L-asparaginase induces IP3R-mediated ER Ca2+ release by targeting µ-OR1 and PAR2 and kills acute lymphoblastic leukemia cells
Bibliographic record
Abstract
Abstract L-asparaginase is a standard therapeutic option for acute lymphoblastic leukemia (aLL), a hematologic cancer that claims the most lives of pediatric cancer patients. Previously, we demonstrated that L-asparaginase kills aLL cells via a lethal rise in [Ca2+]i due to IP3R-mediated ER Ca2+ release followed by calpain-1-Bid-caspase-3/12 activation (Blood, 133, 2222-2232). However, upstream targets of L-asparaginase that trigger IP3R-mediated ER Ca2+ release remain elusive. Here, we show that L-asparaginase targets µ-OR1 and PAR2 and induces IP3R-mediated ER Ca2+ release in aLL cells. In doing so, µ-OR1 plays a major role while PAR2 plays a minor role. Utilizing PAR2- and µ-OR1-knockdown cells, we demonstrate that L-asparaginase stimulation of µ-OR1 and PAR2 relays its signal via Gαi and Gαq, respectively. In PAR2-knockdown cells, stimulation of adenylate cyclase with forskolin or treatment with 8-CPT-cAMP reduces L-asparaginase-induced µ-OR1-mediated ER Ca2+ release, suggesting that activation of µ-OR1 negatively regulates AC and cAMP. In addition, the PKA inhibitor 14-22 amide (myr) alone evokes ER Ca2+ release, and subsequent L-asparaginase treatment does not induce further ER Ca2+ release, indicating the involvement of PKA inhibition in L-asparaginase-induced µ-OR1-mediated ER Ca2+ release, which can bypass the L-asparaginase-µ-OR1-AC-cAMP loop. This coincides with (a) the decreases in PKA-dependent inhibitory PLCβ3 Ser1105 phosphorylation, which prompts PLCβ3 activation and ER Ca2+ release, and (b) BAD Ser118 phosphorylation, which leads to caspase activation and apoptosis. Thus, our findings offer new insights into the Ca2+-mediated mechanisms behind L-asparaginase-induced aLL cell apoptosis and suggest that PKA may be targeted for therapeutic intervention for aLL.
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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".