Identification and validation of reference genes for transcript normalization in strawberry (Fragaria × ananassa) defense responses

Abstract

Strawberry (Fragaria spp) is an emerging model for the development of basic genomics and recombinant DNA studies among rosaceous crops. Functional genomic and molecular studies involve relative quantification of gene expression under experimental conditions of interest. Accuracy and reliability are dependent upon the choice of an optimal reference control transcript. There is no information available on validated endogenous reference genes for use in studies testing strawberry-pathogen interactions. Thirteen potential pre-selected strawberry reference genes were tested against different tissues, strawberry cultivars, biotic stresses, ripening and senescent conditions, and SA/JA treatments. Evaluation of reference candidate's suitability was analyzed by five different methodologies, and information was merged to identify best reference transcripts. A combination of all five methods was used for selective classification of reference genes. The resulting superior reference genes, FaRIB413, FaACTIN, FaEF1α and FaGAPDH2 are strongly recommended as control genes for relative quantification of gene expression in strawberry. This report constitutes the first systematic study to identify and validate optimal reference genes for accurate normalization of gene expression in strawberry plant defense response studies.

Figure 1. Expression levels of candidate reference genes in different experimental sets.

Box plot graphs of Cq values for each reference gene tested in all strawberry samples and subsets. Cq values are inversely proportional to the amount of template and are shown as the first and third quartile. Vertical lines indicate the range of values, and median values are indicated by the black lines. Circles indicate outliers. RCF, Ripening-Camarosa-Fruit; FCF, Fungal-Camarosa-Fruit; FCC, Fungal-Camarosa-Crown; FCP, Fungal-Camarosa-Petiole; FAP, Fungal-Andana-Petiole; HCY, Hormone-Camarosa-Young-in-vitro; HCC, Hormone-Chandler-Cellular-suspensions; All, samples from all seven experiments analyzed together.

Figure 2. Average expression stability value (M_A) of each gene.

Specific M_A values were calculated under seven single experimental conditions tested, as well as by combining all samples together. M_A for genes tested are shown as derived by geNormPLUS analysis. The lowest M_A value indicates the most stable expression.

Figure 3. Determination of the number of genes required to calculate a hypothetical normalization factor.

Pairwise variation (Vn/n+1) analysis was carried out to determine the number of reference genes required for accurate normalization. An asterisk indicates the lowest V value in each experiment. An arrowhead indicates the minimum number of genes required to pass the suggested cut-off value (0.15) . See Table 2 for experiment description.

Figure 4. Rank aggregation of gene lists using the Monte Carlo algorithm.

Visual representation of rank aggregation using Monte Carlo algorithm with the Spearman footrule distances. The solution of the rank aggregation is shown in a plot where genes are ordered based on their rank position according to their stability measurement (grey lines). Mean rank position of each gene is shown in black, as well the model computed by the Monte Carlo algorithm (red line). See Table 2 for experimental description.

Figure 5. Transcript level relative quantification of the FaWRKY1 transcription factor.

FaWRKY1 gene expression was analyzed in strawberry under the seven independent experimental conditions used in this study. Error bars show standard deviation calculated from two biological replicates. Normalization factors were calculated as the geometric mean of the expression levels of the two most stable reference genes as recommended in Figure 4 for each single experiment. Normalization to each gene individually is also shown. Additionally, the least stable reference gene was used for normalization of each experiment to demonstrate the effect of unstable reference genes in the quantification of the relative amount of target gene mRNA. Every sample was calibrated with their corresponding mock sample (see Table 2 for experimental details). Black lines linked to the X axis have been added to f and g to illustrate range of gene induction.