# An Integrative Computational Approach for Identifying Cotton Host Plant MicroRNAs with Potential to Abate CLCuKoV-Bur Infection

## TL;DR

This study identifies cotton microRNAs that could potentially combat a virus causing cotton leaf curl disease using computational methods.

## Contribution

The paper presents the first computational prediction of cotton microRNAs with potential to resist CLCuKoV-Bur infection.

## Key findings

- Eighteen cotton microRNAs were predicted to bind to CLCuKoV-Bur viral sequences using multiple algorithms.
- The ghr-miR399d microRNA was identified as a top candidate for developing an artificial RNAi-based resistance strategy.
- An in silico regulatory network of microRNA interactions with the virus was created using Circos software.

## Abstract

Cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bur) has a circular single-stranded ssDNA genome of 2759 nucleotides in length and belongs to the genus Begomovirus (family, Geminiviridae). CLCuKoV-Bur causes cotton leaf curl disease (CLCuD) and is transmitted by the whitefly Bemisis tabaci cryptic species. Monopartite begomoviruses encode five open reading frames (ORFs). CLCuKoV-Bur replicates through a dsDNA intermediate. Five open reading frames (ORFs) are organized in the small circular, single-stranded (ss)-DNA genome of CLCuKoV-Bur (2759 bases). RNA interference (RNAi) is a naturally occurring process that has revolutionized the targeting of gene regulation in eukaryotic organisms to combat virus infection. The aim of this study was to elucidate the potential binding attractions of cotton-genome-encoded microRNAs (Gossypium hirsutum-microRNAs, ghr-miRNAs) on CLCuKoV-Bur ssDNA-encoded mRNAs using online bioinformatics target prediction tools, RNA22, psRNATarget, RNAhybrid, and TAPIR. Using this suite of robust algorithms, the predicted repertoire of the cotton microRNA-binding landscape was determined for a CLCuKoV-Bur consensus genome sequence. Previously experimentally validated cotton (Gossypium hirsutum L.) miRNAs (n = 80) were selected from a public repository miRNA registry miRBase (v22) and hybridized in silico into the CLCuKoV-Bur genome (AM421522) coding and non-coding sequences. Of the 80 ghr-miRNAs interrogated, 18 ghr-miRNAs were identified by two to four algorithms evaluated. Among them, the ghr-miR399d (accession no. MIMAT0014350), located at coordinate 1747 in the CLCuKoV-Bur genome, was predicted by a consensus or “union” of all four algorithms and represents an optimal target for designing an artificial microRNA (amiRNA) silencing construct for in planta expression. Based on all robust predictions, an in silico ghr-miRNA-regulatory network was developed for CLCuKoV-Bur ORFs using Circos software version 0.6. These results represent the first predictions of ghr-miRNAs with the therapeutic potential for developing CLCuD resistance in upland cotton plants.