Innate immune system signaling and CD11b <sup>+</sup> CD11c <sup>+</sup> CD103 <sup>+</sup> cell migration to the brain underlie changes in mouse behavior after microbial colonization
Bibliographic record
Abstract
Abstract Background and Aims Accumulating evidence suggests the microbiota is a key factor in disorders of gut-brain interaction (DGBI), by affecting host immune and neural systems. However, the underlying mechanisms remain elusive due to their complexity and clinical heterogeneity of patients with DGBIs. We aimed to identify neuroimmune pathways that are critical in microbiota-gut-brain communication during de novo gut colonization. Methods We employed a combination of gnotobiotic and state-of-the-art microbial tools, behavioral analysis, immune and pharmacological approaches. Germ-free wild type, MyD88 −/− Ticam1 −/− and SCID mice were studied before and after colonization with specific pathogen-free microbiota, Altered Schaedler Flora, E. coli or S. typhimurium (permanent or transient colonizers). TLR agonists and antagonists, CCR7 antagonist or immunomodulators were used to study immune pathways. We assessed brain c-Fos, brain-derived neurotrophic factor, and dendritic and glial cells by immunofluorescence, expression of neuroimmune genes by NanoString and performed brain proteomics. Results Bacterial monocolonization, conventionalization or administration of microbial products to germ-free mice altered mouse behavior similarly, acting through Toll-like receptor or nucleotide-binding oligomerization domain signaling. The process required CD11b + CD11c + CD103 + cell activation and migration into the brain. The change in behavior did not require the continued presence of bacteria and was associated with activation of multiple neuro-immune networks in the gut and the brain. Conclusions Changes in neural plasticity occur rapidly upon initial gut microbial colonization and involve innate immune signaling to the brain, mediated by CD11b + CD11c + CD103 + cell migration. The results identify a new target with therapeutic potential for DGBIs developing in context of increased gut and blood-brain barrier permeability. Highlights Microbiota impairment is a key factor in disorders of gut-brain interaction (DGBI) Microbial colonization induces changes in brain and behavior via innate immunity Microbial colonization activates multiple neuro-immune networks in gut and brain Behavioral change is mediated by CD11b + CD11c + CD103 + cells migration to the brain
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".