Application of phage display technology for the production of antibodies against Streptococcus suis serotype 2
Bibliographic record
Abstract
Streptococcus suis (S. suis) serotype 2 infection is a problem in the swine industry and responsible for most cases of human infection worldwide. Since current multiplex PCR cannot differentiate between serotypes 2 and 1/2, then serotype-specific antibodies (Abs) are required for serotype identification to confirm infection by serotype 2. This study aimed to generate Abs specific to S. suis serotype 2 by phage display from a human heavy chain variable domain (VH) antibody library. For biopanning, whole cells of S. suis serotype 2 were used as the target antigen. With increasing selection stringency, we could select the VH Abs that specifically bound to a S. suis serotype 2 surface antigen, which was identified as the capsular polysaccharide (CPS). From ELISA analysis, the specific phage clone 47B3 VH with the highest binding activity to S. suis serotype 2 was selected and shown to have no cross-reactivity with S. suis serotypes 1/2, 1, and 14 that shared a common epitope with serotype 2 and occasionally cause infections in human. Moreover, no cross-reactivity with other bacteria that can be found in septic blood specimens was also observed. Then, 47B3 VH was successfully expressed as soluble 47B3 VH in E. coli TG1. The soluble 47B3 VH crude extract was further tested for its binding ability in a dose-dependent ELISA assay. The results indicated that the activity of phage clone 47B3 was still retained even when the Ab occurred in the soluble form. A quellung reaction demonstrated that the soluble 47B3 VH Ab could show bioactivity by differentiation between S. suis serotypes 2 and 1/2. Thus, it will be beneficial to use this VH Ab in the diagnosis of disease or discrimination of S. suis serotypes Furthermore, the results described here could motivate the use of phage display VH platform to produce serotyping antibodies.
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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) | 0.001 | 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".