Fecal microbiota transplantation (FMT) has been successfully applied to treat recurrent Clostridium difficile infection in humans, but a precise method to measure which bacterial strains stably engraft in recipients and evaluate their association with clinical outcomes is lacking. We assembled a collection of >1,000 different bacterial strains that were cultured from the fecal samples of 22 FMT donors and recipients. Using our strain collection combined with metagenomic sequencing data from the same samples, we developed a statistical approach named Strainer for the detection and tracking of bacterial strains from metagenomic sequencing data. We applied Strainer to evaluate a cohort of 13 FMT longitudinal clinical interventions and detected stable engraftment of 71% of donor microbiota strains in recipients up to 5 years post-FMT. We found that 80% of recipient gut bacterial strains pre-FMT were eliminated by FMT and that post-FMT the strains present persisted up to 5 years later, together with environmentally acquired strains. Quantification of donor bacterial strain engraftment in recipients independently explained (precision 100%, recall 95%) the clinical outcomes (relapse or success) after initial and repeat FMT. We report a compendium of bacterial species and strains that consistently engraft in recipients over time that could be used in defined live biotherapeutic products as an alternative to FMT. Our analytical framework and Strainer can be applied to systematically evaluate either FMT or defined live bacterial therapeutic studies by quantification of strain engraftment in recipients.
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Sequence data files (FASTQ) for all metagenomic sequencing samples are stored in the SRA under project number PRJNA637878. Whole-genome assembled sequences (FASTA) of all the strains have been deposited under project number PRJNA637878. Detailed metadata linking strains and fecal metagenomics to the FMT donor–recipient pair is provided in Supplementary Tables 8 and 9. Source data are provided with this paper.
The code for Strainer, a demo application and comparison with other SNP-based strain tracking algorithms is available at https://bitbucket.org/faithj02/strainer-metagenomics/src/master/ and https://doi.org/10.5281/zenodo.5191788.
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This work was supported in part by the staff and resources of the Microbiome Translational Center and the Scientific Computing Division at Icahn School of Medicine at Mount Sinai. We thank C. Fermin, E. Vazquez and G.N. Escano for gnotobiotic husbandry support and S. Simmons for helpful suggestions. This work was supported by National Institutes of Health grants (nos. NIDDK DK112978, NIDDK DK124133, NIDDK DK123749, NIDDK DK124165), a SUCCESS philanthropic award and Crohn’s and Colitis Foundation RFA awards to G.J.B. (no. 580924), V.A. (no. 650451) and J.F. (nos. 632758, 651867).
J.F. is on the scientific advisory board of Vedanta Biosciences, reports receiving research grants from Janssen Pharmaceuticals and reports receiving consulting fees from Innovation Pharmaceuticals, Janssen Pharmaceuticals, BiomX and Vedanta Biosciences. J.-F.C. reports receiving research grants from AbbVie, Janssen Pharmaceuticals and Takeda, receiving payment for lectures from AbbVie, Amgen, Allergan, Bristol Myers Squibb, Ferring Pharmaceuticals, Shire and Takeda, receiving consulting fees from AbbVie, Amgen, Arena Pharmaceuticals, Boehringer Ingelheim, Bristol Myers Squibb, Celgene Corporation, Celltrion Healthcare, Eli Lilly, Enterome, Ferring Pharmaceuticals, Geneva Pharmaceuticals, Genentech, Gilead, Iterative Scopes, Ipsen, Imedex, Immunic, Inotrem, Janssen Pharmaceuticals, Landos, LimmaTech Biologics AG, Medimmune, Merck, Novartis, O Mass, Otsuka Pharmaceutical, Pfizer, Shire, Takeda, TiGenix and Viela Bio and holds stock options in Intestinal Biotech Development. D.G. is an employee of Janssen Research and Development. The other authors declare no competing interests. A patent has been filed on this work (Patent Cooperation Treaty application PCT/US21/71018, filed 27 July 2021).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Comprehensiveness of our cultured bacterial strain library and algorithm Strainer.
a, Proportion of bacterial reads in the metagenomics sample that are explained by the genome sequences of the cultured strain library for that sample (n = 20 biologically independent samples). Each point in the plot corresponds to a separate sample. The lower and upper bounds of the box in the boxplot corresponds to 25th and 75th percentile respectively, with the median line in centre. Upper whisker extends till the maxima, while the lower whisker extends till 1.5 times the inter-quartile range. Points beyond this lower limit are also plotted. b, Proportion of bacterial reads explained by the cultured strain library for a donor after gavaging (n = 3 independent replicates) germ-free mice with stool from (n = 3) corresponding human donors, and performing metagenomics on the mouse faecal samples. Each point corresponds to a separate sample. Data for mouse replicates for each different donor sample is presented as mean values ± SEM. c, Percentage similarity between (n = 96) different isolates of species Bacteriodes ovatus and the reference strain AAXF00000000.2. Similarity is found by comparing sequence k-mers of length 31 between genomes. Each point in the boxplot corresponds to a separate sample. The lower and upper bounds of the box in the boxplot corresponds to 25th and 75th percentile respectively, with the median line in centre. Upper whisker extends till the maxima, while the lower whisker extends till the minima. d, Proportion of bacterial reads in the metagenomics sample that are explained by the genome sequences of the cultured strain library for that sample. Each point in the boxplot corresponds to a separate sample. e, Overview of our algorithm Strainer. The algorithm has 3 modules, where Module-1 involves finding the unique and likely informative sequence k-mers for each strain by removing those shared extensively with unrelated sequenced strains in NCBI, unrelated metagenomics samples, and those cultured and sequenced in this study. Next, we decompose each sequencing read in the metagenomics sample of interest into its k-mers, and find reads which have k-mers belonging to multiple strains, or have <95% of informative k-mers for a single strain. We further remove these non-informative k-mers from our previous set. In Module-2 we assign sequencing reads from the metagenomics sample of interest, with a majority of informative k-mers (>95%) to each strain. Next, we map these reads to the genome of the corresponding strain, and consider the non-overlapping ones only. This step normalizes for sequencing depth across samples and checks for evenness of read distribution across the bacterial genome. Finally, in Module-3 we compare the read enrichment in a sample to unrelated samples or negative controls and present summary statistics for presence or absence of a strain in a sample.
Extended Data Fig. 2 FMT strain dynamics (donor, pre-FMT recipient and environmental strains) in recipients post-FMT.
a, Trajectory of proportional strain engraftment of donor strains in each recipient at all available timepoints (in days). The donor recipient pair ids are at the top of each plot. b, Number of strains that transmit and engraft for at least 8-weeks in patients post-FMT (single FMT donor to recipient setting) grouped by taxonomic order. c, The number of strains colonized at 8 weeks (short term) that engraft for at least 6-months or more (long-term) in patients post-FMT (both single FMT donor to single and multiple recipients setting) grouped by taxonomic order. d, Trajectory of proportional persistence of recipient’s strains post-FMT at all available timepoints (in days). The donor recipient pair ids are at the top of each plot. e, The number of the recipient’s original strains that persist for at least 8-weeks post-FMT, grouped by taxonomic order. f, The number of environment strains (that is non-donor and non-recipient in origin) that engraft in patients stably over multiple timepoints (>1 week) post-FMT, grouped by taxonomic order.
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Aggarwala, V., Mogno, I., Li, Z. et al. Precise quantification of bacterial strains after fecal microbiota transplantation delineates long-term engraftment and explains outcomes. Nat Microbiol 6, 1309–1318 (2021). https://doi.org/10.1038/s41564-021-00966-0