Pathobionts in Inflammatory Bowel Disease: Origins, Underlying Mechanisms, and Implications for Clinical Care
Bibliographic record
Abstract
The gut microbiota plays a significant role in the pathogenesis of both forms of inflammatory bowel disease (IBD), namely, Crohn's disease (CD) and ulcerative colitis (UC). Although evidence suggests dysbiosis and loss of beneficial microbial species can exacerbate IBD, many new studies have identified microbes with pathogenic qualities, termed "pathobionts," within the intestines of patients with IBD. The concept of pathobionts initiating or driving the chronicity of IBD has largely focused on the putative aggravating role that adherent invasive Escherichia coli may play in CD. However, recent studies have identified additional bacterial and fungal pathobionts in patients with CD and UC. This review will highlight the characteristics of these pathobionts and their implications for IBD treatment. Beyond exploring the origins of pathobionts, we discuss those associated with specific clinical features and the potential mechanisms involved, such as creeping fat (Clostridium innocuum) and impaired wound healing (Debaryomyces hansenii) in patients with CD as well as the increased fecal proteolytic activity (Bacteroides vulgatus) seen as a biomarker for UC severity. Finally, we examine the potential impact of pathobionts on current IBD therapies, and several new approaches to target pathobionts currently in the early stages of development. Despite recognizing that pathobionts likely contribute to the pathogenesis of IBD, more work is needed to define their modes of action. Determining whether causal relationships exist between pathobionts and specific disease characteristics could pave the way for improved care for patients, particularly for those not responding to current IBD therapies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".