Abstract
Real-time PCR is always the method of choice for expression analyses involving comparison of a large number of treatments. It is also the favored method for final confirmation of transcript levels followed by high throughput methods such as RNA sequencing and microarray. Our analysis comprised 16 different permutation and combinations of treatments involving four different Agrobacterium strains and three time intervals in the model plant Arabidopsis thaliana. The routinely used reference genes for biotic stress analyses in plants showed variations in expression across some of our treatments. In this report, we describe how we narrowed down to the best reference gene out of 17 candidate genes. Though we initiated our reference gene selection process using common tools such as geNorm, Normfinder and BestKeeper, we faced situations where these software-selected candidate genes did not completely satisfy all the criteria of a stable reference gene. With our novel approach of combining simple statistical methods such as t test, ANOVA and post hoc analyses, along with the routine software-based analyses, we could perform precise evaluation and we identified two genes, UBQ10 and PPR as the best reference genes for normalizing mRNA levels in the context of 16 different conditions of Agrobacterium infection. Our study emphasizes the usefulness of applying statistical analyses along with the reference gene selection software for reference gene identification in experiments involving the comparison of a large number of treatments.
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Acknowledgements
We thank DST (Department of Science and Technology)-INSPIRE (Fellowship No.IF140978 and Grant No. IFA11-LSPA-04), India, for the doctoral fellowship of Joseph JT and project funding of Shah JM, respectively. We gratefully acknowledge K. Veluthambi (Madurai Kamaraj University, India) and Paul J. Hooykaas (Leiden University, the Netherlands) for providing Agrobacterium strains. We thank Maya N for preliminary support.
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Joseph, J.T., Poolakkalody, N.J. & Shah, J.M. Screening internal controls for expression analyses involving numerous treatments by combining statistical methods with reference gene selection tools. Physiol Mol Biol Plants 25, 289–301 (2019). https://doi.org/10.1007/s12298-018-0608-2
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DOI: https://doi.org/10.1007/s12298-018-0608-2