The microbiota regulate neuronal function and fear extinction learning

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Abstract

Multicellular organisms have co-evolved with complex consortia of viruses, bacteria, fungi and parasites, collectively referred to as the microbiota1. In mammals, changes in the composition of the microbiota can influence many physiologic processes (including development, metabolism and immune cell function) and are associated with susceptibility to multiple diseases2. Alterations in the microbiota can also modulate host behaviours—such as social activity, stress, and anxiety-related responses—that are linked to diverse neuropsychiatric disorders3. However, the mechanisms by which the microbiota influence neuronal activity and host behaviour remain poorly defined. Here we show that manipulation of the microbiota in antibiotic-treated or germ-free adult mice results in significant deficits in fear extinction learning. Single-nucleus RNA sequencing of the medial prefrontal cortex of the brain revealed significant alterations in gene expression in excitatory neurons, glia and other cell types. Transcranial two-photon imaging showed that deficits in extinction learning after manipulation of the microbiota in adult mice were associated with defective learning-related remodelling of postsynaptic dendritic spines and reduced activity in cue-encoding neurons in the medial prefrontal cortex. In addition, selective re-establishment of the microbiota revealed a limited neonatal developmental window in which microbiota-derived signals can restore normal extinction learning in adulthood. Finally, unbiased metabolomic analysis identified four metabolites that were significantly downregulated in germ-free mice and have been reported to be related to neuropsychiatric disorders in humans and mouse models, suggesting that microbiota-derived compounds may directly affect brain function and behaviour. Together, these data indicate that fear extinction learning requires microbiota-derived signals both during early postnatal neurodevelopment and in adult mice, with implications for our understanding of how diet, infection, and lifestyle influence brain health and subsequent susceptibility to neuropsychiatric disorders.

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Fig. 1: ABX and GF mice are less prone to fear extinction.
Fig. 2: Excitatory neurons and microglia are affected in ABX mice.
Fig. 3: Defective extinction-learning-related dendritic spine formation in ABX mice.
Fig. 4: Defective ensemble calcium dynamics in the mPFC of ABX mice.
Fig. 5: Extinction learning deficits in GF mice are associated with alterations in microbiota-derived metabolites.

Code availability

The algorithm used for automated scoring of freezing behaviour is available at https://www.seas.upenn.edu/~molneuro/software.html. The algorithm used for motion artefact correction in 2P calcium imaging data is available at http://www.cs.cmu.edu/~kangli/code/Image_Stabilizer.html. All other analysis code is available from the corresponding author upon reasonable request.

Data availability

RNA-seq data, 16S rRNA-seq data and snRNA-seq data are available at Gene Expression Omnibus and BioProject under accession numbers GSE134808, PRJNA556230 and GSE135326, respectively. All datasets generated and/or analysed during the current study are presented in this published article, the accompanying Source Data or Supplementary Information, or are available from the corresponding author upon reasonable request.

References

  1. 1.

    Belkaid, Y. & Hand, T. W. Role of the microbiota in immunity and inflammation. Cell 157, 121–141 (2014).

  2. 2.

    Hill, D. A. & Artis, D. Intestinal bacteria and the regulation of immune cell homeostasis. Annu. Rev. Immunol. 28, 623–667 (2010).

  3. 3.

    Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).

  4. 4.

    Maren, S. Neurobiology of Pavlovian fear conditioning. Annu. Rev. Neurosci. 24, 897–931 (2001).

  5. 5.

    VanElzakker, M. B., Dahlgren, M. K., Davis, F. C., Dubois, S. & Shin, L. M. From Pavlov to PTSD: the extinction of conditioned fear in rodents, humans, and anxiety disorders. Neurobiol. Learn. Mem. 113, 3–18 (2014).

  6. 6.

    Mielcarz, D. W. & Kasper, L. H. The gut microbiome in multiple sclerosis. Curr. Treat. Options Neurol. 17, 344 (2015).

  7. 7.

    Krajmalnik-Brown, R., Lozupone, C., Kang, D. W. & Adams, J. B. Gut bacteria in children with autism spectrum disorders: challenges and promise of studying how a complex community influences a complex disease. Microb. Ecol. Health Dis. 26, 26914 (2015).

  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).

  9. 9.

    Möhle, L. et al. Ly6Chi monocytes provide a link between antibiotic-induced changes in gut microbiota and adult hippocampal neurogenesis. Cell Rep. 15, 1945–1956 (2016).

  10. 10.

    Hoban, A. E. et al. Regulation of prefrontal cortex myelination by the microbiota. Transl. Psychiatry 6, e774 (2016).

  11. 11.

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

  12. 12.

    Erny, D. et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nat. Neurosci. 18, 965–977 (2015).

  13. 13.

    Hoban, A. E. et al. The microbiome regulates amygdala-dependent fear recall. Mol. Psychiatry 23, 1134–1144 (2018).

  14. 14.

    Lu, J. et al. Microbiota influence the development of the brain and behaviors in C57BL/6J mice. PLoS ONE 13, e0201829 (2018).

  15. 15.

    Arentsen, T., Raith, H., Qian, Y., Forssberg, H. & Diaz Heijtz, R. Host microbiota modulates development of social preference in mice. Microb. Ecol. Health Dis. 26, 29719 (2015).

  16. 16.

    Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).

  17. 17.

    Hepworth, M. R. et al. Group 3 innate lymphoid cells mediate intestinal selection of commensal bacteria-specific CD4+ T cells. Science 348, 1031–1035 (2015).

  18. 18.

    Pattwell, S. S. et al. Altered fear learning across development in both mouse and human. Proc. Natl Acad. Sci. USA 109, 16318–16323 (2012).

  19. 19.

    Soliman, F. et al. A genetic variant BDNF polymorphism alters extinction learning in both mouse and human. Science 327, 863–866 (2010).

  20. 20.

    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).

  21. 21.

    Sgritta, M. et al. Mechanisms underlying microbial-mediated changes in social behavior in mouse models of autism spectrum disorder. Neuron 101, 246–259.e246 (2019).

  22. 22.

    Kipnis, J., Cohen, H., Cardon, M., Ziv, Y. & Schwartz, M. T cell deficiency leads to cognitive dysfunction: implications for therapeutic vaccination for schizophrenia and other psychiatric conditions. Proc. Natl Acad. Sci. USA 101, 8180–8185 (2004).

  23. 23.

    Miyajima, M. et al. Metabolic shift induced by systemic activation of T cells in PD-1-deficient mice perturbs brain monoamines and emotional behavior. Nat. Immunol. 18, 1342–1352 (2017).

  24. 24.

    Filiano, A. J. et al. Unexpected role of interferon-γ in regulating neuronal connectivity and social behaviour. Nature 535, 425–429 (2016).

  25. 25.

    Tovote, P., Fadok, J. P. & Lüthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).

  26. 26.

    Dragunow, M. & Faull, R. The use of c-fos as a metabolic marker in neuronal pathway tracing. J. Neurosci. Methods 29, 261–265 (1989).

  27. 27.

    Gale, G. D. et al. Role of the basolateral amygdala in the storage of fear memories across the adult lifetime of rats. J. Neurosci. 24, 3810–3815 (2004).

  28. 28.

    Thion, M. S. et al. Microbiome influences prenatal and adult microglia in a sex-specific manner. Cell 172, 500–516.e516 (2018).

  29. 29.

    Kierdorf, K. et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013).

  30. 30.

    Yuste, R. & Bonhoeffer, T. Morphological changes in dendritic spines associated with long-term synaptic plasticity. Annu. Rev. Neurosci. 24, 1071–1089 (2001).

  31. 31.

    Yuste, R. & Denk, W. Dendritic spines as basic functional units of neuronal integration. Nature 375, 682–684 (1995).

  32. 32.

    Trachtenberg, J. T. et al. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 420, 788–794 (2002).

  33. 33.

    Matsuzaki, M., Honkura, N., Ellis-Davies, G. C. & Kasai, H. Structural basis of long-term potentiation in single dendritic spines. Nature 429, 761–766 (2004).

  34. 34.

    Lai, C. S., Franke, T. F. & Gan, W. B. Opposite effects of fear conditioning and extinction on dendritic spine remodelling. Nature 483, 87–91 (2012).

  35. 35.

    Sudo, N. et al. Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J. Physiol. (Lond.) 558, 263–275 (2004).

  36. 36.

    Ivanov, I. I. et al. Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139, 485–498 (2009).

  37. 37.

    Atarashi, K. et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 331, 337–341 (2011).

  38. 38.

    Garrett, W. S. et al. Enterobacteriaceae act in concert with the gut microbiota to induce spontaneous and maternally transmitted colitis. Cell Host Microbe 8, 292–300 (2010).

  39. 39.

    Schaedler, R. W. & Dubos, R. J. The fecal flora of various strains of mice. Its bearing on their susceptibility to endotoxin. J. Exp. Med. 115, 1149–1160 (1962).

  40. 40.

    Tautenhahn, R., Patti, G. J., Rinehart, D. & Siuzdak, G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).

  41. 41.

    Shaw, W. Increased urinary excretion of a 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), an abnormal phenylalanine metabolite of Clostridia spp. in the gastrointestinal tract, in urine samples from patients with autism and schizophrenia. Nutr. Neurosci. 13, 135–143 (2010).

  42. 42.

    Keşli, R., Gökçen, C., Buluğ, U. & Terzi, Y. Investigation of the relation between anaerobic bacteria genus Clostridium and late-onset autism etiology in children. J. Immunoassay Immunochem. 35, 101–109 (2014).

  43. 43.

    Yeh, Y. C. et al. Indoxyl sulfate, not p-cresyl sulfate, is associated with cognitive impairment in early-stage chronic kidney disease. Neurotoxicology 53, 148–152 (2016).

  44. 44.

    Hsiao, E. Y. et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 1451–1463 (2013).

  45. 45.

    Kang, M. et al. The effect of gut microbiome on tolerance to morphine mediated antinociception in mice. Sci. Rep. 7, 42658 (2017).

  46. 46.

    Patel, T. P. et al. An open-source toolbox for automated phenotyping of mice in behavioral tasks. Front. Behav. Neurosci. 8, 349 (2014).

  47. 47.

    Dincheva, I. et al. FAAH genetic variation enhances fronto-amygdala function in mouse and human. Nat. Commun. 6, 6395 (2015).

  48. 48.

    Liston, C. et al. Circadian glucocorticoid oscillations promote learning-dependent synapse formation and maintenance. Nat. Neurosci. 16, 698–705 (2013).

  49. 49.

    Mukamel, E. A., Nimmerjahn, A. & Schnitzer, M. J. Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63, 747–760 (2009).

  50. 50.

    Dombeck, D. A., Khabbaz, A. N., Collman, F., Adelman, T. L. & Tank, D. W. Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56, 43–57 (2007).

  51. 51.

    Rajasethupathy, P. et al. Projections from neocortex mediate top-down control of memory retrieval. Nature 526, 653–659 (2015).

  52. 52.

    Dodt, M., Roehr, J. T., Ahmed, R. & Dieterich, C. FLEXBAR—flexible barcode and adapter processing for next-generation sequencing platforms. Biology (Basel) 1, 895–905 (2012).

  53. 53.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  54. 54.

    Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

  55. 55.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  56. 56.

    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).

  57. 57.

    Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protocols 8, 1551–1566 (2013).

  58. 58.

    Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

  59. 59.

    Snel, B., Lehmann, G., Bork, P. & Huynen, M. A. STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res. 28, 3442–3444 (2000).

  60. 60.

    Pino, P. A. & Cardona, A. E. Isolation of brain and spinal cord mononuclear cells using percoll gradients. J. Vis. Exp. 48, 2348 (2011).

  61. 61.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  62. 62.

    Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).

  63. 63.

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

  64. 64.

    Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).

  65. 65.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

  66. 66.

    Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

  67. 67.

    Traag, V. A. Faster unfolding of communities: speeding up the Louvain algorithm. Phys. Rev. E 92, 032801 (2015).

  68. 68.

    Ntranos, V., Yi, L., Melsted, P. & Pachter, L. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat. Methods 16, 163–166 (2019).

  69. 69.

    Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

  70. 70.

    Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

  71. 71.

    McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

  72. 72.

    Dezfuli, G. et al. Subdiaphragmatic vagotomy with pyloroplasty ameliorates the obesity caused by genetic deletion of the melanocortin 4 receptor in the mouse. Front. Neurosci. 12, 104 (2018).

  73. 73.

    Edwards, D. R., Lohman, D. C. & Wolfenden, R. Catalytic proficiency: the extreme case of S-O cleaving sulfatases. J. Am. Chem. Soc. 134, 525–531 (2012).

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Acknowledgements

We thank the members of the Artis and Liston laboratories for discussion and reading of the manuscript; G. Eraslan, S. Simmons, and C. Smillie for discussions about the snRNA-seq analyses; and the Metabolic Phenotyping Center of Weill Cornell Medicine for technical advice and support. The summary cartoon (Extended Data Fig. 10f) was created with BioRender.com. This work was supported by the Jill Roberts Institute (to G.G.P.), JSPS Overseas Research Fellowships (to S.M.), the National Institute of General Medical Sciences (1R35GM118182-01, to K.J.T.), the National Institute of Allergy and Infectious Diseases (1P01AI102852-01A1, to K.J.T. and S.S.C.), the National Institutes of Health (NS052819, to F.S.L), the Rita Allen Foundation, the One Mind Institute, the Klingenstein-Simons Foundations, the Brain and Behavior Research Foundation, and the National Institutes of Mental Health (R01 MH109685, R01 MH118451) (all to C.L.), the National Institutes of Health (AI074878, AI095466, AI095608 and AI102942), the Burroughs Wellcome Fund, the Crohn’s and Colitis Foundation and the Rosanne H. Silbermann Foundation (all to D.A.). F.C.S. is a Faculty Scholar of the Howard Hughes Medical Institute. This work was supported by the Klarman Cell Observatory at the Broad Institute. A.R. is a Howard Hughes Medical Institute Investigator.

Author information

C.C. carried out most of the experiments and analysed the data. M.H.M., D.J., T.H.W., H.C., A.M.K., T.T., M.E.A., L.Z., N.J.B., R.Y., S.M., C.N.P., A.L., H.C.M., F.T., S.S.C., K.J.T., A.R., F.C.S. and F.S.L. helped with experiments. H.C. and A.R. performed snRNA-seq and analysis. G.G.P. performed bulk RNA-seq and 16S rDNA-seq analysis. D.A., C.L. and C.C. conceived the project, analysed data, and wrote the manuscript with input from all co-authors.

Correspondence to Conor Liston or David Artis.

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Competing interests

A.R. is an SAB member of ThermoFisher Scientific and Syros Pharmaceuticals and a co-founder and equity holder of Celsius Therapeutics. D.A. has contributed to scientific advisory boards at MedImmune, Pfizer, FARE, and the KRF. The other authors declare no competing interests.

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Peer review information Nature thanks Drew Kiraly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Antibiotic treatment results in bacterial community restructuring.

ac, Food intake (a), water intake (b) and weight gain (c) of the mice measured using the Promethion Metabolic Cage System. Antibiotic treatment was started two weeks before the experiment and continued for the duration of the experiment. For food (a) and water intake (b), the mice were acclimated to the system for the first four days followed by one day of data collection. Body mass (c) of the mice was measured at the beginning (Start) and the end (End) of the 5-day experiment. n = 4 mice per group. Mean ± s.e.m. Total, full day. Light and Dark denote the light and dark periods of the 12-h cycle. d, 16S rDNA gene copies as quantified by real-time PCR with reverse transcription (RT–PCR) from stool pellets collected from control or ABX mice. Data pooled from two independent experiments. n = 7 mice per group. Mean ± s.e.m.; unpaired two-sided t-test. eg, PCoA (e), alpha-diversity Shannon index (f) and taxonomic classification (g) of 16S rDNA in stool pellets collected from control or ABX mice. Control n = 4, ABX n = 5. For PCoA plot PERMANOVA: F = 33.579, Df = 1, P = 0.00804. For phylogenetic classification ‘f_’, ‘g_’, ‘uncl_c_’, ‘uncl_d_’ and ‘uncl_o_’ stand for ‘family_’, ‘genus_’, ‘unclassified_class_’, ‘unclassified_domain_’ and ‘unclassified_order_’, respectively. ‘uncl_d_Bacteria’ matches exactly to mitochondria or chloroplasts, probably from the food. Mean ± s.e.m. in f. Source data

Extended Data Fig. 2 Antibiotic-treated mice retain deficits in extinction learning after vagotomy.

Fear extinction in sham-operated control (Ctrl_Sham) or ABX mice (ABX_Sham) and in vagotomised ABX mice (ABX_Vx) mice over the course of 3 days or sessions. Ctrl_Sham n = 10, ABX_Sham n = 10, ABX_Vx n = 12. Mean ± s.e.m.; AUC was calculated for each mouse within each group, followed by unpaired two-sided t-test between groups. P values are as follows: i, 2.57 × 10−7; ii, 9.21 × 10−8. Source data

Extended Data Fig. 3 Comparable percentages and numbers of CD45high leukocytes in the brains of control and ABX or GF mice.

a, Gating strategy for T cells, B cells, dendritic cells (DCs) and macrophages (Mφ) in the brain. b, Population frequencies and numbers of brain-resident CD45high leukocytes in control and ABX mice. c, d, Population frequencies of CD4+ T cells, CD8+ T cells, CD19+ B cells (c), CD11c+ DCs and F4/80+ macrophages (d) gated on brain-resident CD45high leukocytes in control and ABX mice. e, Population frequencies and numbers of brain-resident CD45high leukocytes in control and GF mice. f, g, Population frequencies of CD4+ T cells, CD8+ T cells, CD19+ B cells (f), CD11c+ DCs and F4/80+ macrophages (g) gated on brain-resident CD45high leukocytes in control and GF mice. h, Gating strategy of total myeloid cells and Ly6Chigh monocytes in the brain. i, j, Population frequencies of total myeloid cells and Ly6Chigh monocytes gated on brain-resident CD45high leukocytes in control and ABX (i) or GF (j) mice. Data in b, c, g, j are representative of three independent experiments. n = 4 mice per group. Data in d, i are pooled from two independent experiments. n = 8 mice per group. Data in e, f are pooled from two independent experiments. n = 6 mice group. Data are mean ± s.e.m.; unpaired two-sided t-tests were used. P values are indicated on the figures. k, Fear extinction in control, GF and Rag1−/− mice in the single-session 30-tone fear extinction assay. Data are pooled from two independent experiments. Control n = 18, GF n = 16, Rag1−/− n = 18. Mean ± s.e.m.; AUC was calculated for each mouse within each group followed by one-way ANOVA with Tukey’s multiple comparisons test. F(2,49) = 8.558, P = 0.0006. Adjusted P values are as follows: i = 0.0343, ii = 0.0004. l, Fear extinction of SPF-Rag1−/− and GF-Rag1−/− mice in the single-session 30-tone fear extinction assay. n = 7 mice per group. Mean ± s.e.m.; AUC was calculated for each mouse within each group followed by unpaired two-sided t-test between groups. P value is shown. Source data

Extended Data Fig. 4 Comparable transcriptomes of mPFCs dissected from control and ABX mice in the absence of fear conditioning and extinction.

a, PCA of genome-wide transcriptional profiles of mouse mPFC in the absence of fear conditioning and extinction. Control n = 3, ABX n = 4. PERMANOVA test was used: F = 2.52, Df = 1, P = 0.17. b, Volcano plot of differential expression between control (negative log2FC) and ABX (positive log2FC) groups. DEGs (defined as FDR < 0.1, DESeq2 Wald test) are shown in red. cf, Immunofluorescence staining of c-FOS (red) (c, e) and the density of c-FOS+ neurons (d, f) in the BLA (c, d) or IL (e, f) of control and GF mice 90 min after classical fear extinction session 3. Data pooled from two independent experiments. n = 6 mice per group. Mean ± s.e.m.; unpaired two-sided t-tests. P values are shown. Scale bar, 200 μm. Source data

Extended Data Fig. 5 Gene expression patterns of individual cell subsets in the mPFC.

a, Proportion of expressing cells (dot size) and mean normalized expression of representative marker genes (columns) associated with the cell clusters shown in Fig. 2a (rows). Clusters are labelled with post facto annotation based on known marker genes. Ambiguous clusters expressing multiple canonical markers across cell types are annotated with both (for example, exPFC/astrocyte), and are likely to represent doublets. b, Number of significantly differentially expressed genes (z-test calculated on coefficients of mixed linear model, Bonferroni-corrected P < 10−7) by cluster after downsampling each cluster to 500 nuclei, ranked from highest to lowest (clusters of doublets and undetermined annotations not included). exPFC, glutamatergic excitatory neurons from the PFC; GABA, γ-aminobutyric acid (GABA)ergic interneurons; OPC, oligodendrocyte progenitor cells; MO, myelinating oligodendrocytes. Source data

Extended Data Fig. 6 Differential gene expression between control and ABX mice in individual clusters of mPFC cells.

Differential expression of ABX versus control (log2FC) in each cluster in Fig. 2a and the associated significance. Blue, genes that are significantly differentially expressed (z-test calculated on coefficients of mixed linear model, Bonferroni-corrected P < 10−7).

Extended Data Fig. 7 Differentially expressed genes in ABX versus control mPFC samples shared by all excitatory neuronal subsets.

Mean fold change in expression in excitatory neurons (columns) from Fig. 2a of genes (rows) that were significantly differentially expressed (z-test calculated on coefficients of mixed linear model, Bonferroni-corrected P < 10−7) in at least 2 of these clusters, and with absolute log2FC ≥ 0.31 in at least 1 cluster.

Extended Data Fig. 8 Differentially expressed genes in ABX versus control mPFC samples shared by multiple cell types.

Mean fold change in expression across all cell clusters (columns) from Fig. 2a of genes (rows) that were significantly differentially expressed (z-test calculated on coefficients of mixed linear model, Bonferroni-corrected P < 10−7) in at least 4 clusters, and with absolute log2FC ≥ 0.31 in at least 1 cluster.

Extended Data Fig. 9 Microglia in GF and ABX mice exhibit a developmentally immature phenotype.

a, Population frequencies and numbers of microglia in control and GF mice. b, Representative flow cytometry histogram and mean fluorescence intensity (MFI) of F4/80 staining on microglia from control and GF mice. c, Representative flow cytometry plots and population frequencies of CSF1R+ microglia in control and GF mice. d, Representative flow cytometry histogram and MFI of CSF1R expression gated on CSF1R+ microglia from control and GF mice. Data in ad are representative of three independent experiments, n = 4 mice per group. e, Population frequencies and numbers of microglia in control and ABX mice. f, Representative flow cytometry histogram and MFI of F4/80 staining on microglia from control and ABX mice. g, Representative flow cytometry plots and population frequencies of CSF1R+ microglia in control and ABX mice. h, Representative flow cytometry histogram and MFI of CSF1R expression gated on CSF1R+ microglia from control and ABX mice. Data in e are pooled from two independent experiments, n = 8 mice per group. Data in fh are representative of two independent experiments, n = 4 mice per group. Mean ± s.e.m.; unpaired two-sided t-test. P values shown. Source data

Extended Data Fig. 10 Downregulation of metabolites in GF mice.

a, Enzyme-linked immunosorbent assay (ELISA) quantification of plasma corticosterone in control and ABX mice. Data pooled from three independent experiments. Control n = 12; ABX n = 11. b, ELISA quantification of plasma corticosterone in control and GF mice. Data pooled from three independent experiments. Control n = 12; GF n = 11. Mean ± s.e.m. c, Structures of phenyl sulfate, pyrocatechol sulfate, 3-(3-sulfooxyphenyl)propanoic acid and indoxyl sulfate. d, Relative abundances of phenyl sulfate, pyrocatechol sulfate, 3-(3-sulfooxyphenyl)propanoic acid and indoxyl sulfate in faecal samples from Ctrl_fostered, GF and ex-GF_fostered mice as determined by LC–MS. n = 3 mice per group. e, Relative abundances of phenyl sulfate, pyrocatechol sulfate, 3-(3-sulfooxyphenyl)propanoic acid and indoxyl sulfate in CSF samples from control and GF mice as determined by LC–MS. Data are representative of two independent experiments, n = 8 mice per group. Mean ± s.e.m.; unpaired two-sided t-test. P values shown. f, Schematic of the microbiota–gut–brain axis in fear extinction learning. Our data inform a model in which alterations in the microbiota and their metabolites influence neuronal function and learning-related plasticity, which may be due to altered microglia-mediated synaptic pruning, and subsequently regulate fear extinction behaviour. Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Gene ontology (GO) enrichment analyses of the differentially expressed genes between ABX and control mPFC samples in Fig. 1f.

Supplementary Table 2

Gene ontology (GO) enrichment analyses of the differentially expressed genes shared by excitatory neuronal subsets in Fig. 2a.

Supplementary Table 3

Gene ontology (GO) enrichment analyses of the differentially expressed genes shared by multiple cell types in Fig. 2a.

Supplementary Table 4

Gene ontology (GO) enrichment analyses of the differentially expressed genes in microglia in Fig. 2c.

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Chu, C., Murdock, M.H., Jing, D. et al. The microbiota regulate neuronal function and fear extinction learning. Nature 574, 543–548 (2019) doi:10.1038/s41586-019-1644-y

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