GABA-modulating bacteria of the human gut microbiota


The gut microbiota affects many important host functions, including the immune response and the nervous system1. However, while substantial progress has been made in growing diverse microorganisms of the microbiota2, 23–65% of species residing in the human gut remain uncultured3,4, which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence in artificial media of key growth factors that are provided by neighbouring bacteria in situ5,6. In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bioassay-driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter GABA (γ-aminobutyric acid). GABA was the only tested nutrient that supported the growth of KLE1738, and a genome analysis supported a GABA-dependent metabolism mechanism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome-based metabolic modelling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. A transcriptome analysis of human stool samples from healthy individuals showed that GABA-producing pathways are actively expressed by Bacteroides, Parabacteroides and Escherichia species. By coupling 16S ribosmal RNA sequencing with functional magentic resonance imaging in patients with major depressive disorder, a disease associated with an altered GABA-mediated response, we found that the relative abundance levels of faecal Bacteroides are negatively correlated with brain signatures associated with depression.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Co-culture assay to isolate KLE1738.
Fig. 2: In vitro and in silico identification of GABA-modulating bacteria.
Fig. 3: Relative abundance of faecal Bacteroides inversely correlates with functional connectivity between left DLPFC and DMN structures in patients with MDD.

Data availability

The 16S rRNA sequence and genome data for KLE1738 are available from the NCBI (MH636586 and PRJNA482656, respectively). The American Gut sequence data are available from the EBI under accession ERP012803. fMRI data are available at the discretion of M.J.D. All other data that support the findings of this study are available from the corresponding author upon request.


  1. 1.

    Fung, T. C., Olson, C. A. & Hsiao, E. Y. Interactions between the microbiota, immune and nervous systems in health and disease. Nat. Neurosci. 20, 145–155 (2017).

    CAS  Article  Google Scholar 

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 3.

    Lagier, J. C. et al. The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota. Clin. Microbiol. Rev. 28, 237–264 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    Lagkouvardos, I., Overmann, J. & Clavel, T. Cultured microbes represent a substantial fraction of the human and mouse gut microbiota. Gut Microbes 8, 493–503 (2017).

    Article  Google Scholar 

  5. 5.

    D’Onofrio, A. et al. Siderophores from neighboring organisms promote the growth of uncultured bacteria. Chem. Biol. 17, 254–264 (2010).

    Article  Google Scholar 

  6. 6.

    Fenn, K. et al. Quinones are growth factors for the human gut microbiota. Microbiome 5, 161 (2017).

    Article  Google Scholar 

  7. 7.

    Carlier, J. P., Bedora-Faure, M., K’Ouas, G., Alauzet, C. & Mory, F. Proposal to unify Clostridium orbiscindens Winter et al. 1991 and Eubacterium plautii (Seguin 1928) Hofstad and Aasjord 1982, with description of Flavonifractor plautii gen. nov., comb. nov., and reassignment of Bacteroides capillosus to Pseudoflavonifractor capillosus gen. nov., comb. nov. Int. J. Syst. Evol. Microbiol. 60, 585–590 (2010).

    CAS  Article  Google Scholar 

  8. 8.

    Klaring, K. et al. Intestinimonas butyriciproducens gen. nov., sp. nov., a butyrate-producing bacterium from the mouse intestine. Int. J. Syst. Evol. Microbiol. 63, 4606–4612 (2013).

    Article  Google Scholar 

  9. 9.

    Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).

    CAS  Article  Google Scholar 

  10. 10.

    Fodor, A. A. et al. The “most wanted” taxa from the human microbiome for whole genome sequencing. PLoS ONE 7, e41294 (2012).

    CAS  Article  Google Scholar 

  11. 11.

    Lagkouvardos, I. et al. IMNGS: a comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci. Rep. 6, 33721 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011).

    CAS  Article  Google Scholar 

  13. 13.

    Deutscher, J., Francke, C. & Postma, P. W. How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol. Mol. Biol. Rev. 70, 939–1031 (2006).

    CAS  Article  Google Scholar 

  14. 14.

    Feehily, C. & Karatzas, K. A. Role of glutamate metabolism in bacterial responses towards acid and other stresses. J. Appl. Microbiol. 114, 11–24 (2013).

    CAS  Article  Google Scholar 

  15. 15.

    Hardman, J. K. & Stadtman, T. C. Metabolism of omega-amino acids. I. Fermentation of gamma-aminobutyric acid by Clostridium aminobutyricum n. sp. J. Bacteriol. 79, 544–548 (1960).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Fallingborg, J. Intraluminal pH of the human gastrointestinal tract. Dan. Med. Bull. 46, 183–196 (1999).

    CAS  PubMed  Google Scholar 

  17. 17.

    Aziz, R. K. et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9, 75 (2008).

    Article  Google Scholar 

  18. 18.

    Bateman, A. et al. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

    Article  Google Scholar 

  19. 19.

    McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).

    Article  Google Scholar 

  20. 20.

    Sneath, P. H. Principles of bacterial taxonomy. Proc. R. Soc. Med. 65, 851–852 (1972).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Arkin, A. P. et al. The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).

    CAS  Article  Google Scholar 

  22. 22.

    Ni, Y., Li, J. & Panagiotou, G. A molecular-level landscape of diet–gut microbiome interactions: toward dietary interventions targeting bacterial genes. mBio 6, e01263-15 (2015).

    Article  Google Scholar 

  23. 23.

    Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).

    CAS  Article  Google Scholar 

  24. 24.

    Matsumoto, M. et al. Colonic absorption of low-molecular-weight metabolites influenced by the intestinal microbiome: a pilot study. PLoS ONE 12, e0169207 (2017).

    Article  Google Scholar 

  25. 25.

    van Berlo, C. L. et al. Gamma-aminobutyric acid production in small and large intestine of normal and germ-free Wistar rats. Influence of food intake and intestinal flora. Gastroenterology 93, 472–479 (1987).

    Article  Google Scholar 

  26. 26.

    Fujisaka, S. et al. Diet, genetics, and the gut microbiome drive dynamic changes in plasma metabolites. Cell Rep. 22, 3072–3086 (2018).

    CAS  Article  Google Scholar 

  27. 27.

    Luscher, B., Shen, Q. & Sahir, N. The GABAergic deficit hypothesis of major depressive disorder. Mol. Psychiatry 16, 383–406 (2011).

    CAS  Article  Google Scholar 

  28. 28.

    Davidson, R. J., Pizzagalli, D., Nitschke, J. B. & Putnam, K. Depression: perspectives from affective neuroscience. Annu. Rev. Psychol. 53, 545–574 (2002).

    Article  Google Scholar 

  29. 29.

    Greicius, M. D., Krasnow, B., Reiss, A. L. & Menon, V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl Acad. Sci. USA 100, 253–258 (2003).

    CAS  Article  Google Scholar 

  30. 30.

    Greicius, M. D. et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429–437 (2007).

    Article  Google Scholar 

  31. 31.

    Sheline, Y. I. et al. The default mode network and self-referential processes in depression. Proc. Natl Acad. Sci. USA 106, 1942–1947 (2009).

    CAS  Article  Google Scholar 

  32. 32.

    Liston, C. et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol. Psychiatry 76, 517–526 (2014).

    Article  Google Scholar 

  33. 33.

    Koechlin, E. & Hyafil, A. Anterior prefrontal function and the limits of human decision-making. Science 318, 594–598 (2007).

    CAS  Article  Google Scholar 

  34. 34.

    Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A. & Ochsner, K. N. Prefrontal–subcortical pathways mediating successful emotion regulation. Neuron 59, 1037–1050 (2008).

    CAS  Article  Google Scholar 

  35. 35.

    Tillisch, K. et al. Brain structure and response to emotional stimuli as related to gut microbial profiles in healthy women. Psychosom. Med. 79, 905–913 (2017).

    Article  Google Scholar 

  36. 36.

    Hassan, A. M. et al. High-fat diet induces depression-like behaviour in mice associated with changes in microbiome, neuropeptide Y, and brain metabolome. Nutr. Neurosci. (2018).

  37. 37.

    Bravo, J. A. et al. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc. Natl Acad. Sci. USA 108, 16050–16055 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Janik, R. et al. Magnetic resonance spectroscopy reveals oral Lactobacillus promotion of increases in brain GABA, N-acetyl aspartate and glutamate. Neuroimage 125, 988–995 (2016).

    CAS  Article  Google Scholar 

  39. 39.

    Lin, Q. Submerged fermentation of Lactobacillus rhamnosus YS9 for gamma-aminobutyric acid (GABA) production. Braz. J. Microbiol. 44, 183–187 (2013).

    CAS  Article  Google Scholar 

  40. 40.

    Barrett, E., Ross, R. P., O’Toole, P. W., Fitzgerald, G. F. & Stanton, C. Gamma-aminobutyric acid production by culturable bacteria from the human intestine. J. Appl. Microbiol. 113, 411–417 (2012).

    CAS  Article  Google Scholar 

  41. 41.

    Pokusaeva, K. et al. GABA-producing Bifidobacterium dentium modulates visceral sensitivity in the intestine. Neurogastroenterol. Motil. 29, e12904 (2017).

    Article  Google Scholar 

  42. 42.

    Kootte, R. S. et al. Improvement of insulin sensitivity after lean donor feces in metabolic syndrome is driven by baseline intestinal microbiota composition. Cell. Metab. 26, 611–619 (2017).

    CAS  Article  Google Scholar 

  43. 43.

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    CAS  Article  Google Scholar 

  44. 44.

    Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res. 45, D535–D542 (2017).

    CAS  Article  Google Scholar 

  45. 45.

    Wixon, J. & Kell, D. The Kyoto encyclopedia of genes and genomes—KEGG. Yeast 17, 48–55 (2000).

    CAS  Article  Google Scholar 

  46. 46.

    Kitagawa, M. et al. Complete set of ORF clones of Escherichia coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biological research. DNA Res. 12, 291–299 (2005).

    CAS  Article  Google Scholar 

  47. 47.

    Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  Google Scholar 

  48. 48.

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

    CAS  Article  Google Scholar 

  49. 49.

    Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191–16 (2017).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Amir, A. et al. Correcting for microbial blooms in faecal samples during room-temperature shipping. mSystems 2, e00199–16 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Chang, C. & Glover, G. H. Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage 47, 1448–1459 (2009).

    Article  Google Scholar 

  52. 52.

    Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V. & Greicius, M. D. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22, 158–165 (2012).

    CAS  Article  Google Scholar 

Download references


The authors would like to thank S. Rubin for help with the cultivation of KLE1738, and J. Wang of the Small Molecule Mass Spectrometry Facility, a Harvard Faculty of Arts and Science (FAS) Division of Science Core Facility, for analysing the GC/MS samples. This work was supported by grants R01HG005824 to K.L., R01GM086158 to J.C. and F32GM108415 to T.R.R.

Author information




P.S. and K.L. planned the study, analysed the data and wrote the paper. P.S. performed the co-culture screening for helper-dependent pairs and general KLE1738 cultivation. P.S., J.C. and K.H.K. designed and performed the bioassay-driven screening for GABA. P.S. and N.M. performed media exclusion experiments. P.S., E.J.S. and D.D. analysed the KLE1738 genome. P.S., D.D. and T.R.R. performed 13C feeding experiments. P.S. cultivated B. fragilis and D.D. analysed the supernatant for GABA and glutamate. P.S. designed and with A.L. executed the screen for GABA-producing bacteria. P.S. prepared the supernatant of GABA producers identified in the KLE1738 co-culture screen, and D.D. analysed these supernatants for GABA production. P.S., D.M., R.K., J.L., J.K.L. and K.Z. performed the metagenomic analysis for GABA producers and consumers, and P.S., A.S., and J.A.G. analysed the human transcriptome dataset. P.S., K.L., D.T. and M.J.D. designed the MDD study, and P.S., K.L., A.S., J.A.G., D.T., C.L. and M.J.D. analysed the MDD data. All authors helped edit the manuscript.

Corresponding authors

Correspondence to Philip Strandwitz or Kim Lewis.

Ethics declarations

Competing interests

P.S. and K.L. declare competing financial interests as they are founders of Holobiome, Inc. All other authors have no competing interests.

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 Figures 1–8, Supplementary Tables 1–4 and 6–8.

Reporting Summary

Supplementary Table 5

Genome analysis of GABA modulating potential of 1,159 gut bacterial genomes, consisting of 919 species.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Strandwitz, P., Kim, K.H., Terekhova, D. et al. GABA-modulating bacteria of the human gut microbiota. Nat Microbiol 4, 396–403 (2019).

Download citation

Further reading