Microbiome Geographic Population Structure (mGPS) Detects Fine-Scale Geography
Zhang, Yali ; McCarthy, Leo ; Ruff, Emil and Elhaik, Eran LU
(2024)
In Genome Biology and Evolution
16(11).
- Abstract
Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to... (More)
Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning–based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure’s ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology.
(Less)
- author
- Zhang, Yali
; McCarthy, Leo
; Ruff, Emil
and Elhaik, Eran
LU
- organization
- publishing date
- 2024-11-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- antimicrobial resistance (AMR), biogeographical predictions, forensics, machine learning, microbiome, microbiome geographic population structure (mGPS)
- in
- Genome Biology and Evolution
- volume
- 16
- issue
- 11
- article number
- evae209
- publisher
- Oxford University Press
- external identifiers
-
- scopus:85209156045
- pmid:39373631
- ISSN
- 1759-6653
- DOI
- 10.1093/gbe/evae209
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2024.
- id
- f2b35cd4-ace7-4d66-ae66-f7ce3b0c12fc
- date added to LUP
- 2024-12-09 14:55:26
- date last changed
- 2025-07-08 07:42:30
@article{f2b35cd4-ace7-4d66-ae66-f7ce3b0c12fc,
abstract = {{<p>Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning–based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure’s ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology.</p>}},
author = {{Zhang, Yali and McCarthy, Leo and Ruff, Emil and Elhaik, Eran}},
issn = {{1759-6653}},
keywords = {{antimicrobial resistance (AMR); biogeographical predictions; forensics; machine learning; microbiome; microbiome geographic population structure (mGPS)}},
language = {{eng}},
month = {{11}},
number = {{11}},
publisher = {{Oxford University Press}},
series = {{Genome Biology and Evolution}},
title = {{Microbiome Geographic Population Structure (mGPS) Detects Fine-Scale Geography}},
url = {{http://dx.doi.org/10.1093/gbe/evae209}},
doi = {{10.1093/gbe/evae209}},
volume = {{16}},
year = {{2024}},
}