Spinal microglia contribute to cancer-induced pain through system xC −-mediated glutamate release
Bibliographic record
Abstract
Abstract Introduction: Microglial cells, the resident macrophages of the central nervous system, are a key contributor to the generation and maintenance of cancer-induced pain (CIP). In healthy organisms, activated microglia promote recovery through the release of trophic and anti-inflammatory factors to clear toxins and pathogens and support neuronal survival. Chronically activated microglia, however, release toxic substances, including excess glutamate, causing cytotoxicity. Accordingly, rising attention is given to microglia for their role in abnormal physiology and in mediating neurotoxicity. Objectives: To examine the nociceptive relationship between peripherally-released glutamate and microglial xCT. Methods: A validated murine model of 4T1 carcinoma cell–induced nociception was used to assess the effect of peripheral tumour on spinal microglial activation and xCT expression. Coculture systems were then used to investigate the direct effect of glutamate released by wildtype and xCT knockdown MDA-MB-231 carcinoma cells on microglial activation, functional system xC − activity, and protein levels of interferon regulatory factor 8 (IRF8), a transcription factor implicated in microglia-mediated nociception. Results: Blockade of system xC − with sulfasalazine (SSZ) in vivo attenuated nociception in a 4T1 murine model of CIP and attenuates tumour-induced microglial activation in the dorsal horn of the spinal cord. Furthermore, knockdown of xCT in MDA-MB-231 cells mitigated tumour cell–induced microglial activation and functional system xC − activity in vitro. Conclusions: These data collectively demonstrate that the system xCT antiporter is functionally implicated in CIP and may be particularly relevant to pain progression through microglia. Upregulated xCT in chronically activated spinal microglia may be one pathway to central glutamate cytotoxicity. Microglial xCT may therefore be a valuable target for mitigating CIP.
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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 | 0.000 |
| 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 | 0.000 |
| 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".