Long Noncoding RNA MIAT Controls Advanced Atherosclerotic Lesion Formation and Plaque Destabilization
Bibliographic record
Abstract
BACKGROUND Long noncoding RNAs (lncRNAs) are important regulators of biological processes involved in vascular tissue homeostasis and disease development. The present study assessed the functional contribution of the lncRNA myocardial infarction-associated transcript (MIAT) to atherosclerosis and carotid artery disease. METHODS We profiled differences in RNA transcript expression in patients with advanced carotid artery atherosclerotic lesions from the Biobank of Karolinska Endarterectomies. The lncRNA MIAT was identified as the most upregulated noncoding RNA transcript in carotid plaques compared with nonatherosclerotic control arteries, which was confirmed by quantitative real-time polymerase chain reaction and in situ hybridization. RESULTS Experimental knockdown of MIAT, using site-specific antisense oligonucleotides (LNA-GapmeRs) not only markedly decreased proliferation and migration rates of cultured human carotid artery smooth muscle cells (SMCs) but also increased their apoptosis. MIAT mechanistically regulated SMC proliferation through the EGR1 (Early Growth Response 1)-ELK1 (ETS Transcription Factor ELK1)-ERK (Extracellular Signal-Regulated Kinase) pathway. MIAT is further involved in SMC phenotypic transition to proinflammatory macrophage-like cells through binding to the promoter region of KLF4 and enhancing its transcription. Studies using Miat$^{-/-}$ and Miat$^{-/-}$ApoE$^{-/-}$ mice, and Yucatan LDLR$^{-/-}$ mini-pigs, as well, confirmed the regulatory role of this lncRNA in SMC de- and transdifferentiation and advanced atherosclerotic lesion formation. CONCLUSIONS The lncRNA MIAT is a novel regulator of cellular processes in advanced atherosclerosis that controls proliferation, apoptosis, and phenotypic transition of SMCs, and the proinflammatory properties of macrophages, as well.
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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".