Instituto de Biotecnología, CICVyA, INTA Castelar, Las Cabañas y Los Reseros, (1686) Hurlingham, Buenos Aires, Argentina.
Plant Cell Rep. 2011 Jan;30(1):63-74. doi: 10.1007/s00299-010-0944-3. Epub 2010 Nov 13.
The selection and validation of reference genes constitute a key point for gene expression analysis based on qPCR, requiring efficient normalization approaches. In this work, the expression profiles of eight genes were evaluated to identify novel reference genes for transcriptional studies associated to the senescence process in sunflower. Three alternative strategies were applied for the evaluation of gene expression stability in leaves of different ages and exposed to different treatments affecting the senescence process: algorithms implemented in geNorm, BestKeeper software, and the fitting of a statistical linear mixed model (LMModel). The results show that geNorm suggested the use of all combined genes, although identifying α-TUB1 as the most stable expressing gene. BestKeeper revealed α-TUB and β-TUB as stable genes, scoring β-TUB as the most stable one. The statistical LMModel identified α-TUB, actin, PEP, and EF-1α as stable genes in this order. The model-based approximation allows not only the estimation of systematic changes in gene expression, but also the identification of sources of random variation through the estimation of variance components, considering the experimental design applied. Validation of α-TUB and EF-1α as reference genes for expression studies of three sunflower senescence associated genes showed that the first one was more stable for the assayed conditions. We conclude that, when biological replicates are available, LMModel allows a more reliable selection under the assayed conditions. This study represents the first analysis of identification and validation of genuine reference genes for use as internal control in qPCR expression studies in sunflower, experimentally validated throughout six different controlled leaf senescence conditions.
基于 qPCR 的基因表达分析的关键步骤是选择和验证参考基因，这需要有效的归一化方法。在这项工作中，评估了 8 个基因的表达谱，以确定向日葵衰老过程转录研究的新的参考基因。为了评估不同年龄叶片和不同处理条件下与衰老过程相关的基因表达稳定性，应用了三种替代策略：geNorm 中实现的算法、BestKeeper 软件和统计线性混合模型 (LMModel) 的拟合。结果表明，geNorm 建议使用所有组合基因，但鉴定出 α-TUB1 为最稳定表达的基因。BestKeeper 显示 α-TUB 和 β-TUB 为稳定基因，β-TUB 评分最高。统计 LMModel 按此顺序鉴定出 α-TUB、肌动蛋白、PEP 和 EF-1α 为稳定基因。基于模型的逼近不仅允许估计基因表达的系统变化，还允许通过估计方差分量来识别随机变异的来源，同时考虑应用的实验设计。α-TUB 和 EF-1α 作为三种向日葵衰老相关基因表达研究的参考基因的验证表明，在测定条件下，前者更稳定。我们得出结论，当有生物学重复时，LMModel 允许在测定条件下进行更可靠的选择。本研究首次对向日葵中用于 qPCR 表达研究的真实参考基因进行了鉴定和验证分析，通过六种不同的受控叶片衰老条件进行了实验验证。
