Tetracycline transactivator overexpression in keratinocytes triggers a TRPV1 primary sensory neuron-dependent neuropathic itch
Bibliographic record
Abstract
Abstract Mouse models that combine tetracycline-controlled gene expression systems and conditional genetic activation can tightly regulate transgene expression in discrete cell types and tissues. However, the commonly used Tet-Off variant, tetracycline transactivator (tTA), when overexpressed and fully active, can lead to developmental lethality, disease, or more subtle behavioral phenotypes. Here we describe a profound itch phenotype in mice expressing a genetically encoded tTA that is conditionally activated within the Phox2a lineage. Phox2a; tTA mice develop intense, localized scratching and regional skin lesions that can be controlled by the tTA inhibitor, doxycycline. As gabapentin, but not morphine, relieved the scratching, we consider this phenotype to result from chronic neuropathic itch, not pain. In contrast to the Phox2a lineage, mice with tTA activated within the Phox2b lineage, which has many similar areas of recombination within the nervous system, did not recapitulate the scratching phenotype. In Phox2a-Cre mice, but not Phox2b-Cre, intense Cre-dependent reporter expression was found in skin keratinocytes which formed the area at which skin lesions developed. Most interestingly, repeated topical application of the DREADD agonist, CNO, which chronically induced G i signaling in Phox2a-keratinocytes, completely reversed the localized scratching and skin lesions. Furthermore, ablation of TRPV1-expressing, primary afferent neurons reduced the scratching with a time course comparable to that produced by G i -DREADD inhibition. These temporal properties suggest that the neuropathic itch condition arises not only from localized keratinocyte activation of peripheral nerves but also from a persistent, gabapentin-sensitive state of central sensitization.
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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) | 0.001 | 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".