PROS1 released by lung basal cells limits inflammation in epithelial and monocytes during SARS-CoV-2 infection
Bibliographic record
Abstract
Abstract Introduction Factors regulating the severity of pneumonitis during viral infections remain unresolved. We previously found higher expression of protein S (PROS1) in lung epithelium of mild compared to severe coronavirus disease 2019 (COVID-19) patients. We hypothesized that PROS1 may protect the upper airways by regulating epithelial and myeloid cell responses during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods To test this, in vitro air–liquid interface (ALI) cultures of primary healthy human lung epithelial cells were infected with SARS-CoV-2. This model, validated through immunofluorescent staining, confocal microscopy, and single-cell RNA-sequencing, replicated pathogenic changes seen in the lungs of COVID-19. Regulation and secretion of PROS1, along with multiple soluble mediators, were quantified in control and infected cultures using ELISAs. Results We found that PROS1 is present in the basal cells of healthy pseudostratified epithelium and is released during SARS-CoV-2 infection through an IFN-mediated process. Transcriptome analysis revealed that PROS1 downregulated the SARS-CoV-2-induced proinflammatory phenotypes of basal cells, transforming pathogenic CXCL10/11high into a regenerative S100A2posKRThigh basal cell phenotype. In parallel, SARS-CoV-2 increased the secretion of M-CSF from epithelial cells, which induced the expression of PROS1 receptor MERTK on monocytes interacting with the lung epithelium. PROS1, in turn, shifted SARS-CoV-2-induced pathogenic monocyte phenotypes toward a phenotype with increased MHC class II. Conclusion These findings highlight the crucial role of PROS1 in protecting against severe lung pathology caused by SARS-CoV-2, by reducing epithelial- and monocyte-derived inflammation, promoting pro-repair epithelial phenotypes, and enhancing antigen presentation in myeloid cells.
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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 | 0.000 |
| 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".