Differential protein and mRNA expression of CaMKs during osteoclastogenesis and its functional implications
Bibliographic record
Abstract
The calmodulin-dependent kinase (CaMK) family has been recently recognized to participate in the regulation of osteoclastogenesis. However, there are some controversial reports regarding the mRNA expression patterns of CaMKs during osteoclastogenesis, although the protein expression pattern of most CaMKs during osteoclastogenesis have not been studied. In the present study, we attempted to address this issue by using a mouse bone marrow monocyte model and parallel Western blotting and quantitative real-time PCR. Our results revealed some interesting expression patterns of CaMKs during the process. Among all CaMKs examined, only CaMKIIδ exhibited consistent expression patterns between its mRNA and protein with both rising remarkably during osteoclastogenesis. CaMKIV protein was not detectable during the first three days of cell culture, but it rose on Day 5. The CaMK inhibitor, KN93, subdued osteoclastogenesis during the first three days of cell culture, a time when CaMKIV was absent while other KN93-sensitive CaMKs presented. In addition, KN93 was found to inhibit the expression of some early receptor activator of NF-κB (RANK) signaling intermediates (extracellular signal-regulated kinase (ERK) and Akt) in the non-differentiated mouse bone marrow monocytes. Collectively, these data reveal differential expression patterns of KN93-sensitive CaMK proteins and their mRNAs during osteoclastogenesis, supporting a CaMKII-RANK signaling interaction in the regulation of early osteoclastogenesis.
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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 | 0.001 | 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".