Granulocyte-colony–stimulating factor (G-CSF) signaling in spinal microglia drives visceral sensitization following colitis
Bibliographic record
Abstract
Pain is a main symptom of inflammatory diseases and often persists beyond clinical remission. Although we have a good understanding of the mechanisms of sensitization at the periphery during inflammation, little is known about the mediators that drive central sensitization. Recent reports have identified hematopoietic colony-stimulating factors as important regulators of tumor- and nerve injury-associated pain. Using a mouse model of colitis, we identify the proinflammatory cytokine granulocyte-colony-stimulating factor (G-CSF or Csf-3) as a key mediator of visceral sensitization. We report that G-CSF is specifically up-regulated in the thoracolumbar spinal cord of colitis-affected mice. Our results show that resident spinal microglia express the G-CSF receptor and that G-CSF signaling mediates microglial activation following colitis. Furthermore, healthy mice subjected to intrathecal injection of G-CSF exhibit pronounced visceral hypersensitivity, an effect that is abolished by microglial depletion. Mechanistically, we demonstrate that G-CSF injection increases Cathepsin S activity in spinal cord tissues. When cocultured with microglia BV-2 cells exposed to G-CSF, dorsal root ganglion (DRG) nociceptors become hyperexcitable. Blocking CX3CR1 or nitric oxide production during G-CSF treatment reduces excitability and G-CSF-induced visceral pain in vivo. Finally, administration of G-CSF-neutralizing antibody can prevent the establishment of persistent visceral pain postcolitis. Overall, our work uncovers a DRG neuron-microglia interaction that responds to G-CSF by engaging Cathepsin S-CX3CR1-inducible NOS signaling. This interaction represents a central step in visceral sensitization following colonic inflammation, thereby identifying spinal G-CSF as a target for treating chronic abdominal pain.
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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".