Sulfur (S) is a limited macronutrient that is essential for maintaining plant growth and productivity. Since the Clean Air Act, reduction of atmospheric S has led to S-deficient soils worldwide. Farmers that grow crops with relatively high S-demands, such as Brassicas, combat soil deficiencies through S-fertilization. Brassicas require high concentrations (20-40 kg/hectare) of S-additions at least once in the growing cycle, in part due to the production of specialized metabolites known as glucosinolates (GSLs). GSLs are S-intensive and have been shown to oscillate throughout the day, implying that their constituent S can be recycled back into primary metabolism. How this diel turnover is regulated is largely unknown. Addressing this question would require costly and time-intensive metabolomics/flux experiments across several morphotypes.

PhD student Angela Ricono (Plant and Microbial Biology), in a project called “Leveraging Time of Day Transcriptional Networks With High Resolution Metabolomics to Predict Sulfur Dynamics in a Diverse Specialty Crop,” is using a novel approach that associates multi-day, high resolution time series RNA-sequencing and metabolomics data to uncover regulatory genes that are predictive of changes in S-metabolism in Brassica crops. Predictive markers will be used to select candidate lines and key times of day for future S-flux experiments. These findings will strengthen understanding of how plants regulate S-allocation throughout the day and inform breeding efforts to improve S-use efficiency in a variety of specialty crops.

Some funding for this project was provided by a 2023 Research Computing-MnDRIVE PhD Graduate Assistantship. The RC-MnDRIVE Graduate Assistantship program supports U of M PhD candidates pursuing research at the intersection of informatics and any of the five MnDRIVE areas:

  • Robotics
  • Global Food
  • Environment
  • Conditions
  • Cancer Clinical Trials

This project is part of the Global Food MnDRIVE area. See the complete list of the RC-MnDRIVE Graduate Assistantships for 2023.

diagram illustrating project titled Leveraging diel transcriptional networks with high resolution metabolomics to predict sulfur dynamics in a diverse specialty crop