Abstract 456: LncRNA <i>CHROME</i> is Increased in Cardiovascular Disease and Regulates Inflammatory Gene Expression
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are a class of regulatory RNAs capable of binding DNA, RNA, and/or protein to regulate transcriptional and epigenetic networks. Although thousands of lncRNAs have been identified, relatively few have been functionally characterized. Here we identify a primate-specific lncRNA, CHROME , encoded in a locus associated with cardiovascular disease. We found that CHROME expression is increased in the plasma of patients with inflammatory conditions, including coronary artery disease and lupus, compared to control subjects. Using FANTOM, a database of transcriptome analyses, we found the CHROME locus is transcriptionally activated in human monocytes and macrophages stimulated with microbial ligands and inflammatory cytokines. To investigate CHROME’s molecular mechanisms, we used RNA immunoprecipitation (RIP) and chromatin isolation by RNA purification (ChIRP) to map CHROME lncRNA -protein and -DNA interactions, respectively. We found that CHROME has a strong histone binding affinity and its DNA binding pattern was consistent with interaction of CHROME with numerous inflammatory transcription factor motifs, including sites for CEBPβ, SPI1, NFKB, and RELA. To investigate the impact of CHROME’s interaction with DNA in macrophages, we used gain and loss of function studies combined with RNA-sequencing. Ingenuity Pathway Analysis of genes most significantly altered with both CHROME knockdown and overexpression identified the inflammatory response as the pathway most significantly altered by CHROME . In particular, repression of CHROME led to a decrease in the expression of interferon stimulated genes and receptors involved in macrophage motility. Together, these data suggest that CHROME contributes to the transcriptional regulation of inflammatory gene expression and its dysregulation in the setting of atherosclerotic cardiovascular disease may contribute to the maintenance of chronic inflammation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".