AraMultiOmics: A Platform for Generating Multi-Omics Features for Studying Symbiosis in Arabidopsis thaliana and Arbuscular Mycorrhizal Fungi

Abstract

Background: Recent investigation revealed that arbuscular mycorrhizal fungi (AMF) brought major changes in the transcriptome of non-host plant- Arabidopsis thaliana (A. thaliana) within the AM network constructed by the hyphae of AMF connecting multiple plant roots. Although there is enormous omics data available for A. thaliana, most AM-related information has been restricted to transcriptome studies. Objective: We aimed to provide a comprehensive toolset for analyzing AM signaling-driven molecular interactions in A. thaliana. Methods: We developed ten modules: 1) Epigenetic regulation in protein–nucleic acid interactions (PNI), 2) DNA structure and metal binding profiles, 3) Transcription factor (TF) binding profiles, 4) Protein domain–domain interactions (DDI), 5) Profiling of protein-metal and protein-ligand interactions with complex structures (PLP) based on alignment of similar protein structures, 6) Carbohydrate- lipid-protein interactions (CLP) – analysis of lipidome-proteome interactions, Nglycosylation/ glycan structure data, and carbohydrate-active enzyme/substrate predictions, 7) Metabolic pathway analysis, 8) Multiple omics association studies, 9) Gene Ontology (GO) and Plant Ontology (PO) analysis, and 10) Medicago transcriptome and epigenetic information. Results: For the program demonstration, we generated various comparative datasets based on differentially expressed genes (DEGs) from Arabidopsis thaliana (A. thaliana) of non-arbuscular mycorrhizal (non-AM) and arbuscular mycorrhizal (AM) phenotypes, as well as DEGs from Medicago truncatula (M. truncatula). These datasets were analyzed using statistical methods and artificial neural networks. The program demonstrated a range of advantages in studying molecular interactions related to AM symbiosis. Conclusion: To aid in the inference of AM-driven changes and the identification of AM-derived molecules during AM symbiosis, the program offers a user-friendly platform for generating datasets with key features, which can then be integrated with various downstream statistical methods. The program code is freely available for download at www.artfoundation.kr. other: It also provides various multi-omics approaches in A. thaliana study.