Background Quantitative PCR (qPCR) is one of the most common and accurate methods of gene expression analysis. However, the biggest challenge for this kind of examinations is normalization of the results, which requires the application of dependable internal controls. The selection of appropriate reference genes (RGs) is one of the most crucial points in qPCR data analysis and for correct assessment of gene expression. Because of the fact that many reports indicate that the expression profiles of typically used RGs can be unstable in certain experimental conditions, species or tissues, reference genes with stable expression levels should be selected individually for each experiment. In this study, we analysed a set of ten candidate RGs for wheat seedlings under short-term drought stress. Our tests included five 'traditional' RGs (GAPDH, ACT, UBI, TUB, and TEF1) and five novel genes developed by the RefGenes tool from the Genevestigator database. Results Expression stability was assessed using five different algorithms: geNorm, NormFinder, BestKeeper, RefFinder and the delta Ct method. In the final ranking, we identified three genes: CJ705892, ACT, and UBI, as the best candidates for housekeeping genes. However, our data indicated a slight variation between the different algorithms that were used. We revealed that the novel gene CJ705892, obtained by means of in silico analysis, showed the most stable expression in the experimental tissue and condition. Conclusions Our results support the statement, that novel genes selected for certain experimental conditions have a more stable level of expression in comparison to routinely applied RGs, like genes encoding actin, tubulin or GAPDH. Selected CJ705892 gene can be used as a housekeeping gene in the expression analysis in wheat seedlings under short-term drought. The results of our study will be useful for subsequent analyses of gene expression in wheat tissues subjected to drought.

中文翻译：

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短期干旱胁迫下普通小麦 (Triticum aestivum L.) 幼苗 qPCR 研究的稳定参考基因的鉴定。

背景 定量 PCR (qPCR) 是最常见和最准确的基因表达分析方法之一。然而，此类检查的最大挑战是结果的标准化，这需要应用可靠的内部控制。选择合适的参考基因 (RGs) 是 qPCR 数据分析和正确评估基因表达的最关键点之一。由于许多报告表明，通常使用的 RGs 的表达谱在某些实验条件、物种或组织中可能不稳定，因此应为每个实验单独选择具有稳定表达水平的参考基因。在这项研究中，我们分析了一组 10 个小麦幼苗在短期干旱胁迫下的候选 RG。我们的测试包括五个“传统”的 RG（GAPDH、ACT、UBI、TUB 和 TEF1）以及由 Genevestigator 数据库中的 RefGenes 工具开发的五个新基因。结果 使用五种不同的算法评估表达稳定性：geNorm、NormFinder、BestKeeper、RefFinder 和 delta Ct 方法。在最终排名中，我们确定了三个基因：CJ705892、ACT 和 UBI，作为看家基因的最佳候选者。然而，我们的数据表明所使用的不同算法之间存在细微差异。我们发现，通过计算机分析获得的新基因 CJ705892 在实验组织和条件下表现出最稳定的表达。结论 我们的结果支持这样的说法，即为某些实验条件选择的新基因与常规应用的 RG 相比具有更稳定的表达水平，如编码肌动蛋白的基因，微管蛋白或 GAPDH。选择的CJ705892基因可作为管家基因在短期干旱条件下小麦幼苗的表达分析中使用。我们的研究结果将有助于后续分析遭受干旱的小麦组织中的基因表达。