2024-09-20
ENA first public
ERC000050
ENA-CHECKLIST
EMBL Heidelberg
INSDC center name
public
INSDC status
ERS21063008
SRA accession
spire_mag_01842863
Submitter Id
Many fragments with little to no review of assembly other than reporting of standard assembly statistics
assembly quality
megahit 1.2.9
assembly software
default
binning parameters
metabat 2.12.1
binning software
human digestive system
broad-scale environmental context
EBI-EMG
broker name
2010-08-03
collection date
98.76
%
completeness score
checkm2 0.1.3
completeness software
5.07
%
contamination score
This sample represents a Third Party Annotation (TPA) binned metagenome assembled from the metagenomic run SRR10025146 of study SRP219390.
description
stool
environmental medium
USA
geographic location (country and/or sea)
missing: third party data
DD
geographic location (latitude)
missing: third party data
DD
geographic location (longitude)
metagenome-assembled genome
investigation type
human gut metagenome
isolation_source
human gut
local environmental context
human gut metagenome
metagenomic source
Phocaeicola vulgatus
organism
Extreme dysbiosis in the gastrointestinal microbiota is frequent in intensive care unit (ICU) patients. This dysbiosis is associated with increased risk of pathobiont colonization and healthcare-associated infections. Researchers have been increasingly interested in developing predictive microbiota disruption indices (MDIs) that could be used to identify patients at risk of poor health outcomes such as gastrointestinal colonization by drug-resistant bacteria or hospital-acquired infections. We used 16S rRNA gene sequencing and shotgun metagenomic sequencing of DNA extracted stool samples. The stool samples were collected on consecutive days. These samples and clinical/epidemiologic data were used to examine temporal patterns in the microbiome. We learned that the microbiota of ICU patients can fluctuate on a daily basis. Gastrointestinal domination by specific bacteria, which is considered a risk factor for infection, was common, variable, and due to both pathogens (e.g., Enterococcus) and commensal bacteria species (e.g., Lactobacillus). Fluctuations in the microbiota were generally what one would predict based on the specific taxa and resistance genes present and antibiotic(s) administered. The rapid changes in the GI microbiota have implications for the design of MDIs for patient monitoring.
project name
SAMN12617467
sample derived from
Phocaeicola vulgatus
scientific_name
Illumina MiSeq
sequencing method
multi-marker approach
taxonomic identity marker
binned metagenome: spire_mag_01842863
title