Microarray-based uncovering reference genes for quantitative real time PCR in grapevine under abiotic stress

Abstract

Background: Quantitative real time polymerase chain reaction is becoming the primary tool for detecting mRNA and transcription data analysis as it shows to have advantages over other more commonly used techniques. Nevertheless, it also presents a few shortcomings, with the most import being the need for data normalisation, usually with a reference gene. Therefore the choice of the reference gene(s) is of great importance for correct data analysis. Microarray data, when available, can be of great assistance when choosing reference genes. Grapevine was submitted to water stress and heat stress as well as a combination of both to test the stability of the possible reference genes.

Results: Using the analysis of microarray data available for grapevine, six possible reference genes were selected for RT-qPCR validation: PADCP, ubiq, TIF, TIF-GTP, VH1-IK, aladin-related. Two additional genes that are commonly used as reference genes were included: act and L2. The stability of those genes was tested in leaves of grapevine in both field plants and in greenhouse plants under water or heat stress or a combination of both. Gene stability was analyzed with the softwares GeNorm, NormFinder and the ΔCq method resulting in several combinations of reference genes suitable for data normalisation. In order to assess the best combination, the reference genes were tested in putative stress marker genes (PCO, Galsynt, BKCoAS and HSP17) also chosen from the same microarray, in water stress, heat stress and the combination of both.

Conclusions: Each method selected different gene combinations (PADCP + act, TIF + TIF-GTP and ubiq + act). However, as none of the combinations diverged significantly from the others used to normalize the expression of the putative stress marker genes, then any combination is suitable for data normalisation under the conditions tested. Here we prove the accuracy of choosing grapevine reference genes for RT-qPCR through a microarray analysis.

Figure 1

Stability values of the putative reference genes using the software GeNorm. Stability values of reference genes calculated using the software GeNorm, A: all samples; B: greenhouse samples; C: field samples. Because GeNorm does not analyse samples in groups, these were obtained manually.

Figure 2

Stability values of the putative reference genes using the software NormFinder. Stability values of reference genes calculated using the software NormFinder, A: all samples; B: greenhouse samples; C: field samples; D: greenhouse versus field samples.

Figure 3

Optimal number of reference genes required for effective normalisation. Optimal number of reference genes required for effective normalisation. The pairwise variation (V_n/V_n/n+1) was analysed between the normalisation factors NFn and NFn + 1 using the software GeNorm to determine the optimal number of reference genes required for RT-qPCR data normalisation in three different situations (n and n + 1 as in the ranking of Table 3).

Figure 4

Selected scatterplots of normalisation factors before (x-axis) and after (y-axis) inclusion of an (n + 1)th control gene in all samples and in greenhouse samples. Low variation values (V) correspond to high correlation coefficients (r = Spearman rank correlation coefficient). It is clear that there is no need to include more than three control genes for both “all samples” and “greenhouse” samples.

Figure 5

Expression of the stress marker genes in grapevine normalised with the different Best Combinations or L2. Expression of the four possible abiotic stress marker genes in grapevine plants subjected to WS (A), HS (B), a combination of both (C): PCO – negative marker of WS; Galsynt – positive marker of WS; BkcoAS – negative marker of HS; HSP17– positive marker of HS. Expression was normalised using the three Best Combinations of genes and the worst possible gene (L2). Significant differences (p < 0.05) are represented by ★ when compared to the opposite treatment in the case of WS and HS and in comparison with the respective single treatment in the case of WSHS. ★ of different colours represent the significant differences of the gene expression with the correspondent colour of the reference genes combination.