Back

LongTrack: long read metagenomics-based precise tracking of bacterial strains and their genomic changes after fecal microbiota transplantation

Fan, Y.; Ni, M.; Aggarwala, V.; Mead, E. A.; Ksiezarek, M.; Cao, L.; Kamm, M. A.; Borody, T.; Paramsothy, S.; Kaakoush, N. O.; Grinspan, A.; Faith, J. J.; Fang, G.

2024-12-11 microbiology
10.1101/2024.09.30.615906 bioRxiv
Show 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.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.