Nature MicrobiologyMicrobiologyPeer-reviewed journal article
Long read metagenomics-based precise tracking of bacterial strains and genomic changes after fecal microbiota transplantation
Yu FaniD, Mi NiiD, Varun AggarwalaiD, Edward A. MeadiD, Magdalena KsiezarekiD, Lei CaoiD, Michael A. KammiD, Thomas Julius BorodyiD, Sudarshan ParamsothyiD, Nadeem Omar KaakoushiD, Ari M. GrinspaniD, Jeremiah J. FaithiD, Gang FangiD
Abstract
Fecal microbiota transplantation (FMT) has revolutionized the treatment of recurrent Clostridioides difficile infection (rCDI) and is being evaluated across other diseases. Accurate tracking of bacterial strains that stably engraft in recipients is critical for understanding the determinants of strain engraftment, evaluating their correlation with clinical outcomes, and guiding the development of therapeutic bacterial consortia. While short-read sequencing has advanced FMT research, it faces challenges in strain-level de novo metagenomic assembly. In this study, we described a novel framework, LongTrack, which uses long-read metagenomic assemblies and rigorous informatics tailored for FMT strain tracking. We highlighted LongTracks advantage over short-read approaches especially when multiple strains co-exist in the same sample. We showed LongTrack uncovered hundreds of engrafted strains across six FMT cases of rCDI and inflammatory bowel disease patients. Furthermore, long reads also allowed us to assess the genomic and epigenomic stability of engrafted strains during the 5-year follow-ups, revealing structural variations that may be associated with strain adaptation in a new host environment. Combined, our study advocates the use of long-read metagenomics and LongTrack to enhance strain tracking in future FMT studies, paving the way for the development of more effective defined biotherapeutic as an alternative to FMT.
- organism: human
- assay: metagenomics
- research focus: clustering
- research focus: variant calling
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: deep learning
- approach: statistical model
- Citations
- 14
- FWCI
- 3.23
- Field percentile
- top 6%
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