Mutations in FKBP10, which result in Bruck syndrome and recessive forms of osteogenesis imperfecta, inhibit the hydroxylation of telopeptide lysines in bone collagen
Bibliographic record
Abstract
Although biallelic mutations in non-collagen genes account for <10% of individuals with osteogenesis imperfecta, the characterization of these genes has identified new pathways and potential interventions that could benefit even those with mutations in type I collagen genes. We identified mutations in FKBP10, which encodes the 65 kDa prolyl cis-trans isomerase, FKBP65, in 38 members of 21 families with OI. These include 10 families from the Samoan Islands who share a founder mutation. Of the mutations, three are missense; the remainder either introduce premature termination codons or create frameshifts both of which result in mRNA instability. In four families missense mutations result in loss of most of the protein. The clinical effects of these mutations are short stature, a high incidence of joint contractures at birth and progressive scoliosis and fractures, but there is remarkable variability in phenotype even within families. The loss of the activity of FKBP65 has several effects: type I procollagen secretion is slightly delayed, the stabilization of the intact trimer is incomplete and there is diminished hydroxylation of the telopeptide lysyl residues involved in intermolecular cross-link formation in bone. The phenotype overlaps with that seen with mutations in PLOD2 (Bruck syndrome II), which encodes LH2, the enzyme that hydroxylates the telopeptide lysyl residues. These findings define a set of genes, FKBP10, PLOD2 and SERPINH1, that act during procollagen maturation to contribute to molecular stability and post-translational modification of type I procollagen, without which bone mass and quality are abnormal and fractures and contractures result.
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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 | 0.000 |
| 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".