ceRNAs in plants: computational approaches and associated challenges for target mimic research

Area of Science:

• Plant molecular biology
• Bioinformatics
• RNA biology

Background:

• The competing endogenous RNA (ceRNA) hypothesis proposes a global regulatory mechanism involving microRNAs (miRNAs).
• While extensively studied in animals, significant advances in target mimic (TM) discovery and computational/experimental methods have emerged in plants over the last decade.
• Understanding miRNA:TM interactions is crucial for deciphering noncoding RNA functions.

Purpose of the Study:

• To summarize recent progress in computational approaches for identifying miRNA:TM interactions in plants.
• To provide a comprehensive overview of plant TM research, including literature, tools, databases, and computational reports.
• To present a novel bioinformatics approach for predicting TM motifs that cross-target members of miRNA families.

Main Methods:

• Literature review of plant TM research, tools, and databases.
• Description of a standardized protocol for computational and experimental TM analysis.
• Development of a bioinformatics approach to identify consensus miRNA-binding sites in known TMs across plant genomes, transcriptomes, and known miRNAs to predict novel TM motifs.

Main Results:

• Identification of three consensus TM motifs: MIM166, MIM171, and MIM159/319.
• The MIM159/319 motif shows strong experimental validation from recent studies.
• The proposed computational approach is particularly promising for plants due to conserved and large miRNA families.

Conclusions:

• Computational methods are powerful tools for advancing the study of miRNA:TM interactions in plants.
• The developed bioinformatics approach effectively predicts conserved TM motifs.
• Further research is needed to address computational and experimental challenges in plant ceRNA studies.