Genome-wide characterization of the TERT gene in Rosaceae family and expression analysis of its responses to salt, waterlogging and drought stress in apple

被引:1
作者
Kiyak, Ali [1 ]
机构
[1] Mehmet Akif Ersoy Univ, Fac Arts & Sci, Dept Mol Biol, TR-15030 Burdur, Turkiye
关键词
Rosaceae; Malus domestica Borkh; Telomerase reverse transcriptase; Evolution; Biotic stress; Gene expression analysis; TELOMERE-LENGTH; IDENTIFICATION; MODELS;
D O I
10.1016/j.sajb.2024.05.017
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Telomerase reverse transcriptase (TERT) is the catalytic subunit of the telomerase enzyme and TERT expression affects the telomerase activity. The RNA subunit of TERT serves as the template for reverse transcription in the elongation of telomeres. However, the evolutionary history and characteristics of the TERT gene in the Rosaceae family remain largely unclear to date. In this study, 23 TERT gene homologous were identi fied in 14 Rosaceae species. Phylogenetic analysis showed that these TERT genes clustered into six groups within the Plantae kingdom. Gene structure analysis indicated that Rosaceae TERT genes within the same groups showed similar exon numbers and contained similar motifs. Several cis -elements related to light, hormones, and abiotic stress responses were predicted in the Rosaceae TERT promoters. Purifying selection was involved in the evolution of TERT genes in Rosaceae family. The gene ontology of Rosaceae TERTs revealed that these genes are involved in various biological processes beyond telomere biology, which aligns with the network analysis of MdTERT. Expression analysis based on transcriptomic data revealed that MdTERT exhibited different expression patterns in response to growth, development and abiotic stresses. The RT-qPCR results demonstrated that MdTERT responded to salt, drought, and waterlogging stresses in two different apple rootstocks. Overall, these findings provide a valuable resource for understanding the molecular evolution of TERT genes in the Rosaceae family and for future functional characterization analyses with MdTERT . (c) 2024 SAAB. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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页码:318 / 330
页数:13
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