AHR signaling is induced by infection with coronaviruses
Bibliographic record
Abstract
Abstract Coronavirus infection in humans is usually associated to respiratory tract illnesses, ranging in severity from mild to life-threatening respiratory failure. The aryl hydrocarbon receptor (AHR) was recently identified as a host factor for Zika and dengue viruses; AHR antagonists boost antiviral immunity, decrease viral titers and ameliorate Zika-induced pathology in vivo. Here we report that AHR is activated by infection with different coronaviruses, potentially impacting antiviral immunity and lung epithelial cells. Indeed, the analysis of single-cell RNA-seq from lung tissue detected increased expression of AHR and AHR transcriptional targets, suggesting AHR signaling activation in SARS-CoV-2-infected epithelial cells from COVID-19 patients. Moreover, we detected an association between AHR expression and viral load in SARS-CoV-2 infected patients. Finally, we found that the pharmacological inhibition of AHR suppressed the replication in vitro of one of the causative agents of the common cold, HCoV-229E, and the causative agent of the COVID-19 pandemic, SARS-CoV-2. Taken together, these findings suggest that AHR activation is a common strategy used by coronaviruses to evade antiviral immunity and promote viral replication, which may also contribute to lung pathology. Future studies should further evaluate the potential of AHR as a target for host-directed antiviral therapy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".