ABSTRACT
Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management.
IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.
High-throughput sequencing data and antibiotic resistance mechanisms of soil microbial communities in non-irrigated and irrigated soils with raw sewage in African cities
