Optimized combination methods for exploring and verifying disease-resistant transcription factors in melon

Area of Science:

• Plant Science
• Genomics
• Bioinformatics

Background:

• Omics data generation is rapidly increasing, but methods for exploring this data, particularly for identifying key regulatory genes and their functions, remain limited.
• Understanding gene regulation is crucial for biological systems, yet the specific functions of many regulatory genes are unknown.

Purpose of the Study:

• To develop and apply an optimized strategy for exploring omics data to identify key regulatory genes and their functions in Curcurcurbitaceae disease resistance.
• To investigate the roles of transcription factors (TFs) and their interactions with resistance (R) genes in melon.

Main Methods:

• Transcriptome analysis of susceptible and resistant melon genotypes.
• Identification of transcription factors (TFs) using plant TF and cucurbit genomics databases.
• Weighted gene coexpression network analysis (WGCNA) and short time series expression miner (STEM) for module-specific TF screening.
• Cis-acting element analysis, quantitative reverse transcription PCR (RT-qPCR), and phylogenetic analysis.

Main Results:

• 391 transcription factors (TFs) were identified, primarily involved in transcription regulation.
• Key regulatory TFs, including WRKYs and bHLHs, were identified and found to be significantly correlated with resistance (R) genes (e.g., DRP2, RGA3, DRP1, NB-ARC).
• WRKY and bHLH TFs may directly interact with R genes, and WRKY gene expression was validated by RT-qPCR.

Conclusions:

• The study presents a novel, combined bioinformatics strategy for uncovering TF functions in biological processes and predicting gene interactions.
• The identified TFs, particularly WRKYs and bHLHs, play critical roles in Curcurcurbitaceae disease resistance.
• This approach enhances the exploration of omics data for understanding gene regulatory networks and disease resistance mechanisms.