Phosphoproteomic profiling of skeletal muscle twitch torque potentiation in ovarian hormone deficient female mice
Bibliographic record
Abstract
Skeletal muscle makes up ~40% of total body mass in a healthy adult. Muscle strength begins to decline with age. In females, preclinical and clinical studies have shown that reduction in estrogen reduces muscle force (i.e., strength). Previously, we showed the skeletal muscle phosphoproteome in a basal, non‐contracting state was remodeled in estrogen‐deficient females. Therefore, we questioned how estrogen deficiency would impact the skeletal muscle phosphoproteome after force generation. We performed two label‐free quantification phosphoproteomic analyses of the tibialis anterior muscle in ovariectomized (Ovx/Sham) and ovarian senescent (Old/Young) female mice after in vivo post‐tetanic potentiation protocol. We identified 25 and 60 differentially regulated phosphoproteins (p‐value<0.05 and ≥1.4‐fold change) in Ovx/Sham and Old/Young mice, respectively. Comparative analysis using Ingenuity Pathway Analysis’s activation Z‐score found similar patterns of predicted inhibition and activation of canonical pathways, such as inhibition of calcium signaling and activation of 14‐3‐3‐mediated signaling in both datasets. Likewise, parallel patterns in functional analysis were found relating to muscle contraction, fibrogenesis, etc. Overall, our findings suggest that the similarities identified in both datasets could elucidate the molecular characteristics of muscle proteins that might contribute to decrements in muscle function observed in Ovx and Old females due to the loss of estrogen.
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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 | 0.001 | 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) | 0.002 | 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".