Dataset Information

Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean.

ABSTRACT: Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change.

SUBMITTER: Deutschmann IM

PROVIDER: S-EPMC10762198 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean.

Deutschmann Ina M IM Delage Erwan E Giner Caterina R CR Sebastián Marta M Poulain Julie J Arístegui Javier J Duarte Carlos M CM Acinas Silvia G SG Massana Ramon R Gasol Josep M JM Eveillard Damien D Chaffron Samuel S Logares Ramiro R

Nature communications 20240102 1

Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibitin ...[more]

PMID: 38168083

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Project description:BackgroundEcological 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.ResultsWe 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.ConclusionsTo 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.

Dataset Information

Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean.

Publications

Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean.

Similar Datasets

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