Tracking Strains in the Microbiome: Insights from Metagenomics and Models
Name
fmicb-07-00712.pdf
Size
514.21 KB
Format
Adobe PDF
Checksum (MD5)
fd31a87f9230a22f242a35de8a449ef3
Author(s)
Brito, Ilana Lauren
Alm, Eric J
Date Issued
November 2015
Journal
Frontiers in Microbiology
Publisher
Frontiers Research Foundation
Citation
Brito, Ilana L., and Eric J. Alm. “Tracking Strains in the Microbiome: Insights from Metagenomics and Models.” Frontiers in Microbiology 7 (May 2016) © 2016 Brito and Alm
Version
Final published version
Abstract
Transmission usually refers to the movement of pathogenic organisms. Yet, commensal microbes that inhabit the human body also move between individuals and environments. Surprisingly little is known about the transmission of these endogenous microbes, despite increasing realizations of their importance for human health. The health impacts arising from the transmission of commensal bacteria range widely, from the prevention of autoimmune disorders to the spread of antibiotic resistance genes. Despite this importance, there are outstanding basic questions: what is the fraction of the microbiome that is transmissible? What are the primary mechanisms of transmission? Which organisms are the most highly transmissible? Higher resolution genomic data is required to accurately link microbial sources (such as environmental reservoirs or other individuals) with sinks (such as a single person's microbiome). New computational advances enable strain-level resolution of organisms from shotgun metagenomic data, allowing the transmission of strains to be followed over time and after discrete exposure events. Here, we highlight the latest techniques that reveal strain-level resolution from raw metagenomic reads and new studies that are tracking strains across people and environments. We also propose how models of pathogenic transmission may be applied to study the movement of commensals between microbial communities. Keywords: microbiome; metagenomics; models; biological; strain diversity; genotyping techniques; bacterial genomics
MIT Department
Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Attribution 4.0 International (CC BY 4.0)
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/FMICB.2016.00712
