The Fc-effector function of COVID-19 convalescent plasma contributes to SARS-CoV-2 treatment efficacy in mice
Bibliographic record
Abstract
COVID-19 convalescent plasmas (CCPs) are chosen for plasma therapy based on neutralizing titers and anti-Spike immunoglobulin levels. However, specific CCP characteristics that promote SARS-CoV-2 control in recipients are complex and incompletely defined. Using an in vivo imaging approach, we demonstrate that CCPs with low neutralizing and high Fc-effector activity, in contrast to those with poor Fc-function, afford effective prophylaxis and therapy in K18-hACE2 mice lethally challenged with SARS-CoV-2-nLuc. Macrophages and neutrophils significantly contributed to CCP effects during therapy but to a reduced extent under prophylaxis. Both IgG and Ig(M+A) were required during therapy, but the IgG fraction alone was sufficient during prophylaxis. Finally, despite neutralizing poorly, SARS-CoV-2 Wuhan-elicited CCPs delayed Delta and Beta variants of concern (VOC)-induced mortality in mice illustrating the contribution of polyclonal Fc-effector functions in immunity against VOCs . Thus, in addition to neutralization, Fc-effector activity is a significant criterion for CCP selection for therapeutic applications. Funding Information: This work was supported by This work was supported by NIH grant R01AI163395 to WM,le Ministère de l’Économie et de l’Innovation du Québec, Programme de soutien aux organismes de recherche et d’innovation, Foundation du CHUM, CIHR grant nos. 352417 and 177958, a CFI grant, 41027 and a Canada Research Chair on Retroviral Entry no. RCHS0235 950-232424 to AF; Canada’s COVID-19 Immunity Task Force (CITF) & Canada Foundation for Innovation (CFI) #41027 to AF, CIHR fellowships to GBB, le Ministère de l’Économie et de l’Innovation du Québec, Fondation du CHU Sainte-Justine Fonds de recherché du Québec – Santé #281662 to PB. Declaration of Interests: The authors declare no competing interests. Ethical Approval Statement: All participants consented to the study (CER #2020-004). PBMCs from healthy individuals as a source of effector cells in our ADCC assay were obtained under CRCHUM institutional review board (protocol #19.381). Research adhered to the standards indicated by the Declaration of Helsinki. All participants were adults and provided informed written consent prior to enrollment in accordance with Institutional Review Board approval. All animal experiments were approved by the Institutional Animal Care and Use Committees (IACUC) of and Institutional Biosafety Committee of Yale University (IBSCYU).
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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) | 0.001 | 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".