Project description:Gut microbiota were assessed in 540 colonoscopy-screened adults by 16S rRNA gene sequencing of stool samples. Investigators compared gut microbiota diversity, overall composition, and normalized taxon abundance among these groups.
Project description:IL22 induces antimicrobial peptides which influnce microbiota. We used 16s rRNA gene sequencing (16s DNA-seq) to analyze the microbiota with Fc or IL-22Fc treatment.
Project description:Interventions: Case (colorectal cancer) group:a newly diagnosed colorectal cancer( CRC ) by colonoscopy and pathology;Control group:Clinically healthy volunteers with no symptoms or history of intestinal disease(e.g. colonic adenomatous polyps, CRC or inflammatory bowel disease)
Primary outcome(s): composition of gut microbiota;intestinal microbial phytase activity;16s rRNA metagenomic sequencing;diet surveys;phytic acid intake
Study Design: Case-Control study
Project description:Chronic acid suppression by proton pump inhibitor (PPI) has been hypothesized to alter the gut microbiota via a change in intestinal pH. To evaluate the changes in gut microbiota composition by long-term PPI treatment. Twenty-four week old F344 rats were fed with (n = 5) or without (n = 6) lansoprazole (PPI) for 50 weeks. Then, profiles of luminal microbiota in the terminal ileum were analyzed. Pyrosequencing for 16S rRNA gene was performed by genome sequencer FLX (454 Life Sciences/Roche) and analyzed by metagenomic bioinformatics.
Project description:The search for factors beyond the radiotherapy dose that could identify patients more at risk of developing radio-induced toxicity is essential to establish personalised treatment protocols for improving the quality-of-life of survivors. To investigate the role of the intestinal microbiota in the development of radiotherapy-induced gastrointestinal toxicity, the MicroLearner observational cohort study characterised the intestinal microbiota of 136 (discovery) and 79 (validation) consecutive prostate cancer patients at baseline radiotherapy. Gastrointestinal toxicity was assessed weekly during RT using CTCAE. An average grade >1.3 over time points was used to identify patients suffering from persistent acute toxicity (endpoint). The intestinal microbiota of patients was quantified from the baseline faecal samples using 16S rRNA gene sequencing technology.
Project description:To explore the effects of gut microbiota of young (8 weeks) or old mice (18～20 months) on stroke, feces of young (Y1-Y9) and old mice (O6-O16) were collected and analyzed by 16s rRNA sequencing. Then stroke model was established on young mouse receive feces from old mouse (DOT1-15) and young mouse receive feces from young mouse (DYT1-15). 16s rRNA sequencing were also performed for those young mice received feces from young and old mice.
Project description:Interventions: An observational study at the Dutch Screening for Breast Cancer will be performed in 66 postmenopausal women without breast cancer. By acquiring insight into the intestinal microbiota composition of postmenopausal women without breast cancer, a control group will be set up for already existing research lines in microbiota research in breast cancer patients at MUMC+. Fecal samples and questionnaires will be collected. The intestinal microbiota composition and absolute abundance of the fecal samples will be analyzed by with 16S rRNA Next Generation Sequencing (NGS) with subsequent qPCR to convert relative abundance to absolute abundance.
Primary outcome(s): The primary endpoints include the microbiota composition.
Study Design: N/A , unknown, Other
Project description:In this study, we performed a comparative analysis of gut microbiota composition and gut microbiome-derived bacterial extracellular vesicles (bEVs) isolated from patients with solid tumours and healthy controls. After isolating bEVs from the faeces of solid tumour patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of faeces from patientsand controls using 16S rRNA sequencing. Machine learning was used to classify the samples into patients and controls based on their bEVs and faecal microbiomes.
