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Long-read metagenomics for strain tracking after faecal microbiota transplant

Nature Microbiology volume 10, pages 3258–3271 (2025)Cite this article

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Abstract

Accurate tracking of bacterial strains that stably engraft in faecal microbiota transplant (FMT) recipients is critical for understanding the determinants of strain engraftment, evaluating correlations with clinical outcomes and guiding the development of therapeutic consortia. While short-read sequencing has advanced FMT research, it faces challenges in strain-level de novo metagenomic assembly. Here we describe LongTrack, a method that uses long-read metagenomic assemblies for FMT strain tracking. LongTrack shows higher precision and specificity than short-read approaches, especially when multiple strains co-exist in the same sample. We uncovered 648 engrafted strains across six FMT cases involving patients with recurrent Clostridioides difficile infection and inflammatory bowel disease. Furthermore, long reads enabled assessment of the genomic and epigenomic stability of engrafted strains at the 5-year follow-up timepoint, revealing structural variations that may be associated with strain adaptation in a new host environment. Our findings support the use of long-read metagenomics to track microbial strains and their adaptations.

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Fig. 1: Description of FMT samples, matched bacteria isolates and the core design of LongTrack for reliable FMT strain tracking and detecting intrastrain SVs in engrafted strains.
Fig. 2: High-purity, strain-level long-read MAGs serve as a solid foundation for LongTrack.
Fig. 3: Evaluation of LongTrack for FMT strain tracking.
Fig. 4: LongTrack uncovered hundreds of engrafted strains across six FMT cases of patients with rCDI and IBD.
Fig. 5: Long reads uncovered complex SVs in engrafted strains during the 5-year follow-up after rCDI FMT.

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Data availability

All sequencing data generated for this study are available via the Sequence Read Archive under BioProject (PRJNA1101882). Source data are provided with this paper.

Code Availability

Customized scripts of LongTrack and a tutorial with supporting data are available via GitHub at https://github.com/fanglab/LongTrack. Source data are provided with this paper.

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Acknowledgements

This work was supported in part by the staff and resources of the Microbiome Translational Center, Center for AI-Driven Genomic and Microbiome Medicine and Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai. This work was supported by grant no. R35 GM139655 (G.F.) from the National Institutes of Health. G.F. is a Hirschl Research Scholar by Irma T. Hirschl/Monique Weill-Caulier Trust and a Nash Family Research Scholar.

Author information

Author notes

  1. Varun Aggarwala

    Present address: Reliance Foundation Institution of Education and Research, Jio Institute, Navi Mumbai, India

Authors and Affiliations

  1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Yu Fan, Mi Ni, Varun Aggarwala, Edward A. Mead, Magdalena Ksiezarek, Lei Cao, Jeremiah J. Faith & Gang Fang

  2. Department of Gastroenterology and Medicine, St. Vincent’s Hospital, Melbourne, Victoria, Australia

    Michael A. Kamm

  3. Centre for Digestive Diseases, Sydney, New South Wales, Australia

    Thomas J. Borody

  4. University of Sydney, Sydney, New South Wales, Australia

    Sudarshan Paramsothy

  5. School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia

    Nadeem O. Kaakoush

  6. Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ari Grinspan

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Contributions

Initiated study: G.F. and J.J.F. Developed methodology: Y.F. and G.F. Data analysis: Y.F., J.J.F. and G.F. Data interpretation: Y.F., M.N., V.A., E.A.M., M.K., L.C., J.J.F. and G.F. Collection and interpretation of clinical data: M.A.K., T.J.B., S.P., N.O.K. and A.G. Supervised research: G.F. Wrote first draft of paper: Y.F. and G.F. Approved paper: all authors.

Corresponding author

Correspondence to Gang Fang.

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Competing interests

S.P. has served as a consultant/steering committee member for Vedanta Biosciences and has received speaker/advisory board fees from AbbVie, Dr Falk Pharma, Ferring, Janssen and Takeda. J.J.F. is a consultant for Vedanta Biosciences and Genfit. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Description of sample collection and FMT study design for CDI patients.

Stool samples were collected from three donors and four recipients at multiple time points. For the illustration of LongTrack, strain tracking was performed for long-read MAGs assembled from donors and recipients using short-read metagenomic data generated across all the samples shown in the figure (Methods). The Venn diagram displays the number of strains that were recovered by long-read (LR) MAGs (yellow color) or isolate genomes from bacterial culture (red color) from 3 donors and 2 recipients.

Extended Data Fig. 2 Illustration of LongTrack workflow’s implementation in this study for generating strain-level long-read MAGs.

Initially, the raw long reads from donor and pre-FMT recipient underwent metagenomic assembly using the metaFlye (v2.9). The resulting Metagenome-assembled genomes (MAGs) were then partitioned into individual bins corresponding to the species level using MaxBin (v2.0). Strainberry (v1.1) was used to separate the contigs into strain level based on haplotype phasing for each species-level bin. Subsequently, metaWRAP (v1.3.2) was employed to bin the separated contigs into strain level. Finally, the strain-level long-read MAGs with completeness of more than 10% were chosen for FMT strain tracking in post-FMT recipients (Methods).

Extended Data Fig. 3 Comparative evaluation of long-read MAGs and short-read MAGs from Donor 1.

A. Completeness levels of 124 long-read MAGs (LR MAGs) and 135 short-read MAGs (SR MAGs) from Donor 1 reveal no significant differences. B. Contamination levels of 124 LR MAGs and 135 SR MAGs from Donor 1 assessed using CheckM, showing significantly lower contamination in LR MAGs (p = 0.043; two-sided Wilcoxon rank sum test; p < 0.05, *). C. Contig N50 (kb) comparison for 124 LR MAGs and 135 SR MAGs Donor 1, showing significantly higher N50 in LR MAGs (p = 2.2e-16; two-sided Wilcoxon rank sum test; p < 0.01, **).

Source data

Extended Data Fig. 4 Comparative evaluation of long-read MAGs and short-read MAGs from Donor 1.

A. Schematic of contamination level by mapping long-read and short-read MAG to its corresponding isolate genome, calculated as the percentage of unaligned bases contamination; B. Comparative analysis of contamination levels determined by comparing it to the isolate genome and by using the CheckM tool. A total of 60 long-read (LR) MAGs from Donors 1–3 and Recipients 1–4, and 23 short-read (SR) MAGs from Donor 1 (D1) were analyzed. (C–D) Comparison of contamination levels in corresponding strains between LR MAGs and SR MAGs from D1. (C) Bar plots illustrate contamination levels in corresponding strains between LR MAGs and SR MAGs for species with multiple co-existing strains and species with a single strain, emphasizing the consistently lower contamination observed in LR MAGs. D. Contamination levels for 31 LR MAGs and 23 SR MAGs from D1, categorized by species with either a single strain or multiple co-existing strains. LR MAGs consistently demonstrated lower contamination levels (p-value = 0.0048 for single strain; p-value = 0.0086 for multiple strain; two-sided permutation test on the mean difference, n = 10,000 permutations; p-value < 0.01, **) E. Genomic similarity of shared strains between the long-read (LR) MAGs/short-read (SR) MAGs and their corresponding isolate genomes determined by k-mer similarity (Methods).

Source data

Extended Data Fig. 5 Evaluation of FMT strain tracking between LongTrack and StrainFinder as a representative of SNP-based inference approach.

A. For the donor sample D1, in silico spike-in dataset was designed with 3% Clostridium tyrobutyricum and 2% Turicibacter sanguinis added to the microbiome dataset. For the post-FMT recipient R1, the spike-in data set A (low abundance) had a composition of 0.25% Clostridium tyrobutyricum and 0.1% Turicibacter sanguinis, while the spike-in data set B (high abundance) had a composition of 2.5% Clostridium tyrobutyricum and 1% Turicibacter sanguinis (Methods). B. The strain tracking results indicate that the SNP-based approach, StrainFinder, works well for strains with relatively modest-to-high abundance (data set B), but tends to miss strains with modest-to-low abundance (data set A). The chart represents the presence (green) or absence (gray) of strains in post-FMT recipients based on spike-in design (considering ground truth), LongTrack with long-read MAGs, or StrainFinder. Discrepancies with the ground truth are indicated by an “X” marker.

Extended Data Fig. 6 Sensitivity evaluation of LongTrack and culture-based approach (Strainer).

The sensitivity evaluation of LongTrack and the culture-based approach (Strainer) was performed using the union of all strains detected either through isolate genomes or MAGs as the reference set. To evaluate sensitivity across different microbial abundance levels, the reference set was stratified by relative abundance thresholds, which provides a comprehensive benchmark that encompasses the diversity of detectable strains, including those uniquely identified by each method. Numbers in brackets on the x-axis indicate the number of reference strains (n) in each bin.

Extended Data Fig. 7 LongTrack uncovered engraftment of cultured strains across FMT cases in rCDI patients.

A, Proportional engraftment of donor strains (PEDS) for shared cultured strains is shown across individual post-FMT recipients at various time points of the 5-year follow up. B, Proportional persistence of recipient strains (PPRS) for shared cultured strains is shown across individual post-FMT recipients at various time points of the 5-year follow up.

Extended Data Fig. 8 LongTrack uncovered uncultured of engrafted strains of rCDI.

The long-read MAGs uncultured by the previous study were utilized for strain tracking to determine if they were present in the post-FMT recipients for FMT cases of CDI: The results of tracking across three FMT cases D1- > R1 (A), D2- > R3 (B), and D3- > R4 (C), which describe the presence (green) or absence (gray) of strains based on LongTrack.

Extended Data Fig. 9 Reverse strain tracking of earlier samples based on long-read MAGs assembled from post-FMT recipients using LongTrack.

The long-read MAGs assembled from post-FMT recipient samples were utilized for strain tracking to determine if they were also present in the donor and pre-FMT recipient metagenomic data using LongTrack (Reverse tracking). The results of reverse tracking across three FMT cases: D1 - > R1, D2 - > R3 and D3 - > R4 are shown in A-C, which describe the presence (green) or absence (gray) of strains based on LongTrack. (D-F) Illustration of bacterial strains that may have been acquired from environmental sources, as determined by long-read MAGs assembled from the five-year post-FMT recipients’ samples but absent in all the earlier samples. The results across three FMT cases: D1 - > R1, D2 - > R3 and D3 - > R4 are shown in D-F, which describe the presence (green) or absence (gray) of strains based on LongTrack.

Extended Data Fig. 10 Description of samples collection and FMT study design for two IBD patients.

Stool samples were collected from six donors and two recipients at multiple time points. The two recipients (IBD R5 and IBD R6; orange) had received FMT from six donors (IBD D4 - D9; purple and gray). For the illustration of LongTrack, strain tracking was performed for long-read MAGs assembled from two donors (IBD D4 and D5; purple) using short-read metagenomic data generated across all the samples shown in the figure (Methods).

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Fan, Y., Ni, M., Aggarwala, V. et al. Long-read metagenomics for strain tracking after faecal microbiota transplant. Nat Microbiol 10, 3258–3271 (2025). https://doi.org/10.1038/s41564-025-02164-8

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