Electrophysiological and Proteomic Evidence of Protease-Mediated Pro-nociceptive Signaling in Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
Abdominal pain is a debilitating symptom of inflammatory bowel disease (IBD). Despite advances in understanding IBD pathology, the mechanisms underlying pain remain poorly defined. While studies of tissue biopsies from IBD patients and rodent models have highlighted the roles of proinflammatory cytokines and proteases in pain signaling, these approaches predominantly capture host-derived mediators, overlooking the broader luminal environment influenced by the microbiota. Given the compromised barrier in IBD leading to increased mucosal permeability, examining the luminal milieu would characterize a novel source of factors involved in pain modulation in IBD patients with active disease. Fecal supernatants (FS) from healthy volunteers (HV) of either sex had no effect on ex vivo colonic afferent nerve mechanosensitivity or in vitro dorsal root ganglia (DRG) neuron excitability. In contrast, FS from Crohn's disease (CD) and ulcerative colitis (UC) patients of either sex significantly excited colonic afferent nerves and increased mechanosensitivity ex vivo and increased DRG neuronal excitability in vitro. These were blocked by the serine protease inhibitor and a protease-activated receptor 2 (PAR 2 ) antagonist. Proteomic analysis revealed IBD FS contained elevated levels of trypsin- and elastase-like serine proteases compared with HV FS. Proteomics identified CELA3B, ELA2A, and PRSS1 as key proteases enriched in IBD FS, with distinct activity profiles in UC and CD. These findings establish that proteases within FS from IBD patients directly modulate pain-sensing pathways by activation of PAR 2 on colonic afferent nerves, offering a unique insight into luminal contributions to pain and identifying potential therapeutic targets for visceral hypersensitivity in IBD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".