Abstract 661: Regulation of <i>CCL2</i> Expression in Vascular Endothelial Cells by a Long Noncoding RNA
Bibliographic record
Abstract
Vascular inflammation is a critical driver of chronic diseases such as atherosclerosis. A network of NF-κB-dependent leukocyte adhesion molecules and chemokines are induced in endothelial cells (ECs) in response to inflammatory mediators. This includes chemokine (C-C motif) ligand 2 ( CCL2 ), which contributes to atherosclerosis by recruiting monocytes to the endothelium. Recently, long noncoding RNAs (lncRNAs) have been implicated in regulating gene expression through epigenetic mechanisms, but lncRNAs remain poorly studied in the context of vascular inflammation and NF-kB pathway regulation. LncRNAs are frequently retained in the nucleus where they interact with chromatin remodelling complexes to modulate the expression of neighboring protein-coding genes. Hence, identifying NF-kB-regulated neighboring mRNA-lncRNA pairs in vascular endothelial cells may uncover functional lncRNAs that play a role in fine-tuning the expression of their neighboring inflammatory genes. The Arraystar human lncRNA microarray V3 was employed to identify differentially expressed lncRNAs and mRNAs in ECs stimulated with the pro-inflammatory cytokine, IL-1β. Neighboring IL-1β-regulated mRNA-lncRNA pairs demonstrated a larger magnitude of mRNA induction than mRNAs lacking a neighboring lncRNA. This phenomenon was associated with shared regulatory elements and localization within the same topologically associated domain. Follow-up analysis was performed on the nuclear-enriched, lncRNA-CCL2 , which is transcribed through a super-enhancer near CCL2 . Both lncRNA-CCL2 and CCL2 responded to the same inflammatory stimuli. Similar to CCL2 , lncRNA-CCL2 transcript was elevated in unstable human atherosclerotic plaques. Knockdown of lncRNA-CCL2 decreased CCL2 mRNA levels in multiple EC cell lines, but had no effect on other inflammatory genes or distal CCL genes. Hence, our approach has uncovered neighboring IL-1β-regulated mRNA-lncRNA pairs and identified a novel functional lncRNA, lncRNA-CCL2 .
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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) | 0.004 | 0.002 |
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".