Conserved transcriptional programming across sex and species after peripheral nerve injury predicts treatments for neuropathic pain
Bibliographic record
Abstract
Background and Purpose: Chronic pain is a devastating problem affecting 1 in 5 individuals around the globe, with neuropathic pain the most debilitating and poorly treated type of chronic pain. Advances in transcriptomics and data mining have contributed to cataloging diverse cellular pathways and transcriptomic alterations in response to peripheral nerve injury but have focused on phenomenology and classifying transcriptomic responses. Experimental approach: Here, with the goal of identifying new types of pain-relieving agents, we compared transcriptional reprogramming changes in the dorsal spinal cord after peripheral nerve injury cross-sex and cross-species and imputed commonalities, as well as differences in cellular pathways and gene regulation. Key Results : We identified 93 transcripts in the dorsal horn that were increased by peripheral nerve injury in male and female mice and rats. Following gene ontology and transcription factor analyses, we constructed a pain interactome for the proteins encoded by the differentially expressed genes, discovering new, conserved signaling nodes. We interrogated the interactome with the Drug-Gene database to predict FDA-approved medications that may modulate key nodes within the network. The top hit from the analysis was fostamatinib, the molecular target of which is the non-receptor tyrosine kinase Syk, which our analysis had identified as a key node in the interactome. Conclusions & Implications : We found that intrathecally administrating the active metabolite of fostamatinib, R406, significantly reversed pain hypersensitivity in both sexes. Thus, we have identified and shown the efficacy of an agent that could not have been previously predicted to have analgesic properties.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".