Computational tool-assisted primer design for real-time reverse transcription (RT) PCR (qPCR) analysis largely ignores the sequence similarities between sequences of homologous genes in a plant genome. It can lead to false confidence in the quality of the designed primers, which sometimes results in skipping the optimization steps for qPCR. However, the optimization of qPCR parameters plays an essential role in the efficiency, specificity, and sensitivity of each gene’s primers. Here, we proposed an optimized approach to sequentially optimizing primer sequences, annealing temperatures, primer concentrations, and cDNA concentration range for each reference (and target) gene. Our approach started with a sequence-specific primer design that should be based on the single-nucleotide polymorphisms (SNPs) present in all the homologous sequences for each of the reference (and target) genes under study. By combining the efficiency calibrated and standard curve methods with the 2−ΔΔCt method, the standard cDNA concentration curve with a logarithmic scale was obtained for each primer pair for each gene. As a result, an R2 ≥ 0.9999 and the efficiency (E) = 100 ± 5% should be achieved for the best primer pair of each gene, which serve as the prerequisite for using the 2−ΔΔCt method for data analysis. We applied our newly developed approach to identify the best reference genes in different tissues and at various inflorescence developmental stages of Tripidium ravennae, an ornamental and biomass grass, and validated their utility under varying abiotic stress conditions. We also applied this approach to test the expression stability of six reference genes in soybean under biotic stress treatment with Xanthomonas axonopodis pv. glycines (Xag). Thus, these case studies demonstrated the effectiveness of our optimized protocol for qPCR analysis.

中文翻译：

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用于逐步优化实时 RT-PCR 分析的优化方案

用于实时逆转录 (RT) PCR (qPCR) 分析的计算工具辅助引物设计在很大程度上忽略了植物基因组中同源基因序列之间的序列相似性。它可能导致对设计引物质量的错误信心，有时会导致跳过 qPCR 的优化步骤。然而，qPCR 参数的优化对每个基因引物的效率、特异性和敏感性起着至关重要的作用。在这里，我们提出了一种优化方法来顺序优化每个参考（和目标）基因的引物序列、退火温度、引物浓度和 cDNA 浓度范围。我们的方法从序列特异性引物设计开始，该引物设计应基于研究中的每个参考（和目标）基因的所有同源序列中存在的单核苷酸多态性 (SNP)。通过将效率校准和标准曲线方法与 2-ΔΔCt方法，获得每个基因的每个引物对的具有对数刻度的标准 cDNA 浓度曲线。结果，一个R2≥ 0.9999 和效率 (乙) = 每个基因的最佳引物对应达到 100 ± 5%，这是使用 2-ΔΔCt数据分析方法。我们应用我们新开发的方法来鉴定不同组织和不同花序发育阶段的最佳参考基因。滇滇，一种观赏性和生物质草，并在不同的非生物胁迫条件下验证了它们的效用。我们还应用这种方法来测试大豆中六个参考基因在生物胁迫处理下的表达稳定性黄单胞菌光伏。甘氨酸(Xag)。因此，这些案例研究证明了我们优化的 qPCR 分析方案的有效性。