Stress-induced tyrosine phosphorylation of RtcB modulates IRE1 activity and signaling outputs
Bibliographic record
Abstract
Abstract Endoplasmic Reticulum (ER) stress is a hallmark of various diseases, which is dealt with through the activation of an adaptive signaling pathway named the Unfolded Protein Response (UPR). This response is mediated by three ER-resident sensors and the most evolutionary conserved, IRE1α signals through its cytosolic kinase and endoribonuclease (RNase) activities. IRE1α RNase activity can either catalyze the initial step of XBP1 mRNA unconventional splicing or degrade a number of RNAs through Regulated IRE1- Dependent Decay (RIDD). The balance between these two activities plays an instrumental role in cells’ life and death decisions upon ER stress. Until now, the biochemical and biological outputs of IRE1α RNase activity have been well documented, however, the precise mechanisms controlling whether IRE1 signaling is adaptive or pro-death (terminal) remain unclear. This prompted us to further investigate those mechanisms and we hypothesized that XBP1 mRNA splicing and RIDD activity could be co-regulated by the IRE1α RNase regulatory network. We showed that a key nexus in this pathway is the tRNA ligase RtcB which, together with IRE1α, is responsible for XBP1 mRNA splicing. We demonstrated that RtcB is tyrosine phosphorylated by c-Abl and dephosphorylated by PTP1B. Moreover, we identified RtcB Y306 as a key residue which, when phosphorylated, perturbs RtcB interaction with IRE1α, thereby attenuating XBP1 mRNA splicing and favoring RIDD. Our results demonstrate that the IRE1α RNase regulatory network is dynamically fine-tuned by tyrosine kinases and phosphatases upon various stresses and that the nature of the stress determines cell adaptive or death outputs.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".