Oral treatment with Lactobacillus rhamnosus attenuates behavioural deficits and immune changes in chronic social stress
Bibliographic record
Abstract
BACKGROUND: Stress-related disorders involve systemic alterations, including disruption of the intestinal microbial community. Given the putative connections between the microbiota, immunity, neural function, and behaviour, we investigated the potential for microbe-induced gut-to-brain signalling to modulate the impact of stress on host behaviour and immunoregulation. METHODS: Male C57BL/6 mice treated orally over 28 days with either Lactobacillus rhamnosus (JB-1) ™ or vehicle were subjected to chronic social defeat and assessed for alterations in behaviour and immune cell phenotype. 16S rRNA sequencing and mass spectrometry were employed to analyse the faecal microbial community and metabolite profile. RESULTS: Treatment with JB-1 decreased stress-induced anxiety-like behaviour and prevented deficits in social interaction with conspecifics. However, JB-1 did not alter development of aggressor avoidance following social defeat. Microbial treatment attenuated stress-related activation of dendritic cells while increasing IL-10+ regulatory T cells. Furthermore, JB-1 modulated the effect of stress on faecal metabolites with neuroactive and immunomodulatory properties. Exposure to social defeat altered faecal microbial community composition and reduced species richness and diversity, none of which was prevented by JB-1. Stress-related microbiota disruptions persisted in vehicle-treated mice for 3 weeks following stressor cessation. CONCLUSIONS: These data demonstrate that despite the complexity of the gut microbiota, exposure to a single microbial strain can protect against certain stress-induced behaviours and systemic immune alterations without preventing dysbiosis. This work supports microbe-based interventions for stress-related disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".