Azithromycin Downregulates Gene Expression of IL-1β and Pathways Involving TMPRSS2 and TMPRSS11D Required by SARS-CoV-2
Bibliographic record
Abstract
Background TMPRSS2, ACE2 and TMPRSS11D are genes coding for proteins necessary for SARS-CoV-2 activation, infection and transmission. Once SARS-CoV-2 enters the host cell, it leads to an exaggerated inflammatory state of the lungs mediated by overexpressed TNF-, IL-6, and IL-1β. We assessed azithromycin's effect on the aforementioned genes and their associated pathways to evaluate its potential use as a possible treatment. Objective Confirm the role azithromycin may play in the regulation of pathways and genes involved in inflammation and SARS-CoV-2 activation and cell-to-cell transmission. Methods Primary airway nasal epithelial cells collected from nasal biopsies of three patients with chronic rhinosinusitis (CRS) were primary cultured and treated or not with 10µg of azithromycin. RNA was extracted from these samples and analyzed using a microarray chip. Differential gene expression profiles and gene set enrichment analysis (GSEA) were obtained between both groups. Results Cell cultures treated with 10µg of azithromycin significantly downregulated receptor-mediated endocytosis canonical pathways involving TMPRSS2 and TMPRSS11D genes. Downregulated inflammation-associated genes included IL-1β and NDST1. Interestingly, numerous genes in the cholesterol biosynthesis pathway were significantly upregulated as part of a potential process named drug-induced phospholipidosis (DLP). Conclusions This proof of concept demonstrates azithromycin downregulates pathways involving serine proteases TMPRSS2 and TMPRSS11D required for SARS-CoV-2 activation and its cell-to-cell transmission while downregulating pro-inflammatory cytokine IL-1β, NDST-1 and their associated pathways. This may help reduce the characteristic excessive respiratory epithelial inflammation, key feature of SARS-CoV-2 infection. Finally, azithromycin may also decrease available cholesterol in lipid rafts which may hinder SARS-CoV-2 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) | 0.001 | 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 | 0.001 |
| 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".