Development and Characterization of Recombinant Antibody Fragments That Recognize and Neutralize In Vitro Stx2 Toxin from Shiga Toxin-Producing Escherichia coli
Bibliographic record
Abstract
BACKGROUND: Stx toxin is a member of the AB5 family of bacterial toxins: the active A subunit has N-glycosidase activity against 28S rRNA, resulting in inhibition of protein synthesis in eukaryotic cells, and the pentamer ligand B subunits (StxB) bind to globotria(tetra)osylceramide receptors (Gb3/Gb4) on the cell membrane. Shiga toxin-producing Escherichia coli strains (STEC) may produce Stx1 and/or Stx2 and variants. Strains carrying Stx2 are considered more virulent and related to the majority of outbreaks, besides being usually associated with hemolytic uremic syndrome in humans. The development of tools for the detection and/or neutralization of these toxins is a turning point for early diagnosis and therapeutics. Antibodies are an excellent paradigm for the design of high-affinity, protein-based binding reagents used for these purposes. METHODS AND FINDINGS: In this work, we developed two recombinant antibodies; scFv fragments from mouse hybridomas and Fab fragments by phage display technology using a human synthetic antibody library. Both fragments showed high binding affinity to Stx2, and they were able to bind specifically to the GKIEFSKYNEDDTF region of the Stx2 B subunit and to neutralize in vitro the cytotoxicity of the toxin up to 80%. Furthermore, the scFv fragments showed 79% sensitivity and 100% specificity in detecting STEC strains by ELISA. CONCLUSION: In this work, we developed and characterized two recombinant antibodies against Stx2, as promising tools to be used in diagnosis or therapeutic approaches against STEC, and for the first time, we showed a human monovalent molecule, produced in bacteria, able to neutralize the cytotoxicity of Stx2 in vitro.
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 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) | 0.001 | 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 | 0.001 |
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".