Microbial Patterns in Newly Diagnosed Inflammatory Bowel Disease Revealed by Presence and Transcriptional Activity - Relationship to Diagnosis and Outcome
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».