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Updated: Aug 7, 2026

Experimental Design for Laser Microdissection RNA-Seq: Lessons from an Analysis of Maize Leaf Development
Published on: March 5, 2017
Genome-wide identification and comparative expression analysis of LEA genes in watermelon and melon genomes
Yasemin Celik Altunoglu1, Mehmet Cengiz Baloglu1, Pinar Baloglu1,2
1Department of Genetics and Bioengineering, Faculty of Engineering and Architecture, Kastamonu University, 37150 Kastamonu, Turkey.
Abstract:
Late embryogenesis abundant (LEA) proteins are large and diverse group of polypeptides which were first identified during seed dehydration and then in vegetative plant tissues during different stress responses. Now, gene family members of LEA proteins have been detected in various organisms. However, there is no report for this protein family in watermelon and melon until this study. A total of 73 LEA genes from watermelon (ClLEA) and 61 LEA genes from melon (CmLEA) were identified in this comprehensive study. They were classified into four and three distinct clusters in watermelon and melon, respectively. There was a correlation between gene structure and motif composition among each LEA groups. Segmental duplication played an important role for LEA gene expansion in watermelon. Maximum gene ontology of LEA genes was observed with poplar LEA genes. For evaluation of tissue specific expression patterns of ClLEA and CmLEA genes, publicly available RNA-seq data were analyzed. The expression analysis of selected LEA genes in root and leaf tissues of drought-stressed watermelon and melon were examined using qRT-PCR. Among them, ClLEA-12-17-46 genes were quickly induced after drought application. Therefore, they might be considered as early response genes for water limitation conditions in watermelon. In addition, CmLEA-42-43 genes were found to be up-regulated in both tissues of melon under drought stress. Our results can open up new frontiers about understanding of functions of these important family members under normal developmental stages and stress conditions by bioinformatics and transcriptomic approaches.
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