Antibodies against SARS-CoV-2 control complement-induced inflammatory responses to SARS-CoV-2
Bibliographic record
Abstract
Abstract Dysregulated immune responses contribute to pathogenesis of COVID-19 leading to uncontrolled and exaggerated inflammation observed during severe COVID-19. However, it remains unclear how immunity to SARS-CoV-2 is induced and subsequently controlled. Notably, here we have uncovered an important role for complement in the induction of innate and adaptive immunity to SARS-CoV-2. Complement rapidly opsonized SARS-CoV-2 via the lectin pathway. Complement-opsonized SARS-CoV-2 efficiently interacted with dendritic cells (DCs), inducing type I IFN and pro-inflammatory cytokine responses, which were inhibited by antibodies against the complement receptors (CR)3 and CR4. These data suggest that complement is important in inducing immunity via DCs in the acute phase against SARS-CoV-2. Strikingly, serum from COVID-19 patients as well as monoclonal antibodies against SARS-CoV-2 attenuated innate and adaptive immunity induced by complement-opsonized SARS-CoV-2. Blocking the FcyRII, CD32, restored complement-induced immunity. These data strongly suggest that complement opsonization of SARS-CoV-2 is important for inducing innate and adaptive immunity to SARS-CoV-2. Subsequent induction of antibody responses is important to limit the immune responses and restore immune homeostasis. These data suggest that dysregulation in complement and FcyRII signalling might underlie mechanisms causing severe 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".