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Genetic correlation network prediction of forest soil microbial functional organization
Soil ecological functions are largely determined by the activities of soil microorganisms, which, in turn, are regulated by relevant interactions between genes and their corresponding pathways. Therefore, the genetic network can theoretically elucidate the functional organization that supports compl...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6155114/ https://www.ncbi.nlm.nih.gov/pubmed/30046166 http://dx.doi.org/10.1038/s41396-018-0232-8 |
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author | Ma, Bin Zhao, Kankan Lv, Xiaofei Su, Weiqin Dai, Zhongmin Gilbert, Jack A. Brookes, Philip C. Faust, Karoline Xu, Jianming |
author_facet | Ma, Bin Zhao, Kankan Lv, Xiaofei Su, Weiqin Dai, Zhongmin Gilbert, Jack A. Brookes, Philip C. Faust, Karoline Xu, Jianming |
author_sort | Ma, Bin |
collection | PubMed |
description | Soil ecological functions are largely determined by the activities of soil microorganisms, which, in turn, are regulated by relevant interactions between genes and their corresponding pathways. Therefore, the genetic network can theoretically elucidate the functional organization that supports complex microbial community functions, although this has not been previously attempted. We generated a genetic correlation network based on 5421 genes derived from metagenomes of forest soils, identifying 7191 positive and 123 negative correlation relationships. This network consisted of 27 clusters enriched with sets of genes within specific functions, represented with corresponding cluster hubs. The clusters revealed a hierarchical architecture, reflecting the functional organization in the soil metagenomes. Positive correlations mapped functional associations, whereas negative correlations often mapped regulatory processes. The potential functions of uncharacterized genes were predicted based on the functions of located clusters. The global genetic correlation network highlights the functional organization in soil metagenomes and provides a resource for predicting gene functions. We anticipate that the genetic correlation network may be exploited to comprehensively decipher soil microbial community functions. |
format | Online Article Text |
id | pubmed-6155114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61551142018-10-01 Genetic correlation network prediction of forest soil microbial functional organization Ma, Bin Zhao, Kankan Lv, Xiaofei Su, Weiqin Dai, Zhongmin Gilbert, Jack A. Brookes, Philip C. Faust, Karoline Xu, Jianming ISME J Article Soil ecological functions are largely determined by the activities of soil microorganisms, which, in turn, are regulated by relevant interactions between genes and their corresponding pathways. Therefore, the genetic network can theoretically elucidate the functional organization that supports complex microbial community functions, although this has not been previously attempted. We generated a genetic correlation network based on 5421 genes derived from metagenomes of forest soils, identifying 7191 positive and 123 negative correlation relationships. This network consisted of 27 clusters enriched with sets of genes within specific functions, represented with corresponding cluster hubs. The clusters revealed a hierarchical architecture, reflecting the functional organization in the soil metagenomes. Positive correlations mapped functional associations, whereas negative correlations often mapped regulatory processes. The potential functions of uncharacterized genes were predicted based on the functions of located clusters. The global genetic correlation network highlights the functional organization in soil metagenomes and provides a resource for predicting gene functions. We anticipate that the genetic correlation network may be exploited to comprehensively decipher soil microbial community functions. Nature Publishing Group UK 2018-07-25 2018-10 /pmc/articles/PMC6155114/ /pubmed/30046166 http://dx.doi.org/10.1038/s41396-018-0232-8 Text en © International Society for Microbial Ecology 2018 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 Ma, Bin Zhao, Kankan Lv, Xiaofei Su, Weiqin Dai, Zhongmin Gilbert, Jack A. Brookes, Philip C. Faust, Karoline Xu, Jianming Genetic correlation network prediction of forest soil microbial functional organization |
title | Genetic correlation network prediction of forest soil microbial functional organization |
title_full | Genetic correlation network prediction of forest soil microbial functional organization |
title_fullStr | Genetic correlation network prediction of forest soil microbial functional organization |
title_full_unstemmed | Genetic correlation network prediction of forest soil microbial functional organization |
title_short | Genetic correlation network prediction of forest soil microbial functional organization |
title_sort | genetic correlation network prediction of forest soil microbial functional organization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6155114/ https://www.ncbi.nlm.nih.gov/pubmed/30046166 http://dx.doi.org/10.1038/s41396-018-0232-8 |
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