Organic Dust Exposure Enhances SARS-CoV-2 Entry in a PKCα- and ADAM-17-Dependent Manner
Bibliographic record
Abstract
SARS-CoV-2, the causative agent of the COVID-19 pandemic, has had a global impact, affecting millions over the last three years. Pre-existing lung diseases adversely affect the prognosis of infected COVID-19 patients, and agricultural workers routinely exposed to inhalable organic dusts have substantial increased risk for developing chronic lung diseases. In previous studies, we characterized the protein kinase C (PKC)-dependent airway inflammation mediated by organic dust extract (ODE) derived from dust collected from swine confinement facilities in in vitro and in vivo models. Here, we studied the effect of ODE on SARS-CoV-2 pseudoviral infection in mice and human bronchial epithelial cells (BEAS-2B). In wild-type (WT) and transgenic mice expressing the human angiotensin I-converting enzyme 2 (ACE2) receptor (SARS-CoV-2 entry receptor), ODE increased ACE2 shedding by ADAM-17 in the lungs. After repeated ODE treatments, the increased soluble ACE2 correlated to higher pseudovirus titer in the mouse lungs. In the human bronchial epithelial cells, ODE augmented PKCα activity in WT cells, and membrane ACE2 expression was diminished in PKCα-dominant negative cells. Unlike in the mice, increasing membrane ACE2 levels by treating with PKCα or ADAM-17 inhibitors and a low dose of ODE enhanced pseudoviral entry in vitro. Following viral entry, IL-8 secretion by the cells was diminished in a PKCα- and ADAM- 17-independent manner. Together, the complex mechanisms involved in the synergistic effects of agricultural dust and SARS-CoV-2 highlight the importance of studying dust-mediated changes to immunity against circulating pathogens.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".