Mapping <i>C. difficile</i> TcdB interactions with host cell-surface and intracellular factors using proximity-dependent biotinylation labeling
Bibliographic record
Abstract
ABSTRACT Many bacterial toxins exert their cytotoxic effects by enzymatically inactivating one or more cytosolic targets in host cells. To reach their intracellular targets, these toxins possess functional domains or subdomains that interact with and exploit various host factors and biological processes. Despite great progress in identifying many of the key host factors involved in the uptake of toxins, significant knowledge gaps remain as to how partially characterized and newly discovered microbial toxins exploit host factors or processes to intoxicate target cells. Proximity-dependent biotinylation (e.g., BioID) is a powerful method to identify nearby host factors in living cells, offering the potential to identify host targets of microbial toxins. Here, we used BioID to interrogate proximal interactors of the multi-domain Clostridioides difficile TcdB toxin. Expressed fusions of TurboID to different fragments of TcdB identified several high-confidence proteins in the cytosol, including members of the Rho GTPase signaling network and the actin cytoskeletal network. Additionally, we developed an extracellular proximity labeling method using recombinant TurboID-toxin chimeras, which uncovered a limited number of cell-surface targets including LRP1, which was previously identified as a cell-surface receptor of TcdB. Our work reveals surface receptors and intracellular components exploited by bacterial toxins, highlighting key vulnerabilities in host cells. IMPORTANCE Bacterial toxins are the causative agents of many human diseases. Further characterizing the intoxication mechanisms of these proteins is important for the development of vaccines and treatments for toxin-mediated disease. Proximity-dependent biotinylation approaches offer an orthogonal approach to complement genetic screens. Here, we evaluate the potential of this method to identify host-toxin interactions on the cell surface and in the cytosol, where the toxin modifies essential host targets. Critically, we have highlighted several limitations of this method as applied to protein toxins, which are important for researchers to weigh when considering this technique for exotoxin studies.
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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 | 0.001 |
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".