Effects of prenatal maternal immune activation and exposure to circadian disruption during adolescence: exploring the two-hit model of neurodevelopmental disorders
Bibliographic record
Abstract
Abstract Background Around 80% of individuals with neurodevelopmental disorders (NDDs) such as schizophrenia and autism spectrum disorders experience disruptions in sleep/circadian rhythms. We explored whether prenatal infection, an established risk factor for NDDs, and environmental circadian disruption synergistically induced sex-specific deficits in mice. Methods A maternal immune activation (MIA) protocol was used by injecting pregnant mice (at E9.5) with a viral mimic poly IC or saline. Then, juvenile/adolescent offspring (3-7 weeks old) were subjected to either standard lighting (12:12LD) or constant light (LL). Results We found interactions of the two factors on behaviors related to cognition, anxiety, and sociability. Also, poly IC exposure led to a more activated profile of hippocampal microglia in males only, while LL diminished these effects. Using RNA sequencing in the dorsal hippocampus, we found that poly IC exposure led to many differentially expressed genes in males (but not females), and fewer differentially expressed genes were observed after LL exposure. Using the WGCNA analysis, we found several significant gene modules positively associated with poly IC (in comparison to saline exposure) and LL (in comparison to LD exposure) in males, and less so in females. Interestingly, many of the identified hub bottleneck genes were homologous to human genes associated with both sleep/circadian rhythms and neurodevelopmental disorders as identified by GWA studies. Conclusions Our work demonstrates that in a mouse model of prenatal infection, disruptions in circadian rhythms induced by LL play a role in modulating the effects of MIA at behavioral, cellular, and molecular levels.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".