The Candidate Schizophrenia Risk Gene DGCR2 Regulates Early Steps of Corticogenesis
Bibliographic record
Abstract
BACKGROUND: Alterations in early steps of cortical circuit assembly are thought to play a critical role in vulnerability to schizophrenia (SZ), but the pathogenic impact of SZ-risk mutations on corticogenesis remains to be determined. DiGeorge syndrome critical region 2 (DGCR2) is located in the 22q11.2 locus, whose deletion is a major risk factor for SZ. Moreover, exome sequencing of individuals with idiopathic SZ identified a rare missense mutation in DGCR2, further suggesting that DGCR2 is involved in SZ. METHODS: Here we investigated the function of Dgcr2 and the pathogenic impact of the SZ-risk DGCR2 mutation in mouse corticogenesis using in utero electroporation targeted to projection neurons. RESULTS: Dgcr2 knockdown impaired radial locomotion and final translocation of projection neurons, leading to persistent laminar positioning alterations. The DGCR2 missense SZ-risk mutation had a pathogenic impact on projection neuron laminar allocation by reducing protein expression. Mechanistically, we identified Dgcr2 as a novel member of the Reelin complex, regulating the phosphorylation of Reelin-dependent substrates and the expression of Reelin-dependent transcriptional targets. CONCLUSIONS: Overall, this study provides biological evidence that the SZ-risk gene DGCR2 regulates critical steps of early corticogenesis possibly through a Reelin-dependent mechanism. Additionally, we found that the SZ-risk mutation in DGCR2 has a pathogenic impact on cortical formation by reducing protein expression level, suggesting a functional role for DGCR2 haploinsufficiency in the 22q11.2 deletion syndrome.
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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 | 0.000 |
| 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".