The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
The microbiota regulate neuronal function and fear extinction learning
Open accessAbstract
Multicellular organisms have co-evolved with complex consortia of viruses, bacteria, fungi and parasites, collectively referred to as the microbiota 1. 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 diseases 2. Alterations in the microbiota can also modulate host behaviours-such as social activity, stress, and anxiety-related responses-that are linked to diverse neuropsychiatric disorders 3. 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Without a complex microbiota, both types of mice exhibited altered fear-associated behavior, changes in gene expression in brain cells, and alterations in the firing patterns and rewiring ability of neurons.”
From Signalling cognition: The gut microbiota and hypothalamic-pituitary-adrenal axis (2023) · cites this paper as [385]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In 2019, Artis D.’s group showed that SPF mice treated with a cocktail of broad-spectrum antibiotics, GF mice, GF mice recolonized after weaning age with a simple microbiota or a complex microbiota, have defects in fear extinction learning, compared to SPF mice or GF mice colonized with SPF flora at the time of birth (29).”
From The gut-brain axis: how microbiota and host inflammasome influence brain physiology and pathology (2020) · cites this paper as [29]
Experimental — accuracy not yet measuredIn the citing paper
“Selective colonization revealed a critical developmental period, indicating that microbiota-derived signals were required during early post-natal and adult life for normal learning.”
From Signalling cognition: The gut microbiota and hypothalamic-pituitary-adrenal axis (2023) · cites this paper as [385]
In the citing paper
“Moreover, a relevant contribution of gut microbiota is not only restricted to neuroinflammatory and psychiatric disorders (23–32) but also to brain development (13).”
From The gut-brain axis: how microbiota and host inflammasome influence brain physiology and pathology (2020) · cites this paper as [29]
In the citing paper
“The researchers showed that the microbiota-mediated changes in synapse formation and fear extinction behavior were not the results of the hypothalamic-pituitary-adrenal axis but of the reduced level of potential neuroactive metabolites (phenyl sulfate, pyrocatechol sulfate, 3-(3-sulfooxyphenyl)propanoic acid, and indoxyl sulfate) in the cerebrospinal fluid, serum and in fecal samples of GF mice”
From The gut-brain axis: how microbiota and host inflammasome influence brain physiology and pathology (2020) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
McMurdie, P. J., & Holmes, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061217 · Cited by 21,942
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 21,098
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Cited by
Sorted by citations
Items 401–421 of 421
Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621907
Zhao, H., Li, X., Liu, J.-L., Guan, G.-Q., & Dan, X.-G. (2022) · Figshare · doi:10.6084/m9.figshare.21285572
Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621898
Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621913
Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621916
Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621901
Fujita, H., Ushio, M., Suzuki, K., Abe, M., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621883
Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., Canarini, A., Hayashi, I., Fukushima, K., Fukuda, S., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22621922
Manipulating gut microbial metabolites for cancer therapy: A metabolism-centric approach
Karne, A. S., Alam, M. I., Tiwari, A., Singh, H., Singh, R. K., & Yadav, A. K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33459-7.00003-x
Acute Estradiol Bridges Gut Microbiome Composition With Cognition
Hodes, G. E., & Kropp, D. R. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100662
Mild concussion impairs extinction of avoidance and alters respective brain circuits in male rats
Martínez‐Guzmán, O., Cáceres‐Chacón, M., Rivera‐López, M., Hernández-Busot, G., Forty-Díaz, J., Haddock-Martínez, H. G., & Sierra-Mercado, D. (2026) · Experimental Neurology · doi:10.1016/j.expneurol.2026.115734
Roussin, L. (2023) · theses.fr (ABES) · doi:10.70675/998d3bd1z4618z400dz998cz89e524b35aef · Cited by 1
Pathological Remuneration of Oxidative Stress in Limiting Gut-Brain Axis
Kumar, R., Kashyap, A., & Naqvi, S. (2026) · Oxidative stress in applied basic research and clinical practice · doi:10.1007/978-3-032-19837-2_5
Lactobacillus plantarum enhances cognition through acetate-modulated microglia function
Zhou, T., Yue, Y., Leng, J., Jiang, Y., Liu, H., Zhang, S., Yang, Z.-C., zhao, W., & Li, L. (2026) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2026.106821
Toward a New Definition of Psychogastroenterology: From the Brain–Gut Axis to the Human HolobiontPreprint
Agnoletti, M. (2026) · Qeios · doi:10.32388/jcowi7
Advances in the Study of Mechanisms Underlying the Role of Gut Microbiota in Depressive Disorders
张, 慧. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1682895
Cross species single-nucleus characterization of mammillary body
Lin, X., Li, L., Shen, Y., Chu, X., He, A., & Li, X. (2026) · Cell Insight · doi:10.1016/j.cellin.2026.100359
The neurobiotic sense: emerging pathways of microbial detection and gut–brain communication
Banihashemian, S. Z., Yaghmayee, S., Amirpour, S., Alibabaei, N., & Eslami, M. (2026) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-026-03209-5
Chronic gut inflammation potentiates place aversion in intrinsic and conditioned contexts
Cej, B., BEEKMAN, K., Matisz, C. E., Haight, T., Zovoilis, A., McDonald, R. J., & Gruber, A. J. (2026) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2026.101383
Zhang, X., Zhang, Y., Liu, X., Yan, X., Ai, Q., He, Y., & Shi, Y. (2026) · International Immunopharmacology · doi:10.1016/j.intimp.2026.117561
Park, J. C., & Im, S. (2026) · Microbiome Research Reports · doi:10.20517/mrr.2026.48
Metadata
- Authors
- Chu, C., Murdock, M. H., Jing, D., Won, T. H., Chung, H., Kressel, A. M., Tsaava, T., Addorisio, M. E., Putzel, G. G., Zhou, L., Bessman, N. J., Yang, R., Moriyama, S., Parkhurst, C. N., Li, A., Meyer, H. C., Teng, F., Chavan, S. S., Tracey, K. J., Regev, A., Schroeder, F. C., Lee, F. S., Liston, C., & Artis, D.
- Affiliations
- Cornell University (US)
- Broad Institute (US)
- Northwell Health (US)
- Corresponding author
- Liston, Conor
- Artis, David
- ORCID
- Venue
- Nature
- Published
- 2019-10-23
- Volume
- 574
- Issue
- 7779
- Pages
- 543-548
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6818753
- OpenAlex
- W2981552290
- Cited by
- 421
- References
- 78
- Citations by year
- 2026: 27 · 2025: 49 · 2024: 57 · 2023: 72 · 2022: 77 · 2021: 64 · 2020: 69 · 2019: 3
- FWCI
- 30.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH109685, R01MH118451)
- National Institute of General Medical Sciences (R35GM118182, R01GM132672)
- National Institute of Allergy and Infectious Diseases (R01AI095466, R21AI142213, R01AI102942, R01AI151599, F32AI124517, P01AI102852, R01AI074878, U01AI095608)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK126871)
- National Institute of Neurological Disorders and Stroke (R01NS052819)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- gut microbiota, fear extinction learning, germ-free mice, antibiotic-treated mice, medial prefrontal cortex, neuroplasticity, single-nucleus RNA sequencing, metabolomics, neuropsychiatric disorders, anxiety-like behavior
- MeSH terms
- Animals, Anti-Bacterial Agents, Autistic Disorder, Blood, Calcium, Cerebrospinal Fluid, Cues, Extinction, Psychological, Fear, Feces, Germ-Free Life, Indican, Male, Mice, Inbred BALB C, Mice, Inbred C57BL, Neural Inhibition, Neuroglia, Neurons, Phenylpropionates, Schizophrenia, Vagus Nerve, Prefrontal Cortex, Dendritic Spines, Mice, Metabolomics, Transcriptome, Microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
