The RNAome landscape of tomato during arbuscular mycorrhizal symbiosis reveals an evolving RNA layer symbiotic regulatory network

Arbuscular mycorrhizal symbiosis (AMS) is an ancient plant-fungus relationship that is widely distributed in terrestrial plants. The formation of symbiotic structures and bidirectional nutrient exchange requires the regulation of numerous genes. However, the landscape of RNAome during plant AMS involving different types of regulatory RNA is poorly understood. In this study, a combinatorial strategy utilizing multiple sequencing approaches was used to decipher the landscape of RNAome in tomato, an emerging AMS model. The annotation of the tomato genome was improved by a multiple-platform sequencing strategy. A total of 3,174 protein-coding genes were upregulated during AMS, 42% of which were alternatively spliced. Comparative-transcriptome analysis revealed that genes from 24 orthogroups were consistently induced by AMS in eight phylogenetically distant angiosperms. Seven additional orthogroups were specifically induced by AMS in all surveyed dicot AMS host plants. However, these orthogroups were absent or not induced in monocots and/or non-AMS hosts, suggesting a continuously evolving AMS-responsive network in addition to a conserved core regulatory module. Additionally, we detected 587 lncRNAs, ten miRNAs, and 146 circRNAs that responded to AMS, which were incorporated to establish a tomato AMS-responsive, competing RNA-responsive endogenous RNA (ceRNA) network. Finally, a tomato symbiotic transcriptome database (TSTD, https://efg.nju.edu.cn/TSTD) was constructed to serve as a resource for deep deciphering of the AMS regulatory network. These results help elucidate the reconfiguration of the tomato RNAome during AMS and suggest a sophisticated and evolving RNA layer responsive network during AMS processes. This study reveals sophisticated transcriptional reconfiguration in tomato during arbuscular mycorrhizal symbiosis (AMS) through analyzing RNAome using a combinatorial sequencing approach and suggests an evolving RNA layer regulatory network during AMS processes. The integrated RNAome data provides a valuable resource for deep analysis of the AMS regulatory network.