Hyperacute Response Proteins (HARPs) synthesized on γ-tubulin-FTO-MARK4 translation microdomains upon exposure to stress, regulate stress response in cancer
Bibliographic record
Abstract
Summary Compared to normal, cancer cells are particularly resistant to stress, and their immediate response to stress is critical for their subsequent multilayered adaptation programs which pose a major clinical challenge. With unbiased proteomics and transcriptomics analysis, we identified a list of HARPs synthesized from pre-existing mRNAs within 20 min of diverse stresses in A549 cancer cells, despite the known suppressed global translation in stress. HARP mRNAs were translated on microtubule-associated translation microdomains (MATMs) located on γ-tubulin, that host FTO and specialized cytoskeletal ribosomes, structurally and functionally distinct from ER and cytosolic ribosomes. FTO exited the nucleus immediately after stress and was activated by the microtubule-associated stress kinase MARK4 via T6 phosphorylation. Activated FTO demethylated a translation-inhibiting mRNA methylation (m6A) signature, facilitating compartmentalized HARP translation on MATMs, while non-HARP mRNA remained inhibited. FTO or MARK4 inhibition suppressed HARP synthesis and increased apoptosis post various stresses, including chemotherapy. These data were confirmed in 4 additional cancer cell lines and normal fibroblasts. Using the Protein Atlas database, we found that high levels of our identified HARPs had on average a 35% decrease on patient 5-year survival in prevalent and resistant cancers (breast, lung, liver, pancreas). γ-tubulin, FTO and MARK4 are therapeutic targets for many cancers, through their ability to comprehensively promote HARPs translation, a potential Achille’s heel for cancer’s resistance to physiologic or therapeutic stress, offering a new window in stress biology.
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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".