Low‐dose lithium feeding increases the SERCA2a‐to‐phospholamban ratio, improving SERCA function in murine left ventricles
Bibliographic record
Abstract
New Findings What is the central question of this study? Inhibition of glycogen synthase kinase‐3 (GSK3) has been shown to improve cardiac SERCA2a function. Lithium can inhibit GSK3, but therapeutic doses used in treating bipolar disorder can have toxic effects. It has not been determined whether subtherapeutic doses of lithium can improve cardiac SERCA function. What is the main finding and its importance? Using left ventricles from wild‐type mice, we found that subtherapeutic lithium feeding for 6 weeks decreased GSK3 activity and increased cardiac SERCA function compared with control‐fed mice. These findings warrant the investigation of low‐dose lithium feeding in preclinical models of cardiomyopathy and heart failure to determine the therapeutic benefit of GSK3 inhibition. Abstract The sarco(endo)plasmic reticulum Ca2+‐ATPase (SERCA) pump is responsible for regulating calcium (Ca2+) within myocytes, with SERCA2a being the dominant isoform in cardiomyocytes. Its inhibitor, phospholamban (PLN), acts by decreasing the affinity of SERCA for Ca2+. Changes in the SERCA2a:PLN ratio can cause Ca2+ dysregulation often seen in patients with dilated cardiomyopathy and heart failure. The enzyme glycogen synthase kinase‐3 (GSK3) is known to downregulate SERCA function by decreasing the SERCA2a:PLN ratio. In this study, we sought to determine whether feeding mice low‐dose lithium, a natural GSK3 inhibitor, would improve left ventricular SERCA function by altering the SERCA2a:PLN ratio. To this end, male wild‐type C57BL/6J mice were fed low‐dose lithium via drinking water (10 mg kg−1 day−1 LiCl for 6 weeks) and left ventricles were harvested. GSK3 activity was significantly reduced in LiCl‐fed versus control‐fed mice. The apparent affinity of SERCA for Ca2+ was also increased (pCa50; control, 6.09 ± 0.03 versus LiCl, 6.26 ± 0.04, P < 0.0001) along with a 2.0‐fold increase in SERCA2a:PLN ratio in LiCl‐fed versus control‐fed mice. These findings suggest that low‐dose lithium supplementation can improve SERCA function by increasing the SERCA2a:PLN ratio. Future studies in murine preclinical models will determine whether GSK3 inhibition via low‐dose lithium could be a potential therapeutic strategy for dilated cardiomyopathy and heart failure.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".