Dataset Information

Difficulty in inferring microbial community structure based on co-occurrence network approaches.

ABSTRACT:

Background

Co-occurrence networks-ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques-are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics.

Results

We comprehensively evaluated the validity of common methods for inferring microbial ecological networks through realistic simulations. We evaluated how correctly nine widely used methods describe interaction patterns in ecological communities. Contrary to previous studies, the performance of the co-occurrence network methods on compositional data was almost equal to or less than that of classical methods (e.g., Pearson's correlation). The methods described the interaction patterns in dense and/or heterogeneous networks rather inadequately. Co-occurrence network performance also depended upon interaction types; specifically, the interaction patterns in competitive communities were relatively accurately predicted while those in predator-prey (parasitic) communities were relatively inadequately predicted.

Conclusions

Our findings indicated that co-occurrence network approaches may be insufficient in interpreting species interactions in microbiome studies. However, the results do not diminish the importance of these approaches. Rather, they highlight the need for further careful evaluation of the validity of these much-used methods and the development of more suitable methods for inferring microbial ecological networks.

SUBMITTER: Hirano H

PROVIDER: S-EPMC6567618 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Difficulty in inferring microbial community structure based on co-occurrence network approaches.

Hirano Hokuto H Takemoto Kazuhiro K

BMC bioinformatics 20190613 1

<h4>Background</h4>Co-occurrence networks-ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques-are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is c ...[more]

PMID: 31195956

Similar Datasets

Project description:Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Project description:Microorganisms are crucial components of the aquatic ecosystem due to their immense diversity and abundance. They are vital in sustaining ecological services, especially in maintaining essential biogeochemical cycles. Recent years have seen a substantial increase in surplus nitrogenous pollutants in aquatic ecosystems due to the heightened occurrence of anthropogenic activities. Elevated levels of free ammonia (FA, NH3), stemming from the discharge of excess nitrogenous pollutants, have caused notable fluctuations in aquatic ecosystems, leading to water eutrophication and various ecological challenges. The impact of these oscillations on microbial communities in aquatic ecosystems has not been extensively studied. This study employed 16S rRNA gene amplicon sequencing to systematically investigate the dynamics, co-occurrence networks, and assembly processes of microbial communities and their subcommunities (abundant, moderate, and rare) in the Luanhe River Diversion Project in China. Our findings indicate that NH3 concentration significantly influences the dynamics of microbial communities, with a notable decrease in community Richness and Phylogenetic Distance alongside increased community dissimilarity under higher NH3 conditions. The analysis revealed that certain microbial groups, particularly Actinobacteriaota, were notably more prevalent in environments with elevated NH3 levels, suggesting their potential resilience or adaptive responses to NH3 stress. Additionally, through co-occurrence network analysis, we observed dynamic changes in network topology and increased connectedness under NH3 stress. Key nodes, identified as connectors and module hubs, played crucial roles in maintaining network structure, particularly Cyanobacteria and Actinobacteriaota. Furthermore, stochastic processes, particularly drift and dispersal limitation, predominantly shaped the microbial communities. Within the three subcommunities, the impact of drift became more pronounced as the effect of dispersal limitation diminished. Overall, elucidating the dynamics of microbial communities in aquatic ecosystems exposed to NH3 can enhance our comprehension of the ecological mechanisms of microbial communities and provide new insights into the conservation of microbial community diversity and ecological functions.ImportanceThe research presented in this paper explores how varying concentrations of free ammonia impact microbial communities in aquatic ecosystems. By employing advanced gene sequencing techniques, the study reveals significant changes in microbial diversity and network structures in response to increased ammonia levels. Key findings indicate that high ammonia concentrations lead to a decrease in microbial richness and diversity while increasing community dissimilarity. Notably, certain microbial groups, like Actinobacteria, show resilience to ammonia stress. This research enhances our understanding of how pollution affects microbial ecosystems and underscores the importance of maintaining balanced ammonia levels to preserve microbial diversity and ecosystem health.

Dataset Information

Difficulty in inferring microbial community structure based on co-occurrence network approaches.

Background

Results

Conclusions

Publications

Difficulty in inferring microbial community structure based on co-occurrence network approaches.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets