Proteomics reveal cap-dependent translation inhibitors remodel the translation machinery and translatome
Bibliographic record
Abstract
Tactical disruption of protein synthesis is an attractive therapeutic strategy, with the first-in-class eIF4A-targeting compound zotatifin in clinical evaluation for cancer and COVID-19. The full cellular impact and mechanisms of these potent molecules are undefined at a proteomic level. Here, we report mass spectrometry analysis of translational reprogramming by rocaglates, cap-dependent initiation disruptors that include zotatifin. We find effects to be far more complex than simple "translational inhibition" as currently defined. Translatome analysis by TMT-pSILAC (tandem mass tag-pulse stable isotope labeling with amino acids in cell culture mass spectrometry) reveals myriad upregulated proteins that drive hitherto unrecognized cytotoxic mechanisms, including GEF-H1-mediated anti-survival RHOA/JNK activation. Surprisingly, these responses are not replicated by eIF4A silencing, indicating a broader translational adaptation than currently understood. Translation machinery analysis by MATRIX (mass spectrometry analysis of active translation factors using ribosome density fractionation and isotopic labeling experiments) identifies rocaglate-specific dependence on specific translation factors including eEF1ε1 that drive translatome remodeling. Our proteome-level interrogation reveals that the complete cellular response to these historical "translation inhibitors" is mediated by comprehensive translational landscape remodeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".