Plants’ adaptation to local microbiota is poorly understood but this research area aims to untangle the effects of climate and soil type from the impact of root-microbe interactions. A combination of transplantation experiments and exploitation of natural variation will be used to find the plant genetic components responsible for plant adaptation to local microbiota.

We will overlay population differentiation signatures derived from contrasting three distinct subpopulations with GWAS analysis of fitness traits measured in transplantation field experiments. This approach intersects and integrates phenotype-independent data from population differentiation signatures with the results of the trait- and phenotype-dependent GWA methodology in a uniquely suited population.

The second research area will identify key plant and microbial functions controlling state transitions in plant and microbial phenotypes in specific environmental conditions. The role of these functions will be tested using plant genetics and tailored microbial ecosystems with reduced complexity (for Lotus) and during intercropping or in rotation (for wheat). These model legumes will be used to identify the principles guiding microbiota assembly in the rhizosphere and for identifying and studying root-associated microbes.

The plant and microbial components that contribute to the enrichment of core microbiota and host-specific microbiota are unknown, as is their function in plant growth, development and resilience. To help unravel this we will use comparative analysis to identify core and host-specific endophytes. We will then identify the microbial genes controlling the core and host-specific associations by performing comparative genome and transcriptome analysis as well as establishing an unbiased large-scale genetic screen for several endophytic strains using the insertion sequencing (INSeq) strategy. Parallel studies will also be performed on the hosts to identify plant genes controlling endophytic interactions as well as those involved in intercellular infection.

The fourth research area will investigate the modulation of plant responses, plant hormone signaling, nutrient acquisition, and root system architecture that are potential means for microbes to promote plant resilience to environmental stresses. It is thought that microbes might induce and/or exploit these plant responses in order to provide an advantageous environment for their colonization or to obtain novel carbon sources to support their growth.

Genetic resources will be developed to determine the role of plant hormones in root colonization by microbial communities and how plant growth responses to microbial colonization are orchestrated by plant hormone signaling. These experiments will be carried out in soil and the tailored SynComs. We will also identify differentially regulated components in bacteria-plant communication using TRAP-RNAseq and study the function of plant genes using Lotus and Arabisopsis mutant resources or CRISPR in barley for reverse genetics. Biochemical approaches will also be used to complete pathway identification and characterization and elucidate transcriptional networks.

In this research area we will focus on elucidating the mechanisms of intra- and inter-organismal communication, signaling and resource allocation that regulate the local and systemic responses of selected Lotus accessions and their specific interactions with SynComs. This will lead to an understanding of the molecular mechanisms integrating root-microbe interactions into whole-plant physiology.

Peptides, RNAs, hormones, metabolites and other molecules involved in systematic and local responses will be analyzed to identify and characterize the mobile signals/molecules that provide information between plant organs and between plant root and microbiome. InRoot will develop new sensor and chip technologies to overcome current limitations in sensitivity, resolution and throughput of hormone and metabolite changes and movements. In addition, plant resiliency will be assessed non-invasively through the use of the automated, high-throughput controlled environment phenotyping facility PhenoLab.

The sixth research area will integrate the data on experimentally controlled variation with the quantified plant and microbial responses and combine mechanistic and data-driven modeling approaches to capture the drivers of plant yield and resilience resulting from changes in the microbiota structure and function. The goal is to develop a multiscale model to capture changes in plant yield and resilience in response to 1) genes and associated mechanisms that are associate with natural diversity and adaptation; 2) microbial targets associated with the microbe-microbe and microbe-host machinery that affect rhizosphere microbiota; and 3) plant targets associated with reciprocal root to shoot signaling.