In Silico analysis of the sequence and structure of plant microRNAs packaged in extracellular vesicles. (December 2022)

• Record Type: Journal Article
• Authors: Lang, Claudia; Lin, Harris T.; Wu, Chen; Alavi, Maryam
• Is Part Of: Computational biology and chemistry. Volume 101(2022)
• Publication Date: 2022-12
• Subjects: MiRNA -- Extracellular Vesicles -- Plant defenses -- EV packaging -- Cross-kingdom miRNAs -- Cross-kingdom communication; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107771
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Small RNA (sRNA)-mediated RNA interference (RNAi) is a conserved eukaryotic cellular process associated with immune defense and pathogen virulence. The cross-kingdom transfer of noncoding regulatory sRNAs between host and pathogen can be mediated via lipid, membrane-bound extracellular vesicles (EVs). Several studies have reported in mammalian and plant systems there is selective packaging of sRNAs into EVs. In mammals, sequence patterns and structural motifs are implicated in signaling pathways related to EV cargo sorting. However, in the emerging plant EV field, there is a lack of knowledge of the mechanisms involved in selecting sRNAs for EV transport. In this study, we accessed publicly available databases where the sRNA content of plant EVs has been characterized from control plants and those released in response to fungal pathogen infection. An in-depth analysis revealed 158 sRNAs are EV packaged, with ∼60 % sharing a sequence motif and 98.1 % forming a secondary hairpin stem-loop structure. Many of the predicted plant targets for the EV sRNAs were associated with biological pathways involved in metabolism and regulation processes. Overall, our in silico analysis of sRNAs packaged in plant EVs highlight that a computational approach can offer valuable insights into the cross-kingdom EV transport of sRNAs. Highlights: Plant EV miRNA databases were screened to analyze sequence and structural features. In silico analysis predicted 61 % of EV-packaged miRNAs share a sequence motif. In silico analysis predicted 98.1 % of EV-packaged miRNAs form secondary structures. EV-packaged miRNAs have predicted targets in key plant metabolic pathways.