From knowledge graph to topological data analysis: a novel framework to analyze gene regulatory networks for...

Area of Science:

• Plant molecular biology
• Genomics
• Bioinformatics

Background:

• Tomato (Solanum lycopersicum) is a vital global crop facing over 200 diseases.
• Understanding multi-stress responses in tomato is crucial but largely unexplored.
• Gene regulatory networks (GRNs) are key to deciphering complex plant defense mechanisms.

Purpose of the Study:

• To develop and validate a computational framework (GENIAL) for analyzing complex plant GRNs.
• To identify key transcription factors (TFs) involved in tomato's response to multiple pathogens.
• To create TomTom, a FAIR knowledge graph resource for tomato research.

Main Methods:

• Development of GENIAL (Gene rEgulatory Network and topologIcal datA anaLysis) for GRN analysis.
• Utilized transcriptomics data from tomato under six pathogen stresses.
• Employed virus-induced gene silencing for functional validation of identified TFs.
• Integrated 11 public databases into the TomTom knowledge graph.

Main Results:

• GENIAL successfully identified TFs coordinating specific and multiple pathogen responses in tomato.
• ETHYLENE RESPONSE FACTOR 16 and TCP DOMAIN PROTEIN 17 were validated as key TFs against Botrytis cinerea.
• Silencing of these TFs led to increased susceptibility, confirming their crucial role.
• Downstream target validation confirmed the robustness of GENIAL-identified interactions.

Conclusions:

• The GENIAL framework provides a robust approach for dissecting complex plant GRNs.
• Identified TFs offer potential targets for enhancing tomato disease resistance.
• The study demonstrates a proof of concept for a scalable framework applicable to various plant research questions.