Rare variants in PRKCI cause Van der Woude syndrome and other features of peridermopathy
Bibliographic record
Abstract
Van der Woude syndrome (VWS) is an autosomal dominant disorder characterized by lower lip pits and orofacial clefts (OFCs). With a prevalence of ∼1 in 35,000 live births, it is the most common form of syndromic clefting. Most VWS is attributed to variants in IRF6 (∼70%) or GRHL3 (∼5%), leaving up to 25% of individuals without a molecular diagnosis. Both IRF6 and GRHL3 function in a transcriptional regulatory network (TRN) governing differentiation of periderm, a single epithelial cell layer preventing pathological adhesions during palatogenesis. Periderm disruption can elicit a spectrum of phenotypes, including lip pits and OFCs, pterygia, and severe or fatal congenital anomalies. Understanding these mechanisms is vital in improving health outcomes for individuals with peridermopathies. We hypothesized genes encoding members of the periderm TRN, including kinases such as atypical protein kinase C (aPKC) acting upstream of IRF6, could harbor variants resulting in VWS. Consistent with this hypothesis, we identified 7 de novo variants (DNs) and 11 rare variants in PRKCI in 18 individuals with clinical features of syndromic OFCs and peridermopathies. Among the identified DNs, c.1148A>G (p.Asn383Ser) was found in five unrelated individuals, indicating a hotspot mutation. We functionally tested 12 proband-specific alleles in a zebrafish model. Three alleles, c.389G>A (p.Arg130His), c.1148A>G (p.Asn383Ser), and c.1155A>C (p.Leu385Phe), were confirmed loss-of-function variants. We also show that phosphomimetic Irf6 can rescue the effects of aPKC inhibition, supporting placement of PRKCI within this TRN. In summary, we identified PRKCI variants as causative for VWS and syndromic OFC with other features of peridermopathies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".