Hemagglutinin stalk-based monoclonal antibody elicits broadly reactivity against group 1 influenza A virus
Bibliographic record
Abstract
BACKGROUND: Influenza virus remains a continuous and severe threat to public health worldwide, and its prevention and treatment have always been a major international issue. Because of its ability to evade immune surveillance through rapid antigenic drift and antigenic shift, broad-spectrum vaccines seem increasingly important. METHODS: A mAb named 3C12 from an immortalized hybrid cell was generated via immunizing mice with HA2 protein from A/chicken/Anhui/BRI99/2016 (AH/BRI99/16, H9N2) generated by prokaryotic expression. Then, its broad-spectrum activity was analyzed by WB and IFA. Next, the minimal linear epitope was identified via analyzing the reaction of a series of HA truncations with 3C12. Finally, the protective effects of 3C12 were evaluated in vitro and in vivo infection experiments. RESULTS: in full length of HA and localized in the C-helix region of HA2 (residue 95-101, HA2 numbering). What's more, the mAb 3C12 inhibited H1, H2, H5, H8, H9, H12, H13 and H16 virus-replication in vitro and also has shown effectiveness in preventing and treating disease in mice challenged with lethal dose of AH/BRI99/16 (H9N2) virus in vivo. These results suggested that the broadly reactive anti-HA stem mAb 3C12 exhibited prophylactic and therapeutic efficacy. CONCLUSIONS: Here, we have demonstrated that the linear epitope identified in this study could be a novel target for developing broad-spectrum influenza diagnostics or vaccine design, and the HA2-based monoclonal antibody is indeed a promising strategy for broad-spectrum protection against seasonal and pandemic influenza viruses.
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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".