Genome-wide loss-of-function genetic screening identifies opioid receptor μ1 as a key regulator of L-asparaginase resistance in pediatric acute lymphoblastic leukemia
Bibliographic record
Abstract
, causing asparagine deficit and inhibition of protein synthesis and eventually, leukemic cell death. However, patient relapse often occurs due to development of resistance. The molecular mechanism by which ALL cells acquire resistance to L-asparaginase is unknown. Therefore, we sought to identify genes that are involved in L-asparaginase resistance in primary leukemic cells. By unbiased genome-wide RNAi screening, we found that among 10 resistant ALL clones, six hits were for opioid receptor mu 1 (oprm1), two hits were for carbonic anhydrase 1 (ca1) and another two hits were for ubiquitin-conjugating enzyme E2C (ube2c). We also found that OPRM1 is expressed in all leukemic cells tested. Specific knockdown of OPRM1 confers L-asparaginase resistance, validating our genome-wide retroviral shRNA library screening data. Methadone, an agonist of OPRM1, enhances the sensitivity of parental leukemic cells, but not OPRM1-depleted cells, to L-asparaginase treatment, indicating that OPRM1 is required for the synergistic action of L-asparaginase and methadone, and that OPRM1 loss promotes leukemic cell survival likely through downregulation of the OPRM1-mediated apoptotic pathway. Consistent with this premise, patient leukemic cells with relatively high levels of OPRM1 are more sensitive to L-asparaginase treatment compared to OPRM1-depleted leukemic cells, further indicating that OPRM1 loss has a crucial role in L-asparaginase resistance in leukemic patients. Thus, our study demonstrates for the first time, a novel OPRM1-mediated mechanism for L-asparaginase resistance in ALL, and identifies OPRM1 as a functional biomarker for defining high-risk subpopulations and for the detection of evolving resistant clones. Oprm1 may also be utilized for effective treatment of L-asparaginase-resistant ALL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".