Location: Sunflower Improvement Research
Project Number: 3060-21220-034-028-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 1, 2023
End Date: Dec 31, 2024
Objective:
The main goal of the work is to identify genetic regulatory networks and metabolomic pathways that directly associate with disease development. This goal will be achieved using the following objectives: 1) Conduct RNA-seq-based transcriptomic analysis at different stages of S. sclerotiorum infection on canola leaves; 2) perform mass spectrometry imaging-based metabolomic analysis at different stages of S. sclerotiorum infection on canola leaves; and 3) utilize the information from transcriptomic and metabolomic data to develop descriptive models of plant defense and Sclerotinia pathogenicity.
Approach:
We will explore the significant genes and metabolic pathways involved in different stages of fungal infection based on a spatially resolved multi-omics study. The first objective will be accomplished by inoculating five-week-old canola (tolerant and susceptible lines) with actively growing fungal mycelium in a humidity-saturated chamber. At 24 and 48 h post inoculation, we will harvest infected leaves with necrotic, margin, and uninfected areas. The harvested tissues will be subjected to RNA isolation followed by RNA-seq analysis with the Illumina HiSeq 2500 sequencer available in the WSU Genomic Core Facility. For the second objective’s metabolomic analyses, we will prepare the infected tissues using the same experimental design as objective 1 and will collect defined tissues for mass spectrometry imaging analysis to measure the accumulation of defense compounds in the tissue. We will employ a multigrid matrix-assisted laser desorption/ionization mass spectrometry imaging (mMALDI MSI) technique that utilizes automated inkjet printing to place differing matrices onto predefined dot grids. The expected results will aid our understanding of gene expression and metabolomic pathways related to spatial hormonal regulation. For objective 3, we will develop and apply computational systems biology methods to study genes, proteins, metabolites, and pathways involved in the pathosystem between canola and S. sclerotiorum. One major goal is to elucidate and quantify the regulatory networks using all related data generated in obj. 1 and 2. Results will be used to identify genes, gene clusters, and direct or correlative network modules underlying S. sclerotiorum’s pathogenicity. We will further perform Meta Coexpression Network analysis combining our data with public RNA-seq data from NCBI and from previous papers, which will allow us to identify network hubs and their behavior in the pathosystem between canola and S. sclerotiorum.
