The unique ORF8 protein from SARS-CoV-2 binds to human dendritic cells and induces a hyper-inflammatory cytokine storm
Bibliographic record
Abstract
Abstract The novel coronavirus pandemic, whose first outbreak was reported in December 2019 in Wuhan, China (COVID-19), is caused by the s evere a cute respiratory s yndrome c oronavirus 2 (SARS-CoV-2). Tissue damage caused by the virus 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 many viral infections the recruitment of monocytes into the lung and their differentiation to dendritic cells (DCs) are seen as a response to the viral infection. DCs are critical players in the development of the acute lung inflammation that causes ARDS. Here we focus on the interaction of the ORF8 protein, a specific SARS-CoV-2 open reading frame protein, with dendritic cells (DCs). We show that ORF8 binds to dendritic cells, causes a pre-maturation of differentiating DCs, and induces the secretion of multiple pro-inflammatory cytokines by these cells. In addition, we identified dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) as a possible interaction partner of ORF8 on dendritic cells. Blockade of ORF8 signaling leads to reduced production of IL-1β, IL-6, IL-12p70, TNF-α, MCP-1 (CCL2), and IL-10 by dendritic cells. Analysis of patient sera with high anti-ORF8 antibody titers showed that there was nearly no neutralization of the ORF8 protein and its function. Therefore, a neutralizing antibody that has the capacity of blocking the cytokine and chemokine response mediated by ORF8 protein might be an essential and novel additional step in the therapy of severe SARS-CoV-2 cases.
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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".