Identification and characterization of hippuristanol-resistant mutants reveals eIF4A1 dependencies within mRNA 5′ leader regions
Bibliographic record
Abstract
Hippuristanol (Hipp) is a natural product that selectively inhibits protein synthesis by targeting eukaryotic initiation factor (eIF) 4A, a DEAD-box RNA helicase required for ribosome recruitment to mRNA templates. Hipp binds to the carboxyl-terminal domain of eIF4A, locks it in a closed conformation, and inhibits its RNA binding. The dependencies of mRNAs for eIF4A during initiation is contingent on the degree of secondary structure within their 5' leader region. Interest in targeting eIF4A therapeutically in cancer and viral-infected settings stems from the dependencies that certain cellular (e.g. pro-oncogenic, pro-survival) and viral mRNAs show towards eIF4A. Using a CRISPR/Cas9-based variomics screen, we identify functional EIF4A1 Hipp-resistant alleles, which in turn allowed us to link the translation-inhibitory and cytotoxic properties of Hipp to eIF4A1 target engagement. Genome-wide translational profiling in the absence or presence of Hipp were undertaken and our validation studies provided insight into the structure-activity relationships of eIF4A-dependent mRNAs. We find that mRNA 5' leader length, overall secondary structure and cytosine content are defining features of Hipp-dependent mRNAs.
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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".