XBP1s-Mediated ER Proteostasis Network Enhancement Can Selectively Improve the Folding and Secretion of an Osteogenesis Imperfecta-Causing Collagen-I Variant
Bibliographic record
Abstract
ABSTRACT Osteogenesis imperfecta (OI) is typically caused by autosomal dominant mutations in genes encoding collagen type-I, most commonly resulting in Gly→Ser triple-helical domain substitutions that disrupt collagen folding and/or stability. Here, we test the hypothesis that upregulating the endoplasmic reticulum (ER) proteo-stasis network via the unfolded protein response (UPR) can improve the folding and secretion of the clinically severe, prototypical OI-causing COL1A1 p.G425S collagen-α1(I) variant. We first show that small molecules that activate the entire UPR by causing global ER protein misfolding stress severely ablate collagen-I secretion from both G425S Colα1(I)- and wild-type (WT) Colα1(I)-expressing primary fibroblasts. In contrast, stress-independent, specific induction of just the UPR’s XBP1s transcriptional response can enhance collagen-I secretion from G425S Colα1(I) patient primary fibroblasts up to ~300% of basal levels. Notably, the effect is selective – collagen-I secretion from WT Colα1(I)-expressing healthy donor primary fibroblasts is unaltered by XBP1s. XBP1s pathway activation appears to post-translationally enhance the folding/assembly and secretion of G425S Colα1(I), as only modest impacts on collagen-I transcription or synthesis are observed. Consistent with this notion, we find that the stable, triple-helical collagen-I secreted by XBP1s-activated G425S α1(I) patient fibroblasts includes a higher proportion of the mutant α1(I) polypeptide than the collagen-I secreted under basal ER proteostasis conditions. We note that consistent reproducibility of these results is dependent on as yet unascertained experimental variables. Still, these promising observations suggest the potential for ER proteo-stasis network modulation to improve mutant collagen proteostasis in the collagenopathies, motivating further investigation of the effect’s generality, underlying mechanism, and potential therapeutic benefits.
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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) | 0.001 | 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".