Identification and molecular characterization of miRNAs and their target genes associated with seed development through small RNA sequencing in chickpea
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature Publishing AG
Abstract
Multiple studies have attempted to dissect the molecular mechanism underlying seed development in chickpea (Cicer arietinum
L.). These studies highlight the need to focus on the role of miRNAs in regulating storage protein accumulation in seeds.
Therefore, a total of 8,856,691 short-read sequences were generated from a small RNA library of developing chickpea seeds
and were analyzed using miRDeep-P to identify 74 known and 26 novel miRNA sequences. Known miRNAs were classified into
22 miRNA families with miRNA156 family being most abundant. Of the 26 putative novel miRNAs identified, only 22 could be
experimentally validated using stem loop end point PCR. Differential expression analyses led to the identification of known as
well as novel miRNAs that could regulate various stages of chickpea seed development. In silico target prediction revealed
several important target genes and transcription factors like SPL, mediator of RNA Polymerase II transcription subunit 12,
aspartic proteinase and NACs, which were further validated by real-time PCR analysis. A comparative expression analysis in
chickpea genotypes with contrasting seed protein content revealed one known (Car-miR156h) and two novel miRNA (CarnovmiR7 and Car-novmiR23) candidates to be highly expressed in the LPC (low protein content) chickpea genotypes, targets of
which are known to regulate seed storage protein accumulation. Therefore, this study provides a useful resource in the form of
miRNA and their targets which can be further utilized to understand and manipulate various regulatory mechanisms involved in
seed development with the overall aim of improving yield and nutrition attributes in chickpea.
Description
Accepted date: 9 February 2021
Keywords
Chickpea seed, Small RNA-seq, Expression analysis, Target prediction
Citation
Functional & Integrative Genomics, 21(2): 283-298
