For accurate and reliable quantification of the gene expression, some important issues need to be considered when a qPCR approach is used. The sensitivity, specificity and simplicity of this technique is incomparable with other methods such as Northern and in situ hybridization, RNase protection assays and semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR). Quantitative real-time polymerase chain reaction (qPCR) analysis is one of the most currently used approaches for measuring gene expression level. Network analysis of transcriptome data might lead to better understanding of complex traits such as survival, growth and differentiation. With the availability of genome and transcriptome sequence information in most plant species, the identification and characterization of stress-induced genes is more feasible now. Meanwhile, a number of ESTs (expressed sequence tag), genes and promoters induced by the salt and drought stresses were isolated, sequenced and annotated at a molecular level. littoralis have been investigated so far. Several morphological, anatomical, ecological, and physiological traits of A. littoralis can serve as valuable genetic resource for understanding the molecular mechanisms of stress-responses in monocots, and can potentially be used for improving tolerance to abiotic stresses in economically important crops. Aeluropus littoralis can survive where the water salinity is periodically high and tolerate up to 1100 mM sodium chloride. Aeluropus littoralis is a perennial monocot grass with the small haploid genome of 349 Mb (2n = 2X = 14) using the C 4 mechanism for carbon fixation that grows in dry salty areas or marshes. In this view, several researchers focused on the Aeluropus littoralis as a halophyte model for identification and isolation of the novel adaptation genes. The use of wild plant species or their halophytic relatives has been considered in plant breeding programs for developing salt and drought tolerant crops. The application of rDNA-based primers in qPCR analysis was addressed. RPS3 that applied this study had higher expression stability than commonly used housekeeping genes. Overall, the sets of reference genes proposed in this study are ideal normalizers for qPCR analysis in A. ![]() The universality, specificity and sensitivity of these primer pairs were also evaluated in Poaceae. Additionally, for assessing DNA contamination in RNA samples, a set of unique primers were designed based on the conserved region of ribosomal DNA (rDNA). Also, four set of reference genes were proposed for each tissue and condition including: RPS3, EF1A and UBQ for salt stress and root samples RPS3, EF1A, UBQ as well as GAPDH for recovery condition U2SURP and GTF for leaf samples. RPS3, EF1A, GTF and RPS12 could be used as general reference genes for the all selected conditions and tissues. Here, our results show that a set of four housekeeping genes (HKGs) e.g. According to results obtained, ten candidate reference genes were ranked based on the stability of their expression. To determine the most stably expressed reference genes, three statistical methods (geNorm, NormFinder and BestKeeper) were applied. Ten candidate reference genes participating in different biological processes were analyzed in four groups of samples including root and leaf tissues, salt stress and recovery condition. ![]() Also, a qPCR-based approach for monitoring contamination with gDNA was conducted. The aim of this study was to validate the most stably expressed reference genes in two different tissues of Aeluropus littoralis-halophyte grass at salt stress and recovery condition. It is estimated that the gDNA contamination in gene expression analysis lead to an overestimation of the RNA transcript level. The genomic DNA contamination in RNA samples is another important factor that also influence the interpretation of the data obtained from qPCR. Recent studies have shown that, the expression levels of common housekeeping genes are varying in different tissues and experimental conditions. ![]() The use of stably expressed genes as normalizers has crucial role in accurate and reliable expression analysis estimated by quantitative real-time polymerase chain reaction (qPCR).
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