Abstract 396: <i>CHROMR</i> Coordinates Interferon Signaling And Lipid Metabolism In Viral Infection
Bibliographic record
Abstract
COVID-19, caused by SARS-CoV-2 infection, is associated with atherosclerotic cardiovascular complications like acute coronary syndrome, myocardial infarction, and stroke, but the underlying mechanisms are poorly understood. Long non-coding RNAs (lncRNAs) have emerged as important regulators of gene expression in the immune response. RNA-seq of whole blood from hospitalized patients with COVID-19, influenza A virus and matched controls identified 190 lncRNAs deregulated in both viral infections. Among the top mutually upregulated lncRNAs, we noted CHROMR (alias CHROME ) , a primate-specific lncRNA previously identified as a competing endogenous RNA that regulates cholesterol efflux and fatty acid oxidation via microRNA sequestration. Here, we report a complementary role for CHROMR in coordinating the interferon (IFN) signaling response to respiratory viruses. CHROMR expression is induced in macrophages in response to SARS-CoV-2 and influenza A infection and accumulates in the nucleus where it binds the transcriptional co-repressor IRF2BP2, a negative regulator of IFN-stimulated gene (ISG) expression. CHROMR is essential for mounting an anti-viral response, as its depletion in macrophages reduces histone acetylation at ISGs, activation of IRF signaling, and ISG expression. These findings suggest that CHROMR sequesters the nuclear IRF-2/IRF2BP2 repressor complex releasing its inhibitory effect on transcription of ISGs. Consistent with this, CHROMR expression is required to restrict influenza virus replication in macrophages. Notably, many viruses rewire host lipid synthesis and metabolism to facilitate replication, and thus, increased CHROMR expression in virus infected cells would both mitigate cellular lipid accumulation and increase ISG transcription to mount an anti-viral immune response. Collectively, our findings underscore the merit of investigating lncRNAs to decipher novel regulatory mechanisms that govern lipid metabolism and inflammation 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".