ADP‐Ribosylation of Eukaryotic Elongation Factor 2 by Bacterial Toxins Affects Translation Elongation In vivo and In vitro
Bibliographic record
Abstract
Eukaryotic translation elongation factor 2 (eEF2) facilitates the movement of the peptidyl tRNA‐mRNA complex from the A site of the ribosome to the P site during protein synthesis. ADP‐ribosylation (ADPR) of eEF2 by bacterial toxins on a histidine modified to diphthamide inhibits its translocation activity. Mechanistically, it is unclear how ADPR inhibits eEF2 function. We have developed a novel S. cerevisiae system to directly address this question. This system employs eEF2 mutations which display dominant resistance to diphtheria toxin (DT) expression. These eEF2 mutants lack the diphthamide modification and are not a target of DT; however, their expression allows growth in the presence of wild type ADPR‐eEF2. This is an excellent system to study the in vivo effects of ADPR‐eEF2. For example, yeast expressing both mutant and wild type eEF2 display an increase in parmomycin sensitivity specifically upon DT induction demonstrating a link between ADPR‐eEF2 and translation defects in vivo . These effects are due to the presence of the ADPR‐eEF2, as determined by MS analysis. In addition, we conducted a genetic screen of random yeast 35S rDNA mutations to identify those which confer resistance to DT expression as these mutations may provide insight into how ADPR‐eEF2 functionally interacts with the ribosome. Two 25S rRNA mutations were identified and are being validated. This work was supported by the NIH.
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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 | 0.000 |
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".