Laboratoire de Recherche en Sciences Végétales, Université Toulouse III, UPS, CNRS, BP 42617, Auzeville, 31326 Castanet Tolosan, France.
Plant Cell Physiol. 2012 Dec;53(12):2101-16. doi: 10.1093/pcp/pcs152. Epub 2012 Nov 18.
Interest in the genomics of Eucalyptus has skyrocketed thanks to the recent sequencing of the genome of Eucalyptus grandis and to a growing number of large-scale transcriptomic studies. Quantitative reverse transcription-PCR (RT-PCR) is the method of choice for gene expression analysis and can now also be used as a high-throughput method. The selection of appropriate internal controls is becoming of utmost importance to ensure accurate expression results in Eucalyptus. To this end, we selected 21 candidate reference genes and used high-throughput microfluidic dynamic arrays to assess their expression among a large panel of developmental and environmental conditions with a special focus on wood-forming tissues. We analyzed the expression stability of these genes by using three distinct statistical algorithms (geNorm, NormFinder and ΔCt), and used principal component analysis to compare methods and rankings. We showed that the most stable genes identified depended not only on the panel of biological samples considered but also on the statistical method used. We then developed a comprehensive integration of the rankings generated by the three methods and identified the optimal reference genes for 17 distinct experimental sets covering 13 organs and tissues, as well as various developmental and environmental conditions. The expression patterns of Eucalyptus master genes EgMYB1 and EgMYB2 experimentally validated our selection. Our findings provide an important resource for the selection of appropriate reference genes for accurate and reliable normalization of gene expression data in the organs and tissues of Eucalyptus trees grown in a range of conditions including abiotic stresses.
由于近期对大果桉基因组的测序以及越来越多的大规模转录组学研究,人们对桉树基因组学的兴趣大增。定量逆转录聚合酶链式反应(RT-PCR)是基因表达分析的首选方法,现在也可以用作高通量方法。选择合适的内参对于确保桉树的准确表达结果变得至关重要。为此,我们选择了 21 个候选参考基因,并使用高通量微流控动态阵列在大量发育和环境条件下评估它们的表达情况,特别关注木质组织。我们使用三种不同的统计算法(geNorm、NormFinder 和 ΔCt)分析这些基因的表达稳定性,并使用主成分分析比较方法和排名。结果表明,最稳定的基因不仅取决于所考虑的生物样本面板,还取决于所使用的统计方法。然后,我们综合了三种方法的排名,并为 17 个不同的实验集确定了最佳参考基因,这些实验集涵盖了 13 个器官和组织以及各种发育和环境条件。实验验证的桉树主基因 EgMYB1 和 EgMYB2 的表达模式证实了我们的选择。我们的研究结果为在包括非生物胁迫在内的一系列条件下生长的桉树器官和组织中准确可靠地归一化基因表达数据选择合适的参考基因提供了重要资源。