Elucidation of proteostasis defects caused by osteogenesis imperfecta mutations in the collagen-α2(I) C-propeptide domain
Bibliographic record
Abstract
Intracellular collagen assembly begins with the oxidative folding of ∼30-kDa C-terminal propeptide (C-Pro) domains. Folded C-Pro domains then template the formation of triple helices between appropriate partner strands. Numerous C-Pro missense variants that disrupt or delay triple-helix formation are known to cause disease, but our understanding of the specific proteostasis defects introduced by these variants remains immature. Moreover, it is unclear whether or not recognition and quality control of misfolded C-Pro domains is mediated by recognizing stalled assembly of triple-helical domains or by direct engagement of the C-Pro itself. Here, we integrate biochemical and cellular approaches to illuminate the proteostasis defects associated with osteogenesis imperfecta-causing mutations within the collagen-α2(I) C-Pro domain. We first show that "C-Pro-only" constructs recapitulate key aspects of the behavior of full-length Colα2(I) constructs. Of the variants studied, perhaps the most severe assembly defects are associated with C1163R C-Proα2(I), which is incapable of forming stable trimers and is retained within cells. We find that the presence or absence of an unassembled triple-helical domain is not the key feature driving cellular retention versus secretion. Rather, the proteostasis network directly engages the misfolded C-Pro domain itself to prevent secretion and initiate clearance. Using MS-based proteomics, we elucidate how the endoplasmic reticulum (ER) proteostasis network differentially engages misfolded C1163R C-Proα2(I) and targets it for ER-associated degradation. These results provide insights into collagen folding and quality control with the potential to inform the design of proteostasis network-targeted strategies for managing collagenopathies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".