A Dominant-Negative Approach That Prevents Diphthamide Formation Confers Resistance to Pseudomonas Exotoxin A and Diphtheria Toxin
Bibliographic record
Abstract
Diphtheria toxin (DT), Pseudomonas aeruginosa Exotoxin A (ETA) and cholix toxin from Vibrio cholerae share the same mechanism of toxicity; these enzymes ADP-rybosylate elongation factor-2 (EF-2) on a modified histidine residue called diphthamide, leading to a block in protein synthesis. Mutant Chinese hamster ovary cells that are defective in the formation of diphthamide have no distinct phenotype except their resistance to DT and ETA. These observations led us to predict that a strategy that prevents the formation of diphthamide to confer DT and ETA resistance is likely to be safe. It is well documented that Dph1 and Dph2 are involved in the first biochemical step of diphthamide formation and that these two proteins interact with each other. We hypothesized that we could block diphthamide formation with a dominant negative mutant of either Dph1 or Dph2. We report in this study the first cellular-targeted strategy that protects against DT and ETA toxicity. We have generated Dph2(C-), a dominant-negative mutant of Dph2, that could block very efficiently the formation of diphthamide. Cells expressing Dph2(C-) were 1000-fold more resistant to DT than parental cells, and a similar protection against Pseudomonas exotoxin A was also obtained. The targeting of a cellular component with this approach should have a reduced risk of generating resistance as it is commonly seen with antibiotic treatments.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".