Editor’s Choice Platform for identifying human glycan-specific antibodies against bacterial pathogens using synthetic glycan fragments
Bibliographic record
Abstract
Bacterial infections represent a substantial global health challenge, impacting both human and veterinary health. The ongoing evolution of antibiotic-resistant pathogens, coupled with limited new antibiotic discoveries, urges the need for alternative strategies to treat and prevent these infections. Passive immunization with monoclonal antibodies (mAbs) is gaining interest as a promising alternative. Here, we report an experimental pipeline for generating human mAbs from healthy donor B cells using synthetic mimics of complex bacterial glycans. We identified functional mAbs recognizing discrete and unique epitopes on the surface glycans of two bacterial priority pathogens; Staphylococcus aureus and Streptococcus pyogenes. The use of chemically-defined synthetic glycans was critical for the discovery and systematic characterization of mAbs. From a heterogeneous mix of B cell specificities, antibody sequences were identified, leading to the production of mAbs with distinct reactivities against immunodominant but also to less common or even masked epitopes. The pipeline can be adapted to different glycan targets, donor material or specific antibody isotypes. This work thereby paves the way for the discovery of glycan-specific mAbs with clinical relevance to treat, prevent or diagnose infections with S. aureus, S. pyogenes or other bacterial pathogens.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.069 |
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".