Collaborative Research: RESEARCH-PGR: Deciphering Host- and Environment-dependencies in the Legume-Rhizobia Symbiosis by Dual-Seq Transcriptomics and Directed Genome Engineering

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Plants live in close association with bacteria. Some of these associations have little effect on plant growth, some are harmful to plants, and some benefit plants by providing essential nutrients or other benefits. The most important of these beneficial associations occurs between rhizobia bacteria and their legume hosts, which include agriculturally important species such as soybeans, peas, and alfalfa. This association is important because the bacteria, when living with plants, provide plants with nitrogen through a process called nitrogen fixation. Because nitrogen is an essential nutrient that often limits plant growth, this association supports plant productivity in both natural and agricultural settings while greatly reducing the need for nitrogen fertilizer, an economically and environmentally expensive input to agricultural systems. This project will use an integrative approach to identify the plant legume and rhizobia genes that work together to control the efficacy of nitrogen fixation. The researchers will use manipulative experiments to measure the benefits that each of eighteen host species gain when growing in association with each of two species of rhizobia bacteria. These same experiments will also be used to assay which plant and rhizobial genes are being expressed in each plant-rhizobia pair, and then statistical analyses will identify groups of genes that have similar expression patterns. The experiment promises to identify gene modules that contain plant genes that control rhizobia genes and rhizobia genes that control plant genes. By examining multiple plant species and multiple environments, the proposed work will identify genes essential for nitrogen fixation and genes that can be modified to manipulate nitrogen fixation in specific environments. To verify gene function, the researchers will engineer bacteria genomes with genes of interest and then measure how these engineered bacteria affect plant growth. The results of the work will provide tools to manipulate the legume-rhizobia symbiosis to increase the benefits it provides to agricultural systems. The project will train scientists in new approaches and data analyses and develop materials for hands-on STEM courses for undergraduate students. Most genetic analyses of the legume-rhizobia symbiosis have been conducted in unrealistic environments, where plants rely entirely on nitrogen supplied by a single rhizobium strain. The extent to which results from these studies can be extrapolated across species and environments remains an open question that is critical for refining predictions about symbiosis genomics, including the societal goal of improving plant health. This project will build on the foundational knowledge from the Medicago truncatula-Sinorhizobium meliloti symbiosis by using dual-seq host-symbiont transcriptome data from a broad range of Medicago host species (18 species) and two Sinorhizobium species, across a range of field-relevant nitrogen fertilizer levels. The researchers will use differential expression analyses and two-species coexpression networks to identify both host and symbiont genes with expression that is associated with symbiotic performance. Of particular interest are coexpression modules that are enriched for both plant and microbe genes as well as plant-microbe gene pairs with coordinated expression (i.e., strong edges in the coexpression network). The function of a subset of candidates will be validated by adding them to a minimum symbiotic genome, a powerful genomic engineering approach for gain of function assays. By identifying genes that play a role in adaptation to specific hosts and nitrogen environments, the project will contribute to the goal of untangling the interspecific genetic crosstalk that control plant-microbe symbiosis and that hold the key to optimizing this symbiosis for plant health. All project outcomes will be freely available through long term data and resource repositories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.