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


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 The code to compute GBM abundances from an ortholog abundance table is freely available at:, and a web application is also available at

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,, 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 ( following the standard protocol for data access. FGFP and TR-MDD shotgun sequencing data and metadata are available at the EGA (accession no. EGAS00001003298).


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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).

Author information




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 Information

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

Reporting Summary

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).

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