritis. Arthritis Rheumatol. 69, 114–121 (2017).

    CAS  PubMed  Google Scholar 

  55. 55.

    Hildebrand, F., Tadeo, R., Voigt, A. Y., Bork, P. & Raes, J. LotuS: an efficient and user-friendly OTU processing pipeline. Microbiome 2, 30 (2014).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Schmieder, R. & Edwards, R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6, e17288 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

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

    CAS  PubMed  Google Scholar 

  62. 62.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  Google Scholar 

  64. 64.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2015).

    Google Scholar 

  65. 65.

    Oksanen, J. et al. vegan: Community Ecology. R package version 2.4-2 http://CRAN.R-project.org/package=vegan (2017).

  66. 66.

    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Gloor, G. B. & Reid, G. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).

    CAS  PubMed  Google Scholar 

  68. 68.

    Fletcher, T. D. QuantPsyc: Quantitative Psychology Tools. R package version 1.5 http://cran.r-project.org/package=QuantPsyc (2012).

  69. 69.

    Morgan, M. DirichletMultinomial: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data. Bioconductor version 1.20.0 http://bioconductor.org/packages/release/bioc/html/DirichletMultinomial.html (2017).

  70. 70.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).

  71. 71.

    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Google Scholar 

  72. 72.

    Wei, T. et al. corrplot: Visualization of a Correlation Matrix. R package version 0.77 http://CRAN.R-project.org/package=corrplot (2016).

  73. 73.

    Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 42, 459–471 (2014).

    Google Scholar 

  74. 74.

    Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).

    CAS  PubMed  Google Scholar 

  75. 75.

    Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016).

    CAS  PubMed  Google Scholar 

  76. 76.

    Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    CAS  PubMed  Google Scholar 

  77. 77.

    Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).

    CAS  PubMed  Google Scholar 

  78. 78.

    Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012).

    CAS  PubMed  Google Scholar 

  79. 79.

    Cheng, F. et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52, 3099–3105 (2012).

    CAS  PubMed  Google Scholar 

  80. 80.

    Federhen, S. The NCBI Taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).

    CAS  PubMed  Google Scholar 

  81. 81.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).

    Google Scholar 

  82. 82.

    Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029 (2015).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the FGFP, TR-MDD and LLD participants and staff for their collaboration, and all members of the Raes Laboratory for participating in scientific discussions regarding the manuscript. This study is partially funded by JPND grant JPCOFUND_FP-829-047. The FGFP was funded with support from the Flemish government (grant number IWT130359), Research Fund–Flanders (FWO) Odysseus program (grant number G.0924.09), King Baudouin Foundation (grant number 2012-J80000-004), VIB, Rega Institute for Medical Research and KU Leuven. The LLD study was funded by the Top Institute Food and Nutrition, Cardiovasculair Onderzoek Nederland, Netherlands Organization for Scientific Research (NWO-VIDI, grant number 864.13.013) and an EU FP7 European Research Council (ERC) Advanced Grant (to C.W.; grant number 322698). M.V.-C., J.W., M.J. and S.V.-S. are funded by (post-)doctoral fellowships from Research Foundation-Flanders. A.Z. is funded by an ERC starting grant (grant number 715772) and by a NWO-VIDI grant (grant number 016-178-056). C.W. has an NWO Spinoza prize (number NWO SPI 92-266).

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M.V.-C., G.F., C.W., A.Z., S.V.-S. and J.R. conceived and designed the study. M.V.-C., G.F., E.F.T., R.Y.T., C.S., A.K., M.J., C.W., S.C., A.Z., S.V.-S. and J.R. acquired the data and participated in cohort recruitment. M.V.-C., Y.D., J.W. and R.Y.T. performed data preprocessing. M.V.-C., G.F. and S.V.-S. performed the data analysis. M.V.-C., G.F., L.V.O., S.V.-S. and J.R. interpreted the data. M.V.-C., G.F., S.V.-S. and J.R. wrote the manuscript with all authors providing critical revision of the manuscript. All authors approved the final version for publication.

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Correspondence to Jeroen Raes.

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Supplementary Information

Supplementary Figs. 1–3, legends for Supplementary Dataset 1 and 2, legends for Supplementary Tables 1–18.

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Supplementary Dataset 1

Gut–brain module (GBM) description, containing module input and output compounds and database and literature references used to assemble each module. Each line corresponds to one module step. Tabs separate alternative orthologues (OR operator), while commas correspond to orthologues being subunits of enzymatic complexes (AND operator). For the latter, all subunits of a complex need to be detected in other to consider it present.

Supplementary Dataset 2

Excel file containing Supplementary Tables 1–18.

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Valles-Colomer, M., Falony, G., Darzi, Y. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol 4, 623–632 (2019). https://doi.org/10.1038/s41564-018-0337-x

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