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Comparison of predictive methods and biological validation for qPCR reference genes in sunflower leaf senescence transcript analysis

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Abstract

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.

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Acknowledgments

This research was supported by CONICET PIP 5788, ANPCyT/FONCYT, Préstamo BID PICT 15-32905, INTA-PE AEBIO 241001and 245001, INTA-PE AEBIO 245711, INTA-AEBI0 243532, INTA PN CER 1336, and UNMdP, AGR212, AGR260. Dr. PdCF, Dr. RAH, Dr. NBP, Dr. GAAD, and Dr. LANA are career members of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina), J.A.D.R., M.Sc., is a Professor at the Agricultural College in National University of Cordoba and Dr. HEH is a career member of the Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC) and a Professor at the Facultad de Ciencias Exactas y Naturales, University of Buenos Aires (UBA). We are grateful to Dr. Marisa Farber for critical reading of the manuscript.

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Authors and Affiliations

  1. Instituto de Biotecnología, CICVyA, INTA Castelar, Las Cabañas y Los Reseros, (1686) Hurlingham, Buenos Aires, Argentina

    Paula Fernandez, Sebastián Moschen, H. Esteban Hopp, Norma Paniego & Ruth A. Heinz

  2. Cátedra de Estadística y Biometría, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba, Argentina

    Julio A. Di Rienzo

  3. Laboratorio de Fisiología Vegetal, Unidad Integrada (Facultad de Ciencias Agrarias UNMdP/EEA INTA) Balcarce, C.C. 276, 7620, Balcarce, Argentina

    Sebastián Moschen, Guillermo A. A. Dosio & Luis A. N. Aguirrezábal

  4. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina

    H. Esteban Hopp & Ruth A. Heinz

  5. Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina

    Paula Fernandez

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  1. Paula Fernandez
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Fernandez, P., Di Rienzo, J.A., Moschen, S. et al. Comparison of predictive methods and biological validation for qPCR reference genes in sunflower leaf senescence transcript analysis. Plant Cell Rep 30, 63–74 (2011). https://doi.org/10.1007/s00299-010-0944-3

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