Muscle weakness and selective muscle atrophy in osteoprotegerin-deficient mice
Bibliographic record
Abstract
Bone and muscle are tightly coupled and form a functional unit under normal conditions. The receptor-activator of nuclear factor κB/receptor-activator of nuclear factor κB ligand/osteoprotegerin (RANK/RANKL/OPG) triad plays a crucial role in bone remodeling. RANKL inhibition by OPG prevents osteoporosis. In contrast, the absence of OPG results in elevated serum RANKL and early onset osteoporosis. However, the impacts of OPG deletion on muscle structure and function are unknown. Our results showed that 1-, 3- and 5-month-old Opg-/- mice have reduced tibial and femoral bone biomechanical properties and higher levels of circulating RANKL. OPG-deficient mice displayed reduced locomotor activity and signs of muscle weakness at 5 months of age. Furthermore, OPG deficiency did not affect the skeletal muscles in 1- and 3-month-old mice. However, it impaired fast-twitch EDL but not slow-twitch Sol muscles in 5-month-old Opg-/- mice. Moreover, 5-month-old Opg-/- mice exhibited selective atrophy of fast-twitch-type IIb myofibers, with increased expression of atrophic proteins such as NF-kB, atrogin-1 and MuRF-1. We used an in vitro model to show that RANKL-stimulated C2C12 myotubes significantly increased the expression of NF-kB, atrogin-1 and MuRF-1. A 2-month anti-RANKL treatment starting at 3 months of age in Opg-/- mice improved voluntary activity, the ex vivo maximum specific force (sP0) of EDL muscles, and whole limb grip force performance and rescued the biomechanical properties of bone. In conclusion, the deletion of OPG and the disruption of the RANKL/OPG balance induced osteoporosis as well as the selective weakness and atrophy of the powerful fast-twitch IIb myofibers, which was partly alleviated by an anti-RANKL treatment.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".