miR‐17 targets tissue inhibitor of metalloproteinase 1 and 2 to modulate cardiac matrix remodeling
Bibliographic record
Abstract
We aimed to investigate the role of miR‐17 in cardiac matrix remodeling following myocardial infarction (MI). Using real‐time PCR, we quantified endogenous miR‐17 in infarcted mouse hearts. Compared with related microRNAs, miR‐17 was up‐regulated most dramatically: 3.7‐fold and 2.4‐fold in the infarct region 3 and 7 d post‐MI, respectively, and 2.4‐fold in the border zone at d 3 compared to sham control ( P <0.01). Chimeric luciferase reporter constructs were cloned for miR‐17 target validation. miR‐17 targeted the 3'‐UTR of TIMP2 and the protein coding region of TIMP1. The miR‐17 mimic decreased TIMP2 ( P <0.01) and TIMP1 ( P <0.05) protein expression compared with the scrambled control. Inhibition of endogenous miR‐17 by in vivo antagomir delivery enhanced TIMP2 ( P <0.01) and TIMP1 ( P <0.05) protein expression compared to the mismatch group, decreased MMP9 activity ( P <0.05), reduced infarct size as early as 7 d post‐MI ( P <0.05), and improved cardiac function (fractional shortening and fractional area contraction, P <0.05) at d 21 and 28 post‐MI. Transgenic mice overexpressing miR‐17 in the heart confirmed the deleterious role of miR‐17 in matrix modulation. Our study suggests that miR‐17 participates in the regulation of cardiac matrix remodeling and provides a novel therapeutic approach using miR‐17 inhibitors to prevent remodeling and heart failure after MI.—Li, S.‐H., Guo, J., Wu, J., Sun, Z., Han, M., Shan, S. W., Deng, Z., Yang, B. B., Weisel, R D., Li, R‐K. miR‐17 targets tissue inhibitor of metalloproteinase 1 and 2 to modulate cardiac matrix remodeling. FASEB J. 27, 4254–4265 (2013). www.fasebj.org
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".