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 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".