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A network approach to elucidate and prioritize microbial dark matter in microbial communities
Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to fir...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852563/ https://www.ncbi.nlm.nih.gov/pubmed/32963345 http://dx.doi.org/10.1038/s41396-020-00777-x |
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author | Zamkovaya, Tatyana Foster, Jamie S. de Crécy-Lagard, Valérie Conesa, Ana |
author_facet | Zamkovaya, Tatyana Foster, Jamie S. de Crécy-Lagard, Valérie Conesa, Ana |
author_sort | Zamkovaya, Tatyana |
collection | PubMed |
description | Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats. |
format | Online Article Text |
id | pubmed-7852563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78525632021-02-08 A network approach to elucidate and prioritize microbial dark matter in microbial communities Zamkovaya, Tatyana Foster, Jamie S. de Crécy-Lagard, Valérie Conesa, Ana ISME J Article Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats. Nature Publishing Group UK 2020-09-22 2021-01 /pmc/articles/PMC7852563/ /pubmed/32963345 http://dx.doi.org/10.1038/s41396-020-00777-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zamkovaya, Tatyana Foster, Jamie S. de Crécy-Lagard, Valérie Conesa, Ana A network approach to elucidate and prioritize microbial dark matter in microbial communities |
title | A network approach to elucidate and prioritize microbial dark matter in microbial communities |
title_full | A network approach to elucidate and prioritize microbial dark matter in microbial communities |
title_fullStr | A network approach to elucidate and prioritize microbial dark matter in microbial communities |
title_full_unstemmed | A network approach to elucidate and prioritize microbial dark matter in microbial communities |
title_short | A network approach to elucidate and prioritize microbial dark matter in microbial communities |
title_sort | network approach to elucidate and prioritize microbial dark matter in microbial communities |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852563/ https://www.ncbi.nlm.nih.gov/pubmed/32963345 http://dx.doi.org/10.1038/s41396-020-00777-x |
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