The colonic mucosal virome in inflammatory bowel disease reveals Crassvirales depletion and disease-specific virome features
Bibliographic record
Abstract
The mucosal virome is increasingly recognized for its potential role in shaping intestinal health and disease. Building on previous findings, we analyzed the mucosal virome from 51 individuals, including newly diagnosed treatment naïve participants with ulcerative colitis (UC), Crohn’s disease (CD), and non-inflammatory bowel disease (non-IBD) controls, incorporating longitudinal sampling for a subset of the participants. Viromes were highly individualized, with no shared or core components across participants. Unlike fecal virome studies, we observed no significant associations between mucosal virome diversity and mucosal inflammation, disease subtype, or sampling site. However, there was positive correlation between virome and bacteriome diversity, particularly in CD, suggesting the presence of dynamic interactions that influence microbial community structure. Crassvirales was abundant in the mucosa layer and, consistent with prior studies, Crassvirales abundance was reduced in IBD, irrespective of inflammation status or IBD subtype. These findings highlight their potential as biomarkers of virome health. Our data also revealed the potential presence of altered bacteriome-virome interactions and longitudinal sampling revealed a persistent subset of viruses, potentially shaping disease progression and remission dynamics. Our study underscores the importance of distinguishing microbial community dynamics across IBD subtypes and highlights Crassvirales as key players in mucosal immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".