Fibre Type Specific Distribution of SERCA1a, SERCA2a and Phospholamban in Human Vastus Lateralis
Bibliographic record
Abstract
Previous studies in rodents have shown that the expression patterns of sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) isoforms and myosin heavy chain (MHC) isoforms in skeletal muscle are closely matched. Specifically, fast-twitch skeletal muscles contain SERCA1a and MHCIIb/IIx/IIa whereas slow-twitch muscles contain SERCA2a and MHCI. Phospholamban (PLN), a known regulator of the SERCA pumps, has been shown to be coexpressed almost exclusively with SERCA2a in rodent skeletal muscle. PURPOSE: To examine the fibre type specific expression of SERCA1a, SERCA2a and PLN in human skeletal muscle. METHODS: Muscle biopsies from the vastus lateralis were extracted from five healthy university males and serial cross sections were immunohistochemically stained for SERCA1a (A52), SERCA2a (2A7-A1) and PLN (2D12). Immunofluorescence analysis of MHC expression was also performed with primary antibodies against MHCI (BA-F8), MHCIIa (SC-71) and MHCIIx (6H1). RESULTS: Although the general fibre type distribution of SERCA and MHC isoforms, which is well-defined for rodent skeletal muscle, also clearly exists in human vastus lateralis, we found that SERCA1a was expressed in 58.2±7.5% of muscle fibres containing MHCI and SERCA2a was expressed in 17.7±9.5% of muscle fibres containing MHCIIa. Fibres containing MHCIIx expressed the SERCA1a isoform exclusively. PLN expression was found in all fibres containing MHCI and SERCA2a and in a large population of fibres containing MHCIIa and SERCA1a suggesting that PLN may interact with both SERCA2a and SERCA1a. CONCLUSION: These data suggest differences in the regulation of genes encoding muscle contractile and sarcoplasmic reticulum proteins between rodents and humans and underscore the importance of applying caution when translating findings from animal models to humans. Supported by NSERC Canada Discovery grant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".