Ontology highlight
ABSTRACT:
PROVIDER: PRJNA603432 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:To investigate the TVA diet's effect on mouse gut microbiome, we fed C57/BL6 mice with TVA diet or CON diet for 18 days We then collected feces of the mice and performed 16S ribosomal RNA (rRNA) sequencing.
2023-09-21 | GSE202266 | GEO
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.
2021-10-15 | E-MTAB-10837 | biostudies-arrayexpress
Project description:Aging is associated with declining immunity and inflammation as well as alterations in the gut microbiome with a decrease of beneficial microbes and increase in pathogenic ones. The aim of this study was to investigate aging associated gut microbiome in relation to immunologic and metabolic profile in a non-human primate (NHP) model. 12 old (age>18 years) and 4 young (age 3-6 years) Rhesus macaques were included in this study. Immune cell subsets were characterized in PBMC by flow cytometry and plasma cytokines levels were determined by bead based multiplex cytokine analysis. Stool samples were collected by ileal loop and investigated for microbiome analysis by shotgun metagenomics. Serum, gut microbial lysate and microbe-free fecal extract were subjected to metabolomic analysis by mass-spectrometry. Our results showed that the old animals exhibited higher inflammatory biomarkers in plasma and lower CD4 T cells with altered distribution of naïve and memory T cell maturation subsets. The gut microbiome in old animals had higher abundance of Archaeal and Proteobacterial species and lower Firmicutes than the young. Significant enrichment of metabolites that contribute to inflammatory and cytotoxic pathways was observed in serum and feces of old animals compared to the young. We conclude that aging NHP undergo immunosenescence and age associated alterations in the gut microbiome that has a distinct metabolic profile.
2022-02-01 | E-MTAB-10160 | biostudies-arrayexpress
Project description:Interventions: Group 1: Arm 1 - colon cancer
-> 16sRNA microbiome analysis (stool at time of initial diagnosis/ stool post operative/ stool during follow up; healthy normal tissue of the proximal and distal resection margin; tumor tissue)
Group 2: Arm 2 - rectal cancer
-> 16sRNA microbiome analysis (stool examination at time of initial diagnosis/ if neoad. therapy than stool examination after completion of neoadj. therapy/ stool examination post operative and during follow up; healthy normal tissue of the proximal and distal resection margin; tumor tissue)
Primary outcome(s): 16sRNA analysis of the microbiome (stool at initial diagnosis and postoperatively at primary surgery, or before neoadj. therapy and after completion, tumor tissue intraoperatively) in patients with CRC.
Study Design: Allocation: ; Masking: ; Control: ; Assignment: ; Study design purpose: other
| 2439238 | ecrin-mdr-crc
Project description:Soil microbiome using 16sRNA sequencing
| PRJEB77421 | ENA
Project description:Exposure to high-dose radiation causes life-threatening serious intestinal damage. Histological analysis is the most accurate method for judging the extent of intestinal damage after death. However, it is difficult to predict the extent of intestinal damage to body samples. Here we focused on extracellular microRNAs (miRNAs) released from cells and investigated miRNA species that increased or decreased in serum and feces using a radiation-induced intestinal injury mouse model. A peak of small RNA of 25–200 nucleotides was detected in mouse serum and feces 72 h after radiation exposure, and miRNA presence in serum and feces was inferred. MiRNAs expressed in the small intestine and were increased by more than 2.0-fold in serum or feces following a 10 Gy radiation exposure were detected by microarray analysis and were 4 in serum and 19 in feces. In this study, miR-375-3p, detected in serum and feces, was identified as the strongest candidate for a high-dose radiation biomarker in serum and/or feces using a radiation-induced intestinal injury model.
2023-12-15 | GSE247876 | GEO
Project description:In rodents, brown adipose tissue (BAT) contributes to whole body energy expenditure and low BAT activity is related to hepatic fat accumulation, partially attributable to the gut microbiome. Little is known of these relationships in humans. In adults (n=60), we assessed hepatic fat and cold-stimulated BAT activity utilizing magnetic resonance imaging and the gut microbiome with 16S sequencing. We transplanted gnotobiotic mice with feces from humans to assess the transferability of BAT activity and NAFLD through the microbiome. Individuals with NAFLD (n=29) had lower BAT activity than those without and BAT activity was inversely related to hepatic fat. Although the fecal microbiome was different in those with NAFLD, no differences were observed in relation to BAT activity and neither of these phenotypic traits were transmissible through fecal transplant to gnotobiotic mice. Thus, low BAT activity is associated with hepatic steatosis but this is not mediated through the gut microbiota.
2021-09-20 | GSE162608 | GEO
Project description:Microbiome sequencing model is a Named Entity Recognition (NER) model that identifies and annotates microbiome nucleic acid sequencing method or platform in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sequencing metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
2022-02-21 | MODEL2202170012 | BioModels