Cassol Daniela, Cruz Fernanda P, Espindola Kauê, Mangeon Amanda, Müller Caroline, Loureiro Marcelo Ehlers, Corrêa Régis L, Sachetto-Martins Gilberto
Department of Genetics, Federal University of Rio de Janeiro, Rio de Janeiro, 21944-970, Brazil.
Department of Genetics, Federal University of Rio de Janeiro, Rio de Janeiro, 21944-970, Brazil; Department of Plant Biology, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil.
Plant Physiol Biochem. 2016 Sep;106:101-7. doi: 10.1016/j.plaphy.2016.02.024. Epub 2016 Mar 19.
Quantitative real-time PCR (RT-qPCR) is one of the most powerful and sensitive techniques to the study of gene expression. Several factors influence RT-qPCR performance though, including the stability of the reference genes used for data normalization. While the selection of appropriate reference genes is crucial for accurate and reliable gene expression analysis, no suitable reference genes have been previously identified in castor bean under drought stress. In this study, the expression stability of eleven mRNAs, thirteen microRNAs (miRNAs) and one small nuclear RNA were analyzed in roots and leaves across different levels of water deficit. Three different algorithms were employed to analyze the RT-qPCR data, and the resulting outputs were merged using a non-weighted unsupervised rank aggregation method. Our analysis indicated that the Elongation factor 1-beta (EF1B), Protein phosphatase 2A (PP2A) and ADP-ribosylation factor (ADP) ranked as the best candidates across diverse samples submitted to different levels of drought conditions. EF1B and Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and EF1B and SKP1/ASK-interacting protein 16 (SKIP16) were found as the most suitable reference genes for expression analysis in roots and leaves, respectively. In addition, miRNAs miR168, miR160 and miR397 were selected as optimal reference genes across all tissues and treatments. miR168 and miR156 were recommended as reference for roots, while miR168 and miR160 were recommended for leaves. Together, our results constitute the first attempt to identify and validate the most suitable reference genes for accurate normalization of gene expression in castor bean under drought stress.
定量实时聚合酶链反应（RT-qPCR）是研究基因表达最强大、最灵敏的技术之一。然而，有几个因素会影响RT-qPCR的性能，包括用于数据标准化的参考基因的稳定性。虽然选择合适的参考基因对于准确可靠的基因表达分析至关重要，但此前在蓖麻干旱胁迫下尚未鉴定出合适的参考基因。在本研究中，分析了11种mRNA、13种微小RNA（miRNA）和1种小核RNA在不同水分亏缺水平下根和叶中的表达稳定性。采用三种不同的算法分析RT-qPCR数据，并使用非加权无监督秩聚合方法合并所得输出结果。我们的分析表明，在经受不同干旱条件水平的各种样本中，延伸因子1-β（EF1B）、蛋白磷酸酶2A（PP2A）和ADP-核糖基化因子（ADP）被列为最佳候选基因。发现EF1B和甘油醛-3-磷酸脱氢酶（GAPDH），以及EF1B和SKP1/ASK相互作用蛋白16（SKIP16）分别是根和叶中表达分析最合适的参考基因。此外，miRNA miR168、miR160和miR397被选为所有组织和处理中的最佳参考基因。建议将miR168和miR156作为根的参考基因，而将miR168和miR160作为叶的参考基因。总之，我们的结果首次尝试鉴定和验证在干旱胁迫下蓖麻中用于准确标准化基因表达的最合适参考基因。
