Metagenome-assembled genome: ERR2198699_bin.55_CONCOCT_v1.1_MAG
Pediococcus acidilactici
This sample represents a Third Party Annotation (TPA) Metagenome-Assembled Genome (MAG) assembled from the metagenomic run ERR2198699 of study ERP105282.
ENA-FIRST-PUBLIC
2023-01-03
ENA-LAST-UPDATE
2023-01-03
External Id
SAMEA14083999
INSDC center alias
EBI
INSDC center name
European Bioinformatics Institute
INSDC first public
2023-01-03T00:33:19Z
INSDC last update
2023-01-03T00:33:19Z
INSDC status
public
Submitter Id
ERR2198699_bin.55_CONCOCT_v1.1_MAG
assembly quality
Many fragments with little to no review of assembly other than reporting of standard assembly statistics
assembly software
metaSPAdes v3.12.0
binning parameters
Default
binning software
CONCOCT v1.1
broker name
EMG broker account, EMBL-EBI
collection date
2015-01-01
completeness score
99.38
completeness software
CheckM
contamination score
0.62
environment (biome)
Host-associated
environment (feature)
Human
environment (material)
Digestive system
geographic location (country and/or sea)
USA
geographic location (latitude)
42.0
geographic location (longitude)
71.0
investigation type
metagenome-assembled genome
isolation_source
human gut metagenome
metagenomic source
human gut metagenome
project name
The metadata for this study is available on Dropbox:dropbox [DOT] com/sh/kfiu4wb53oemn6j/AAB13sgaKS7MV4lVqXLCxTuMa?dl=0Abstract: Fecal microbiota transplantation (FMT) is a treatment for microbiome-associated diseases in which gut microbiota are transferred from a healthy donor to a patient. Although the success of FMT requires donor bacteria to engraft in the patient&apos;s gut, the forces governing bacterial engraftment in humans are unknown. Here, we use a vast, ongoing clinical experiment - the treatment of recurrent Clostridium difficile infection with FMT - to uncover the rules of engraftment in humans. First, we built a machine learning model that accurately predicts which bacterial species will engraft in a given host. We then developed a maximum-likelihood strain inference method, Strain Finder, allowing us to infer the genotypes of donor strains and to track them through patients&apos; guts over time. Surprisingly, engraftment could be predicted largely from the abundance and phylogeny of bacteria in the donor and the pre-FMT patient. We also found that donor strains within a species engraft in an all-or-nothing manner and that previously undetected strains frequently colonize the patient after FMT. We validated these findings in another disease context, metabolic syndrome, suggesting that the same principles of engraftment extend to other indications. These findings may guide the design of bacterial therapeutics that target diseases ranging from ulcerative colitis to cancer.
sample derived from
SAMEA104393774
sample name
ERR2198699_bin.55_CONCOCT_v1.1_MAG
scientific_name
Pediococcus acidilactici
sequencing method
Illumina Genome Analyzer IIx
taxonomic identity marker
multi-marker approach