Interleukin 15 modulates the effects of poly I:C maternal immune activation on offspring behaviour
Bibliographic record
Abstract
Maternal infections during pregnancy are linked with an increased risk for disorders like Autism Spectrum Disorder and schizophrenia in the offspring. Although precise mechanisms are still unclear, clinical and preclinical evidence suggest a strong role for maternal immune activation (MIA) in the neurodevelopmental disruptions caused by maternal infection. Previously, studies using the Polyinosinic:Polycytidylic (Poly I:C) MIA preclinical model showed that cytokines like Interleukin 6 (Il6) are important mediators of MIA's effects. In this study, we hypothesized that Il15 may similarly act as a mediator of Poly I:C MIA, given its role in the antiviral immune response. To test this hypothesis, we induced Poly I:C MIA at gestational day 9.5 in wildtype (WT) and Il15−/− rat dams and tested their offspring in adolescence and adulthood. Poly I:C MIA and Il15 knockout produced both independent and synergistic effects on offspring behaviour. Poly I:C MIA decreased startle reactivity in adult WT offspring but resulted in increased adolescent anxiety and decreased adult locomotor activity in Il15−/− offspring. In addition, Poly I:C MIA led to genotype-independent effects on locomotor activity and prepulse inhibition. Finally, we showed that Il15−/− offspring exhibit distinct phenotypes that were unrelated to Poly I:C MIA including altered startle reactivity, locomotion and signal transduction in the auditory brainstem. Overall, our findings indicate that the lack of Il15 can leave offspring either more or less susceptible to Poly I:C MIA, depending on the phenotype in question. Future studies should examine the contribution of fetal versus maternal Il15 in MIA to determine the precise developmental mechanisms underlying these changes.
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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) | 0.001 | 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".