A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis.

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn's disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD ( = 671) and UC ( = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center.

Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn's Disease.

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn's disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA).

Potential Oral Microbial Markers for Differential Diagnosis of Crohn's Disease and Ulcerative Colitis Using Machine Learning Models.

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: = 175, CD: = 127) and healthy controls (HC: = 100) to analyze their oral microbiota using 16S rRNA gene sequencing.

Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn's Disease Using Transcriptome Imputed from Genotypes.

Almost half of patients show no primary or secondary response to monoclonal anti-tumor necrosis factor α (anti-TNF) antibody treatment for inflammatory bowel disease (IBD). Thus, the exact mechanisms of a non-durable response (NDR) remain inadequately defined. We used our genome-wide genotype data to impute expression values as features in training machine learning models to predict a NDR.

Development of a Machine Learning Model to Distinguish between Ulcerative Colitis and Crohn's Disease Using RNA Sequencing Data.

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 ( = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues.

Reversion of Gut Microbiota during the Recovery Phase in Patients with Asymptomatic or Mild COVID-19: Longitudinal Study.

Patients with COVID-19 have been reported to experience gastrointestinal symptoms as well as respiratory symptoms, but the effects of COVID-19 on the gut microbiota are poorly understood. We explored gut microbiome profiles associated with the respiratory infection of SARS-CoV-2 during the recovery phase in patients with asymptomatic or mild COVID-19. A longitudinal analysis was performed using the same patients to determine whether the gut microbiota changed after recovery from COVID-19.

Exocyst subunit Exo70B2 is linked to immune signaling and autophagy.

During the immune response, activation of the secretory pathway is key to mounting an effective response, while gauging its output is important to maintain cellular homeostasis. The Exo70 subunit of the exocyst functions as a spatiotemporal regulator by mediating numerous interactions with proteins and lipids. However, a molecular understanding of the exocyst regulation remains challenging.

Detection of SARS-CoV-2 in Fecal Samples From Patients With Asymptomatic and Mild COVID-19 in Korea.

Background & Aims: Although coronavirus disease 2019 (COVID-19) is characterized by fever and respiratory symptoms, some patients have no or mild symptoms. Severe acute respiratory syndrome-coronavirus (SARS-CoV-2) has been detected in feces of patients. We investigated gastrointestinal symptoms and shedding of virus into feces of patients with asymptomatic or mild COVID-19.

Differentially Abundant Bacterial Taxa Associated with Prognostic Variables of Crohn's Disease: Results from the IMPACT Study.

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.

Regulation of oncogene expression in T-DNA-transformed host plant cells.

Virulent Agrobacterium tumefaciens strains integrate their T-DNA into the plant genome where the encoded agrobacterial oncogenes are expressed and cause crown gall disease. Essential for crown gall development are IaaH (indole-3-acetamide hydrolase), IaaM (tryptophan monooxygenase) and Ipt (isopentenyl transferase), which encode enzymes for the biosynthesis of auxin (IaaH, IaaM) and cytokinin (Ipt). Although these oncogenes are well studied as the tumor-inducing principle, nothing is known about the regulation of oncogene expression in plant cells.

Targeted proteomics analysis of protein degradation in plant signaling on an LTQ-Orbitrap mass spectrometer.

Targeted proteomics has become increasingly popular recently because of its ability to precisely quantify selected proteins in complex cellular backgrounds. Here, we demonstrated the utility of an LTQ-Orbitrap Velos Pro mass spectrometer in targeted parallel reaction monitoring (PRM) despite its unconventional dual ion trap configuration. We evaluated absolute specificity (>99%) and sensitivity (100 amol on column in 1 μg of total cellular extract) using full and mass range scans as survey scans together with data-dependent (DDA) and targeted MS/MS acquisition.

Agrobacterium tumefaciens promotes tumor induction by modulating pathogen defense in Arabidopsis thaliana.

Agrobacterium tumefaciens causes crown gall disease by transferring and integrating bacterial DNA (T-DNA) into the plant genome. To examine the physiological changes and adaptations during Agrobacterium-induced tumor development, we compared the profiles of salicylic acid (SA), ethylene (ET), jasmonic acid (JA), and auxin (indole-3-acetic acid [IAA]) with changes in the Arabidopsis thaliana transcriptome. Our data indicate that host responses were much stronger toward the oncogenic strain C58 than to the disarmed strain GV3101 and that auxin acts as a key modulator of the Arabidopsis-Agrobacterium interaction.

A central role of abscisic acid in drought stress protection of Agrobacterium-induced tumors on Arabidopsis.

Crown gall tumors induced by Agrobacterium tumefaciens represent a sink that has to be provided with nutrients and water by the host plant. The lack of an intact epidermis or cuticle results in uncontrolled loss of water. However, neither the tumor nor the host plant displays wilting.