Microbial Patterns in Newly Diagnosed Inflammatory Bowel Disease Revealed by Presence and Transcriptional Activity - Relationship to Diagnosis and Outcome
Bibliographic record
Abstract
Simen Svendsen Vatn,1– 3,* Simen Hyll Hansen,1,4,* Tone Møller Tannæs,5 Stephan Brackmann,1,2 Christine Olbjørn,1,6 Daniel Bergemalm,7 Åsa V Keita,8 Fernando Gomollon,9 Trond Espen Detlie,1,2 Rahul Kalla,10 Jack Satsangi,10,11 Jørgen Jahnsen,1,2 Morten Harald Vatn,1 Jonas Halfvarson,7 Johannes Roksund Hov,1,4,12 Petr Ricanek,2,13 Aina EF Moen1,5,14 On behalf of the IBD-Character Consortium1Institute of Clinical Medicine, University of Oslo, Oslo, Norway; 2Department of Gastroenterology, Division of Medicine, Akershus University Hospital, Lørenskog, Norway; 3Innlandet Hospital Trust, Gjøvik, Norway; 4Norwegian PSC Research Center and Research Institute of Internal Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital, Oslo, Norway; 5Section for Clinical Molecular Biology (Epigen), Akershus University Hospital, Lørenskog, Norway; 6Department of Pediatric and Adolescent Medicine, Akershus University Hospital, Lørenskog, Norway; 7Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; 8Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden; 9Digestive Diseases Unit, IIS Aragón, Zaragoza, Spain; 10Gastrointestinal Unit, Centre for Genomics and Molecular Medicine, Division of Medical and Radiological Sciences, University of Edinburgh, Edinburgh, UK; 11Translational Gastroenterology Unit, Medical Sciences/ Experimental Medicine Division, University of Oxford, Oxford, UK; 12Section of Gastroenterology, Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; 13Department of Gastroenterology, lovisenberg Diaconal Hospital, Oslo, Norway; 14Department of Virology, Norwegian Institute of Public Health, Oslo, Norway*These authors contributed equally to this workCorrespondence: Simen Svendsen Vatn, Innlandet Hospital Trust, Gjøvik, Norway, Tel +47 94277594, Email bikkjas@hotmail.comBackground: As part of the IBD Character initiative, we examined an inception cohort and investigated mucosal microbiota composition and transcriptional activity in relation to clinical outcomes.Methods: A cohort of 237 individuals were included from five countries: Crohn’s disease (CD, n = 72), ulcerative colitis (UC, n = 57), symptomatic non-IBD controls (SC, n = 78) and healthy controls (HC, n = 30). Rectal/colonic biopsies were obtained at inclusion, and DNA and RNA were extracted from the same biopsy and examined by sequencing the 16S rRNA V4 region.Results: Beta diversity measurements separated IBD from both HC and SC. IBD and SC exhibited reduced intra-individual diversity compared with HC. When comparing taxonomy at DNA and RNA level, six bacteria were found to differ in abundance and/or transcriptional activity between IBD and symptomatic control, while there were 14 and three between symptomatic control and CD and UC, respectively. A limited number of bacterial taxa were responsible for the largest difference between presence and activity, separating patients and controls. Multiple bacterial taxa were associated with treatment escalation in both UC and CD. Machine-learning models separated IBD from symptomatic controls and treatment escalators from non-escalators (AUC > 0.8). However, the differential effects were mainly driven by clinical biomarkers, such as f-calprotectin, s-albumin, and b-hemoglobin.Conclusion: Differences between presence and transcriptional activity were found among multiple taxa when assessing 16S rRNA at DNA and RNA level. Symptomatic controls were more similar to the IBD patients compared to HC. The analyses suggest that the mucosal microbiota carries a moderate diagnostic and predictive potential, outcompeted by f-calprotectin.Keywords: microbiota, RNA, DNA, IBD, biomarkers
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".