Date Published: April 18, 2019
Publisher: Public Library of Science
Author(s): Maria R. V. Coêlho, Rebeca Rivas, José Ribamar C. Ferreira-Neto, Valesca Pandolfi, João P. Bezerra-Neto, Ana Maria Benko-Iseppon, Mauro G. Santos, Haibing Yang.
Calotropis procera is a perennial Asian shrub with significant adaptation to adverse climate conditions and poor soils. Given its increased salt and drought stress tolerance, C. procera stands out as a powerful candidate to provide alternative genetic resources for biotechnological approaches. The qPCR (real-time quantitative polymerase chain reaction), widely recognized among the most accurate methods for quantifying gene expression, demands suitable reference genes (RGs) to avoid over- or underestimations of the relative expression and incorrect interpretation. This study aimed at evaluating the stability of ten RGs for normalization of gene expression of root and leaf of C. procera under different salt stress conditions and different collection times. The selected RGs were used on expression analysis of three target genes. Three independent experiments were carried out in greenhouse with young plants: i) Leaf100 = leaf samples collected 30 min, 2 h, 8 h and 45 days after NaCl-stress (100 mM NaCl); ii) Root50 and iii) Root200 = root samples collected 30 min, 2 h, 8 h and 1day after NaCl-stress (50 and 200 mM NaCl, respectively). Stability rank among the three algorithms used showed high agreement for the four most stable RGs. The four most stable RGs showed high congruence among all combination of collection time, for each software studied, with minor disagreements. CYP23 was the best RG (rank of top four) for all experimental conditions (Leaf100, Root50, and Root200). Using appropriated RGs, we validated the relative expression level of three differentially expressed target genes (NAC78, CNBL4, and ND1) in Leaf100 and Root200 samples. This study provides the first selection of stable reference genes for C. procera under salinity. Our results emphasize the need for caution when evaluating the stability RGs under different amplitude of variable factors.
Calotropis procera (Aiton) W. T. Aiton (Apocynaceae) is an evergreen shrub highly tolerant to drought and salt stresses with remarkable invasive ability in arid and semiarid regions . Due to its pharmacognostic features, this shrub has been used in traditional medicine for the treatment of various diseases . Ecophysiological studies have emphasized the superior physiology of C. procera, which show reduced stomatal conductance with high photosynthetic rate under water deficit [2,3]. These characteristics point this species as rich and attractive source of genes to be used in plant breeding programs for enhancing drought and salinity tolerance. In this sense, gene expression analysis can be used to evaluate the molecular mechanisms involved in plant response to different stresses. In the past years, advances in next-generation sequencing techniques have revolutionized transcriptomics and quickly established RNA-Seq as a robust methodology for gene expression analysis [4–7].
The advent of high-throughput next-generation DNA sequencing (NGS) platforms has provided more comprehensive and maximized studies on diverse genomes, including non-model plant species [7,43,44]. At the same time, advances in RNA sequencing (RNA-Seq) methods have effectively aided in characterization and quantification of transcriptomes (even without a reference genome). They contributed to the understanding of genes expression regulation under different experimental conditions [6,45,46]. However, due to the existence of potential errors during the preparation, synthesis, sequencing and analysis of gene expression libraries (including RNA-Seq), a second method is required to validate the results indicated by the first. The qPCR is currently the most appropriated method for such purpose [12,47], and quality control measures are necessary to mitigate potential errors in qPCR results. Thus, the selection of suitable reference genes is a fundamental requisite. The use of inappropriate RGs may overestimate or underestimate the relative expression of the target genes and lead to .