Dwarf open reading frame (DWORF) peptide is a direct activator of the sarcoplasmic reticulum calcium pump SERCA
Bibliographic record
Abstract
ABSTRACT The cardiac sarcoplasmic reticulum calcium pump, SERCA, sequesters calcium in the sarco-endoplasmic reticulum (SR/ER) and plays a critical role in the contraction-relaxation cycle of the heart. A well-known regulator of SERCA in cardiac muscle is phospholamban (PLN), which interacts with the pump and reduces its apparent calcium affinity. A newly discovered SERCA regulatory subunit in cardiac muscle, dwarf open reading frame (DWORF), has added a new level of SERCA regulation. In this report, we modeled the structure of DWORF and evaluated it using molecular dynamics simulations. DWORF structure was modeled as a discontinuous helix with an unwound region at Pro 15 . This model orients an N-terminal amphipathic helix along the membrane surface and leaves a relatively short C-terminal transmembrane helix. We determined the functional regulation of SERCA by DWORF using a membrane reconstitution system. Surprisingly, we observed that DWORF directly activated SERCA by increasing its turnover rate. Furthermore, in-cell imaging of calcium dynamics demonstrated that DWORF increased SERCA-dependent ER calcium load, calcium reuptake rate, and spontaneous calcium release. Together, these functional assays suggest opposing effects of DWORF and PLN on SERCA function. The results agree with fluorescence resonance energy transfer experiments, which revealed changes in the affinity of DWORF for SERCA at low versus high cytosolic calcium concentrations. We found that DWORF has a higher affinity for SERCA in the presence of calcium, while PLN had the opposite behavior, a higher affinity for SERCA in low calcium. We propose a new mechanism for DWORF regulation of cardiac calcium handling in which DWORF directly enhances SERCA turnover by stabilizing the conformations of SERCA that predominate during elevated cytosolic calcium.
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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 | 0.001 |
| 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".