Maternal Immune Activation during Gestation Interacts with<i>Disc1</i>Point Mutation to Exacerbate Schizophrenia-Related Behaviors in Mice
Bibliographic record
Abstract
Schizophrenia is thought to result from interactions between susceptible genotypes and environmental risk factors. DISC1 is an important gene for schizophrenia and mood disorders based on both human and animal studies. In the present study we sought to investigate interactions between two distinct point mutations in the mouse Disc1 gene (L100P and Q31L) and maternal immune activation (MIA) during pregnancy with polyinosinic:polycytidylic acid (polyI:C). PolyI:C given at 5 mg/kg impaired cognitive and social behavior in both wild-type (WT) and Disc1-Q31L(+/-) offspring, and reduced prepulse inhibition at 16 but not 8 weeks of age. Disc1-L100P(+/-) mutants were more sensitive to MIA than WT or Disc1-Q31L(+/-) mice. Interleukin-6 (IL-6) is a critical cytokine for mediating the behavioral and transcriptional effects of polyI:C. We found a more pronounced increase of IL-6 in response to polyI:C in fetal brain in Disc1-L100P(+/-) mice compared with WT or Disc1-Q31L(+/-) mice. Coadministration of an anti-IL-6 antibody with polyI:C reversed schizophrenia-related behavioral phenotypes in Disc1-L100P(+/-) mice. In summary, we found specific interactions between discrete genetic (Disc1-L100P(+/-)) and environmental factors (MIA) that exacerbate schizophrenia-related phenotypes. IL-6 may be important in the pathophysiology of this interaction.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".