GABA-modulating bacteria of the human gut microbiota

Philip Strandwitz, Ki Hyun Kim, Darya Terekhova, Joanne K Liu, Anukriti Sharma, Jennifer Levering, Daniel McDonald, David Dietrich, Timothy R Ramadhar, Asama Lekbua, Nader Mroue, Conor Liston, Eric J Stewart, Marc J Dubin, Karsten Zengler, Rob Knight, Jack A Gilbert, Jon Clardy, Kim Lewis, Philip Strandwitz, Ki Hyun Kim, Darya Terekhova, Joanne K Liu, Anukriti Sharma, Jennifer Levering, Daniel McDonald, David Dietrich, Timothy R Ramadhar, Asama Lekbua, Nader Mroue, Conor Liston, Eric J Stewart, Marc J Dubin, Karsten Zengler, Rob Knight, Jack A Gilbert, Jon Clardy, Kim Lewis

Abstract

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.

Conflict of interest statement

Competing interests: P.S. and K. L. declare competing financial interests as they are founders of Holobiome, Inc.

Figures

Fig. 1.. Co-culture assay to isolate KLE1738.
Fig. 1.. Co-culture assay to isolate KLE1738.
(A) Diluted human fecal sample was plated on Fastidious Anaerobe Agar with 0.5% yeast extract (FAAy), and slower growing, smaller colonies (formed after 4–7 days -- “candidate dependent”, example in blue box) were tested for dependence on their faster growing, larger neighboring colonies (formed after 1–3 days -- “candidate helper”, example in green box) by co-culture. (B) To identify growth factors, the candidate dependent was also tested for growth promotion with Escherichia coli BW25113. Using this method we isolated KLE1738, which grew around the helper (C) Bacteroides fragilis KLE1758, but not Escherichia coli (D). Experiments describing dependency phenotypes were repeated in triplicate. (E) Phylogenetic tree of closely related type and representative genomes belonging to the Ruminococcaceae family. Tree assembled using Randomized Axelerated Maximum Likelihood in PATRIC. Parts of Figure modified from Fenn, 20176; the culture plate and colonies highlighted in (A) is used for an illustration, and is not the source plate for KLE1738. A single stool sample yielded the KLE1738-KLE1758 helper-dependent pair.
Fig. 2.. In vitro and in silico…
Fig. 2.. In vitro and in silico identification of GABA modulating bacteria.
(A) To screen for GABA-producing bacteria, homogenized human stool sample was diluted and mixed with molten FAAy, with or without pH 7.0 MOPS buffer. KLE1738 was then spread on top of the agar and plates were incubated anaerobically for a week. Colonies that KLE1738 grew around were GABA producers (Inset). (B) Identified GABA producers were grown in liquid medium buffered at a pH of 5.0 and 7.0, and GABA levels of the spent medium was quantified using LC/MS and final pH of the medium was tested with pH strips. N=2 independent samples, and error is based on standard error. (C) 1,159 available gut genomes (consisting of 919 species) were analyzed for the genetic potential to produce and/or consume GABA (pathways associated with production or consumption highlighted in Supplemental Information Table 5). (D). The biochemical potential of 533 available gut-related metabolic models in KBase were examined for the capability to produce GABA or consume GABA. Shown are genera that represent at least 0.5% of the 533 models and contain either GABA producers or consumers.
Fig. 3.. Fecal Bacteroides relative abundance inversely…
Fig. 3.. Fecal Bacteroides relative abundance inversely correlates with functional connectivity between left DLPFC and DMN structures in patients with Major Depressive Disorder (MDD).
(A) 3-Dimensional plots of the medial surface of the left and right hemispheres in patients with MDD (n=23). Significant cluster of 387 voxels in which fecal Bacteroides relative abundance correlated inversely with functional connectivity between this cluster and the left DLPFC. Colorbar shows Z scores of beta weights of the Bacteroides relative abundance covariate of a multiple linear regression with functional connectivity as the dependent variable. (B) Scatter plot of the average functional connectivity (Z score) over a sphere of radius 5mm centered at the voxel of peak significance (+12, −57, 0 in MNI coordinates) and abundance of fecal Bacteroides (Pearson r = −0.67, p=.0005). n=23.

References

    1. Fung TC, Olson CA & Hsiao EY Interactions between the microbiota, immune and nervous systems in health and disease. Nat Neurosci 20, 145–155, (2017).
    1. Browne HP et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546, (2016).
    1. Lagier JC 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).
    1. 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).
    1. D’Onofrio A et al. Siderophores from neighboring organisms promote the growth of uncultured bacteria. Chem Biol 17, 254–264, (2010).
    1. Fenn K et al. Quinones are growth factors for the human gut microbiota. Microbiome 5, 161, (2017).
    1. Carlier JP, 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. International journal of systematic and evolutionary microbiology 60, 585–590, (2010).
    1. Klaring K et al. Intestinimonas butyriciproducens gen. nov., sp. nov., a butyrate-producing bacterium from the mouse intestine. International journal of systematic and evolutionary microbiology 63, 4606–4612, (2013).
    1. 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).
    1. Fodor AA et al. The “most wanted” taxa from the human microbiome for whole genome sequencing. PLoS One 7, e41294, (2012).
    1. 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).
    1. Goodman AL et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc Natl Acad Sci U S A 108, 6252–6257, (2011).
    1. Deutscher J, Francke C & Postma PW How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol Mol Biol Rev 70, 939–1031, (2006).
    1. Feehily C & Karatzas KA Role of glutamate metabolism in bacterial responses towards acid and other stresses. Journal of applied microbiology 114, 11–24, (2013).
    1. Hardman JK & Stadtman TC Metabolism of omega-amino acids. I. Fermentation of gamma-aminobutyric acid by Clostridium aminobutyricum n. sp. J Bacteriol 79, 544–548, (1960).
    1. Fallingborg J Intraluminal pH of the human gastrointestinal tract. Dan Med Bull 46, 183–196, (1999).
    1. Aziz RK et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9, 75, (2008).
    1. Bateman A et al. UniProt: a hub for protein information. Nucleic Acids Research 43, D204–D212, (2015).
    1. McDonald D, Hyde ER, Debelius JW, Morton JT, Gonzalez A, Ackermann G American gut: an open platform for citizen‐science microbiome research. mSystems, (2018).
    1. Sneath PH Principles of bacterial taxonomy. Proc R Soc Med 65, 851–852, (1972).
    1. Arkin A. P. e. a. The DOE Systems Biology Knowledgebase. bioRxiv, (2016).
    1. Ni Y, Li J & Panagiotou G A Molecular-Level Landscape of Diet-Gut Microbiome Interactions: Toward Dietary Interventions Targeting Bacterial Genes. MBio 6, e01263–01215, (2015).
    1. Haas BJ 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).
    1. Matsumoto M et al. Colonic Absorption of Low-Molecular-Weight Metabolites Influenced by the Intestinal Microbiome: A Pilot Study. PLoS One 12, e0169207, (2017).
    1. van Berlo CL 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).
    1. Fujisaka S et al. Diet, Genetics, and the Gut Microbiome Drive Dynamic Changes in Plasma Metabolites. Cell Rep 22, 3072–3086, (2018).
    1. Luscher B, Shen Q & Sahir N The GABAergic deficit hypothesis of major depressive disorder. Mol Psychiatry 16, 383–406, (2011).
    1. Davidson RJ, Pizzagalli D, Nitschke JB & Putnam K Depression: perspectives from affective neuroscience. Annu Rev Psychol 53, 545–574, (2002).
    1. Greicius MD, Krasnow B, Reiss AL & Menon V Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 100, 253–258, (2003).
    1. Greicius MD et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological psychiatry 62, 429–437, (2007).
    1. Sheline YI et al. The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A 106, 1942–1947, (2009).
    1. Liston C et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biological psychiatry 76, 517–526, (2014).
    1. Koechlin E & Hyafil A Anterior prefrontal function and the limits of human decision-making. Science 318, 594–598, (2007).
    1. Wager TD, Davidson ML, Hughes BL, Lindquist MA & Ochsner KN Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron 59, 1037–1050, (2008).
    1. Tillisch K et al. Brain structure and response to emotional stimuli as related to gut microbial profiles in healthy women. Psychosom Med, (2017).
    1. Hassan AM et al. High-fat diet induces depression-like behaviour in mice associated with changes in microbiome, neuropeptide Y, and brain metabolome. Nutr Neurosci, 1–17, (2018).
    1. Bravo JA 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 U S A 108, 16050–16055, (2011).
    1. 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).
    1. Lin Q Submerged fermentation of Lactobacillus rhamnosus YS9 for gamma-aminobutyric acid (GABA) production. Braz J Microbiol 44, 183–187, (2013).
    1. Barrett E, Ross RP, O’Toole PW, Fitzgerald GF & Stanton C gamma-Aminobutyric acid production by culturable bacteria from the human intestine. Journal of applied microbiology 113, 411–417, (2012).
    1. Pokusaeva K et al. GABA-producing Bifidobacterium dentium modulates visceral sensitivity in the intestine. Neurogastroenterol Motil 29, (2017).
    1. Kootte RS 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 e616, (2017).
    1. Stamatakis A RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313, (2014).
    1. Wattam AR et al. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res 45, D535–D542, (2017).
    1. Wixon J & Kell D The Kyoto encyclopedia of genes and genomes--KEGG. Yeast 17, 48–55, (2000).
    1. 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).
    1. Hyatt D et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119, (2010).
    1. Hamilton M A rating scale for depression. J Neurol Neurosurg Psychiatry 23, 56–62, (1960).
    1. Caporaso JG et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335–336, (2010).
    1. Amir A et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2, (2017).
    1. Amir A et al. Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping. mSystems 2, (2017).
    1. Chang C & Glover GH Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage 47, 1448–1459, (2009).
    1. Shirer WR, Ryali S, Rykhlevskaia E, Menon V & Greicius MD Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex 22, 158–165, (2012).

Source: PubMed

3
订阅