Autophagy and 3-Phosphoinositide-Dependent Kinase 1 (PDK1)-Related Kinome in Pagetic Osteoclasts
Bibliographic record
Abstract
In Paget's disease of bone (PDB), a major contributory factor are osteoclasts (OCs) that are larger, more numerous, resistant to apoptosis, and hyperactive. The aim of this human in vitro study was to identify kinase cascades involved in the OC phenotype and to determine their impact on downstream processes. Basal phosphorylation levels of Akt and ERK were found to be elevated in PDB OCs. Given our previous findings that 3-phosphoinositide-dependent protein kinase 1 (PDK1) associates with the crucial adaptor p62 in OCs, we hypothesized that PDK1 may play an important role in OC-related kinome regulation. The increased phosphorylation of Akt and its substrate GSK3β observed in PDB OCs was reduced significantly upon PDK1 inhibition, as well as that of 4EBP1 and Raptor. This suggests a PDK1/Akt-dependent activation of mammalian target of rapamycin complex 1 (mTORC1) in PDB OCs. The resistance to apoptosis and the bone resorption were also overcome upon PDK1 inhibition. Studying autophagy by LC3B expression, we found a less inducible autophagy compared with control cells, which was reversed by PDK1 inhibition. In addition, PBD OCs exhibited higher LC3B-II/LC3B-I ratios and numbers of p62 and LC3B puncta per OC area, which did not further increase in the presence of lysosomal protease inhibitors, suggesting an accumulation of non-degradative autophagosomes. Together these results indicate a strong potential regulatory role for PDK1 in OC stimulatory pathways (Akt, ERK) and autophagy induction (via mTORC1), which may contribute to the OC phenotype in PDB. We also identified defects in late autophagosome maturation in these cells, the mechanism of which remains to be determined. © 2016 American Society for Bone and Mineral Research.
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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".