Disentangling leaf-microbiome interactions in Arabidopsis thaliana by network mapping

Abstract

The leaf microbiota plays a key role in plant development, but a detailed mechanism of microbe-plant relationships remains elusive. Many genome-wide association studies (GWAS) have begun to map leaf microbes, but few have systematically characterized the genetics of how microbes act and interact. Previously, we integrated behavioral ecology and game theory to define four types of microbial interactions – mutualism, antagonism, aggression, and altruism, in a microbial community assembly. Here, we apply network mapping to identify specific plant genes that mediate the topological architecture of microbial networks. Analyzing leaf microbiome data from an Arabidopsis GWAS, we identify several heritable hub microbes for leaf microbial communities and detect 140–728 SNPs (Single nucleotide polymorphisms) responsible for emergent properties of microbial network. We reconstruct Bayesian genetic networks from which to identify 22–43 hub genes found to code molecular pathways related to leaf growth, abiotic stress responses, disease resistance and nutrition uptake. A further path analysis visualizes how genetic variants of Arabidopsis affect its fecundity through the internal workings of the leaf microbiome. We find that microbial networks and their genetic control vary along spatiotemporal gradients. Our study provides a new avenue to reveal the “endophenotype” role of microbial networks in linking genotype to end-point phenotypes in plants. Our integrative theory model provides a powerful tool to understand the mechanistic basis of structural-functional relationships within the leaf microbiome and supports the need for future research on plant breeding and synthetic microbial consortia with a specific function.

The Arabidopsis holobiont: a (re)source of insights to understand the amazing world of plant–microbe interactions

“…For instance, the plant-life history traits, epigenome, evolutionary, ecological, and climate (as recently addressed by [ 53 ] aspects, and concomitant multiple layers of the abiotic and biotic aspects of the environment should be included in future research. Networking mapping analysis [ 107 ] and multi-genome metabolic modeling [ 127 ] are some of the ways to move forward in this issue. This will foster our ability to predict the effects of a particular plant-microbiota interaction and to engineer the use of microbiomes for more sustainable agriculture.…”