Abstract
Here, we utilize the available next-generation sequencing (NGS) data to develop robust markers differentiating between salt-tolerant and sensitive wheat cultivars. First, we analyzed multi-transcriptomic datasets for salt-tolerant cultivars to identify the shared differentially expressed genes (DEGs). The identified shared DEGs (31 sDEGs) were extracted and compared to available wheat reference genomes to obtain all single-nucleotide polymorphisms (SNPs). Interestingly, six shared SNPs (sSNPs) were identified across four genes including the MIOX-1 gene. For in silico validation of MIOX-1 expression, we compared transcriptomes of salt-tolerant and sensitive wheat cultivars. Interestingly, the expression of the MIOX-1 gene was significantly down-regulated in the tolerant compared to the sensitive. Therefore, the SNP located in the promotor area of the MIOX-1 gene was further analyzed to predict the transcription factor binding sites. The results revealed that the occurrence of this sSNP is irreplaceable and positioned in the WRKY-1 binding site. The expression of the WRKY-1 gene was consistent between salt-tolerant and salt-sensitive wheat cultivars, indicating that the WRKY-1 had no influence on the MIOX-1 gene expression and these SNP might positively affect the salinity tolerance. For SNP in vitro validation, we used two Egyptian cultivars with opposite salinity responses (tolerant: Sakha-93 and sensitive: Gemmeiza-9). The allele-specific polymerase chain reaction results revealed that Gemmeiza-9 was heterozygous (C/G), whereas the Sakha-93 was homozygous (C/C), indicating that these SNPs might affect positively the response of wheat cultivars to tolerate the salinity. Ultimately, these SNPs could serve as a powerful marker for accelerating wheat breeding programs.
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Karam, A., El-Assal, S. E. D. S., Hussein, B. A., & Atia, M. A. M. (2022). Transcriptome data mining towards characterization of single nucleotide polymorphisms (SNPs) controlling salinity tolerance in bread wheat. Biotechnology and Biotechnological Equipment, 36(1), 389–400. https://doi.org/10.1080/13102818.2022.2081516
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