Project description:We explore whether a low-energy diet intervention for Metabolic dysfunction-associated steatohepatitis (MASH) improves liver disease by means of modulating the gut microbiome. 16 individuals were given a low-energy diet (880 kcal, consisting of bars, soups, and shakes) for 12 weeks, followed by a stepped re-introduction to whole for an additional 12 weeks. Stool samples were obtained at 0, 12, and 24 weeks for microbiome analysis. Fecal microbiome were measured using 16S rRNA gene sequencing. Positive control (Zymo DNA standard D6305) and negative control (PBS extraction) were included in the sequencing. We found that low-energy diet improved MASH disease without lasting alterations to the gut microbiome.
Project description:Total DNA was extracted from stool specimens, amplified to collect amplicons of variable V3–V4 regions of the bacterial 16s rRNA gene and sequenced with MiSeq (2x300bp) Illumina platform.
Project description:Primary outcome(s): Analysis of the diversity and composition of the gut microbiome by 16S rRNA sequencing
Study Design: Observational Study Model : Others, Time Perspective : Prospective, Enrollment : 60, Biospecimen Retention : Collect & Archive- Sample with DNA, Biospecimen Description : Blood, Stool
Project description:The Gut health in multiple joint osteoarthritis (MJOA) study leverages data from parallel community-based cohorts in humans and in pet dogs to elucidate the role of altered microbiota in MJOA. One hundred Johnston County Health Study human participants were 35 to 70 years of age at enrollment (2022-2023), self-identified as Hispanic, White, or Black, and lived in Johnston County, North Carolina. Demographic, clinical information, multiple joint radiographs, and stool samples for microbiome profiling by 16S rRNA gene sequencing were obtained from all participants. Similar data were collected from an independent group of pet dogs (N=115) from the local community, at the North Carolina State University (NCSU) College of Veterinary Medicine. The central hypothesis of the study is that intestinal permeability, with or without dysbiosis, is a major driver in the development and worsening of MJOA.
Project description:The human gut is colonized by trillions of microorganisms that influence human health and disease through the metabolism of xenobiotics, including therapeutic drugs and antibiotics. The diversity and metabolic potential of the human gut microbiome have been extensively characterized, but it remains unclear which microorganisms are active and which perturbations can influence this activity. Here, we use flow cytometry, 16S rRNA gene sequencing, and metatranscriptomics to demonstrate that the human gut contains distinctive subsets of active and damaged microorganisms, primarily composed of Firmicutes, which display marked temporal variation. Short-term exposure to a panel of xenobiotics resulted in significant changes in the physiology and gene expression of this active microbiome. Xenobiotic-responsive genes were found across multiple bacterial phyla, encoding novel candidate proteins for antibiotic resistance, drug metabolism, and stress response. These results demonstrate the power of moving beyond DNA-based measurements of microbial communities to better understand their physiology and metabolism. RNA-Seq analysis of the human gut microbiome during exposure to antibiotics and therapeutic drugs.
Project description:Hundreds of microbial species were found to be transcriptionally active in the human gut microbiome based on the expression profiling of ca. 680.000 microbial genes As a part of the MetaHIT cohort 233 human stool samples were transcriptionally profiled using a custom made microarray that included probes for most prevalent microbial genes in the cohort as established by whole-genome sequencing of the same samples
Project description:This study investigates the gut microbiome composition and diversity in three groups of rats: control, radiation enteritis model, and treatment (TG) groups. Total DNA was extracted from stool samples, PCR-amplified targeting 16S rRNA gene variable regions, and sequenced using Illumina MiSeq or NovaSeq platforms. Downstream bioinformatics analyses included sequence quality control, denoising (DADA2/OTU clustering), taxonomic classification, alpha and beta diversity evaluation, differential species abundance analysis, and functional prediction. The processed data include ASV/OTU tables, taxonomy assignments, and sample metadata.
Project description:Gut microbial profiling of uterine fibroids (UFs) patients comparing control subjects. The gut microbiota was examined by 16S rRNA quantitative arrays and bioinformatics analysis. The goal was to reveal alterations in the gut microbiome of uterine fibroids patients.
