Role of rat sensory neuron-specific receptor (rSNSR1) in inflammatory pain: Contribution of TRPV1 to SNSR signaling in the pain pathway
Bibliographic record
Abstract
Sensory neuron-specific receptors (SNSRs) belong to a large family of GPCRs, known as Mrgs (Mas-related genes), many of which are preferentially expressed in primary afferent nociceptors. Selective SNSR agonists produce pain-like behaviors in rats, showing that SNSR activation is sufficient to produce pain. However, it is unknown whether SNSR activation is necessary for pain either in the normal condition or in pathological pain states. Here we used small interfering RNA (siRNA) to acutely knockdown rat SNSR1 and test the hypothesis that this receptor mediates pain responses. Administration of siRNA to the lumbar spinal cord in rats dose-dependently knocked down rSNSR1 mRNA and protein and abolished heat hyperalgesia evoked by intradermal administration of specific rSNSR1 agonists. In rats with levels of rSNSR1 knockdown sufficient to block responses to the SNSR1 agonists, there was no effect on normal pain responses, but there was a significant reduction of heat hyperalgesia in an inflammatory pain model (Complete Freund's Adjuvant), supporting a role for rSNSR1 in inflammatory pain. Further in vivo studies revealed that SNSR1 knockdown had no effect on responses to intradermal capsaicin, a selective TRPV1 agonist. In contrast, a selective TRPV1 antagonist abolished heat hyperalgesia produced by an SNSR agonist, suggesting that TRPV1 receptors mediate rSNSR1-evoked responses. We also found that rSNSR1-like immunoreactivity, like TRPV1, is localized in the superficial dorsal horn of the spinal cord. We propose that rSNSR1 represents a new member of the receptors expressed on chemosensitive nociceptors responsible for detecting the "inflammatory soup" of mediators generated by tissue damage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".