Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
08
10
2021
accepted:
21
07
2022
pubmed:
16
9
2022
medline:
28
9
2022
entrez:
15
9
2022
Statut:
ppublish
Résumé
Fecal microbiota transplantation (FMT) is highly effective against recurrent Clostridioides difficile infection and is considered a promising treatment for other microbiome-related disorders, but a comprehensive understanding of microbial engraftment dynamics is lacking, which prevents informed applications of this therapeutic approach. Here, we performed an integrated shotgun metagenomic systematic meta-analysis of new and publicly available stool microbiomes collected from 226 triads of donors, pre-FMT recipients and post-FMT recipients across eight different disease types. By leveraging improved metagenomic strain-profiling to infer strain sharing, we found that recipients with higher donor strain engraftment were more likely to experience clinical success after FMT (P = 0.017) when evaluated across studies. Considering all cohorts, increased engraftment was noted in individuals receiving FMT from multiple routes (for example, both via capsules and colonoscopy during the same treatment) as well as in antibiotic-treated recipients with infectious diseases compared with antibiotic-naïve patients with noncommunicable diseases. Bacteroidetes and Actinobacteria species (including Bifidobacteria) displayed higher engraftment than Firmicutes except for six under-characterized Firmicutes species. Cross-dataset machine learning predicted the presence or absence of species in the post-FMT recipient at 0.77 average AUROC in leave-one-dataset-out evaluation, and highlighted the relevance of microbial abundance, prevalence and taxonomy to infer post-FMT species presence. By exploring the dynamics of microbiome engraftment after FMT and their association with clinical variables, our study uncovered species-specific engraftment patterns and presented machine learning models able to predict donors that might optimize post-FMT specific microbiome characteristics for disease-targeted FMT protocols.
Identifiants
pubmed: 36109637
doi: 10.1038/s41591-022-01964-3
pii: 10.1038/s41591-022-01964-3
pmc: PMC9499858
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1913-1923Subventions
Organisme : NCI NIH HHS
ID : U01 CA230551
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022. The Author(s).
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