Transcriptional and Epigenetic Modulation of Human Rhinovirus–Induced CXCL10 Production by Cigarette Smoke
Bibliographic record
Abstract
Human rhinovirus (HRV) triggers exacerbations of asthma and chronic obstructive pulmonary disease. Cigarette smoking is the primary risk factor for the development of chronic obstructive pulmonary disease, and 25% of individuals with asthma smoke. Smokers experience both longer and more severe colds. We previously showed that cigarette smoke extract (CSE) inhibited HRV-induced expression of a range of epithelial antiviral molecules. Here, we use CXCL10 as a model antiviral gene to examine the mechanisms by which CSE inhibits epithelial antiviral immunity. HRV-induced CXCL10 transcription depends on activation of NF-ĸB and IFN-regulatory factor-1 (IRF-1), and we now also implicate two signal transducer and activator of transcription (STAT) consensus sequences in the CXCL10 promoter in HRV-induced CXCL10 expression. CSE inhibited HRV-induced activation and nuclear translocation/binding of both NF-ĸB, and IRF-1 to their respective recognition sequences in the CXCL10 promoter. HRV also induced formation of complexes at the STAT region in the CXCL10 promoter, and HRV-induced activation of STAT-1 was inhibited by CSE. In addition, CSE inhibited HRV-induced chromatin accessibility around the transcriptional start site of the CXCL10 promoter. Although CSE inhibited HRV-induced expression of both the viral double-stranded RNA sensors, retinoic acid-inducible gene-I and melanoma differentiation-associated gene (MDA) 5, only specific short interfering RNA (siRNA) to MDA5, but not nontargeting siRNA, or siRNA to retinoic acid-inducible gene-I, inhibited HRV-induced CXCL10 induction. We conclude that CSE reduces chromatin accessibility and inhibits viral signaling via NF-ĸB, IRF-1, STAT-1, and MDA5. Thus, we show that CSE can simultaneously modulate multiple pathways linked to innate immune responses to HRV infection.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".