Agroecosystem engineering extended from plant-microbe interactions revealed by multi-omics data.

In an agroecosystem, plants and microbes coexist and interact with environmental factors such as climate, soil, and pests. However, agricultural practices that depend on chemical fertilizers, pesticides, and frequent tillage often disrupt the beneficial interactions in the agroecosystem. To reconcile the improvement of crop performance and reduction in environmental impacts in agriculture, we need to understand the functions of the complex interactions and develop an agricultural system that can maximize the potential benefits of the agroecosystem. Therefore, we are developing a system called the agroecosystem engineering system, which aims to optimize the interactions between crops, microbes, and environmental factors, using multi-omics analysis. This review first summarizes the progress and examples of omics approaches, including multi-omics analysis, to reveal complex interactions in the agroecosystem. The latter half of this review discusses the prospects of data analysis approaches in the agroecosystem engineering system, including causal network analysis and predictive modeling.

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks.

Network-based assessments are important for disentangling complex microbial and microbial-host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka-Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.