Inhibition of Proprotein Convertase SKI-1 Blocks Transcription of Key Extracellular Matrix Genes Regulating Osteoblastic Mineralization
Bibliographic record
Abstract
Mineralization, a characteristic phenotypic property of osteoblastic lineage cells, was blocked by 4-(2-aminoethyl) benzenesulfonyl fluoride hydrochloride (AEBSF) and decanoyl-Arg-Arg-Leu-Leu-chloromethyl ketone (dec-RRLL-cmk), inhibitors of SKI-1 (site 1; subtilisin kexin like-1) protease. Because SKI-1 is required for activation of SREBP and CREB (cAMP-response element-binding protein)/ATF family transcription factors, we tested the effect of these inhibitors on gene expression. AEBSF decreased expression of 140 genes by 1.5-3.0-fold including Phex, Dmp1, COL1A1, COL11A1, and fibronectin. Direct comparison of AEBSF and dec-RRLL-cmk, a more specific SKI-1 inhibitor, demonstrated that expression of Phex, Dmp1, COL11A1, and fibronectin was reduced by both, whereas COL1A2 and HMGCS1 were reduced only by AEBSF. AEBSF and dec-RRLL-cmk decreased the nuclear content of SKI-1-activated forms of transcription factors SREBP-1, SREBP-2, and OASIS. In contrast to AEBSF, the actions of dec-RRLL-cmk represent the sum of its direct actions on SKI-1 and indirect actions on caspase-3. Specifically, dec-RRLL-cmk reduced intracellular caspase-3 activity by blocking the formation of activated 19-kDa caspase-3. Conversely, overexpression of SKI-1-activated SREBP-1a and CREB-H in UMR106-01 osteoblastic cells increased the number of mineralized foci and altered their morphology to yield mineralization nodules, respectively. In summary, SKI-1 regulates the activation of transmembrane transcription factor precursors required for expression of key genes required for mineralization of osteoblastic cultures in vitro and bone formation in vivo. Our results indicate that the differentiated phenotype of osteoblastic cells and possibly osteocytes depends upon the non-apoptotic actions of SKI-1.
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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) | 0.001 | 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 | 0.001 |
| 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".