L-asparaginase is a PAR2 N-terminal protease that unmasks the PAR2 tethered ligand
Bibliographic record
Abstract
Abstract L-asparaginase is an indispensable chemotherapeutic drug for patients with acute lymphoblastic leukemia (aLL), a life-threatening lymphoid neoplasm and the prime cause of cancer death among children. Previously, we reported that L-asparaginase kills aLL cells via an excessive rise in [Ca2+]i due to IP3R-mediated ER Ca2+ release followed by stimulation of the intrinsic apoptotic pathway (Blood, 133, 2222-2232). We also demonstrated that L-asparaginase triggers ER Ca2+ release by targeting the G-protein-coupled receptor (GPCR), protease-activated receptor 2 (PAR2) (Cell Death & Discovery, 10:366). However, how L-asparaginase stimulates PAR2 remains unknown. Here, we show that elastase, which can disarm trypsin-mediated PAR2 activation by cleaving a S67-V68 residue downstream of the tethered ligand (TL) and removing it from PAR2, abrogates L-asparaginase-induced ER Ca2+ release, indicating that L-asparaginase targets the TL-containing PAR2 N-terminal extracellular domain to induce ER Ca2+ release. Inactive forms (T111V/K184T or D112T/K184T) of L-asparaginase do not induce ER Ca2+ release in μ-opioid receptor 1 (µ-OR1)-knockdown aLL cells, suggesting that L-asparaginase action on PAR2 requires its enzymatic activity. Time-lapse confocal microscopy of cells expressing mRFP-hPAR2-eYFP and nanoluciferase (Nluc) reporter release assays of cells expressing Nluc-hPAR2-eYFP showed that L-asparaginase cleaves PAR2 at the N-terminal extracellular I26-G71 domain. Cleavage assay of a PAR2 N-terminal peptide by L-asparaginase and subsequent LC-MS/MS analysis show that L-asparaginase is a PAR2 protease that cleaves N30-R31 and R31-S32 residues, unmasking the PAR2 TL. Thus, our findings reveal for the first time the molecular mechanism through which L-asparaginase activates PAR2, leading to perturbation of intracellular Ca2+ homeostasis and aLL cell apoptosis.
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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 | 0.001 |
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".