Background/Aims: In ulcerative colitis (UC) patients, Escherichia coli Nissle 1917 (EcN) is equivalent to mesalazine for preventing disease relapse; however, evidence of the ability of EcN to increase health-related quality of life or induce remission remains scarce. We investigated the efficacy of EcN as an add-on therapy for UC.Methods: In this multicentre, double-blind, randomised, placebo-controlled study, a total of 133 UC patients were randomly assigned to receive either EcN or placebo once daily for 8 weeks. Inflammatory bowel disease questionnaire (IBDQ) scores (primary endpoint) and clinical remission and response rates (secondary endpoints) were compared (Clinical trial registration number: NCT04969679).Results: In total, 118 patients (EcN, 58; placebo, 60) completed the study. The number of patients reaching the primary endpoint did not differ between the EcN and placebo groups (30 [51.7%] vs. 31 [51.7%]; per-protocol analysis, p = 1.0; intention-to-treat analysis, p = 0.86). However, significantly fewer patients in the EcN group exhibited a decreased IBDQ score (1 [1.7%] vs. 8 [13.3%]; per-protocol analysis, p = 0.03; intention- to-treat analysis, p = 0.02). Moreover, a significantly higher number of patients in the EcN group displayed clinical response at 4 weeks (23 [39.7%] vs. 13 [21.7%], p = 0.04) and endoscopic remission at 8 weeks (26 [46.4%] vs. 16 [27.1%], p = 0.03).Conclusions: Although the number of patients reaching the primary endpoint did not differ between the EcN and placebo groups, EcN was found to be safe and effective in preventing the exacerbation of IBDQ scores and achieving clinical responses and endoscopic remission in patients with mild-to-moderate UC.

Crohn’s disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn’s disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

Early intestinal resection in patients with Crohn’s disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients recruited from 15 hospitals were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical–genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001). Identification of genetic polymorphisms and clinical features enhanced the prediction of early intestinal resection in patients with CD.

Limited studies have examined the intestinal microbiota composition in relation to Crohn’s disease (CD) prognosis. We analyzed the differences in microbial communities and relevant metabolic pathways associated with prognostic variables in patients with CD. We applied 16S rRNA gene sequencing to analyze a cohort of 1110 CD and healthy control (HC) fecal samples. We categorized patients with CD into good (CD-G), intermediate (CD-I) and poor (CD-P) prognosis groups, according to the history of using biologics and intestinal resection. Microbiota α-diversity decreased more in CD-P than CD-G and CD-I. Microbiota ß-diversity in CD-P differed from that in CD-G and CD-I. Thirteen genera and 10 species showed differential abundance between CD-G and CD-P groups. Escherichia coli (p = 0.001) and species Producta (p = 0.01) and genera Lactobacillus (p = 0.003) and Coprococcus (p = 0.01) consistently showed differences between CD-G and CD-P groups after adjusting for confounding variables. Functional profiling suggested that the microbial catabolic pathways and pathways related to enterobacterial common antigen and lipopolysaccharide biosynthesis were better represented in the CD-P group than in the CD-G group, and E. coli were the top contributors to these pathways. CD prognosis is associated with altered microbiota composition and decreased diversity, and E. coli might be causally involved in CD progression, and may have adapted to live in inflammatory environments.