Maternal exposure to prostaglandin E2 modifies expression of Wnt genes in mouse brain – An autism connection
Bibliographic record
Abstract
Prostaglandin E2 (PGE2) is a lipid signaling molecule important for brain development and function. Various genetic and environmental factors can influence the level of PGE2 and increase the risk of developing Autism Spectrum Disorder (ASD). We have previously shown that in neuronal cell lines and mouse brain, PGE2 can interfere with the Wnt canonical pathway, which is essential during early brain development. Higher levels of PGE2 increased Wnt-dependent motility and proliferation of neuroectodermal stem cells, and modified the expression of Wnt genes previously linked to autism disorders. We also recently established a cross-talk between these two pathways in the prenatal mouse brain lacking PGE2 producing enzyme (COX-/-). The current study complements the published data and reveals that PGE2 signaling also converges with the Wnt canonical pathway in the developing mouse brain after maternal exposure to PGE2 at the onset of neurogenesis. We found significant changes in the expression level of Wnt-target genes, Mmp7, Wnt2, and Wnt3a, during prenatal and early postnatal stages. Interestingly, we observed variability in the expression level of these genes between genetically-identical pups within the same pregnancy. Furthermore, we found that all the affected genes have been previously associated with disorders of the central nervous system, including autism. We determined that prenatal exposure to PGE2 affects the Wnt pathway at the level of β-catenin, the major downstream regulator of Wnt-dependent gene transcription. We discuss how these results add new knowledge into the molecular mechanisms by which PGE2 may interfere with neuronal development during critical periods.
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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 | 0.001 | 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".