Tracking Strains in the Microbiome: Insights from Metagenomics and Models
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 importan...
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| Format: | Article |
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Frontiers Research Foundation
2018
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| Online Access: | http://hdl.handle.net/1721.1/114226 https://orcid.org/0000-0001-8294-9364 |
| _version_ | 1826208817326587904 |
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| author | Brito, Ilana Lauren Alm, Eric J |
| author2 | Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics |
| author_facet | Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics Brito, Ilana Lauren Alm, Eric J |
| author_sort | Brito, Ilana Lauren |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T14:13:06Z |
| format | Article |
| id | mit-1721.1/114226 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:13:06Z |
| publishDate | 2018 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1142262022-09-28T19:17:20Z Tracking Strains in the Microbiome: Insights from Metagenomics and Models Brito, Ilana Lauren Alm, Eric J Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics Massachusetts Institute of Technology. Department of Biological Engineering Brito, Ilana Lauren Alm, Eric J 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 2018-03-19T19:32:26Z 2018-03-19T19:32:26Z 2015-11 2015-11 2018-02-16T19:37:41Z Article http://purl.org/eprint/type/JournalArticle 1664-302X http://hdl.handle.net/1721.1/114226 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 https://orcid.org/0000-0001-8294-9364 http://dx.doi.org/10.3389/FMICB.2016.00712 Frontiers in Microbiology Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Research Foundation Frontiers |
| spellingShingle | Brito, Ilana Lauren Alm, Eric J Tracking Strains in the Microbiome: Insights from Metagenomics and Models |
| title | Tracking Strains in the Microbiome: Insights from Metagenomics and Models |
| title_full | Tracking Strains in the Microbiome: Insights from Metagenomics and Models |
| title_fullStr | Tracking Strains in the Microbiome: Insights from Metagenomics and Models |
| title_full_unstemmed | Tracking Strains in the Microbiome: Insights from Metagenomics and Models |
| title_short | Tracking Strains in the Microbiome: Insights from Metagenomics and Models |
| title_sort | tracking strains in the microbiome insights from metagenomics and models |
| url | http://hdl.handle.net/1721.1/114226 https://orcid.org/0000-0001-8294-9364 |
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