Antibody microarray and immunoblotting analyses of the EGF signaling phosphorylation network in human A431 epidermoid carcinoma cells
Bibliographic record
Abstract
Post-homogenization instability of phosphorylation sites in proteins, and the insensitivity and high costs of most proteomics analytical methods have been major barriers in tracking cell signalling networks in minute specimens of cells and tissues.While antibody microarrays have been promising tools for quantifying changes in protein expression and phosphorylation, the interpretation of findings from their applications have been complicated by issues of protein-protein interactions and cross-reactivity with off-target proteins.We used the human A431 cervical carcinoma cell line that over-expresses the epidermal growth factor (EGF) receptor to investigate its signal transduction mechanisms.Chemical cleavage of proteins at cysteine residues at the time of homogenizing the A431 cells permitted preservation of the autophosphorylation of this receptor in response to brief stimulation of these cells with EGF and revealed many downstream signalling events.Most of the EGF-induced changes in protein phosphorylation observed with the antibody microarray were validated by Western blotting with the intended targets, although many of these also false positives that arose from cross-reactivity with the EGF receptor itself.Some false-negatives from blocked epitopes may have stemmed from interactions with SH2 domain-containing adapter proteins that were resistant to chemical cleavage and internal interactions of flanking arginine and lysine residues with phosphosites.
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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) | 0.001 | 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".