A highly sensitive technique to measure myosin regulatory light chain phosphorylation: the first quantification in renal arterioles
Bibliographic record
Abstract
Phosphorylation of the 20-kDa myosin regulatory light chains (LC(20)) plays a key role in the regulation of smooth muscle contraction. The level of LC(20) phosphorylation is governed by the relative activities of myosin light chain kinase and phosphatase pathways. The regulation of these two pathways differs in different smooth muscle types and in the actions of different vasoactive stimuli. Little is known concerning the regulation of LC(20) phosphorylation in the renal microcirculation. The available pharmacological probes are often nonspecific, and current techniques to directly measure LC(20) phosphorylation are not sensitive enough for quantification in small arterioles. We describe here a novel approach to address this important issue. Using SDS-PAGE with polyacrylamide-bound Mn(2+)-phosphate-binding tag and enhanced Western blot analysis, we were able to detect LC(20) phosphorylation using as little as 5 pg (250 amol) of isolated LC(20). Phosphorylated and unphosphorylated LC(20) were detected in single isolated afferent arterioles, and LC(20) phosphorylation levels could be accurately quantified in pooled samples of three arterioles (<300 cells). The phosphorylation level of LC(20) in the afferent arteriole was 6.8 +/- 1.7% under basal conditions and increased to 34.7 +/- 5.1% and 44.6 +/- 6.6% in response to 30 mM KCl and 10(-8) M angiotensin II, respectively. The application of this technique will enable investigations of the different determinants of LC(20) phosphorylation in afferent and efferent arterioles and provide insights into the signaling pathways that regulate LC(20) phosphorylation in the renal microvasculature under physiological and pathophysiological conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".