A285 MICROBIAL DYSBIOSIS ENHANCES PAIN PERCEPTION AND DRG NEURON EXCITABILITY
Bibliographic record
Abstract
Abdominal pain is a major symptom of IBD and IBS, which are associated with microbial dysbiosis. Disruption of the microbiota with antibiotics increases visceral pain and germ- free mice are prone to pain. However, the mechanisms underlying microbial modulation of pain remain elusive. We hypothesized that disruption of the intestinal microbiota modulates the excitability of dorsal root ganglion (DRG) neurons. We aim to demonstrate the impact of microbial dysbiosis on pain sensitivity and identify the the mechanism by which it works. Patch clamp electrophysiological recordings of DRG neuron excitability (decreased rheobase = increased excitability) were obtained from control mice and mice treated with the non-absorbable antibiotic vancomycin (50mg/ml in drinking water) for one week. DRG neurons from vancomycin-treated mice were hyperexcitable (~25 % decrease in rheobase, p < 0.01) compared to controls. Interestingly, this effect was not restricted to gut-projecting DRG neurons, suggesting an effect of gut dysbiosis on somatic pain pathways. Consistent with this, mice treated with vancomycin were approximately 20% more sensitive to noxious thermal stimuli applied to hind paws than control mice (p < 0.01). Incubation of DRG neurons from naïve mice in serum from vancomycin-treated mice increased DRG neuron excitability by ~25%, suggesting that microbial dysbiosis alters circulating mediators that influence nociception. Multiplex ELISA measurements did not detect any differences in serum cytokines or chemokines between vancomycin-treated and control mice. The cysteine protease inhibitor E64 (10µM) and the protease-activated receptor 2 antagonist GB-83 (10µM) each blocked the increase in DRG neuron excitability in response to serum from vancomycin-treated mice. Together, these data suggest that microbial dysbiosis is sufficient to alter pain sensitivity, and identify circulating cysteine proteases as potential mediators of this effect. CCC
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".