Transcription profile of soybean-root-knot nematode interaction reveals a key role of phythormones in the resistance reaction

Background: Root-knot nematodes (RKN- Meloidogyne genus) present extensive challenges to soybean crop. The soybean line (PI 595099) is known to be resistant against specific strains and races of nematode species, thus its differential gene expression analysis can lead to a comprehensive gene expression profiling in the incompatible soybean-RKN interaction. Even though many disease resistance genes have been studied, little has been reported about phytohormone crosstalk on modulation of ROS signaling during soybean-RKN interaction. Results: Using 454 technology to explore the common aspects of resistance reaction during both parasitism and resistance phases it was verified that hormone, carbohydrate metabolism and stress related genes were consistently expressed at high levels in infected roots as compared to mock control. Most noteworthy genes include those encoding glycosyltransferases, peroxidases, auxin-responsive proteins and gibberellin-regulated genes. Our data analysis suggests the key role of glycosyltransferases, auxins and components of gibberellin signal transduction, biosynthesis and deactivation pathways in the resistance reaction and their participation in jasmonate signaling and redox homeostasis in mediating aspects of plant growth and responses to biotic stress. Conclusions: Based on this study we suggest a reasonable model regarding to the complex mechanisms of crosstalk between plant hormones, mainly gibberellins and auxins, which can be crucial to modulate the levels of ROS in the resistance reaction to nematode invasion. The model also includes recent findings concerning to the participation of DELLA-like proteins and ROS signaling controlling plant immune or stress responses. Furthermore, this study provides a dataset of potential candidate genes involved in both nematode parasitism and resistance, which can be tested further for their role in this biological process using functional genomics approaches.