Chronic SR Ca <sup>2+</sup> -ATPase Inhibition Causes Adaptive Changes in Cellular Ca <sup>2+</sup> Transport
Bibliographic record
Abstract
Phospholamban, the critical regulator of the cardiac SERCA2a Ca2+ affinity, is phosphorylated at Ser16 and Thr17 during beta-adrenergic stimulation (eg, isoproterenol). To assess the impact of nonphosphorylatable phospholamban, a S16A, T17A double-mutant (DM) was introduced into phospholamban knockout mouse hearts. Transgenic lines expressing DM phospholamban at levels similar to wild types (WT) were identified. In vitro phosphorylation confirmed that DM phospholamban could not be phosphorylated, but produced the same shift in EC50 of SERCA2a for Ca2+ as unphosphorylated WT phospholamban. Rates of basal twitch [Ca2+]i decline were not different in DM versus WT cardiomyocytes. Isoproterenol increased the rates of twitch [Ca2+]i decline in WT, but not DM myocytes, confirming the prominent role of phospholamban phosphorylation in this response. Increased L-type Ca2+ current (ICa) density, with unaltered characteristics, was the major compensation in DM myocytes. Consequently, the normal beta-adrenergic-induced increase in ICa caused larger dynamic changes in absolute ICa density. Isoproterenol increased Ca2+ transients to a comparable amplitude in DM and WT. There were no changes in myofilament Ca2+ sensitivity, or the expression levels and Ca2+ removal activities of other Ca2+-handling proteins. Nor was there evidence of cardiac remodeling up to 10 months of age. Thus, chronic inhibition of SERCA2a by ablation of phospholamban phosphorylation (abolishing its adrenergic regulation) results in a unique cellular adaptation involving greater dynamic ICa modulation. This ICa modulation may partly compensate for the loss in SERCA2a responsiveness and thereby partially normalize beta-adrenergic inotropy in DM phospholamban mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".