Expanding the clinical spectrum of recessive truncating mutations of <i>KLHL7</i> to a Bohring-Opitz-like phenotype
Bibliographic record
Abstract
Background Bohring-Opitz syndrome (BOS) is a rare genetic disorder characterised by a recognisable craniofacial appearance and a typical ‘BOS’ posture. BOS is caused by sporadic mutations ofASXL1. However, several typical patients with BOS have no molecular diagnosis, suggesting clinical and genetic heterogeneity. Objectives To expand the phenotypical spectrum of autosomal recessive variants of KLHL7, reported as causing Crisponi syndrome/cold-induced sweating syndrome type 1 (CS/CISS1)-like syndrome. Methods We performed whole-exome sequencing in two families with a suspected recessive mode of inheritance. We used the Matchmaker Exchange initiative to identify additional patients. Results Here, we report six patients with microcephaly, facial dysmorphism, including exophthalmos, nevus flammeus of the glabella and joint contractures with a suspected BOS posture in five out of six patients. We identified autosomal recessive truncating mutations in the KLHL7 gene. KLHL7 encodes a BTB–kelch protein implicated in the cell cycle and in protein degradation by the ubiquitin–proteasome pathway. Recently, biallelic mutations in the KLHL7 gene were reported in four families and associated with CS/CISS1, characterised by clinical features overlapping with our patients. Conclusion We have expanded the clinical spectrum of KLHL7 autosomal recessive variants by describing a syndrome with features overlapping CS/CISS1 and BOS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".