Maternal Immune Activation Alters Fetal Brain Development and Enhances Proliferation of Neural Precursor Cells in Rats
Bibliographic record
Abstract
Maternal immune activation (MIA) caused by exposure to pathogens or inflammation during critical periods of neurodevelopment is a major risk factor for behavioral deficits and psychiatric illness in offspring. A spectrum of behavioral abnormalities can be recapitulated in rodents by inducing MIA using the viral mimetic, PolyI:C. Many studies have focused on long-term changes in brain structure and behavioral outcomes in offspring following maternal PolyI:C exposure, but acute changes in prenatal development are not well-characterized. Using RNA-Sequencing, we profiled acute transcriptomic changes in rat conceptuses (decidua along with nascent embryo and placenta) after maternal PolyI:C exposure during early gestation, which enabled us to capture gene expression changes provoked by MIA inclusive to the embryonic milieu. We identified a robust increase in expression of genes related to antiviral inflammation following maternal PolyI:C exposure, and a corresponding decrease in transcripts associated with nervous system development. At mid-gestation, regions of the developing cortex were thicker in fetuses prenatally challenged with PolyI:C, with females displaying a thicker ventricular zone and males a thicker cortical mantle. Along these lines, neural precursor cells (NPCs) isolated from fetal brains prenatally challenged with PolyI:C exhibited a higher rate of self-renewal. Expression of Notch1 and the Notch ligand, delta-like ligand 1, which are both highly implicated in maintenance of NPCs and nervous system development, was increased following PolyI:C exposure. These results suggest that MIA elicits rapid gene expression changes within the conceptus, including repression of neurodevelopmental pathways, resulting in profound alterations in fetal brain development.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".