SARS-CoV-2 and SARS-CoV-2 Spike protein S1 subunit Trigger Proinflammatory Response in Macrophages in the Absence of Productive Infection
Bibliographic record
Abstract
Abstract One of the hallmarks of critically ill COVID-19 patients infected with SARS-CoV-2 is exaggerated inflammatory response. Though macrophages mediate inflammatory responses and can produce pro-inflammatory cytokines to eliminate pathogens, infection with SARS-CoV-2 has been shown to cause immune dysfunction, leading to hyperinflammation in the lungs. To further understand the role of macrophages in hyperinflammatory responses during SARS-CoV-2 infection, we infected a THP-1 human derived macrophage cell line with SARS-CoV-2. Our results show that, though macrophages do not support viral replication, infection with SARS-CoV-2 still results in the upregulation of the mRNA of cytokines TNFα and CXCL10, which are markers of COVID-related hyperinflammation. In addition, we identified SARS-CoV-2 Spike protein S1 subunit as one viral factor involved in the upregulation of cytokines in macrophages. We show that glycosylated, soluble S1 protein can upregulate TNFα and CXCL10 mRNAs, as well as the secretion of TNFα, in macrophages in the absence of virus infection. Therefore, macrophage activation by the S1 subunit and SARS-CoV-2 infection may contribute to the hyperinflammation in the lungs seen in critically ill patients through the upregulation of proinflammatory cytokines such as TNFα and CXCL10. Supported by NIH grant (R01 AI083387), Washington Research Foundation, and NIH NIGMS Predoctoral Biotechnology Training Grant 5T32GM008336
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".