SITS-NSF-UKRI: Reverse engineering the soil microbiome: detecting, modeling, and optimizing signal impacts on microbiome metabolic functions

Chemical signaling among microorganisms in the soil determines microbial behavior, including whether or not soil microbes suppress plant diseases, enhance crop growth, or grow on particular soil nutrients. However, little is known about the specific chemical signals that mediate these behaviors, limiting the potential for practical management to optimize microbial activities to support healthy crops and ecosystems. The objectives of this project at the University of Minnesota and the University of Manchester in the UK are to develop and test a set of 100 novel, microbial recorders that can sense specific signals and report on whether each of 100 particular genes in the microbe responds. This project will provide a valuable means of identifying specific chemical signals among soil microbes that can optimize beneficial functions or suppress detrimental functions. The research will shed light on the complex chemical and metabolic interactions that determine how well soil microbiomes can support healthy crops and ecosystems, and provide insight into novel, practical ways to harness microbiomes for beneficial functions. Tools created here will also guide improvements in understanding the ecology and functional potential of soil microbiomes in agricultural and natural habitats. Exchanges between U.S. and U.K. scientists will be integral to the success of the research effort, strengthening the capacities and output of scientists in both countries.

The research will provide fundamental insights into the roles of signals in mediating the ecology of soil microbes and suppression of plant diseases. This work establishes a foundation for engineering functional soil microbiomes for precision agriculture. Specific objectives are to: 1) Develop and test genetic recorder (GR) strains to 'listen and report' on signals in the soil that regulate primary and secondary metabolic pathways in Streptomyces spp. isolated from disease suppressive soils; 2) Model and test how species-species interactions that rely on primary and secondary metabolic induction impact multi-species communities; and 3) Discover effects of potential signals on Streptomyces metabolism and harness signals to optimize microbial functional capacities in soil. Methods: 1) GRs will be created to detect the activation of genes/pathways of interest in soil microbes using serine integrase-mediated recombination. The GRs will be quantified using Next-Generation Sequencing technology, and will be able to simultaneously record the activation of hundreds of metabolic activities in a single high-throughput experiment. 2) Genome-scale metabolic models, transcriptomics, and metabolomics will be used to connect signals to functions. Existing metabolic modeling platforms will be extended to incorporate novel functionality to understand how signals influence the physiology of individual bacteria and alter emergent ecosystem dynamics. 3) Potential signals will be screened for their direct effects on Streptomyces antibiotic inhibitory and nutrient use phenotypes in vitro, providing both a signal discovery platform and a direct comparison with phenotypic data.

This project was awarded through the 'Signals in the Soil (SitS) opportunity, a collaborative solicitation that involves the ENG/CBET and BIO/IOS divisions of the National Science Foundation (NSF), the United States Department of Agriculture National Institute of Food and Agriculture (USDA NIFA) and the following United Kingdom Research and Innovation (UKRI) research councils: 1) The Natural Environment Research Council (NERC), 2) the Biotechnology and Biological Sciences Research Council (BBSRC), 3) the Engineering and Physical Sciences Research Council (EPSRC), and the Science and Technology Facilities Council (STFC).

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.