A Significant Contribution of the Classical Pathway of Complement in SARS-CoV-2 Neutralization of Convalescent and Vaccinee Sera
Bibliographic record
Abstract
Although high titers of neutralizing Abs in human serum are associated with protection from reinfection by SARS-CoV-2, there is considerable heterogeneity in human serum-neutralizing Abs against SARS-CoV-2 during convalescence between individuals. Standard human serum live virus neutralization assays require inactivation of serum/plasma prior to testing. In this study, we report that the SARS-CoV-2 neutralization titers of human convalescent sera were relatively consistent across all disease states except for severe COVID-19, which yielded significantly higher neutralization titers. Furthermore, we show that heat inactivation of human serum significantly lowered neutralization activity in a live virus SARS-CoV-2 neutralization assay. Heat inactivation of human convalescent serum was shown to inactivate complement proteins, and the contribution of complement in SARS-CoV-2 neutralization was often >50% of the neutralizing activity of human sera without heat inactivation and could account for neutralizing activity when standard titers were zero after heat inactivation. This effect was also observed in COVID-19 vaccinees and could be abolished in individuals who were undergoing treatment with therapeutic anti-complement Abs. Complement activity was mainly dependent on the classical pathway with little contributions from mannose-binding lectin and alternative pathways. Our study demonstrates the importance of the complement pathway in significantly increasing viral neutralization activity against SARS-CoV-2 in spike seropositive individuals.
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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 | 0.001 |
| 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".