CCL2 Promotes Novel Coronavirus-Mediated Inflammatory Responses on Macrophages
Bibliographic record
Abstract
PURPOSE: The hyperinflammatory response is one of the main complications associated with novel coronavirus disease 2019 (COVID-19), and there is no effective treatment for cytokine storm. Therefore, it is important to investigate the key genes associated with severity of the disease. METHODS: In this study, we used a microarray data set to analyze the key genes associated with severe illness in patients with COVID-19. The proportion of immune cells was determined using the CIBERSORT algorithm. The key genes were further verified by detecting the levels of cytokines and chemokines in the serum of patients. Additionally, macrophages were stimulated with SARS-CoV-2 spike protein and chemokine ligand (CCL) 2. The expression of cytokines, ERK1/2, and NF-κB in macrophages was detected. RESULTS: Four hub genes were identified. Among them, C-C motif chemokine receptor 2 (CCR2) was an upregulated hub gene, while killer cell lectin-like receptor subfamily K member 1 (KLRK1), macrophage colony-stimulating factor receptor (CSF1R), and CD3D human recombinant protein (CD3D) were downregulated genes. Immune cell type identification found that the proportion of monocytes was higher in patients with severe COVID-19 than that in controls. Moreover, levels of CCL2 were significantly higher in patients with COVID-19. When stimulated with SARS-CoV-2 S protein and CCL2, macrophages secreted more inflammatory cytokines. The expression level of ERK1/2 was elevated. CONCLUSIONS: These results suggested that S protein and CCL2 may mediate macrophage inflammatory responses through the ERK1/2 signaling pathway. This study provides a basis for clinical treatment and improves the prognosis of critically ill patients with COVID-19.
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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 | 0.000 |
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".