Unmasking SARS-CoV-2 ORF3a; a hidden trigger of immune overdrive in COVID-19
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), became a global health crisis in 2020, leading to widespread severe cases. The severe cases are often linked to an overactive immune response that is characterized by excessive release of pro-inflammatory cytokines. SARS-CoV-2 can abortively infect lung-resident macrophages, leading to inflammasome activation and the release of pro-inflammatory cytokines, which often escalates into a cytokine storm, driving COVID-19 pathology. However, the mechanism of inflammasome activation in macrophages during SARS-CoV-2 infection remains unclear. Here, we demonstrate that SARS-CoV-2 open reading frame 3a (ORF3a) significantly activates inflammasome in THP-1-derived macrophages (TDM), resulting in the secretion of IL1β and IL-18 cytokines, whose elevated levels correlate with COVID-19 pathogenicity. This provides insight into inflammasome activation and heightened innate immune reaction seen in severe cases of COVID-19. While the ORF3a-transfected A549 lung epithelial cells did not release significant amounts of IL-1β, their supernatant was sufficient to induce robust inflammasome activation in TDM. This ability of epithelial cells to promote inflammasome activation in immune cells, even when they are not the primary source of inflammatory cytokines, sheds light on the process of macrophage activation syndrome (MAS), cytokine storm, and systemic inflammation in severe COVID-19. Taken together, we show that ORF3a plays an important role in the pathogenesis of SARS-CoV-2, and targeting ORF3a could be an effective therapeutic strategy to target both the virus and the disease, given ORF3a’s role in virus production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".