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Microbiome source tracking using single nucleotide variants

View ORCID ProfileLeah Briscoe, View ORCID ProfileEran Halperin, View ORCID ProfileNandita R. Garud
doi: https://doi.org/10.1101/2022.05.28.493810
Leah Briscoe
1Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, Los Angeles, CA, United States of America
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  • For correspondence: leah.briscoe@ucla.edu
Eran Halperin
2Department of Computer Science, University of California Los Angeles, Los Angeles, CA, United States of America
3Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
4Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
5Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
6Institute of Precision Health, University of California Los Angeles, CA, United States of America
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Nandita R. Garud
3Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America
7Department of Ecology and Evolutionary Biology, University of California Los Angeles, CA, United States of America
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Abstract

Microbiomes are composed of hundreds to thousands of species of microorganisms living on and in our body and also in our environment. Elucidating the sources of these community members has been of great interest in the field to understand underlying ecological and colonization dynamics. Microbiomes are likely mixtures of several other microbiomes. The estimation of the contribution of various source microbiomes to a given community is known as source tracking. While emphasis has been placed on source tracking using species composition, single nucleotide variants (SNVs) within species may be more informative because rare variants can be highly specific to certain sources. However, to date, SNV frequencies have not been leveraged for source tracking despite their success with strain tracking, in which individual strains per species rather than contributions from whole communities are tracked. We assess the ability of SNVs versus species in a previously designed source tracking algorithm FEAST (Shenhav et al., 2019) and find that SNVs can more accurately identify sources and their contributions. With SNV source tracking, we recapitulate previous findings that transmissions from mothers to their infants decreases with the age of the infant and that the built environment of NICUs play an important role in seeding infant microbiomes. Additionally, with SNV source tracking, we track migration of microbes across oceanic regions, including across the Suez and Panama canals, and observe a distance-decay relationship in the source contribution, which we do not observe with species source tracking. In sum, source tracking with SNVs can offer new insights into microbiome transmission and colonization sources that species cannot.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 29, 2022.
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Microbiome source tracking using single nucleotide variants
Leah Briscoe, Eran Halperin, Nandita R. Garud
bioRxiv 2022.05.28.493810; doi: https://doi.org/10.1101/2022.05.28.493810
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Microbiome source tracking using single nucleotide variants
Leah Briscoe, Eran Halperin, Nandita R. Garud
bioRxiv 2022.05.28.493810; doi: https://doi.org/10.1101/2022.05.28.493810

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