Role of CaMKII and sarcolipin in muscle adaptations to strength training with different levels of fatigue in the set
Bibliographic record
Abstract
Abstract Strength training promotes a IIX‐to‐IIA shift in myosin heavy chain (MHC) composition, likely due to changes in sarcoplasmic [Ca2+] which are sensed by CaMKII. Sarcoplasmic [Ca2+] is in part regulated by sarcolipin (SLN), a small protein that when overexpressed in rodents stimulates mitochondrial biogenesis and a fast‐to‐slow fiber type shift. The purpose of this study was to determine whether CaMKII and SLN are involved in muscle phenotype and performance changes elicited by strength training. Twenty‐two men followed an 8‐week velocity‐based resistance training program using the full squat exercise while monitoring repetition velocity. Subjects were randomly assigned to two resistance training programs differing in the repetition velocity loss allowed in each set: 20% (VL20) vs 40% (VL40). Strength training caused muscle hypertrophy, improved 1RM and increased total CaMKII protein expression, particularly of the δD isoform. Phospho‐Thr287‐CaMKII δD expression increased only in VL40 (+89%), which experienced greater muscle hypertrophy, and a reduction in MHC‐IIX percentage. SLN expression was increased in VL20 (+33%) remaining unaltered in VL40. The changes in phospho‐Thr287‐CaMKII δD were positively associated with muscle hypertrophy and the number of repetitions during training, and negatively with the changes in MHC‐IIX and SLN. Most OXPHOS proteins remained unchanged, except for NDUFB8 (Complex I), which was reduced after training (−22%) in both groups. The amount of fatigue allowed in each set critically influences muscle CaMKII and SLN responses and determines muscle phenotype changes. With lower intra‐set fatigue, the IIX‐to‐IIA MHC shift is attenuated.
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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".