The unique ORF8 protein from SARS-CoV-2 binds to human dendritic cells and induces a hyper-inflammatory cytokine storm
Bibliographic record
Abstract
The novel coronavirus pandemic, first reported in December 2019, was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 infection leads to a strong immune response and activation of antigen-presenting cells, which can elicit acute respiratory distress syndrome (ARDS) characterized by the rapid onset of widespread inflammation, the so-called cytokine storm. In response to viral infections, monocytes are recruited into the lung and subsequently differentiate into dendritic cells (DCs). DCs are critical players in the development of acute lung inflammation that causes ARDS. Here, we focus on the interaction of a specific SARS-CoV-2 open reading frame protein, ORF8, with DCs. We show that ORF8 binds to DCs, causes pre-maturation of differentiating DCs, and induces the secretion of multiple proinflammatory cytokines by these cells. In addition, we identified DC-SIGN as a possible interaction partner of ORF8 on DCs. Blockade of ORF8 leads to reduced production of IL-1β, IL-6, IL-12p70, TNF-α, MCP-1 (also named CCL2), and IL-10 by DCs. Therefore, a neutralizing antibody blocking the ORF8-mediated cytokine and chemokine response could be an improved therapeutic strategy against SARS-CoV-2.
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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) | 0.001 | 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".