Metagenomic Analysis of Microbiome in Colon Tissue from Subjects with Inflammatory Bowel Diseases Reveals Interplay of Viruses and Bacteria
Bibliographic record
Abstract
Inflammatory bowel diseases (IBD), Crohn's disease and ulcerative colitis, are poorly understood disorders affecting the intestinal tract. The current model for disease suggests that genetically susceptible patients develop intolerance to gut microflora, and chronic inflammation develops as a result of environmental insults. Although interest has mainly focused on studying genetic variants and gut bacterial flora, little is known about the potential of viral infection to contribute to disease. Accordingly, we conducted a metagenomic analysis to document the baseline virome in colonic biopsy samples from patients with IBD in order to assess the contribution of viral infection to IBD. Libraries were generated from colon RNA to create approximately 2 GB sequence data per library. Using a bioinformatic pipeline designed to detect viral sequences, more than 1000 viral reads were derived directly from tissue without any coculture or isolation procedure. Herein, we describe the complexity and abundance of viruses, bacteria/bacteriophage, and human endogenous retroviral sequences from 10 patients with IBD and 5 healthy subjects undergoing surveillance colonoscopy. Differences in gut microflora and the abundance of mammalian viruses and human endogenous retroviruses were readily detected in the metagenomic analyses. Specifically, patients with herpesviridae sequences in their colon demonstrated increased expression of human endogenous viral sequences and differences in the diversity of their microbiome. This study provides a promising metagenomic approach to describe the colonic microbiome that can be used to better understand virus-host and phage-bacteria interactions in IBD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".