Prenatal maternal stress induces gut microbiota dysbiosis and long-term programming of autoreactive CD4 T cells in the context of neuroinflammation 2132
Bibliographic record
Abstract
Abstract Description The prenatal period is a sensitive time during which intrauterine exposure to environmental factors can shape the developing immune system in offspring. Evidence suggests that maternal stress during pregnancy weakens adaptive immune responses in offspring, increasing their risk of infectious diseases and cancer. However, the mechanisms through which prenatal stress (PS) impairs immune responses in adulthood are not fully understood. To address this knowledge gap, we developed a PS animal model where pregnant females experience stress during a specific gestational period. We examined the effect of PS on autoimmune responses in adult offspring using experimental autoimmune encephalomyelitis, a well-suited model for studying T lymphocyte functions. Our findings indicate that PS reduces the pathogenic potential of CD4 T cells by limiting their production of inflammatory cytokines and their ability to infiltrate the central nervous system. Adoptive transfer experiments confirmed that PS has a stable and intrinsic effect on CD4+ T cells, potentially due to epigenetic changes. We found that this effect is linked to intestinal dysbiosis and identified a microbiota metabolite that may play a role. Overall, our study demonstrates that PS leaves a lasting “immune imprint” on CD4 T cell function, associated with microbiota dysbiosis. The discovery of a relevant metabolite could serve as a potential therapeutic target for multiple sclerosis patients. Funding Sources Supported by INSERM, ARSEP and FRC Topic Categories Neuroimmunology (NEUR)
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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".