ICAM-1-related noncoding RNA accelerates atherosclerosis by amplifying NF-κB signaling
Bibliographic record
Abstract
Long noncoding RNAs (lncRNAs) are critical regulators of inflammation with great potential as new therapeutic targets. However, the role of lncRNAs in early atherosclerosis remains poorly characterized. This study aimed to identify the key lncRNA players in activated endothelial cells (ECs). The lncRNAs in response to pro-inflammatory factors in ECs were screened through RNA sequencing. ICAM-1-related non-coding RNA (ICR) was identified as the most potential candidate for early atherosclerosis. ICR is essential for intercellular adhesion molecule-1 (ICAM1) expression, EC adhesion and migration. In a high fat diet-induced atherosclerosis model in mice, ICR is upregulated in the development of atherosclerosis. After intravenous injection of adenovirus carrying shRNA for mouse ICR, the atherosclerotic plaque area was markedly reduced with the declined expression of ICR and ICAM1. Mechanistically, ICR stabilized the mRNA of ICAM1 in quiescent ECs; while under inflammatory stress, ICR upregulated ICAM1 in a nuclear factor kappa B (NF-κB) dependent manner. RNA-seq analysis showed pro-inflammatory targets of NF-κB were regulated by ICR. Furthermore, the chromatin immunoprecipitation assays showed that p65 binds to ICR promoter and facilitates its transcription. Interestingly, ICR, in turn, promotes p65 accumulation and activity, forming a positive feedback loop to amplify NF-κB signaling. Preventing the degradation of p65 using proteasome inhibitors rescued the expression of NF-κB targets suppressed by ICR. Taken together, ICR acts as an accelerator to amplify NF-κB signaling in activated ECs and suppressing ICR is a promising early intervention for atherosclerosis through ICR/p65 loop blockade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".