Microbial interaction-driven community differences as revealed by network analysis

Diversity and compositional analysis are the most common approaches in deciphering microbial community differences. However, these approaches neglect microbial structural differences driven by microbial interactions. In this study, the microbiota data were generated from 12 rectal digesta samples co...

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Autores principales:	Zhe Pan, Yanhong Chen, Mi Zhou, Tim A. McAllister, Le Luo Guan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Shiga toxin Microbial interactions Co-occurrence network Group-specific taxa Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/a34f6c87f3cf4455916d519608225ed5
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spelling	oai:doaj.org-article:a34f6c87f3cf4455916d519608225ed52021-11-14T04:31:39ZMicrobial interaction-driven community differences as revealed by network analysis2001-037010.1016/j.csbj.2021.10.035https://doaj.org/article/a34f6c87f3cf4455916d519608225ed52021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004578https://doaj.org/toc/2001-0370Diversity and compositional analysis are the most common approaches in deciphering microbial community differences. However, these approaches neglect microbial structural differences driven by microbial interactions. In this study, the microbiota data were generated from 12 rectal digesta samples collected from steers in which the Shiga toxin 2 gene (stx2) was not expressed (defined as Stx2− group) in the bacteria, and those with stx2 expressed (defined as Stx2+ group) and used to explore whether microbial networks affect gut microbiota and foodborne pathogen virulence in cattle. Although the Shannon and Chao1 indices of rectal digesta microbial communities did not differ between the two groups (P > 0.05), 24 and 13 taxa were identified to be group-specific genera for Stx2− and Stx2+ microbial communities, respectively. The network analysis indicated 12 and 14 generalists (microbes that were densely connected with other taxa) in microbial communities for Stx2− and Stx2+ groups, and 8 out of 12 generalists and 6 out of 14 generalists were designated to Stx2− and Stx2+ group-specific genera, respectively. However, the 66 core genera were not classified as network generalists. Natural connectivity measurements revealed that the higher stability of the Stx2− microbial network in comparison to the Stx2+ network, suggesting that the structure of each microbial community was inherently different even when their diversity and composition were comparable. Group-specific genera intensely interacted with other taxa in the co-occurrence network, indicating that characterizing microbial networks together with group-specific genera could be an alternative approach to identify variation in microbial communities.Zhe PanYanhong ChenMi ZhouTim A. McAllisterLe Luo GuanElsevierarticleShiga toxinMicrobial interactionsCo-occurrence networkGroup-specific taxaBiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6000-6008 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Shiga toxin Microbial interactions Co-occurrence network Group-specific taxa Biotechnology TP248.13-248.65
spellingShingle	Shiga toxin Microbial interactions Co-occurrence network Group-specific taxa Biotechnology TP248.13-248.65 Zhe Pan Yanhong Chen Mi Zhou Tim A. McAllister Le Luo Guan Microbial interaction-driven community differences as revealed by network analysis
description	Diversity and compositional analysis are the most common approaches in deciphering microbial community differences. However, these approaches neglect microbial structural differences driven by microbial interactions. In this study, the microbiota data were generated from 12 rectal digesta samples collected from steers in which the Shiga toxin 2 gene (stx2) was not expressed (defined as Stx2− group) in the bacteria, and those with stx2 expressed (defined as Stx2+ group) and used to explore whether microbial networks affect gut microbiota and foodborne pathogen virulence in cattle. Although the Shannon and Chao1 indices of rectal digesta microbial communities did not differ between the two groups (P > 0.05), 24 and 13 taxa were identified to be group-specific genera for Stx2− and Stx2+ microbial communities, respectively. The network analysis indicated 12 and 14 generalists (microbes that were densely connected with other taxa) in microbial communities for Stx2− and Stx2+ groups, and 8 out of 12 generalists and 6 out of 14 generalists were designated to Stx2− and Stx2+ group-specific genera, respectively. However, the 66 core genera were not classified as network generalists. Natural connectivity measurements revealed that the higher stability of the Stx2− microbial network in comparison to the Stx2+ network, suggesting that the structure of each microbial community was inherently different even when their diversity and composition were comparable. Group-specific genera intensely interacted with other taxa in the co-occurrence network, indicating that characterizing microbial networks together with group-specific genera could be an alternative approach to identify variation in microbial communities.
format	article
author	Zhe Pan Yanhong Chen Mi Zhou Tim A. McAllister Le Luo Guan
author_facet	Zhe Pan Yanhong Chen Mi Zhou Tim A. McAllister Le Luo Guan
author_sort	Zhe Pan
title	Microbial interaction-driven community differences as revealed by network analysis
title_short	Microbial interaction-driven community differences as revealed by network analysis
title_full	Microbial interaction-driven community differences as revealed by network analysis
title_fullStr	Microbial interaction-driven community differences as revealed by network analysis
title_full_unstemmed	Microbial interaction-driven community differences as revealed by network analysis
title_sort	microbial interaction-driven community differences as revealed by network analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/a34f6c87f3cf4455916d519608225ed5
work_keys_str_mv	AT zhepan microbialinteractiondrivencommunitydifferencesasrevealedbynetworkanalysis AT yanhongchen microbialinteractiondrivencommunitydifferencesasrevealedbynetworkanalysis AT mizhou microbialinteractiondrivencommunitydifferencesasrevealedbynetworkanalysis AT timamcallister microbialinteractiondrivencommunitydifferencesasrevealedbynetworkanalysis AT leluoguan microbialinteractiondrivencommunitydifferencesasrevealedbynetworkanalysis
_version_	1718429975428726784

Microbial interaction-driven community differences as revealed by network analysis

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