Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases

作者：Gianluca Ianiro, Michal Punčochář, Nicolai Karcher, Serena Porcari, Federica Armanini, Francesco Asnicar, Francesco Beghini, Aitor Blanco‐Míguez, Fabio Cumbo, Paolo Manghi, Federica Pinto, Luca Masucci, Gianluca Quaranta, Silvia De Giorgi, Giusi Desirè Sciumè, Stefano Bibbò, Federica D. Del Chierico, Lorenza Putignani, Maurizio Sanguinetti, Antonio Gasbarrini, Mireia Valles‐Colomer, Giovanni Cammarota, Nicola Segata · 发表于：Nature Medicine · 年份：2022 · DOI：10.1038/s41591-022-01964-3 · 被引用次数：374 · 研究领域：Clostridium difficile and Clostridium perfringens research、Gut microbiota and health、Helicobacter pylori-related gastroenterology studies

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 releva...