A Novel Intronic Variant in the <scp>KH3</scp> Domain of <scp>HNRNPK</scp> Leads to a Mild Form of Au‐Kline Syndrome
Bibliographic record
Abstract
Despite the massive adoption of sequencing technologies, disease-specific diagnosis remains challenging, particularly for genes with highly homologous pseudogenes like HNRNPK. Pathogenic HNRNPK variants cause Au-Kline syndrome (AKS), a neurodevelopmental disorder with malformations and distinctive facial features. We validated a novel de novo HNRNPK intronic variant (c.1192-3 C>A, p.Leu398ValfsTer21) in a patient previously misdiagnosed with Kabuki Syndrome (KS). By combining sequencing, in vitro splicing assays, molecular modelling, and protein function analysis, we characterised the molecular defect. A unique DNA methylation (DNAm) signature was recently identified in AKS, with missense variants showing an intermediate DNAm pattern, suggesting an epi-genotype-phenotype correlation linked to milder clinical features. The DNAm signature is a valuable tool for variant interpretation, especially in unclear AKS cases. We demonstrate that two independent approaches-functional characterisation and DNAm evaluation-confirmed a partial loss of HNRNPK function and validated an AKS diagnosis with a mild phenotype. Our findings highlight that a multidisciplinary approach integrating genomic and epigenomic analyses with functional studies and clinical assessment significantly improves rare disease diagnosis.
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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 | 0.001 |
| 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 | 0.001 | 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".