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Microbiome. 2018 Sep 18;6(1):166. doi: 10.1186/s40168-018-0549-6.

Predicting recurrence of Clostridium difficile infection following encapsulated fecal microbiota transplantation.

Author information

1
Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
2
BioTechnology Institute, University of Minnesota, 140 Gortner Lab, 1479 Gortner Ave, St. Paul, MN, 55108, USA.
3
Division of Gastroenterology, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA.
4
BioTechnology Institute, University of Minnesota, 140 Gortner Lab, 1479 Gortner Ave, St. Paul, MN, 55108, USA. sadowsky@umn.edu.
5
Department of Soil, Water and Climate, University of Minnesota, St. Paul, Minnesota, USA. sadowsky@umn.edu.
6
Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, USA. sadowsky@umn.edu.

Abstract

BACKGROUND:

Fecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection (rCDI). The use of freeze-dried, encapsulated donor material for FMT (cap-FMT) allows for an easy route of administration and remains clinically effective in the majority of rCDI patients. We hypothesized that specific shifts in the microbiota in response to cap-FMT could predict clinical outcome. We further evaluated the degree of donor microbiota engraftment to determine the extent that donor transfer contributed to recovery.

RESULTS:

In total, 89 patients were treated with 100 separate cap-FMTs, with a success rate (no rCDI 60 days post cap-FMT) of 80%. Among responders, the lower alpha diversity (ANOVA P < 0.05) observed among patient's pre-FMT samples was restored following cap-FMT. At 1 week post-FMT, community composition varied by clinical outcome (ANOSIM P < 0.001), with similar abundances among families (Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae) in responder and donor samples. Families that showed differential abundances by outcome (response vs. recurrence) from samples collected 7 days following cap-FMT were used to construct a regression tree-based model to predict recurrence. Results showed a training accuracy of 100% to predict recurrence and the model was 97% accurate against a test data set of samples collected 8-20 days following cap-FMT. Evaluation of the extent of engraftment using the Bayesian algorithm SourceTracker revealed that approximately 50% of the post-FMT communities of responders were attributable to donor microbiota, while an additional 20-30% of the communities were similar to a composite healthy microbiota consisting of all donor samples.

CONCLUSIONS:

Regression tree-based analyses of microbial communities identified taxa significantly related to clinical response after 7 days, which can be targeted to improve microbial therapeutics. Furthermore, reinstatement of a healthy assemblage following cap-FMT was only partially attributable to explicit donor engraftment and continued to develop towards an overall healthy assemblage, independent of donor.

KEYWORDS:

Clostridium difficile; Encapsulated microbiota; Fecal microbiota transplantation; Machine learning; Microbial community structure; Prediction model

PMID:
30227892
PMCID:
PMC6145197
DOI:
10.1186/s40168-018-0549-6
[Indexed for MEDLINE]
Free PMC Article

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