Early-life gut microbiome and stress-axis perturbations dysregulate the development of systemic, mucosal, and brain immunity
Bibliographic record
Abstract
Early-life disruptions to the gut microbiome and stress-axis significantly influence the development of immune, neuroendocrine, and other physiological systems. However, the precise microbial species and pathways mediating these effects remain poorly characterized. Using a murine model, we investigated the individual and combined effects of early-life antibiotic exposure and chronic maternal separation stress, on gut microbiota composition, short-chain fatty acid (SCFA) production, hypothalamic-pituitary-adrenal (HPA) axis activity, and systemic, mucosal, and neuroimmune responses. Broad-spectrum antibiotic treatments severely reduced microbial diversity and SCFA concentrations, with changes persisting into adulthood. Chronic early-life stress exerted more modest but notable effects, reducing key SCFA-producing taxa and impacting microbiome metabolic output. Combined disruptions led to altered microglial active phenotype and cytokine profiles, impaired immune cell populations, and suppressed HPA axis activity. Multi - omic correlational analyses revealed strong associations between SCFAs, specific gut microbes, and immune responses, implicating SCFAs as critical mediators of gut-brain communication. Notably, antibiotic exposure exacerbated susceptibility to allergic airway inflammation, highlighting the systemic consequences of early-life microbiome disturbances. These findings demonstrate that early microbial perturbations impair neuroimmune maturation, HPA axis regulation, and host resilience to inflammatory diseases. Our study underscores the importance of preserving the early-life microbiome to support long-term immune and neurodevelopmental health, offering insights into potential therapeutic interventions for mitigating the impact of early-life microbiota disruptions.
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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) | 0.001 | 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 | 0.001 |
| 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".