Mutations in chaperonin-like BBS genes are a major contributor to disease development in a multiethnic Bardet–Biedl syndrome patient population
Bibliographic record
Abstract
BACKGROUND: Bardet-Biedl syndrome is a pleiotropic disorder with 14 BBS genes identified. BBS1, BBS2, BBS4, BBS5, BBS7, BBS8, and BBS9 form a complex called the BBSome, which is believed to recruit Rab8(GTP) to the primary cilium and promote ciliogenesis. The second group, the chaperonin-like proteins BBS6, BBS10, and BBS12, have been defined as a vertebrate-specific branch of the type II chaperonin superfamily. These may play a role in the regulation of BBSome assembly. METHODS AND RESULTS: Using sequence analysis, the role of BBS6, 10 and 12 was assessed in the patient population comprising 93 cases from 74 families. Systemic and ocular phenotypes were defined. In the study, chaperonin-like BBS gene mutations accounted for the disease in approximately 36.5% of BBS families. A total of 38 different non-polymorphic exonic sequence variants were identified in 40.5% of BBS families (41.9% cases), of which 26 were novel (68%). Six cases had mutations present in more than one chaperonin-like BBS gene. One case with four mutations in BBS10 had a phenotype of overall greater severity. The phenotypes observed were beyond the classic BBS phenotype as they overlapped with characteristics of MKKS (congenital heart defect, vaginal atresia, hydrometrocolpos, cryptorchidism), as well as Alström syndrome (diabetes, hearing loss, liver abnormalities, endocrine anomalies, cardiomyopathy). CONCLUSIONS: While overlap between the MKKS and BBS phenotypes has previously been reported for cases with BBS6 mutations, we also observed MKKS phenotypes involving BBS10 and BBS12 and Alström-like phenotypes associated with mutations in BBS1, BBS2, BBS6, BBS7, BBS9, BBS10 and BBS12 for the first time.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".