RT Journal Article
SR Electronic
T1 High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 090993
DO 10.1101/090993
A1 Sonny TM Lee
A1 Stacy A. Kahn
A1 Tom O. Delmont
A1 Nathaniel J. Hubert
A1 Hilary G. Morrison
A1 Dionysios A. Antonopoulos
A1 David T. Rubin
A1 A. Murat Eren
YR 2016
UL http://biorxiv.org/content/early/2016/12/02/090993.abstract
AB Fecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection and shows promise for treating other medical conditions associated with intestinal dysbioses. However, we lack a sufficient understanding of which microbial populations successfully colonize the recipient gut, and the widely used approaches to study the microbial ecology of FMT experiments fail to provide enough resolution to identify populations that are likely responsible for FMT-derived benefits. Here we used shotgun metagenomics to reconstruct 97 metagenome-assembled genomes (MAGs) from fecal samples of a single donor and followed their distribution in two FMT recipients to identify microbial populations with different colonization properties. Our analysis of the occurrence and distribution patterns post-FMT revealed that 22% of the MAGs transferred from the donor to both recipients and remained abundant in their guts for at least eight weeks. Most MAGs that successfully colonized the recipient gut belonged to the order Bacteroidales. The vast majority of those that lacked evidence of colonization belonged to the order Clostridiales and colonization success was negatively correlated with the number of genes related to sporulation. Although our dataset showed a link between taxonomy and the ability of a MAG to colonize the recipient gut, we also identified MAGs with different colonization properties that belong to the same taxon, highlighting the importance of genome-resolved approaches to explore the functional basis of colonization and to identify targets for cultivation, hypothesis generation, and testing in model systems for mechanistic insights.