| collection date | 2015-01-01 |
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| broad-scale environmental context | Host-associated |
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| local-scale environmental context | Human |
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| environmental medium | Digestive system |
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| geographic location | USA |
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| investigation type | metagenome-assembled genome |
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| isolation source | human gut metagenome |
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| 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'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' 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. |
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| sample name | ERR2198685_bin.59_CONCOCT_v1.1_MAG |
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| ENA-CHECKLIST | ERC000047 |
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| ENA-FIRST-PUBLIC | 2023-01-03 |
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| ENA-LAST-UPDATE | 2023-01-03 |
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| External Id | SAMEA14083994 |
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| INSDC center alias | EBI |
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| INSDC center name | European Bioinformatics Institute |
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| INSDC first public | 2023-01-03T00:33:17Z |
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| INSDC last update | 2023-01-03T00:33:17Z |
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| INSDC status | public |
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| Submitter Id | ERR2198685_bin.59_CONCOCT_v1.1_MAG |
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| assembly quality | Many fragments with little to no review of assembly other than reporting of standard assembly statistics |
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| assembly software | metaSPAdes v3.12.0 |
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| binning parameters | Default |
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| binning software | CONCOCT v1.1 |
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| broker name | EMG broker account, EMBL-EBI |
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| completeness score | 96.2 |
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| completeness software | CheckM |
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| contamination score | 1.75 |
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| geographic location (latitude) | 42.0 |
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| geographic location (longitude) | 71.0 |
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| metagenomic source | human gut metagenome |
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| sample derived from | SAMEA104393760 |
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| scientific_name | uncultured Streptococcus sp. |
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| sequencing method | Illumina Genome Analyzer IIx |
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| taxonomic identity marker | multi-marker approach |
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