SameStr - a highly sensitive and universal bioinformatics tool "Made in Hohenheim" for the identification of matching microbiome strains in metagenomic shotgun sequence datasets [12.06.22]
The understanding of how microbiomes assemble, function, and evolve requires metagenomic tools that can resolve microbiota compositions at the strain level. However, the identification and tracking of microbial strains in fecal metagenomes is challenging and available tools variably classify subspecies lineages, which affects their applicability to infer microbial persistence and transfer. Florian W. Fricke and partners present an alternative tool that can be used to improve our understanding of microbiome organization and function, thereby advancing many areas of microbiome research.
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Original Article:
Podlesny D (a), Arze C.(a,b), Dörner E (a)., Verma S (c)., Dutta S.(c), Walter J.(d), Fricke W.F.(a,e) (2022) Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation. Microbiome, 10 (1), art. no. 53
- a) Department of Microbiome Research and Applied Bioinformatics, University of Hohenheim, Stuttgart, Germany
- b) Current address: Ring Therapeutics, Cambridge, MA, United States
- c) Division of Gastroenterology, Sinai Hospital of Baltimore, Baltimore, MD, United Statesd APC Microbiome Ireland, School of Microbiology, and Department of Medicine,
- d) University College Cork, Cork, Irelande Institute for Genome Sciences,
- e) University of Maryland School of Medicine, Baltimore, MD, United States
Abstract
Background: The understanding of how microbiomes assemble, function, and evolve requires metagenomic tools that can resolve microbiota compositions at the strain level. However, the identification and tracking of microbial strains in fecal metagenomes is challenging and available tools variably classify subspecies lineages, which affects their applicability to infer microbial persistence and transfer.
Results: We introduce SameStr, a bioinformatic tool that identifies shared strains in metagenomes by determining single-nucleotide variants (SNV) in species-specific marker genes, which are compared based on a maximum variant profile similarity. We validated SameStr on mock strain populations, available human fecal metagenomes from healthy individuals and newly generated data from recurrent Clostridioides difficile infection (rCDI) patients treated with fecal microbiota transplantation (FMT). SameStr demonstrated enhanced sensitivity to detect shared dominant and subdominant strains in related samples (where strain persistence or transfer would be expected) when compared to other tools, while being robust against false-positive shared strain calls between unrelated samples (where neither strain persistence nor transfer would be expected). We applied SameStr to identify strains that are stably maintained in fecal microbiomes of healthy adults over time (strain persistence) and that successfully engraft in rCDI patients after FMT (strain engraftment). Taxonomy-dependent strain persistence and engraftment frequencies were positively correlated, indicating that a specific core microbiota of intestinal species is adapted to be competitive both in healthy microbiomes and during post-FMT microbiome assembly. We explored other use cases for strain-level microbiota profiling, as a metagenomics quality control measure and to identify individuals based on the persisting core gut microbiota.
Conclusion: SameStr provides for a robust identification of shared strains in metagenomic sequence data with sufficient specificity and sensitivity to examine strain persistence, transfer, and engraftment in human fecal microbiomes. Our findings identify a persisting healthy adult core gut microbiota, which should be further studied to shed light on microbiota contributions to chronic diseases.
© 2022, The Author(s).
Keywords:Clostridium Infections; Fecal Microbiota Transplantation; Feces; Gastrointestinal Microbiome; Humans; Metagenome; Metagenomics; Treatment Outcome
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