An alternative kinase activity assay for primary cultures derived from clinical isolates
Bibliographic record
Abstract
PURPOSE: The measurement of protein kinase activity is central to understanding the signaling pathways that regulate cellular proliferation and apoptosis in virtually all disease processes. These measurements typically involve either indirect, time consuming assessment methods that require large amounts of sample, such as western immunoblotting, or the use of high maintenance, specialized equipment not typically available to a small clinical research facility. The purpose of this project was to determine if a benchtop Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) unit could be used to detect and directly assess kinase activity of the serine/threonine kinase Akt. METHOD: Biotinylated substrate peptides, predicted to be recognized and phosphorylated by Akt to varying extents, were incubated in crude lysates of primary cells derived directly from clinical isolates. Streptavidin-coated chips were then used to isolate the substrate peptides from the lysates after incubation. Finally SELDI-TOF-MS was used to detect the peptide substrates and identify any changes in mass resulting from phosphorylation. RESULTS: The biotinylated peptide substrates were readily detected and a simple, rapid procedure that allows direct measurement of Akt activity in less than 1 microg of cell lysate in a 2microL volume was developed. Further, a linear correlation between native to phospho-peptide ratios and SELDI-TOF-MS output demonstrated that this approach is semi-quantitative. CONCLUSION: This assay avoids many of the pitfalls associated with the currently available kinase protocols as well as labour-intensive mass-spectrometry analysis by specialist laboratories. We propose that this approach may be a viable alternative for clinical research laboratories aiming to measure the activity of kinases in clinical isolates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".