Selection of Reference Genes for Transcription Studies Considering Co-Regulation and Average Transcriptional Stability: Case Study on Adventitious Root Induction in Olive (Olea europaea L.) Microshoots

Selection of reference genes (RGs) for normalization of PCR-gene expression data includes two crucial steps: determination of the between-sample transcriptionally more stable genes, and subsequent choosing of the most suitable genes as internal controls. Both steps can be carried out through generally accepted strategies, each having different strengths and weaknesses. The present study proposes reinforcement of the normalization of gene expression data by integrating analytical revision at critical steps of those accepted procedures. In vitro olive adventitious rooting was used as an experimental system. Candidate RGs were ranked according to transcriptional stability according to several methods. An algorithm of one of these programs (GeNorm) was adapted to allow for partial automatization of RG selection for any strategy of transcriptional-gene stability ordering. In order to choose the more appropriate set of RGs, the achieved results were analytically revised, with special emphasis on biasing effects such as co-regulation. The obtained putative RG sets were also tested for cases restricted to fewer variables. The set formed by the genes H2B, OUB and ACT is valid for normalization in transcriptional studies on olive microshoot rooting when comparing treatments, time points and assays. Such internal reference is now available for wider expression studies on any target gene in similar biological systems. The overall methodology aims to constitute a guide for general application.

Automated summary

This study strengthens the selection strategy of reference genes for normalization of gene expression data by integrating analytical revision. Using in vitro olive adventitious rooting as the experimental system, candidate reference genes were ranked according to their transcriptional stability. The results were analytically revised to select the most suitable combination of reference genes. This study provides a general application guide, which is of significant importance for gene expression studies in similar biological systems.