The neuronal tyrosine kinase receptor ligand ALKAL2 mediates persistent pain
Bibliographic record
Abstract
The anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase known for its oncogenic potential that is involved in the development of the peripheral and central nervous system. ALK receptor ligands ALKAL1 and ALKAL2 were recently found to promote neuronal differentiation and survival. Here, we show that inflammation or injury enhanced ALKAL2 expression in a subset of TRPV1+ sensory neurons. Notably, ALKAL2 was particularly enriched in both mouse and human peptidergic nociceptors, yet weakly expressed in nonpeptidergic, large-diameter myelinated neurons or in the brain. Using a coculture expression system, we found that nociceptors exposed to ALKAL2 exhibited heightened excitability and neurite outgrowth. Intraplantar CFA or intrathecal infusion of recombinant ALKAL2 led to ALK phosphorylation in the lumbar dorsal horn of the spinal cord. Finally, depletion of ALKAL2 in dorsal root ganglia or blocking ALK with clinically available compounds crizotinib or lorlatinib reversed thermal hyperalgesia and mechanical allodynia induced by inflammation or nerve injury, respectively. Overall, our work uncovers the ALKAL2/ALK signaling axis as a central regulator of nociceptor-induced sensitization. We propose that clinically approved ALK inhibitors used for non-small cell lung cancer and neuroblastomas could be repurposed to treat persistent pain conditions.
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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".