Crohn’s disease proteolytic microbiota enhances inflammation through PAR2 pathway in gnotobiotic mice
Bibliographic record
Abstract
Emerging evidence implicates microbial proteolytic activity in ulcerative colitis (UC), but whether it also plays a role in Crohn’s disease (CD) remains unclear. We investigated the effects of colonizing adult and neonatal germ-free C57BL/6 mice with CD microbiota, selected based on high (CD-HPA) or low fecal proteolytic activity (CD-LPA), or microbiota from healthy controls with LPA (HC-LPA) or HPA (HC-HPA). We then investigated colitogenic mechanisms in gnotobiotic C57BL/6, and in mice with impaired Nucleotide-binding Oligomerization Domain-2 (NOD2) and Protease-Activated Receptor 2 (PAR2) cleavage resistant mice (Nod2−/−; R38E-PAR2 respectively). At sacrifice, total fecal proteolytic, elastolytic, and mucolytic activity were analyzed. Microbial community and predicted function were assessed by 16S rRNA gene sequencing and PICRUSt2. Immune function and colonic injury were investigated by inflammatory gene expression (NanoString) and histology. Colonization with HC-LPA or CD-LPA lowered baseline fecal proteolytic activity in germ-free mice, which was paralleled by lower acute inflammatory cell infiltrate. CD-HPA further increased proteolytic activity compared with germ-free mice. CD-HPA mice had lower alpha diversity, distinct microbial profiles and higher fecal proteolytic activity compared with CD-LPA. C57BL/6 and Nod2−/− mice, but not R38E-PAR2, colonized with CD-HPA had higher colitis severity than those colonized with CD-LPA. Our results indicate that CD proteolytic microbiota is proinflammatory, increasing colitis severity through a PAR2 pathway.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".