De Novo Sequencing and Comparative Analysis of Schima superba Seedlings to Explore the Response to Drought Stress

Abstract

Schima superba is an important dominant species in subtropical evergreen broadleaved forests of China, and plays a vital role in community structure and dynamics. However, the survival rate of its seedlings in the field is low, and water shortage could be a factor that limits its regeneration. In order to better understand the response of its seedlings to drought stress on a functional genomics scale, RNA-seq technology was utilized in this study to perform a large-scale transcriptome sequencing of the S. superba seedlings under drought stress. More than 320 million clean reads were generated and 72218 unique transcripts were obtained through de novo assembly. These unigenes were further annotated by blasting with different public databases and a total of 53300 unique transcripts were annotated. A total of 31586 simple sequence repeat (SSR) loci were presented. Through gene expression profiling analysis between drought treatment and control, 11038 genes were found to be significantly enriched in drought-stressed seedlings. Based on these differentially expressed genes (DEGs), Gene Ontology (GO) terms enrichment and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis indicated that drought stress caused a number of changes in the types of sugars, enzymes, secondary mechanisms, and light responses, and induced some potential physical protection mechanisms. In addition, the expression patterns of 18 transcripts induced by drought, as determined by quantitative real-time PCR, were consistent with their transcript abundance changes, as identified by RNA-seq. This transcriptome study provides a rapid method for understanding the response of S. superba seedlings to drought stress and provides a number of gene sequences available for further functional genomics studies.

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Transcriptome analysis of the tea oil camellia (Camellia oleifera) reveals candidate drought stress genes

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