From knowledge graph to topological data analysis: a novel framework to analyze gene regulatory networks for tomato-multi-pathogen interactions.

Tomato (Solanum lycopersicum), despite being the most important vegetable crop world-wide, remains vulnerable to over 200 diseases caused by different pests. Although tomato molecular response to individual stresses is well studied, the gene regulatory network (GRN) representing the crosstalk and trade-offs of multi-stress responses remains almost unexplored. We developed GENIAL (Gene rEgulatory Network and topologIcal datA anaLysis) to refine and analyze complex GRNs and TomTom, a knowledge graph that gathers 11 publicly available databases in a unique FAIR (Findable, Accessible, Interoperable, Reproducible) resource. To test GENIAL, we used transcriptomics data from tomato subjected to six distinct pathogens from the literature. GENIAL yielded the identification of transcription factors (TFs) coordinating the specific and multiple pathogen response. Functional validation using the virus-induced gene silencing system in tomato demonstrated that ETHYLENE RESPONSE FACTOR 16 and TCP DOMAIN PROTEIN 17 act as key TFs in the response to Botrytis cinerea, as silencing of either TF resulted in increased susceptibility. The validation of selected downstream targets allowed the validation of the robustness of the interactions highlighted by GENIAL. This study represents a proof of concept of our framework and can be extended to include other molecular layers and scaled to other questions involving tomato and beyond.