Disentangling environmental effects in microbial association networks

Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association net...

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Autores principales:	Ina Maria Deutschmann, Gipsi Lima-Mendez, Anders K. Krabberød, Jeroen Raes, Sergio M. Vallina, Karoline Faust, Ramiro Logares
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Microbial interactions Association network Effect of indirect dependencies Environmentally driven edge detection Microbial ecology QR100-130
Acceso en línea:	https://doaj.org/article/ffae6cf055f94567a6e77a79dc7e997a
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spelling	oai:doaj.org-article:ffae6cf055f94567a6e77a79dc7e997a2021-11-28T12:08:12ZDisentangling environmental effects in microbial association networks10.1186/s40168-021-01141-72049-2618https://doaj.org/article/ffae6cf055f94567a6e77a79dc7e997a2021-11-01T00:00:00Zhttps://doi.org/10.1186/s40168-021-01141-7https://doaj.org/toc/2049-2618Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstractIna Maria DeutschmannGipsi Lima-MendezAnders K. KrabberødJeroen RaesSergio M. VallinaKaroline FaustRamiro LogaresBMCarticleMicrobial interactionsAssociation networkEffect of indirect dependenciesEnvironmentally driven edge detectionMicrobial ecologyQR100-130ENMicrobiome, Vol 9, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbial interactions Association network Effect of indirect dependencies Environmentally driven edge detection Microbial ecology QR100-130
spellingShingle	Microbial interactions Association network Effect of indirect dependencies Environmentally driven edge detection Microbial ecology QR100-130 Ina Maria Deutschmann Gipsi Lima-Mendez Anders K. Krabberød Jeroen Raes Sergio M. Vallina Karoline Faust Ramiro Logares Disentangling environmental effects in microbial association networks
description	Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses. Video abstract
format	article
author	Ina Maria Deutschmann Gipsi Lima-Mendez Anders K. Krabberød Jeroen Raes Sergio M. Vallina Karoline Faust Ramiro Logares
author_facet	Ina Maria Deutschmann Gipsi Lima-Mendez Anders K. Krabberød Jeroen Raes Sergio M. Vallina Karoline Faust Ramiro Logares
author_sort	Ina Maria Deutschmann
title	Disentangling environmental effects in microbial association networks
title_short	Disentangling environmental effects in microbial association networks
title_full	Disentangling environmental effects in microbial association networks
title_fullStr	Disentangling environmental effects in microbial association networks
title_full_unstemmed	Disentangling environmental effects in microbial association networks
title_sort	disentangling environmental effects in microbial association networks
publisher	BMC
publishDate	2021
url	https://doaj.org/article/ffae6cf055f94567a6e77a79dc7e997a
work_keys_str_mv	AT inamariadeutschmann disentanglingenvironmentaleffectsinmicrobialassociationnetworks AT gipsilimamendez disentanglingenvironmentaleffectsinmicrobialassociationnetworks AT anderskkrabberød disentanglingenvironmentaleffectsinmicrobialassociationnetworks AT jeroenraes disentanglingenvironmentaleffectsinmicrobialassociationnetworks AT sergiomvallina disentanglingenvironmentaleffectsinmicrobialassociationnetworks AT karolinefaust disentanglingenvironmentaleffectsinmicrobialassociationnetworks AT ramirologares disentanglingenvironmentaleffectsinmicrobialassociationnetworks
_version_	1718408237155352576

Disentangling environmental effects in microbial association networks

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