The brinker repressor system regulates injury-induced nociceptive sensitization in <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Chronic pain is a debilitating condition affecting millions of people worldwide, and an improved understanding of the pathophysiology of chronic pain is urgently needed. Nociceptors are the sensory neurons that alert the nervous system to potentially harmful stimuli such as mechanical pressure or noxious thermal temperature. When an injury occurs, the nociceptive threshold for pain is reduced and an increased pain signal is produced. This process is called nociceptive sensitization. This sensitization normally subsides after the injury is healed. However, dysregulation can occur which results in sensitization that persists after the injury has healed. This process is thought to perpetuate chronic pain. The Hedgehog (Hh) signaling pathway has been previously implicated in nociceptive sensitization in response to injury in Drosophila melanogaster. Downstream of Hh signaling, the Bone Morphogenetic Protein (BMP) pathway has also been shown to be necessary for this process. Here, we describe a role for nuclear components of BMP’s signaling pathway in the formation of injury-induced nociceptive sensitization. Brinker (Brk), and Schnurri (Shn) were suppressed in nociceptors using an RNA-interference (RNAi) “knockdown” approach. Knockdown of Brk resulted in hypersensitivity in the absence of injury, indicating that it normally acts to suppress nociceptive sensitivity. Animals in which transcriptional activator Shn was knocked down in nociceptors failed to develop normal allodynia after ultraviolet irradiation injury, indicating that Shn normally acts to promote hypersensitivity after injury. These results indicate that Brk-related transcription regulators play a crucial role in the formation of nociceptive sensitization and may therefore represent valuable new targets for pain-relieving medications.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".