Transgenic Expression of a Dominant-Negative ASIC3 Subunit Leads to Increased Sensitivity to Mechanical and Inflammatory Stimuli
Bibliographic record
Abstract
Molecular and behavioral evidence suggests that acid-sensing ion channels (ASICs) contribute to pain processing, but an understanding of their precise role remains elusive. Existing ASIC knock-out mouse experiments are complicated by the heteromultimerization of ASIC subunits. Therefore, we have generated transgenic mice that express a dominant-negative form of the ASIC3 subunit that inactivates all native neuronal ASIC-like currents by oligomerization. Using whole-cell patch-clamp recordings, we examined the response properties of acutely isolated dorsal root ganglion neurons to protons (pH 5.0). We found that whereas 33% of the proton-responsive neurons from wild-type mice exhibited an ASIC-like transient response, none of the neurons from the transgenic mice exhibited a transient inward current. Capsaicin-evoked responses mediated by the TRPV1 receptor were unaltered in transgenic mice. Adult male wild-type and transgenic mice were subjected to a battery of behavioral nociceptive assays, including tests of thermal, mechanical, chemical/inflammatory, and muscle pain. The two genotypes were equally sensitive to thermal pain and to thermal hypersensitivity after inflammation. Compared with wild types, however, transgenic mice were more sensitive to a number of modalities, including mechanical pain (von Frey test, tail-clip test), chemical/inflammatory pain (formalin test, 0.6% acetic acid writhing test), mechanical hypersensitivity after zymosan inflammation, and mechanical hypersensitivity after intramuscular injection of hypotonic saline. These data reinforce the hypothesis that ASICs are involved in both mechanical and inflammatory pain, although the increased sensitivity of transgenic mice renders it unlikely that they are direct transducers of nociceptive stimuli.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".