Regulation of <i>CCL2</i> expression in human vascular endothelial cells by a neighboring divergently transcribed long noncoding RNA
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease that is driven, in part, by activation of vascular endothelial cells (ECs). In response to inflammatory stimuli, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway orchestrates the expression of a network of EC genes that contribute to monocyte recruitment and diapedesis across the endothelium. Although many long noncoding RNAs (lncRNAs) are dysregulated in atherosclerosis, they remain poorly characterized, especially in the context of human vascular inflammation. Prior studies have illustrated that lncRNAs can regulate their neighboring protein-coding genes via interaction with protein complexes. We therefore identified and characterized neighboring interleukin-1β (IL-1β)−regulated messenger RNA (mRNA)−lncRNA pairs in ECs. We found these pairs to be highly correlated in expression, especially when located within the same chromatin territory. Additionally, these pairs were predominantly divergently transcribed and shared common gene regulatory elements, characterized by active histone marks and NF-κB binding. Further analysis was performed on lncRNA-CCL2 , which is transcribed divergently to the gene, CCL2 , encoding a proatherosclerotic chemokine. LncRNA-CCL2 and CCL2 showed coordinate up-regulation in response to inflammatory stimuli, and their expression was correlated in unstable symptomatic human atherosclerotic plaques. Knock-down experiments revealed that lncRNA-CCL2 positively regulated CCL2 mRNA levels in multiple primary ECs and EC cell lines. This regulation appeared to involve the interaction of lncRNA-CCL2 with RNA binding proteins, including HNRNPU and IGF2BP2. Hence, our approach has uncovered a network of neighboring mRNA−lncRNA pairs in the setting of inflammation and identified the function of an lncRNA, lncRNA-CCL2 , which may contribute to atherogenesis in humans.
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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".