Expressions of matrix metalloproteinases and their inhibitor are modified by beta-adrenergic agonist Ractopamine in skeletal fibroblasts and myoblasts
Bibliographic record
Abstract
Cha, M. C. and Purslow, P. P. 2012. Expressions of matrix metalloproteinases and their inhibitor are modified by beta-adrenergic agonist Ractopamine in skeletal fibroblasts and myoblasts. Can. J. Anim. Sci. 92: 159-166. The beta-adrenergic agonist ractopamine is known to promote growth and improve feed efficiency in animal production, in part by suppressing muscle protein degradation. This investigation aims to determine whether ractopamine modifies the expression of enzymes principally involved in intramuscular connective tissue turnover, the matrix metalloproteinases (MMPs) and their inhibitors, in the principal cell types of skeletal muscle. Mouse skeletal muscle fibroblasts (NOR-10 cells) and myoblasts (C2C12 cells) were cultured with or without 2 or 10 µM ractopamine for 6 or 24 h. Cellular MMP-2 expression was increased (P<0.05) by ractopamine in both cell lines. Cellular MMP-3 expression was also increased in response to ractopamine in myoblasts (P<0.03). The amount of a tissue inhibitor of MMPs (TIMP-1) in cell lysates of both cell lines was increased (P<0.05) by the 6-h ractopamine treatment. The extracellular expression of MMP-2 and TIMP-1 was increased (P<0.05) in myoblasts, but not in fibroblasts. The elevated TIMP-1 expression in medium is in the order of three times higher (P<0.02) than the increased activity of MMP-2 expressed by myoblasts at 6 h. In summary, ractopamine treatment results in a higher cellular expression of MMP-2 and MMP-3 as compared with the expression of their inhibitor TIMP-1. However, the increased extracellular MMP-2 activity is counterbalanced by the increased presence of TIMP-1. The findings show that ractopamine has the potential to alter connective tissue turnover in treated animals.
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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 | 0.001 |
| 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".