Hydrophobic Imbalance in the Cytoplasmic Domain of Phospholamban Is a Determinant for Lethal Dilated Cardiomyopathy
Bibliographic record
Abstract
The sarco(endo)plasmic reticulum calcium ATPase (SERCA) and its regulatory partner phospholamban (PLN) are essential for myocardial contractility. Arg(9) → Cys (R9C) and Arg(14) deletion (R14del) mutations in PLN are associated with lethal dilated cardiomyopathy in humans. To better understand these mutations, we made a series of amino acid substitutions in the cytoplasmic domain of PLN and tested their ability to inhibit SERCA. R9C is a complete loss-of-function mutant of PLN, whereas R14del is a mild loss-of-function mutant. When combined with wild-type PLN to simulate heterozygous conditions, the mutants had a dominant negative effect on SERCA function. A series of targeted mutations in this region of the PLN cytoplasmic domain ((8)TRSAIRR(14)) demonstrated the importance of hydrophobic balance in proper PLN regulation of SERCA. We found that Arg(9) → Leu and Thr(8) → Cys substitutions mimicked the behavior of the R9C mutant, and an Arg(14) → Ala substitution mimicked the behavior of the R14del mutant. The results reveal that the change in hydrophobicity resulting from the R9C and R14del mutations is sufficient to explain the loss of function and persistent interaction with SERCA. Hydrophobic imbalance in the cytoplasmic domain of PLN appears to be a predictor for the development and progression of dilated cardiomyopathy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".