GRPR/PI3Kγ: Partners in Central Transmission of Itch
Bibliographic record
Abstract
The gastrin-releasing peptide (GRP) and its receptor (GRPR) are important components of itch transmission. Upstream, but not downstream, aspects of GRPR signaling have been investigated extensively. We hypothesize that GRPR signals in part through the PI3Kγ/Akt pathway. We used pharmacological, electrophysiological, and behavioral approaches to further evaluate GRPR downstream signaling pathways. Our data show that GRP directly activates small-size capsaicin-sensitive DRG neurons, an effect that translates into transient calcium flux and membrane depolarization (∼ 20 mV). GRPR activation also induces Akt phosphorylation, a proxy for PI3Kγ activity, in ex vivo naive mouse spinal cords and in GRPR transiently expressing HEK293 cells. The intrathecal injection of GRP led to intense scratching, an effect largely reduced by either GRPR antagonists or PI3Kγ inhibitor. Scratching behavior was also induced by the intrathecal injection of an Akt activator. In a dry skin model of itch, we show that GRPR blockade or PI3Kγ inhibition reversed the scratching behavior. Altogether, these findings are highly suggestive that GRPR is expressed by the central terminals of DRG nociceptive afferents, which transmit itch via the PI3Kγ/Akt pathway. SIGNIFICANCE STATEMENT: Itch is the most common symptom of the skin and is related to noncutaneous diseases. It severely impairs patients' quality of life when it becomes chronic and there is no specific or effective available therapy, mainly because itch pathophysiology is not completely elucidated. Our findings indicate that the enzyme PI3Kγ is a key central mediator of itch transmission. Therefore, we suggest PI3Kγ as an attractive target for the development of new anti-pruritic drugs. With this study, we take a step forward in our understanding of the mechanisms underlying the central transmission of itch sensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".