Crop performance is predicted by soil microbial diversity across phylogenetic scales

Selective shifts in the rhizosphere microbiome during the drought season could explain the success of the invader Nicotiana glauca in semiarid ecosystems

The rhizosphere microbiome plays a crucial role in the ability of plants to colonize and thrive in stressful conditions such as drought, which could be decisive for the success of exotic plant invasion in the context of global climate change. The aim of this investigation was to examine differences in the composition, structure, and functional traits of the microbial community of the invader Nicotiana glauca R.C. Graham and native species growing at seven different Mediterranean semiarid locations under two distinct levels of water availability, corresponding to the wet and dry seasons. The results show that the phylum Actinobacteriota was an indicator phylum of the dry season as well as for the community of N. glauca. The dominant indicator bacterial families of the dry season were 67-14 (unclassified family), Pseudonocardiaceae, and Sphingomonadaceae, being relatively more abundant in the invasive rhizosphere. The relative abundances of the indicator fungal families Aspergillaceae (particularly the indicator genus Aspergillus), Glomeraceae, and Claroideoglomeraceae were higher in the invasive rhizosphere. The relative abundance of mycorrhizal fungi was higher in the invasive rhizosphere in the dry season (by about 40 % in comparison to that of native plants), without significant differences between invasive and native plants in the wet season. Bacterial potential functional traits related to energy and precursor metabolites production and also biosynthesis of cell wall, cofactors, vitamins, and amino acids as well as catabolic enzymes involved in the P cycle prevailed in the invasive rhizosphere under drought conditions. This study shows that the pronounced and beneficial shifts in the microbiome assembly and functions in the rhizosphere of N. glauca under conditions of low soil water availability can represent a clear advantage for its establishment.

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.