Proteomics Analyses of Activated Human Optic Nerve Head Lamina Cribrosa Cells following Biomechanical Strain
Bibliographic record
Abstract
PURPOSE: To determine protein regulation following activation of human, optic nerve head (ONH), lamina cribrosa (LC) cells in response to mechanical strain. METHODS: LC cells were isolated and grown from donor tissue in specific media at 37°C and 5% CO(2) humidified incubator. Cells were grown to confluence on collagen I-coated flexible-bottom culture plates, rinsed with Dulbecco's phosphate-buffered saline, and left for 24 hours in serum-free media. They were subjected to 3% or 12% cyclic equiaxial stretch for 2 or 24 hours using a commercial strain-unit system. Control cells were serum-deprived and incubated without stretch for 24 hours. Nano liquid chromatography-mass spectrometry analysis with isobaric tags for relative and absolute quantitation labeling was used to determine protein regulation. RESULTS: In all, 526 proteins were discovered at a 95% confidence limit. Analysis of associated pathways and functional annotation indicated that the LC cells reacted in vitro to mechanical strain by activating pathways involved in protein synthesis, cellular movement, cell-to-cell signaling, and inflammation. These pathways indicated consistent major protein hubs across all stretch/time conditions involving transforming growth factor-β1 (TGFβ1), tumor necrosis factor (TNF), caspase-3 (CASP3), and tumor protein-p53 (p53). Among proteins of particular interest, also found in multiple stretch/time conditions, were bcl-2-associated athanogene 5 (BAG5), nucleolar protein 66 (NO66), and eukaryotic translation initiation factor 5A (eIF-5A). CONCLUSIONS: Pathway analysis identified major protein hubs (TGFβ1, TNF, CASP3, p53) and pathways all previously implicated in cellular activation and in the pathogenesis of glaucomatous optic neuropathy. Several specific proteins of interest (BAG5, NO66, eIF-5A) were identified for future investigation as to their role in ONH glial activation.
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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".