Proteomics based detection of differentially expressed proteins in human osteoblasts subjected to mechanical stress
Bibliographic record
Abstract
Mechanical stress is essential for bone development. Mechanical stimuli are transduced to biochemical signals that regulate proliferation, differentiation, and cytoskeletal reorganization in osteoblasts. In this study, we used proteomics to evaluate differences in the protein expression profiles of untreated Saos-2 osteoblast cells and Saos-2 cells subjected to mechanical stress loading. Using 2-D electrophoresis, MALDI-TOF mass spectroscopy, and bioinformatics, we identified a total of 26 proteins differentially expressed in stress loaded cells compared with control cells. Stress loaded Saos-2 cells exhibited significant upregulation of 17 proteins and significant downregulation of 9 proteins compared with control cells. Proteins that were most significantly upregulated in mechanically loaded cells included those regulating osteogenesis, energy metabolism, and the stress response, such as eukaryotic initiation factor 2 (12-fold), mitochondrial ATP synthase (8-fold), and peptidylprolyl isomerase A (cyclophilin A)-like 3 (6.5-fold). Among the proteins that were significantly downregulated were those involved in specific signaling pathways and cell proliferation, such as protein phosphatase regulatory (inhibitor) subunit 12B (13.8-fold), l-lactate dehydrogenase B (9.4-fold), Chain B proteasome activator Reg (Alpha) PA28 (7.7-fold), and ubiquitin carboxyl-terminal esterase L1 (6.9-fold). Our results provide a platform to understand the molecular mechanisms underlying mechanotransduction.
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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 | 0.001 |
| 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".