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.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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/.
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).
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).
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).
Braniste, V. et al. The gut microbiota influences blood–brain barrier permeability in mice. Sci. Transl. Med. 6, 263ra158 (2014).
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).
Mcdonald, D. et al. American Gut: an open platform for citizen science. mSystems 3, e00031-18 (2018).
Naseribafrouei, A. et al. Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil. 26, 1155–1162 (2014).
Jiang, H. et al. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav. Immun. 48, 186–194 (2015).
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).
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).
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).
Burokas, A., Moloney, R. D., Dinan, T. G. & Cryan, J. F. Microbiota regulation of the mammalian gut–brain axis. 91, 1–62 (2015).
Falony, G., Vieira-Silva, S. & Raes, J. Richness and ecosystem development across faecal snapshots of the gut microbiota. Nat. Microbiol. 3, 526–528 (2018).
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).
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).
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
Zhernakova, A. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).
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).
Hays, R. D., Sherbourne, C. D. & Mazel, R. M. The RAND 36-Item Health Survey 1.0. Health Econ. 2, 217–227 (1993).
Hays, R. D. & Morales, L. S. The RAND-36 measure of health-related quality of life. Ann. Med. 33, 350–357 (2001).
Ware, J. E., Keller, S. D. & Kosinski, M. SF-36: Physical and Mental Health Summary Scales (Health Institute, New England Medical Center, Boston, 1994).
Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997).
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).
Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661–672 (2014).
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
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).
Watten, R. G., Syversen, J. L. & Myhrer, T. Quality of life, intelligence and mood. Soc. Indic. Res. 36, 287–299 (1995).
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).
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).
Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).
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).
Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).
Dinan, T. G., Stanton, C. & Cryan, J. F. Psychobiotics: a novel class of psychotropic. Biol. Psychiatry 74, 720–726 (2013).
Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbialmetagenomics. PLoS ONE 7, e30126 (2012).
Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).
Markowitz, V. M. et al. IMG 4 version of the integrated microbial genomes comparative analysis system. Nucleic Acids Res. 42, D560–D567 (2014).
Lyte, M. & Ernst, S. Catecholamine induced growth of gram negative bacteria. Life Sci. 50, 203–212 (1992).
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).
Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).
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).
Lyte, M. & Cryan, J. F. (eds) Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health (Springer, New York, 2014).
Mazzoli, R. & Pessione, E. The neuro-endocrinological role of microbial glutamate and GABA signaling. Front. Microbiol. 7, 1934 (2016).
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).
Nogueira, T. et al. Horizontal gene transfer of the secretome drives the evolution of bacterial cooperation and virulence. Curr. Biol. 19, 1683–1691 (2009).
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).
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).
Bienenstock, J., Forsythe, P., Karimi, K. & Kunze, W. Neuroimmune aspects of food intake. Int. Dairy J. 20, 253–258 (2010).
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).
Inoshita, M. et al. Elevated peripheral blood glutamate levels in major depressive disorder. Neuropsychiatr. Dis. Treat. 14, 945–953 (2018).
Chow, S., Shao, J. & Wang, H. Sample Size Calculations in Clinical Trial Research (Chapman and Hall, Boca Raton, 2008).
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).
WHO Collaborating Centre for Drug Statistics Methodology ATC Classification Index with DDDs (WHO, 2017).
American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders: Text Revision 4th edn (American Psychiatric Association, Washington, 2002).
Mujagic, Z. et al. A novel biomarker panel for irritable bowel syndrome and the application in the general population. Sci. Rep. 6, 26420 (2016).
Tito, R. Y. et al. Brief report: Dialister as a microbial marker of disease activity in spondyloarthritis. Arthritis Rheumatol. 69, 114–121 (2017).
Hildebrand, F., Tadeo, R., Voigt, A. Y., Bork, P. & Raes, J. LotuS: an efficient and user-friendly OTU processing pipeline. Microbiome 2, 30 (2014).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
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).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Schmieder, R. & Edwards, R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6, e17288 (2011).
Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).
Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
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).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2015).
Oksanen, J. et al. vegan: Community Ecology. R package version 2.4-2 http://CRAN.R-project.org/package=vegan (2017).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Gloor, G. B. & Reid, G. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).
Fletcher, T. D. QuantPsyc: Quantitative Psychology Tools. R package version 1.5 http://cran.r-project.org/package=QuantPsyc (2012).
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).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Wei, T. et al. corrplot: Visualization of a Correlation Matrix. R package version 0.77 http://CRAN.R-project.org/package=corrplot (2016).
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).
Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).
Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016).
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).
Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).
Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012).
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).
Federhen, S. The NCBI Taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).
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).
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).
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).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–3, legends for Supplementary Dataset 1 and 2, legends for Supplementary Tables 1–18.
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.
Excel file containing Supplementary Tables 1–18.
About this article
Cite this article
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
Trends in Ecology & Evolution (2020)
PLOS Biology (2020)
Review article: bugs, inflammation and mood-a microbiota-based approach to psychiatric symptoms in inflammatory bowel diseases
Alimentary Pharmacology & Therapeutics (2020)
Journal of Affective Disorders (2020)
Feeling down? A systematic review of the gut microbiota in anxiety/depression and irritable bowel syndrome
Journal of Affective Disorders (2020)