The overarching goal of this project is to increase soybean protein production in non-optimal environmental conditions. Soybean is grown on over 120 million ha worldwide, from which over 179 billion kg of protein-rich soybean meal per year is produced for livestock feed (FAO). Demand for soybean protein is increasing rapidly as the global population approaches 9 billion and more people can afford diversified diets that include meat. To meet this growing nutritional demand and to keep food prices stable, soybean protein production needs to be resilient to unpredictable growing-season weather, especially temperature stress. Temperature stressed soybean plants show low germination rates, growth delay, and reduced photosynthesis, yield, and seed protein production. Temperature stress-tolerant crops are difficult to develop through conventional breeding, due to the logistical difficulty of screening large numbers of plants for temperature stress response at critical developmental stages, which makes strategies such as genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping impractical or impossible. Furthermore, many temperature stress responses are regulated post-translationally and are thus difficult to detect with conventional genetic markers.
To improve crop temperature stress tolerance, novel strategies are needed to identify key temperatures stress regulators and develop new biomarkers for use in crop breeding. This multidisciplinary team will use state-of-the-art phosphoproteomics analysis, genotypic data, and physiological information together with machine learning to link key post-translational regulators with the desired physiological and agronomic outcomes, i.e. stable germination and increased yield and protein production during temperature stress. These key post-translational, phosphoprotein regulators can be used in breeding programs as novel biomarkers, or phosphomarkers, to select genotypes that are primed for temperature stress tolerance.
The first critical outcome from this project will be phosphomarkers that identify temperature stress-tolerant soybean genotypes, which will help improve the sustainability of soybean protein production and yields during periods of temperature stress without using additional land or inputs. These phosphomarkers will enable breeding selection based on post- translational regulation, which is a novel approach to overcome the limitations of predicting abiotic stress responses based on genetic data alone. The phosphomarkers will be field-validated and integrated into a high-throughput, antibody-based assay that will enable breeders to rapidly screen hundreds of genotypes and select those with protein phosphorylation states that are primed for advantageous temperature stress responses. This will benefit growers, whose livelihoods will be less vulnerable to capricious weather and who will have greater flexibility in soybean planting dates and farm management. It will also benefit livestock producers, who will experience less fluctuation in feed prices, and ultimately consumers, who will have more affordable options for protein in their diets.
The second critical outcome from this project will be the development of a novel, machine-learning based approach to infer stress-activated signaling networks to identify phosphomarkers, as well as new mechanistic models to understand different determinants of soybean yield and protein content. This outcome will be achieved by generating orthogonal
data, including phosphoproteomic, physiological, and agronomic temperature stress responses, and by using them as input for feedforward deep learning models to generate a more holistic view of temperature stress trajectories. This modeling-driven approach has the computational power to link key phosphomarkers with stress tolerance traits using less infrastructure and fewer genotypes than would be required for GWAS or QTL mapping. This will open up a new path
for crop improvement using post-translational regulatory networks that can guide traditional plant breeding as well as biotechnology-based crop improvements. Improving the sustainability of protein production for food and feed is a national and global concern, but soybean growers in lower latitudes, including North Carolina, will benefit the most directly from improved soybean heat tolerance. Improving the yield and protein that can be produced per acre in adverse
weather conditions will directly benefit growers.