Blocking Vibrio cholerae-mediated hemagglutination with short peptide antagonists
Bibliographic record
Abstract
Many bacteria use repeats-in-toxin (RTX) adhesins to mediate binding to host cells and facilitate subsequent colonisation and infection by forming biofilms. Vibrio cholerae, the causative agent of cholera, uses a 230-kDa RTX adhesin, FrhA, to facilitate intestinal colonization. FrhA also mediates hemagglutination of red-blood cells (erythrocytes). Here we have demonstrated that the hemagglutination capability of FrhA is localized to a ~ 20-kDa domain near its C terminus. Bioinformatic analyses indicated this erythrocyte-binding domain (VcEBD) is 65% identical to a peptide-binding module found in the 1.5-MDa ice-binding RTX adhesin that helps its Antarctic bacterium, Marinomonas primoryensis, form symbiotic biofilms with diatoms on the underside of sea ice. This suggested that the FrhA binds V. cholerae to proteins present on the cell surface of erythrocytes. X-ray crystallography revealed that VcEBD has an oblong β-sandwich fold with a shallow, Ca2+-dependent ligand-binding cavity that can anchor a peptidyl ligand with a free terminal carboxyl group. Using a structure-guided approach, we screened a small library of ~ 60 short peptides and optimized the affinity of VcEBD’s peptidyl ligands by roughly 1,000-fold. Importantly, the high-affinity ligands are effective at blocking V. cholerae from binding to erythrocytes at nano-molar concentrations. Structures of VcEBD in complex with three different peptides further elucidated the molecular basis for their interactions, which sets the stage for the development of ligand-based antagonists that may help disrupt V. cholerae interaction with intestinal cells to prevent or treat cholera. With the spread of antibiotic-resistant pathogenic bacteria, this work sheds light on an anti-adhesion approach for combating bacterial infections without the excessive use of antibiotics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".