Characterization of monoclonal antibodies against foot-and-mouth disease virus serotype O and application in identification of antigenic variation in relation to vaccine strain selection
Bibliographic record
Abstract
BACKGROUND: Foot-and-mouth disease (FMD) has severe implications for animal farming which leads to considerable financial losses because of its rapid spread, high morbidity and loss of productivity. For these reasons, the use of vaccine is often favoured to prevent and control FMD. Selection of the proper vaccine is extremely difficult because of the antigenic variation within FMDV serotypes. The aim of the current study was to produce a panel of mAbs and use it for the characterization of new isolates of FMDV serotype O. RESULTS: A panel of FMDV/O specific mAb was produced. The generated mAbs were then characterized using the peptide array and mAb resistant mutant selection. Seven out of the nine mAbs reacted with five known antigenic sites, thus the other two mAbs against non-neutralizing sites were identified. The mAbs were then evaluated by antigenic ELISA for the detection of forty-six FMDV serotype O isolates representing seven of ten known topotypes. Isolates ECU/4/10 and HKN/2/11 demonstrated the highest antigenic variation compared to the others. Furthermore, the panel of mAbs was used in vaccine matching by antigenic profiling ELISA with O1/Manisa as the reference strain. However, there was no correlation between vaccine matching by antigenic ELISA and the gold standard method, virus neutralisation test (VNT), for the forty-six FMDV/O isolates. Nine isolates had particularly poor correlation with the reference vaccine strain as revealed by the low r1 values in VNT. The amino acid sequences of the outer capsid proteins for these nine isolates were analyzed and compared with the vaccine strain O1/Manisa. The isolate ECU/4/10 displayed three unique amino acid substitutions around the antigenic sites 1, 3 and 4. CONCLUSIONS: The panel of mAbs is useful to monitor the emergence of antigenically different strains and determination of relevant antigenic site differences. However, for vaccine matching VNT remains the preferred method but a combination of VNT, antigenic profiling with a panel of mAbs and genetic sequencing would probably be more ideal for full characterization of any new outbreak isolates as well as for selection of vaccine strains from FMDV antigen banks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".