# Computational prediction of the interaction network between long non-coding RNAs and microRNAs in maize based on the transcriptome of the fuzzy tassel mutant line

## TL;DR

This study predicts interactions between long non-coding RNAs and microRNAs in a maize mutant using transcriptome data and machine learning, revealing how these RNA molecules may regulate gene expression.

## Contribution

A novel computational approach combining RNA-seq data and machine learning to predict miRNA–lncRNA interactions in a maize Dicer-like1 mutant.

## Key findings

- 10 lncRNAs in shoots and 34 in tassels showed differential expression in the maize mutant line.
- PmliPred and IntaRNA identified potential miRNA–lncRNA interactions, forming ceRNA networks.
- Some lncRNAs bind multiple miRNAs, supporting their role as miRNA sponges in post-transcriptional regulation.

## Abstract

Long non-coding RNAs (lncRNAs) play an important role in the regulation of gene expression, including interactions with microRNAs (miRNAs), acting as molecular “sponges”. Bioinformatics methods are generally used to predict such interactions. To refine computational predictions, additional evidence based on the co-expression of miRNAs and lncRNAs can be incorporated. In the present study, we investigated potential interactions between lncRNAs and miRNAs in the maize mutant line fuzzy tassel (fzt), which is characterized by reduced expression of certain miRNAs due to a mutation in the Dicer-like1 (DCL1) gene in shoot and tassel tissues. Transcriptome assembly was performed based on RNA-seq data from maize shoot and tassel tissues of control and mutant lines, with data obtained from the NCBI SRA archive. In the shoot, 10 lncRNAs with significantly altered expression levels between control and mutant groups were identified, 9 of which were upregulated in the mutant plants. In the tassel, 34 differentially expressed lncRNAs were identified, with 20 showing increased expression in the mutant line. For lncRNAs with increased expression and miRNAs with decreased expression in the mutant line, potential interactions were predicted using the machine learning algorithm PmliPred. The IntaRNA program was used to confirm possible complementary binding for the identified miRNA–lncRNA pairs, which enabled the construction of competing endogenous RNA (ceRNA) networks. Structural analysis of these networks revealed that certain lncRNAs are capable of binding multiple miRNAs simultaneously, supporting their regulatory role as “sponges” for miRNAs. The results obtained deepen our understanding of post-transcriptional regulation in maize and open new perspectives for breeding strategies aimed at improving stress tolerance and crop productivity.