Ontology highlight
ABSTRACT:
PROVIDER: PRJNA921941 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Microbiota assembly in the infant gut is influenced by time and duration of dietary exposure to breast-milk, infant formula and solid foods. In this randomized controlled intervention study, longitudinal sampling of infant stools (n=998) showed similar development of fecal bacterial communities between formula- and breast-fed infants during the first year of life (N=210). Infant formula supplemented with galacto-oligosaccharides (GOS) was most efficient to sustain high levels of bifidobacteria compared to formula containing B. longum and B. breve or placebo. Metabolite (untargeted) and bacterial profiling (16S rRNA/shallow metagenomics sequencing) revealed 24-hour oscillations and integrated data analysis identified circadian networks. Rhythmicity in bacterial diversity, specific taxa and functional pathways increased with age and was most pronounced following breast-feeding and GOS-supplementation. Circadian rhythms in dominant taxa were discovered ex-vivo in a chemostat model. Hence microbiota rhythmicity develops early in life, likely due to bacterial intrinsic clock mechanism and is affected by diet.
2024-01-10 | MSV000093802 | MassIVE
Project description:Feces 16s rRNA seq experiments is required to demonstrate the AZ1 doesn’t impact the intestinal microbiota.
2020-02-14 | GSE143003 | GEO
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
| 2667847 | ecrin-mdr-crc
Project description:In this study we developed metaproteomics based methods for quantifying taxonomic composition of microbiomes (microbial communities). We also compared metaproteomics based quantification to other quantification methods, namely metagenomics and 16S rRNA gene amplicon sequencing. The metagenomic and 16S rRNA data can be found in the European Nucleotide Archive (Study number: PRJEB19901). For the method development and comparison of the methods we analyzed three types of mock communities with all three methods. The communities contain between 28 to 32 species and strains of bacteria, archaea, eukaryotes and bacteriophage. For each community type 4 biological replicate communities were generated. All four replicates were analyzed by 16S rRNA sequencing and metaproteomics. Three replicates of each community type were analyzed with metagenomics. The "C" type communities have same cell/phage particle number for all community members (C1 to C4). The "P" type communities have the same protein content for all community members (P1 to P4). The "U" (UNEVEN) type communities cover a large range of protein amounts and cell numbers (U1 to U4). We also generated proteomic data for four pure cultures to test the specificity of the protein inference method. This data is also included in this submission.
2017-05-12 | PXD006118 | Pride
Project description:Huanglongbing (HLB) is a worldwide devastating disease of citrus. There are no effective control measures for this newly emerging but century-old disease. A powerful oligonucleotide microarray of high-density 16S rRNA genes, the PhyloChip microarray, has been developed and effectively used to study bacterial diversity, especially from environmental samples. In this article, we aim to decipher the bacterial microbiome in HLB-affected citrus versus non-infected citrus as well as in citrus plants treated with ampicillin and gentamicin using PhyloChip-based metagenomics.
2013-05-09 | GSE46728 | GEO
Project description:The nasopharyngeal microbiota of healthy cattle vs. cattle diagnosed with BRD in a commercial feedlot setting was compared using a high-density 16S rRNA microarray (Phylochip). Nasopharyngeal samples were taken from both groups of animals (n=5) at feedlot entry (day 0) and >60 days later.
2015-06-21 | E-MTAB-3430 | biostudies-arrayexpress
Project description:Metadata for 16S-V4 rRNA gene sequence data for intestinal biopsies from NCT02749630 trial participants.
| EGAD00001008823 | EGA
Project description:A metaproteomics analysis was conducted on the infant fecal microbiome to characterize global protein expression in 8 samples obtained from infants with a range of early-life experiences. Samples included breast-, formula- or mixed-fed, mode of delivery, and antibiotic treatment and one set of monozygotic twins. Although label-free mass spectrometry-based proteomics is routinely used for the identification and quantification of thousands of proteins in complex samples, the metaproteomic analysis of the gut microbiome presents particular technical challenges. Among them: the extreme complexity and dynamic range of member taxa/species, the need for matched, well-annotated metagenomics databases, and the high inter-protein sequence redundancy/similarity between related members. In this study, a metaproteomic approach was developed for assessment of the biological phenotype and functioning, as a complement to 16S rRNA sequencing analysis to identify constituent taxa. A sample preparation method was developed for recovery and lysis of bacterial cells, followed by trypsin digestion, and pre-fractionation using Strong Cation Exchange chromatography. Samples were then subjected to high performance LC-MS/MS. Data was searched against the Human Microbiome Project database, and a homology-based meta-clustering strategy was used to combine peptides from multiple species into representative proteins. Bacterial taxonomies were also identified, based on species-specific protein sequences, and protein metaclusters were assigned to pathways and functional groups. The results obtained demonstrate the applicability of this approach for performing qualitative comparisons of human fecal microbiome composition, physiology and metabolism, and also provided a more detailed assessment of microbial composition in comparison to 16S rRNA.
2019-03-29 | PXD006033 | Pride