Phos-Tag-Based Analysis of Myosin Regulatory Light Chain Phosphorylation in Human Uterine Myocytes
Bibliographic record
Abstract
BACKGROUND: The 'phosphate-binding tag' (phos-tag) reagent enables separation of phospho-proteins during SDS-PAGE by impeding migration proportional to their phosphorylation stoichiometry. Western blotting can then be used to detect and quantify the bands corresponding to the phospho-states of a target protein. We present a method for quantification of data regarding phospho-states derived from phos-tag SDS-PAGE. The method incorporates corrections for lane-to-lane loading variability and for the effects of drug vehicles thus enabling the comparison of multiple treatments by using the untreated cellular set-point as a reference. This method is exemplified by quantifying the phosphorylation of myosin regulatory light chain (RLC) in cultured human uterine myocytes. METHODOLOGY/PRINCIPAL FINDINGS: We have evaluated and validated the concept that, when using an antibody (Ab) against the total-protein, the sum of all phosphorylation states in a single lane represents a 'closed system' since all possible phospho-states and phosphoisotypes are detected. Using this approach, we demonstrate that oxytocin (OT) and calpeptin (Calp) induce RLC kinase (MLCK)- and rho-kinase (ROK)-dependent enhancements in phosphorylation of RLC at T18 and S19. Treatment of myocytes with a phorbol ester (PMA) induced phosphorylation of S1-RLC, which caused a mobility shift in the phos-tag matrices distinct from phosphorylation at S19. CONCLUSION/SIGNIFICANCE: We have presented a method for analysis of phospho-state data that facilitates quantitative comparison to a reference control without the use of a traditional 'loading' or 'reference' standard. This analysis is useful for assessing effects of putative agonists and antagonists where all phospho-states are represented in control and experimental samples. We also demonstrated that phosphorylation of RLC at S1 is inducible in intact uterine myocytes, though the signal in the resting samples was not sufficiently abundant to allow quantification by the approach used here.
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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) | 0.002 | 0.001 |
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".