Co-Expression of SERCA Isoforms, Phospholamban and Sarcolipin in Human Skeletal Muscle Fibers
Bibliographic record
Abstract
Sarcolipin (SLN) and phospholamban (PLN) inhibit the activity of sarco(endo)plasmic reticulum Ca(2+)-ATPases (SERCAs) by reducing their apparent affinity for Ca(2+). A ternary complex between SLN, PLN, and SERCAs results in super-inhibition of SERCA activity. Analysis of skeletal muscle homogenate has limited our current understanding of whether SLN and PLN regulate SERCA1a, SERCA2a, or both in skeletal muscle and whether SLN and PLN are co-expressed in skeletal muscle fibers. Biopsies from human vastus lateralis were analyzed through single fiber Western blotting and immunohisto/fluorescence staining to circumvent this limitation. With a newly generated SLN antibody, we report for the first time that SLN protein is present in human skeletal muscle. Addition of the SLN antibody (50 µg) to vastus lateralis homogenates increased the apparent Ca(2+) affinity of SERCA (K Ca, pCa units) (-Ab, 5.85 ± 0.02 vs. +Ab, 5.95 ± 0.02) and maximal SERCA activity (μmol/g protein/min) (-Ab, 122 ± 6.4 vs. +Ab, 159 ± 11) demonstrating a functional interaction between SLN and SERCAs in human vastus lateralis. Specifically, our results suggest that although SLN and PLN may preferentially regulate SERCA1a, and SERCA2a, respectively, physiologically they both may regulate either SERCA isoform. Furthermore, we show that SLN and PLN co-immunoprecipitate in human vastus lateralis homogenate and are simultaneously expressed in 81% of the fibers analyzed with Western blotting which implies that super-inhibition of SERCA may exist in human skeletal muscle. Finally, we demonstrate unequivocally that mouse soleus contains PLN protein suggesting that super-inhibition of SERCA may also be important physiologically in rodent skeletal muscle.
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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".