Engagement of the GABA to KCC2 Signaling Pathway Contributes to the Analgesic Effects of A<sub>3</sub>AR Agonists in Neuropathic Pain
Bibliographic record
Abstract
More than 1.5 billion people worldwide suffer from chronic pain, yet current treatment strategies often lack efficacy or have deleterious side effects in patients. Adenosine is an inhibitory neuromodulator that was previously thought to mediate antinociception through the A1 and A2A receptor subtypes. We have since demonstrated that A3AR agonists have potent analgesic actions in preclinical rodent models of neuropathic pain and that A3AR analgesia is independent of adenosine A1 or A2A unwanted effects. Herein, we explored the contribution of the GABA inhibitory system to A3AR-mediated analgesia using well-characterized mouse and rat models of chronic constriction injury (CCI)-induced neuropathic pain. The deregulation of GABA signaling in pathophysiological pain states is well established: GABA signaling can be hampered by a reduction in extracellular GABA synthesis by GAD65 and enhanced extracellular GABA reuptake via the GABA transporter, GAT-1. In neuropathic pain, GABAAR-mediated signaling can be further disrupted by the loss of the KCC2 chloride anion gradient. Here, we demonstrate that A3AR agonists (IB-MECA and MRS5698) reverse neuropathic pain via a spinal mechanism of action that modulates GABA activity. Spinal administration of the GABAA antagonist, bicuculline, disrupted A3AR-mediated analgesia. Furthermore, A3AR-mediated analgesia was associated with reductions in CCI-related GAD65 and GAT-1 serine dephosphorylation as well as an enhancement of KCC2 serine phosphorylation and activity. Our results suggest that A3AR-mediated reversal of neuropathic pain increases modulation of GABA inhibitory neurotransmission both directly and indirectly through protection of KCC2 function, underscoring the unique utility of A3AR agonists in chronic pain.
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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".