SERCA2a tyrosine nitration coincides with impairments in maximal SERCA activity in left ventricles from tafazzin‐deficient mice
Bibliographic record
Abstract
The sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) is imperative for normal cardiac function regulating both muscle relaxation and contractility. SERCA2a is the predominant isoform in cardiac muscles and is inhibited by phospholamban (PLN). Under conditions of oxidative stress, SERCA2a may also be impaired by tyrosine nitration. Tafazzin (Taz) is a mitochondrial-specific transacylase that regulates mature cardiolipin (CL) formation, and its absence leads to mitochondrial dysfunction and excessive production of reactive oxygen/nitrogen species (ROS/RNS). In the present study, we examined SERCA function, SERCA2a tyrosine nitration, and PLN expression/phosphorylation in left ventricles (LV) obtained from young (3-5 months) and old (10-12 months) wild-type (WT) and Taz knockdown (TazKD) male mice. These mice are a mouse model for Barth syndrome, which is characterized by mitochondrial dysfunction, excessive ROS/RNS production, and dilated cardiomyopathy (DCM). Here, we show that maximal SERCA activity was impaired in both young and old TazKD LV, a result that correlated with elevated SERCA2a tyrosine nitration. In addition PLN protein was decreased, and its phosphorylation was increased in TazKD LV compared with control, which suggests that PLN may not contribute to the impairments in SERCA function. These changes in expression and phosphorylation of PLN may be an adaptive response aimed to improve SERCA function in TazKD mice. Nonetheless, we demonstrate for the first time that SERCA function is impaired in LVs obtained from young and old TazKD mice likely due to elevated ROS/RNS production. Future studies should determine whether improving SERCA function can improve cardiac contractility and pathology in TazKD 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".