Background

Nitraria sibirica Pall. is a typical salt-diluting halophyte shrub that can adapt to diverse adverse stresses, such as high salinity, alkali, drought and extreme temperatures. Therefore, N. sibirica is often used as a pioneer tree for windbreaks, sand fixation and the improvement of heavily saline-alkali soils [1, 2]. As a sessile plant that grows in extreme environments, N. sibirica has had to develop a series of complex mechanisms to resist or adapt to adverse environmental conditions. In addition, N. sibirica can also serve as an economic plant in saline–alkali areas due to its medicinal value and edible fruit [9]. However, the accuracy and reliability of RT–qPCR results are affected by many factors, such as the quality of the RNA, the efficiency of cDNA synthesis, and the efficiency of amplification [10, 11]. To obtain accurate gene expression results, the data generated by RT–qPCR should be normalized with the use of appropriate reference genes [7, 11]. Housekee** genes that maintain the cytoskeleton or participate in basic biochemical metabolism, such as actin (ACT), β-tubulin (TUB), elongation factor 1-α (EF-1α) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH), are usually selected as internal reference genes [12,13,14]. The expression levels of reference genes should ideally be consistent in different tissues and under different environmental conditions [15, 16]. However, numerous studies have found that the stability of reference gene expression is different in different species, tissues and experimental treatments [17, 18]. For example, ACT was a stable reference gene in Platycladus orientalis [19] but unstable in teak (Tectona grandis L.f.) [20]. Ubiquitin conjugating enzyme (UBC) and GAPDH were the most stable reference genes in the flowers of Iris germanica L. ‘00246’ and ‘Elizabeth’, while the most stable reference genes in the flowers of Iris germanica L ‘2010200’ were TUB and UBC [21]. In Glehnia littoralis, expressed protein 1 (EXP1) and serine/threonine-protein phosphatase PP2A (PP2A) were the most stable reference genes under salt stress, while cyclophilin 2 (CYP2) and α-TUB were the most stable genes under MeJA treatment [22]. In addition, it has been reported that some newly characterized genes may be more stable than traditional housekee** genes. Under salt and drought stress, the expression of the new reference genes RG1, RG3 and RG5 in poplar was more stable than that of 18S ribosomal RNA (18S rRNA), Actin and ubiquitin (UBQ) [27], comparative ΔCt [28] and RefFinder [29]) were applied to evaluate their expression stability for normalization. In addition, the expression of Δ1-pyrroline-5-carboxylate synthetase (NsP5CS) was analysed to demonstrate the effectiveness of the selected reference genes. Our work will facilitate further studies of gene expression in N. sibirica and will accelerate the understanding of the molecular mechanisms underlying stress-induced responses in N. sibirica.

Results

Primer specificity and amplification efficiency test of candidate reference genes

A total of 10 genes were selected from transcriptome data and used as candidate reference genes. qPCR primers were designed based on these sequences. All primers showed a signal and clear band of the expected size, and there was no primer-dimer formation observed on gel electrophoresis (Fig. S1), indicating the specificity of the primer pairs. The single peaks presented in the melting curve assays of each gene further verified the specificity of the primer pairs (Fig. S2). The amplification efficiency of the 10 candidate reference genes ranged from 89.67% (His) to 113.81% (ACT11), and the correlation coefficients (R2) varied from 0.984 to 0.999 (Table 1). These results indicated that the primers of the 10 genes met the standards for qPCR and could be used in subsequent experiments. The details of the candidate reference genes, primer sequences, amplification lengths and efficiencies, and correlation coefficients are shown in Table 1.

Table 1 Ten candidate reference genes and primer sequences (forward and reverse 5’-3’) used in the study
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Expression levels of candidate reference genes in leaves and roots

The quantification cycle (Cq) value reflects the mRNA transcript level. Reference genes with higher Cq values are considered to have lower expression abundance. Based on RT–qPCR, we determined the expression levels of 10 candidate reference genes in root and leaf tissues under salt, alkali, drought, cold, heat and ABA treatments. As shown in Fig. 1, the Cq values of the 10 candidate reference genes varied from 17.56 (EF-1α) to 29.95 (ERF3A), indicating a wide range of expression abundance. EF-1α and ERF3A were the most [mean Cq of 21.02 ± 2.46 (standard deviation, SD)] and least (mean Cq of 27.25 ± 1.66) expressed genes. In addition, it was observed that TBCB, R3H and DIM1 had relatively small SD values of 0.87, 0.99 and 1.01, respectively, whereas EF-1α and ERF3A showed relatively high variation, with SD values of 2.46 and 1.66, respectively (Fig. 1, Table S1). Further analysis of the distribution of Cq values showed that the Cq values of the 10 candidate reference genes varied differently in different tissues and treatments. For example, ACT11 and TUB had Cq values of 23.06 ± 0.93 and 23.48 ± 1.06 in leaves, respectively, but their Cq values in roots were 25.22 ± 0.90 and 24.72 ± 1.61, respectively (Fig. S3). The Cq value of EF-1α was 19.66 ± 1.13 under salt treatment, but the Cq value under heat treatment was 22.80 ± 3.14 (Fig. S4). Therefore, screening suitable reference genes for specific experimental treatments and tissues is needed.

Fig. 1
figure 1

The raw Cq values for 10 candidate reference genes in all samples of N. sibirica. The box indicates the 25th to 75th percentiles, the line across the box represents the median, the inner square represents the mean, and the whiskers represent the maximum and minimum values

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Expression stability of candidate reference genes

Four specialized analytical tools, geNorm, NormFinder, BestKeeper and the comparative ΔCt method, were used to examine the expression stability of the 10 candidate reference genes. Subsequently, the RefFinder tool was employed to evaluate the expression stability of all these candidate reference genes and select the most suitable genes.

geNorm analysis

geNorm evaluated the stability of all 10 candidate reference genes using the M value (reference expression stability measure). The default threshold of M is 1.5. A lower M value indicates more stable gene expression [26]. As illustrated in Fig. 2, the M values of all 10 candidate reference genes in every treatment were lower than the default limit of 1.5, and all the candidate reference genes had different levels of stability under different treatments. ACT7 and R3H, which had the same M values, were the most stable genes for salt-treated leaves (SL), drought-treated leaves (DL) and drought-treated roots (DR); TUB showed good stability for salt-treated roots (SR), alkali-treated roots (AR), heat-treated roots (HR) and abscisic acid-treated roots (ABR); and GAPDH was one of the most stable genes in SR, alkali-treated leaves (AL), heat-treated leaves (HL) and abscisic acid-treated leaves (ABL). EF-1α and His were determined to be the least stable reference genes across treatments. geNorm software also provides information on the optimum number of reference genes to be used in an experiment based on the pairwise variation between ranked genes (Vn/n + 1), and a cut-off of 0.15 (V value) is usually applied to determine whether an additional reference gene needs to be added [26]. If Vn/n + 1 > 0.15, then n + 1 reference genes need to be used; otherwise, only n reference genes are needed. As shown in Fig. 3, the V2/3 values for the SL (0.054), SR (0.071), AL (0.068), AR (0.081), DL (0.106), DR (0.100), ABL (0.142), ABR (0.097), HL (0.123) and HR (0.145) samples were lower than 0.15 (Fig. 3), indicating that two reference genes would be sufficient for the accurate normalization of these treatment samples. The V2/3 values for cold-treated leaves (CL) and cold-treated roots (CR) exceeded 0.15, while the V3/4 values were 0.110 and 0.140, respectively, indicating that three reference genes were needed.

Fig. 2
figure 2

Expression stability values (M) of 10 candidate reference genes validated via the geNorm program. The least stable genes with higher M-values are on the left, and the most stable genes with lower M-values are on the right. SL and SR: Salt-treated leaves and roots, respectively; AL and AR: alkali-treated leaves and roots, respectively; DL and DR: drought-treated leaves and roots, respectively; CL and CR: cold-treated leaves and roots, respectively; HL and HR: heat-treated leaves and roots, respectively; ABL and ABR: abscisic acid-treated leaves and roots, respectively

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Fig. 3
figure 3

Pairwise variation (V) of the 10 candidate reference genes calculated by geNorm to determine the optimal number of reference genes for accurate normalization. A cut-off of 0.15 (Vn/n + 1 value) is usually applied

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NormFinder analysis

NormFinder software directly evaluates the stability of reference genes according to the variance within and between groups, with lower values indicating higher stability [10]. The stability values of the candidate reference genes in each treatment are shown in Fig. 4 and Table S2. The stability rankings of the 10 reference genes under different experimental conditions were not completely consistent. For example, ACT7 was the most stable reference gene in the SL samples, while TUB was ranked first in the SR and ABR samples, and GAPDH was the top ranked gene for the AL, DL and HR samples. The stability ranking of the 10 candidate reference genes generated with NormFinder was slightly different from that produced by geNorm in most of the samples. For example, His and ACT11 were the top ranked reference genes for the DR and CR samples in the NormFinder analysis, while they ranked third and fourth, respectively, in the geNorm analysis.

BestKeeper analysis

BestKeeper evaluates the stability of gene expression by calculating the standard deviation (SD) and coefficient of variation (CV) of the Cq values. A smaller SD and CV indicate better stability of gene expression. If SD > 1, the gene was considered unstable [27]. The analysis results are listed in Fig. 4 and Table S3. GAPDH was the most stable gene in the AL samples. His and TUB were ranked as the most stably expressed genes in the SR and DR samples, respectively, but exhibited the lowest stability in the HL and SL samples, respectively. DIM1 was the most stable reference gene in the SL and HL samples, and ACT11 was the most stable reference gene in the HR and ABR samples. For the AR, DL, CL and CR samples, the most stable gene was TBCB. EF-1α was ranked as the least stably expressed gene in most of the samples, including the AL, AR, DL, DR, CL, HL, HR and ABL samples. Overall, the stability ranking of the 10 candidate reference genes generated with BestKeeper was different from that of geNorm and NormFinder.

Comparative ΔCt analysis

This method evaluated gene expression stability by calculating the mean standard deviation (SD) value of each gene. Here, the smaller the value is, the higher the stability [28]. In SL, AL, AR, DR, HL, and ABR samples, ACT7 was one of the two best reference genes for gene normalization. For ABL, DL, CL, and HR samples, one of the two most stable reference genes was R3H. In addition, the comparative ΔCt analysis also showed that EF-1α was the least stably expressed gene in multiple samples, such as AL, DR, CL and HR samples (Fig. 4 and Table S4).

RefFinder analysis

To reduce the influence of the limitations of a single algorithm, comprehensive stability rankings of the candidate reference genes were determined with the RefFinder program (https://www.heartcure.com.au/reffinder/). The ranking of genes was computed as the geometric mean, and a lower geometric, a higher stability []. As shown in Fig. 4 and Table S5, ACT7 combined with R3H, TUB, GAPDH or His were the two most stable reference genes in the SL, SR, AL, and DR samples; R3H and GAPDH were suggested to be the most suitable combination for DL, ABL, and HR samples; for AR samples, the two most stable reference genes were DIM1 and His; and TUB and R3H were the most stable reference genes for ABR samples. TBCB and GAPDH were the most stable reference genes for HL samples; TBCB, R3H, and ERF3A were found to be the three most stable reference genes in CL samples; and the three most stable reference genes for CR samples were TBCB, ACT11 and DIM1. EF-1α was the least stable reference gene in most samples.

Fig. 4
figure 4

Stability analysis of the 10 candidate reference genes by NormFinder, BestKeeper, comparative ΔCt and RefFinder. Within a pane, the smaller the value, the lighter the colour, and the higher the stability of the candidate reference genes

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Reference gene validation

To verify the results generated through the analyses described above, the expression pattern of NsP5CS was examined in SL, DR, AL, CR, HL and ABR samples. P5CS is a rate-limiting enzyme in proline biosynthesis and plays an important role in controlling plant stress tolerance [30, 31]. The two most stable reference genes (alone and in combination) and the two most unstable reference genes based on the comprehensive ranking results for each sample set were used in the validation test. As shown in Fig. 5, the relative expression levels of NsP5CS normalized with the two most stable reference genes in combination showed significant changes in SL, DR, CR and ABR samples and different trends among different sample sets. In the SL and CR samples, NsP5CS was continuously induced, and its relative expression reached the highest level at 48 h, with values of 9.08 and 6.45, respectively. In DR samples, NsP5CS was rapidly induced and reached a maximum expression level at 3 h and then fluctuated at a lower level with time. NsP5CS was also rapidly induced and reached a maximum value at 3 h and remained at a higher value at 6 h for ABR samples. The expression patterns of NsP5CS normalized with the two most stable reference genes alone or in combination exhibited consistency in SL, DR, CR and ABR samples. However, the expression patterns of NsP5CS were significantly different when unstable reference genes were used. In the AL and HL samples, the expression level of NsP5CS varied less, but there were also similar expression patterns of NsP5CS normalized by the two most stable reference genes (alone or in combination) and were significantly different by the two unstable reference genes.

Fig. 5
figure 5

Relative expression of NsP5CS at 0, 3, 6, 12, 24, and 48 h following stress treatment using the selected reference genes for normalization. The validated reference gene(s) used as normalization factors were the two most stable reference genes (alone or in combination) and the two most unstable reference genes in different treatment groups. Error bars show the standard error calculated from three biological replicates

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Discussion

RT–qPCR is one of the primary methods used to detect gene expression and thereby help to reveal the response mechanism of plants under different stresses [24] and poplar [13]. This result indicates that the expression level of the same gene may not be constant across multiple species [15]. In addition, GAPDH was the best reference gene in the AL, DL, ABL, HL and HR samples of N. sibirica, while it exhibited poor stability in SL and AR. The same reference gene in the same species may respond differently to different stresses, as in the case in Kentucky bluegrass [38] and A. stolonifera [9]. In summary, it is usually necessary to select reference genes specifically according to species, tissues, and treatments.

Numerous studies have reported significant variations in the expression levels of target genes when unstable reference genes were used as internal controls [57] was used to design the candidate reference gene primers. The parameters were set as follows: melting temperature (Tm), 55–65℃; GC content, 40–60%; primer length, 17–26 bp; and amplification product size, 150–300 bp [21, 58]. All primers (Table 1) were synthesized by RuiBiotech company (Bei**g, China). All primers were verified by electrophoresis on a 1.5% agarose gel.

RT–qPCR conditions and amplification efficiency test

RT–qPCR was performed in 96-well plates by using a LightCycler® 480II Real-Time PCR System (Roche Molecular Systems, Germany). cDNA was amplified by using UItraSYBR Mixture (CoWin Biosciences, Bei**g, China). Each reaction mixture contained 10 µl of 2×UItraSYBR Mixture, 0.8 µl of cDNA, 0.4 µl of each primer (10 µM) and 8.4 µl of ddH2O. The amplification conditions were as follows: an initial step of 95 °C for 10 min, followed by 40 cycles of 95 °C for 10 s, 55 °C for 30 s and 72 °C for 32 s. The final melting curve was produced by shifting the amplification temperature from 65 to 95 °C. RT–qPCR analysis of each sample was performed in triplicate, and template-free controls were included in parallel. The standard curve was constructed with a tenfold dilution (10, 100, 1000, 10,000) of a cDNA mixture comprising equal volumes of cDNA from all samples, and the amplification efficiency (E) and correlation coefficient (R2) values of the primers were calculated using the standard curve. The E value of each primer pair was calculated by the curve slope using E = 10(−1/slope) [59].

Stability analysis of candidate reference genes

To assess the stability of candidate reference genes, we first analysed the resulting RT–qPCR data using four software programs: geNorm, NormFinder, BestKeeper and comparative ΔCt method. Then, RefFinder (https://www.heartcure.com.au/reffinder/) was used to generate a comprehensive ranking of the candidate reference genes according to data obtained by geNorm, NormFinder, BestKeeper and comparative ΔCt [42]. For geNorm and NormFinder analysis, the raw quantification cycle values (Cq) need to be converted into relative quantities, and for BestKeeper and comparative ΔCt algorithms, raw Cq values could meet the requirements.

Validation of reference gene stability

To validate the reliability of the selected reference genes, the relative expression level of NsP5CS was analysed in SL, AL, DR, CR, HL and ABR samples. The primer pair of NsP5CS (Table 1) was designed with Primer Premier 5.0 software. The RT‒qPCR conditions were set up the same as the RT‒qPCR conditions described above. The expression of NsP5CS was calculated using (Etarget) ΔCqtarget(control−sample)/(Eref) ΔCqref(control−sample) [60] with the two worst and two best reference genes (alone and in combination) obtained by the comprehensive assessment used as references.