Sensitive method of detecting myosin phosphorylation: effects of angiotensin II on the renal afferent arteriole
Bibliographic record
Abstract
Phosphorylation of myosin light chain (LC20) plays a key role in smooth muscle regulation, and is governed by the relative activation of myosin light chain kinase and phosphatase. The role of these two pathways in the renal microcirculation is not known. Since pharmacologic probes are often not selective, a capability to measure LC20 phosphorylation in small resistance vessels is needed and current techniques have inadequate sensitivity. To address this issue, we developed a highly sensitive method using Phos‐tag PAGE (Mol Cell Proteomics 5:749, 2006). SDS was included during sample preparation and electrophoresis to enhance protein extraction and minimize loss due to nonspecific binding. Sensitivity of western blotting was increased by fixing proteins on the membrane and optimizing all conditions. Using isolated afferent arterioles, we were able to detect LC20 phosphorylation in as little as one afferent arteriole (<50 pg LC20). LC20 phosphorylation levels could be reproducibly measured in pooled samples of 3 vessels and concentration‐dependent increases in LC20 phosphorylation were seen in response to 1–100 nM angiotensin II. Parallel studies using fura‐2 demonstrated corresponding increases in intracellular [Ca 2+ ]. This new method will facilitate studies into the physiologic roles of Ca 2+ sensitization pathways in the renal microcirculation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".