Selection and Optimization of Reference Genes for MicroRNA Expression Normalization by qRT-PCR in Chinese Cedar (Cryptomeria fortunei) under Multiple Stresses

Abstract

MicroRNA (miRNA) expression analysis is very important for investigating its functions. To date, no research on reference genes (RGs) for miRNAs in gymnosperms, including Cryptomeria fortunei, has been reported. Here, ten miRNAs (i.e., pab-miR159a, cln-miR162, cas-miR166d, pab-miR395b, ppt-miR894, cln-miR6725, novel1, novel6, novel14 and novel16) and three common RGs (U6, 5S and 18S) were selected as candidate RGs. qRT-PCR was used to analyse their expressions in C. fortunei under various experimental conditions, including multiple stresses (cold, heat, drought, salt, abscisic acid and gibberellin) and in various tissues (roots, stems, tender needles, cones and seeds). Four algorithms (delta Ct, geNorm, NormFinder and BestKeeper) were employed to assess the stability of candidate RG expression; the geometric mean and RefFinder program were used to comprehensively evaluate RG stability. According to the results, novel16, cln-miR6725, novel1 and U6 were the most stable RGs for studying C. fortunei miRNA expression. In addition, the expression of three target miRNAs (aly-miR164c-5p, aly-miR168a-5p and smo-miR396) was examined to verify that the selected RGs are suitable for miRNA expression normalisation. This study may aid further investigations of miRNA expression/function in the response of C. fortunei to abiotic stress and provides an important basis for the standardisation of miRNA expression in other gymnosperm species.

Keywords: abiotic stress; hormone treatment; miRNAs; qRT-PCR; reference gene; tissue.

Figure 1

Specificity of each candidate reference gene (RG) primer pair. (a) Confirmation of the specificity of qPCR primer amplification of candidate RGs by agarose gel electrophoresis. (b) Melting-curve analysis of quantitative real-time PCR (qRT-PCR) amplification of 13 candidate RGs in Cryptomeria fortunei.

Figure 2

Quantification cycle values of 13 candidate reference genes in different tissues and under multiple stresses. The box indicates the 25th and 75th percentiles, with the line across the box representing the median. Whiskers and asterisks represent 99% confidence intervals and outliers, respectively. Upper and lower horizontal lines indicate the maximum and minimum values, respectively, and small circles represent the average values.

Figure 3

Average standard deviation (STDEV) by delta Ct analysis. Results from (a) 4 °C cold stress; (b) 40 °C heat stress; (c) drought stress simulated by 15% PEG-6000 treatment; (d) salt stress stimulated by 200 mM NaCl treatment; (e) 200 μM ABA treatment; (f) 200 μM GA₃ treatment; (g) multiple stresses; (h) different tissues (roots, stems, tender needles, cones and seeds); (i) total samples.

Figure 4

Average expression stability values and pairwise variations by geNorm analysis. (a) Expression-stability values (M) and rankings of the 13 candidate reference genes of C. fortunei calculated using geNorm. The most and least stable genes are on the right and left, respectively. (b) Determination of the optimal number of reference genes for C. fortunei.

Figure 5

Stacked histograms showing the rankings of 13 candidate reference genes using the BestKeeper algorithm. Results from (a) 4 °C cold stress; (b) 40 °C heat stress; (c) drought stress simulated by 15% PEG-6000 treatment; (d) salt stress stimulated by 200 mM NaCl treatment; (e) 200 μM ABA treatment; (f) 200 μM GA₃ treatment; (g) multiple stresses; (h) different tissues (roots, stems, tender needles, cones and seeds); (i) total samples. Dark grey bars represent standard deviations (SDs) of average Ct values, and light grey bars represent coefficients of variance (CVs).

Figure 6

Comprehensive ranking of 13 RGs calculated as the geometric means of four types of rankings. Results from (a) 4 °C cold stress; (b) 40 °C heat stress; (c) drought stress simulated by 15% PEG-6000 treatment; (d) salt stress stimulated by 200 mM NaCl treatment; (e) 200 μM ABA treatment; (f) 200 μM GA₃ treatment; (g) multiple stresses; (h) different tissues (roots, stems, tender needles, cones and seeds); (i) total samples.

Figure 7

The top 5 most stable reference genes (RGs) were generated by delta-Ct, geNorm, NormFinder and BestKeeper. Blue, pink, green and sky-blue circles each contain the top 5 most stable RGs identified with delta-Ct, geNorm, NormFinder and BestKeeper, respectively. Genes in the overlapping area were confirmed as the top 5 most stable RGs by more than one algorithm.

Figure 8

Relative expression levels of aly-miR164c-5p under different experimental conditions. Results for (a) 4 °C cold stress; (b) 40 °C heat stress; (c) drought stress simulated by 15% PEG-6000 treatment; (d) salt stress stimulated by 200 mM NaCl treatment; (e) 200 μM ABA treatment; (f) 200 μM GA₃ treatment; (g) different tissues (roots, stems, tender needles, cones and seeds). Error bars represent standard deviations (SD) (n = 3).

Figure 9

Relative expression levels of aly-miR168a-5p under different experimental conditions. Results from (a) 4 °C cold stress; (b) 40 °C heat stress; (c) drought stress simulated by 15% PEG-6000 treatment; (d) salt stress stimulated by 200 mM NaCl treatment; (e) 200 μM ABA treatment; (f) 200 μM GA₃ treatment; (g) different tissues (roots, stems, tender needles, cones and seeds). Error bars represent standard deviations (SD) (n = 3).

Figure 10

Relative expression levels of smo-miR396 under different experimental conditions. Results from (a) 4 °C cold stress; (b) 40 °C heat stress; (c) drought stress simulated by 15% PEG-6000 treatment; (d) salt stress stimulated by 200 mM NaCl treatment; (e) 200 μM ABA treatment; (f) 200 μM GA₃ treatment; (g) different tissues (roots, stems, tender needles, cones and seeds). Error bars represent standard deviations (SD) (n = 3).