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2000
Volume 21, Issue 6
  • ISSN: 1570-1646
  • E-ISSN: 1875-6247

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, N-glycosylation/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.

© 2024 Bentham Science Publishers
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2025-07-25

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AraMultiOmics: A Platform for Generating Multi-Omics Features for Studying Symbiosis in Arabidopsis thaliana and Arbuscular Mycorrhizal Fungi
Curr. Proteomics 21, 882 (2024); https://doi.org/10.2174/0115701646360927250129095105
/content/journals/cp/10.2174/0115701646360927250129095105
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  • Article Type:     Research Article
Keyword(s): AM symbiosis; AM-driven signaling; arbuscular mycorrhizal fungi; comparative omics; feature generation; multi-omics analysis

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