Antibody ‘Hotspots’ induce antibody-dependent cell-mediated cytotoxicity against SARS-CoV-2 spike-expressing lung fibroblasts
Bibliographic record
Abstract
Abstract Since COVID-19 emerged in 2019, neutralizing antibody (Ab) responses have dominated as surrogate measures of protection against infection and severe illness. The mostly self-limiting course of SARS-CoV-2 infection suggests the innate immune system, including natural killer (NK) cells, plays a role in its control. Therefore, we investigated SARS-CoV-2-specific antibody-dependent cell-mediated cytotoxicity (ADCC) and if Abs against Wuhan and early SARS-CoV-2 variants activate ADCC against emergent highly transmissible variants. ADCC was evaluated by measuring NK cell cytotoxicity (51Cr) against plasma (1:1000)-sensitized MRC-5 cells stably transduced to express Wuhan or Delta spike. ADCC mediated by plasma from double-vaccinated and convalescent adults was generally low (range 1.2 – 24.9%; median 7.2% (IQR 5.2 – 10.6%), and plasma from those with previous natural infection mediated significant ADCC (> 10% lysis) to the same extent as plasma from fully vaccinated individuals (13/34 vs 11/44). Hybrid immunity imparted robust ADCC (27.9 ± 10.8% target lysis) in 32/34 individuals tested and levels of ADCC significantly correlated with IgG3 responses (P = 0.0093) against spike stalk (S2) domain. A peptide scan identified four distinct ‘hotspot’ regions associated with robust ADCC in individuals with reactive Abs. Overall, a minor decline in ADCC against Delta variant spike-expressing relative to Wuhan targets occurred in all groups; however, individuals with Abs against the region containing D614G mediated greater ADCC against Delta variant than Wuhan targets. Determining which Abs provide robust and broad ADCC can inform future COVID-19 prevention and treatment strategies and predict ADCC responses for emergent variants. Supported by grants from CIHR through the COVID Immunity Task Force (VR1-173202)
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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) | 0.002 | 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".