<scp>SARS</scp> ‐ <scp>CoV</scp> ‐2 Nucleocapsid Protein Does Not Induce Inflammation in Endothelial Cells or Monocytes
Bibliographic record
Abstract
The nucleocapsid (N) protein of SARS-CoV-2 is critical for viral replication and genome packaging. However, whether it induces inflammation through endothelial cell and monocyte activation remains controversial. One aspect that has been overlooked is the consideration of residual endotoxins in recombinant proteins when conducting immune response studies. We aimed to assess whether N-protein induces an inflammatory response in mouse and human microvascular endothelial cells (MEC and HMEC) and THP-1 cells, independent of endotoxin contamination. MEC, HMEC, and THP-1 cells were treated with vehicle, lipopolysaccharide (LPS), or recombinant N-protein in the presence or absence of the LPS-neutralizing agent polymyxin B. The inflammatory response was assessed through quantification of mRNA and protein levels of inflammatory markers and monocyte adhesion to endothelial cells. In MEC, treatment with N-protein resulted in at least a 15-fold increase in inflammatory marker levels. Similarly, in THP-1 cells, N-protein caused a significant increase in mRNA inflammatory marker levels, which were brought back to control levels with polymyxin B. LPS or N-protein alone resulted in a tripling of monocyte adhesion to MEC, and this was reduced to control levels with polymyxin B. In addition, in HMEC, endotoxin-depleted N-protein did not cause a significant increase in inflammatory marker levels. In conclusion, our data show that N-protein does not induce inflammation in endothelial cells or monocytes. Some of its observed inflammatory effects may be due to endotoxin contamination, suggesting that it does not directly induce inflammation.
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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) | 0.001 | 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 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".