Genome-wide identification and tissue-specific profiling of microRNAs in lentil (Lens culinaris Medik.)
MicroRNAs (miRNAs) are key post-transcriptional regulators that influence plant development, metabolism, and stress adaptation. Despite the agronomic importance of lentil (Lens culinaris Medik.), its miRNA landscape remains poorly characterized. This study aimed to establish a comprehensive, tissue-specific atlas of conserved and novel miRNAs to improve understanding of gene regulation in lentil. Small RNA libraries from seven tissues—root, shoot, stem, mature leaf, flower, flower bud, and young pod—were sequenced using the Illumina HiSeq platform, generating over 432 million reads. A total of 1,490 miRNA candidates were identified, comprising 415 conserved and 1,075 novel miRNAs. Among 370 miRNAs with sufficient expression for analysis, 61% showed significant tissue-dependent variation, revealing extensive spatial regulation. Conserved miRNAs such as miR156, miR159, miR164, and miR172 displayed expression patterns consistent with their known developmental roles, whereas most novel miRNAs exhibited tissue-restricted accumulation, particularly in roots and reproductive organs. Predicted target and enrichment analyses indicated that conserved miRNAs primarily regulate transcription factors and hormone signaling components, while novel miRNAs are associated with redox homeostasis, lipid metabolism, and transport processes. This study provides the first comprehensive overview of miRNA expression in lentil, revealing a dual-layer regulatory architecture comprising conserved miRNAs that coordinate core developmental pathways and novel miRNAs that fine-tune tissue-specific and adaptive responses. The expanded lentil miRNA repertoire established here represents a valuable resource for future studies in legumes, including functional, evolutionary, and breeding-related applications. Overall design: miRNA expression profiling in lentil tissues (from seven tissues—root, shoot, stem, mature leaf, flower, flower bud, and young pod) using high-throughput sequencing.