Abstract
Quantitative real-time PCR (qRT-PCR) is a potent technique for gene expression analysis, but its accuracy relies heavily on the selection of stable reference genes. In bermudagrass (Cynodon dactylon) roots under alkaline salt stress, we sought to identify suitable reference genes. Seven candidates-EF1α, PP2A, TIP41, GAPDH, Actin, β-tubulin, and CACS-were assessed for specificity, amplification efficiency, and expression stability using geNorm, NormFinder, BestKeeper, and RefFinder. All primers exhibited high specificity and efficiency, as evidenced by single, strong bands on agarose gels and single melting peaks in qRT-PCR. Initial ΔCt value analysis identified EF1α, TIP41, and GAPDH as the most stable genes, with further analysis consistently ranking EF1α as the top reference gene across all software. Validation through transcriptome data and qRT-PCR of selected core genes confirmed EF1α’s stability and suitability for gene expression studies in bermudagrass roots under stress. This study offers a thorough evaluation of reference genes for qRT-PCR in bermudagrass and enhances our comprehension of gene expression quantification in this species under alkaline salt stress.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12298-025-01603-4.
Keywords: Cynodon dactylon, Reference gene selection, Gene expression analysis, qRT-PCR
Introduction
In the field of plant science, accurate quantification of gene expression is vital for understanding plant growth, development, stress responses, and genetic improvement (Wang et al. 2022). Real-time fluorescent quantitative PCR (qRT-PCR) has emerged as the preferred method for this purpose, owing to its high sensitivity, specificity, and reproducibility (Wang et al. 2022). The accuracy of qRT-PCR results, however, hinges on the selection of stable and reliable reference genes (Li et al. 2022). These genes, which maintain constant expression levels under specific conditions, are essential for calibrating sample differences and mitigating experimental errors (Chen et al. 2022). Consequently, identifying and validating appropriate reference genes are prerequisites for ensuring qRT-PCR data accuracy (Dong et al. 2022).
Bermudagrass, a widespread grass in temperate and tropical regions, is noted for its adaptability and stress resistance (Ye et al. 2016). Under alkaline salt stress, it demonstrates robust growth and tolerance, making it an ideal model for studying plant alkaline tolerance mechanisms (Tang et al. 2024a). Although physiological and morphological responses of bermudagrass to alkaline salt stress have been extensively studied (Tang et al. 2024b), knowledge of its molecular mechanisms, especially gene expression regulation, is limited. This gap is primarily due to the absence of stable reference genes for gene expression studies in bermudagrass under such stress conditions.
Previous studies have extensively evaluated the stability of reference genes across various plant species and under different abiotic stress conditions (Abbas et al. 2021). In Aegilops tauschii (Coss.), HSP and PP2A were identified as highly stable reference genes under salt stress, whereas GAPDH exhibited instability. Conversely, in durum wheat, the housekeeping genes GAPDH and UBC36 proved to be the most stable under diverse abiotic stresses, including drought (Abbas et al. 2021). For Chinese cabbage, EF1α was reported as the most stable reference gene, with GAPDH being optimal for drought stress conditions (Niu et al. 2015). Additionally, the stability of reference genes in bermudagrass under multiple abiotic stresses, such as drought, salt, heat, and cold, has been assessed, revealing that PP2A, CACS, EF1α, TIP41, UPL7, TUB, and GAPDH exhibit relatively stable expression across these stresses (Chen et al. 2015). However, due to the variability among plant species and the specificity of stress conditions, these reference genes stable in other species or under different stresses may not necessarily be suitable for gene expression studies in bermudagrass under alkali-salt stress. Research on reference genes under alkali-salt stress remains scarce, and the assessment of reference gene stability under specific environmental stresses is crucial for accurate quantification of gene expression (Cheng et al. 2022). Therefore, it is imperative to screen and validate reference genes suitable for qRT-PCR analysis in the roots of bermudagrass under alkali-salt stress.
This study aims to assess the expression stability of seven candidate reference genes (EF1α, PP2A, TIP41, GAPDH, Actin, β-tubulin, and CACS) in the roots of bermudagrass. These candidates, selected based on the EST database of bermudagrass and prior research, are widely present in plants and are considered to potentially exhibit stable expression under various stress conditions (Chen et al. 2015; Li et al. 2022). Employing a systematic approach, which includes primer specificity verification, amplification efficiency evaluation, and comprehensive analysis using multiple algorithms (geNorm, NormFinder, BestKeeper, and RefFinder), we seek to identify reliable reference genes for gene expression studies in bermudagrass under alkali-salt stress. Ultimately, this research endeavors to facilitate a deeper understanding of the stress tolerance mechanisms in this species and to provide insights for related studies in other closely related species.
Materials and methods
Plant materials and growth conditions
Two bermudagrass genotypes, alkali-tolerant "C14" and alkali-sensitive "C40", were selected as experimental materials. Stolons with 1–2 nodes were used as vegetative propagules and cultivated in plastic trays filled with a mixture of nutrient soil and vermiculite (2:1, pH 7.07). Plants grew under natural light with a humidity range of 33–58% and a temperature range of 25–40 °C. Half-strength Hoagland nutrient solution was applied every 7 days, with daily watering. Uniformly grown plants at the 5–8 leaf stage were transplanted into 12 × 12 × 15 cm pots (350 g per pot, vermiculite: perlite = 2:1), with 8 plants per pot, and maintained under the aforementioned growth conditions. After 45 days of growth in soil culture and reaching the 8–10 leaf stage, plants were subjected to alkaline salt stress treatment.
Alkaline salt stress treatment
Three treatment groups were established: CK (distilled water, pH 7.0), MA (100 mmol/L NaHCO₃, pH 8.4), and SA (100 mmol/L NaHCO₃: Na₂CO₃ = 1:1, pH 9.9), with three replicates per group. On the first day of stress treatment, 250 ml of the corresponding solution was added to each pot. Maintaining a constant salt concentration, we placed trays under the pots to collect runoff and reapplied it daily with additional solution. Following six days of stress treatment, samples were promptly frozen in liquid nitrogen and stored at -80 °C.
RNA extraction, quality assessment, and concentration determination
RNA was extracted from 0.2 g of root tissue of bermudagrass using a modified Trizol method. Tissue was ground in liquid nitrogen, followed by the addition of 1 ml of pre-cooled Trizol and vortexing for 5 min. Samples were centrifuged at 13,000 g for 5 min at 4 °C, and the supernatant was transferred to a new tube. Chloroform was added at a ratio of Trizol: chloroform = 1:0.2, mixed, allowed to stand for 5 min, and then centrifuged for 15 min. A 400 μl aliquot of the supernatant was taken, and RNA was precipitated with isopropanol, followed by centrifugation for 10 min and discarding of the supernatant. The RNA was washed with 75% ethanol, centrifuged at 12,000 g for 5 min at 4 °C, and air-dried for 3–5 min before being dissolved in 20–50 μl of 0.1% DEPC water. RNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer, while RQN values were assessed with an Agilent 5300 Bioanalyzer. Quality assessment of the RNA indicated it was free of contaminants such as solids, pigments, proteins, or sugars, highlighting its high purity. The concentration of rRNA bands suggested good RNA quality, suitable for subsequent experimental analysis. Eighteen individual RNA samples for sequencing had concentrations ranging from 20.4 to 94.8 μg (all ≥ 10 μg). The total sample amount met the requirements for library construction, with 28S/18S ratios > 1.8 and RQN values ≥ 6.5, indicating high integrity of all RNA samples and their suitability for subsequent experiments.
Selection of reference sequences and design of PCR primers
We identified 12 genes with significant expression differences in our unpublished RNAseq dataset for further investigation of differences between "C14" and "C40" genotypes under alkaline salt stress(Table 1). These candidate genes were selected for RT-qPCR validation based on BLAST alignments of differentially expressed sequences from bermudagrass in NCBI. Potential reference genes for bermudagrass under other abiotic stresses, including elongation factor 1-alpha (EF1α), protein phosphatase 2A (PP2A), TIP41-like protein (TIP41), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Actin, β-tubulin, and calcium-binding protein (CACS), were chosen based on the EST database and previous studies. Specific primers were designed using Primer 5.0 software, with melting temperatures set at 58–62 °C, primer lengths of 18–22 bp, GC contents of 40–60%, and amplicon lengths ranging from 106 to 183 bp (Table 2).
Table 1.
Primer sequences for fluorescence quantitative qPCR
| Gene ID | Forward primer (5′-3′) | Reverse primer (5′-3′) |
|---|---|---|
| Cd197221 | GGAATAGAGGGTGGATACAAGG | GATAGAACGGTGACTCAGGGAT |
| Cd2106 | AACCAACCCCTATTCAGTCTCA | CACCAAGTATGTCACTCTGCGTA |
| Cd218750 | CCGAAAAGGAGCACTACCGT | CTTCAGGGAGATGGGCAATG |
| Cd62337 | AGCAGAGTTCAAAGCACGGA | CCTACCACAAACAATGCGAGTA |
| Cd144698 | TGGGCTCAACATTGGGTGCT | TCGTCCCGTCTTCCGTTTTCTC |
| Cd214003 | TTCCTCAAGTCAAAAGGTCACA | TCCAAGGCCATCAGCATTA |
| Cd235170 | GGTTGCTCTTGTTACCATTCC | CTCCTTCAGGGTCTTGTATTCC |
| Cd260327 | CGCACTTCCTGTCCCCTCTT | CGTGAATGCTGTGACCTTTTGA |
| Cd35923 | ACAACGGCACCAATCTCG | CAGGTCTTCGTCCAGCAAA |
| Cd161983 | TATTCGGGTCGTCGCATC | GTCGCACTTGGTCGTCACA |
| Cd218315 | CAACGATGGAAACAACGAGTG | CCTGAGCAGCAAATAAGGAAAG |
| Cd283400 | CCTGATGATGATTGCTACGGC | CGAGCTGCTTGGTCTTCCAC |
Table 2.
Primer information for candidate reference genes in the roots of bermudagrass under alkaline salt stress
| Gene ID | Primer sequence(5′-3′) | Amplification product length (bp) | Amplification efficiency | R2 |
|---|---|---|---|---|
| β-tubulin | GCCGAGAACTGCGACTG | 108 | 85.76 | 0.986 |
| CCACATCTGCTGCGTGAG | ||||
| Actin | GAATGGTCAAGGCGGGTTT | 106 | 93.56 | 0.992 |
| CGTAGTCCAGGGCAATGTAG | ||||
| PP2A | ACTACTTCCCCTTGACGGC | 183 | 119.50 | 0.991 |
| AAATACCCCAACCACATCG | ||||
| CACS | TCCTGAAGGTTTGGGAGAAG | 169 | 97.72 | 0.942 |
| AAATAACAGTTCCGAAGAGCC | ||||
| EF1α | TGTCCCTGTCGGTCGTGTT | 112 | 100.80 | 0.996 |
| AGCCTCATGGTGCATCTCA | ||||
| TIP41 | AATTGACCTTGTCGCTCTTTC | 126 | 89.55 | 0.994 |
| GTCGGCATTACCCTCACTCT | ||||
| GAPDH | CCCTTCATCACCACCGACTA | 157 | 117.46 | 0.995 |
| GAATCTCCTCTGGGTTCCTG |
qRT-PCR Analysis
Real-time fluorescent quantitative PCR (qRT-PCR) was performed on an ABI 7500 Fast instrument using the PerfectStart® Uni RT&qPCR Kit (TRAN). The amplification protocol consisted of an initial denaturation step at 94 °C for 30 s, followed by 45 cycles of 94 °C for 5 s and 60 °C for 30 s, during which fluorescence signals were collected. Gene expression was calculated using the 2‒ΔΔCt method for comparative analysis.
Data statistical analysis
The expression stability of 12 candidate reference genes was comprehensively analyzed using geNorm, NormFinder, BestKeeper, and RefFinder algorithms (Li et al. 2020). Based on the relative quantification data (Cq values) of the reference genes, geNorm and NormFinder calculated the average expression stability value (M) and stability value (S) for each gene, respectively, with higher S values indicating more unstable gene expression. BestKeeper computed the paired standard deviation (SD), coefficient of variation (CV), and correlation coefficient (R2) across different samples for each gene; higher R2 values and lower SD and CV values indicated more stable expression of the reference gene. To validate the reliability of the selected reference genes, quantitative analysis was conducted on the expression levels of different alkaline salt-resistant genotypes of bermudagrass under alkaline salt stress, using the identified best reference gene as a reference. Ct values for each reaction were recorded, and subsequently, the expression stability of the candidate reference genes was further analyzed using geNorm, NormFinder, and BestKeeper. Specifically, 2‒ΔΔCt values were calculated based on the minimum Ct value of each candidate gene across all samples and used as input data for geNorm and NormFinder analyses. BestKeeper directly utilized Ct values to calculate SD and CV for candidate gene expression. The gene with the smallest SD and CV values was selected as the reference gene. RefFinder provided a comprehensive evaluation by integrating data from geNorm (M values), NormFinder (stability S), BestKeeper (CV and SD), and ∆Cq values. To ensure accuracy, each analysis was repeated six times.
Results
Assessment of primer specificity and PCR amplification efficiency
Employing an equimolar mixture of cDNA derived from 18 distinct samples of bermudagrass roots subjected to alkaline salt stress, PCR amplification was executed on targeted gene fragments of seven reference genes (Table 1). All primer sets yielded singular, intense bands on agarose gels, devoid of any detectable primer dimers or nonspecific amplification artifacts(Fig. 1). The resultant amplicon sizes were in accordance with our expectations, thereby attesting to the efficacy of the primers. Subsequent qRT-PCR analysis further corroborated the specificity of the reference gene primers, as evidenced by the emergence of singular melting peaks for each amplicon (Fig. 2). The amplification efficiencies ranged between 85.76% and 119.50%, with correlation coefficients spanning from 0.942 to 0.996, signifying robust amplification performance and suitability for downstream experimental procedures.
Fig. 1.
Electrophoresis of amplification products of candidate reference genes in the roots of bermudagrass under alkaline salt stress
Fig. 2.
Melting curves of PCR products for candidate reference genes in the roots of bermudagrass under alkaline salt stress
Stability analysis of reference gene Ct values
Ct values for each reference gene were obtained via qRT-PCR, with lower Ct values indicating higher mRNA transcription levels. Among the seven reference genes, EF1α and GAPDH exhibited the lowest Ct values of 24.83 and 24.43, respectively, while Actin had the highest at 35.58 (Fig. 3). The coefficients of variation (CVs) for β-tubulin, Actin, PP2A, CACS, EF1α, TIP41, and GAPDH were 6.03%, 4.33%, 4.81%, 4.89%, 5.95%, 2.78%, and 6.31%, respectively, with lower CVs indicating more stable expression in qRT-PCR experiments.
Fig. 3.
Ct values of candidate reference genes in the roots of bermudagrass subjected to alkaline salt stress. The box plots represent the 25th to 75th percentiles, with the middle line indicating the mean and the whiskers denoting the maximum and minimum values
The experimental design incorporated eighteen treatments systematically categorized into four groups for optimized evaluation of reference gene stability. These included the C14 genotype group (C14CK, C14MA, C14SA), C40 genotype group (C40CK, C40MA, C40SA), control group (C14CK and C40CK), and treatment group (C14MA, C14SA, C40MA, C40SA). Based on △Ct values (Fig. 3), the most stable reference genes were identified as EF1α (0.63), TIP41 (0.55), GAPDH (0.44), and EF1α again in the control group (0.84). The △Ct values in the control group were significantly lower than those in the alkaline salt treatment group, suggesting that alkaline salt stress increased the instability of reference gene expression. Collectively, considering both the range of Ct values and △Ct values, EF1α, TIP41, and GAPDH demonstrated the most stable expression profiles and are potential optimal reference gene candidates for studies involving alkaline salt stress.
Candidate reference genes expression stability geNorm analysis
geNorm analysis calculated the average variation M values, revealing high stability among all seven reference genes, with M values below 1.5. The M values were ranked as follows: EF1α and PP2A (0.63), TIP41 (0.68), GAPDH (0.75), β-tubulin (0.83), Actin (0.92), and CACS (1.07). EF1α and PP2A exhibited the lowest M values, indicating the most stable expression. Data from 18 treatments were categorized into four groups: C14 genotype, C40 genotype, control, and treatment (Fig. 4). M value ranges were 0.14–0.82 for C14, 0.11–0.76 for C40, 0.01–0.66 for the control, and 0.47–1.06 for the treatment group. The most stable reference genes in each group were: β-tubulin and Actin (0.14) for C14; β-tubulin and PP2A (0.11) for C40; EF1α and GAPDH (0.01) for the control; and EF1α and β-tubulin (0.47) for the treatment. M values in the control group were significantly lower compared to the alkaline salt-treated group, suggesting increased instability of reference genes under alkaline salt stress. PP2A demonstrated markedly lower stability in C14 (0.55) compared to C40 (0.11), whereas EF1α showed no significant difference between the two genotypes, indicating greater overall stability of EF1α relative to PP2A.
Fig. 4.
Analysis of expression stability of candidate reference genes in bermudagrass roots subjected to alkaline salt stress. C14 is C14 genotype group (including control C14CK, and treated groups C14MA and C14SA). C40 is C40 genotype group (including control C40CK, and treated groups C40MA and C40SA). Control group: Combined untreated samples (C14CK + C40CK). Treatment group: Combined treated samples (C14MA + C14SA + C40MA + C40SA)
Candidate reference genes expression stability NormFinder analysis
NormFinder analysis identified EF1α as the most stable reference gene among the seven candidates, with the lowest S value indicating high expression stability (Fig. 4). This finding underscores the importance of EF1α for subsequent experimental designs. The S values, analogous to stability measures, were inversely related to gene stability. Specifically, the stability ranking was EF1α (0.13) < TIP41 (0.56) < PP2A (0.57) < GAPDH (0.63) < Actin (0.81) < β-tubulin (1.04) < CACS (1.29). Across all groups, including C14, C40, control, and treatment groups, the S values ranged from 0.22 to 1.05, 0.16 to 1.32, 0.003 to 1.35, and 0.25 to 1.11, respectively. The most stable reference genes within these groups were EF1α (0.22) for C14, TIP41 (0.16) for C40, GAPDH (0.003) for the control, and EF1α (0.25) for the treatment group.
Candidate reference genes expression stability bestkeeper analysis
BestKeeper analysis revealed that TIP41 ranked first among the seven reference genes in the C14 group, control group, and treatment group, while β-tubulin ranked first in the C40 group. These results indicate significant differences in reference gene stability between the two genotype materials (Table 3).
Table 3.
Assessment of expression stability and ranking of candidate reference genes in bermudagrass using BestKeeper software (with CV representing Coefficient of Variation and SD representing Standard Deviation)
| Samples | Control group | Treatment group | C14 | C40 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CV | SD | Rank | CV | SD | Rank | CV | SD | Rank | CV | SD | Rank | |
| β-tubulin | 7.17 | 2.48 | 7 | 4.4 | 1.47 | 4 | 2.68 | 0.95 | 5 | 1.28 | 0.41 | 1 |
| Actin | 3.49 | 1.27 | 3 | 3.13 | 1.1 | 2 | 2.86 | 1.04 | 6 | 3.89 | 1.36 | 6 |
| PP2A | 5.4 | 1.51 | 4 | 3.31 | 0.9 | 3 | 2.14 | 0.61 | 2 | 1.83 | 0.48 | 2 |
| CACS | 3.11 | 0.95 | 2 | 4.77 | 1.5 | 7 | 2.2 | 0.7 | 3 | 5.5 | 1.69 | 7 |
| EF1α | 5.71 | 1.46 | 5 | 4.49 | 1.1 | 5 | 2.38 | 0.62 | 4 | 3.09 | 0.73 | 5 |
| TIP41 | 2.35 | 0.81 | 1 | 2.03 | 0.67 | 1 | 1.73 | 0.59 | 1 | 2.25 | 0.74 | 4 |
| GAPDH | 5.85 | 1.46 | 6 | 4.7 | 1.14 | 6 | 3.94 | 1.01 | 7 | 2.17 | 0.5 | 3 |
Responses of growth indices of bermudagrass to alkaline salt stress
We utilized the online tool RefFinder in order to minimize potential biases stemming from single software algorithms, comprehensively assessing the expression stability of seven reference genes across three different software programs (Table 4). The geometric mean values were calculated, with the results displayed in Fig. 4. The stability of these genes was ranked as follows, from most to least stable: EF1α (1.41), PP2A (1.86), TIP41 (2.06), GAPDH (4.43), Actin (4.61), β-tubulin (5.96), and CACS (6.44). Our findings revealed that CACS (6.44) showed the greatest instability, making it unsuitable as a reference gene for qRT-PCR analysis in dog tooth grass roots subjected to alkaline salt stress. Conversely, EF1α (1.41) demonstrated the highest stability among the genes examined and was thus determined to be the optimal reference gene for qRT-PCR analysis in this particular scenario.
Table 4.
Ranking of reference gene stability in bermudagrass under alkaline salt stress
| Method | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| △CT | EF1α | PP2A | TIP41 | GAPDH | Actin | β-tubulin | CACS |
| BestKeeper | TIP41 | PP2A | Actin | EF1α | CACS | GAPDH | β-tubulin |
| Normfinder | EF1α | TIP41 | PP2A | GAPDH | Actin | β-tubulin | CACS |
| geNorm | EF1α | PP2A | TIP41 | GAPDH | β-tubulin | Actin | CACS |
|
Recommended comprehensive ranking |
EF1α | PP2A | TIP41 | GAPDH | Actin | β-tubulin | CACS |
Validation of reference gene EF1α using transcriptome data and random hub gene expression
We conducted qRT-PCR analysis on the roots of bermudagrass under alkaline salt stress to validate the stability of the reference gene EF1α. Expression levels obtained from qRT-PCR were compared with those derived from transcriptome data. EF1α was chosen as the reference gene based on its demonstrated stability, and this choice was further supported by the consistency observed between qRT-PCR and transcriptome results. For a more rigorous validation, we randomly selected 12 Hub genes that were differentially expressed, based on criteria of |Log2FC|≥ 3 and upregulation, for qRT-PCR analysis. As illustrated in Fig. 5, the fold changes measured by qRT-PCR aligned well with the differential expression patterns revealed by the transcriptome data (based on FPKM values). This alignment not only confirmed the reliability of the transcriptome data but also reaffirmed the stability of EF1α as a suitable reference gene for validating gene expression in bermudagrass roots under alkaline salt stress.
Fig. 5.
Validation of the stability of EF1α as a reference gene in the roots of bermudagrass
Discussion
Gene expression analysis, crucial for elucidating the molecular mechanisms of plant responses to abiotic stresses (Zhou et al. 2021), is highly dependent on precise quantification methods, notably quantitative real-time PCR (qRT-PCR). The reliability of qRT-PCR data depends on the selection of appropriate reference genes (Qiu et al. 2022), which can show considerable variation in expression stability among different experimental conditions and plant species (Li et al. 2022). As of now, no universally stable reference gene has been identified that is applicable across all species and conditions. Using unselected reference genes without critique often results in erroneous conclusions, highlighting the necessity to carefully choose reference genes tailored to specific experimental materials and conditions before conducting fluorescent quantitative experiments.
Recent research has shown that bermudagrass can thrive in alkaline salt soils with a pH of 9.3, leading to the identification of an alkali-salt-tolerant genotype (C14) and an alkali-salt-sensitive genotype (C40). Morphological and physiological evaluations have been conducted, yet studies on molecular mechanisms, including transcriptome and gene expression analyses, are lacking. Previous studies have assessed reference gene stability in bermudagrass under various abiotic stresses, such as drought, salinity, cold, and heat stress (Chen et al. 2015). Although genes like PP2A, CACS, EF1α, TIP41, UPL7, TUB, and GAPDH exhibited stable expression under these stresses, research on reference genes under alkaline salt stress is limited. Recognizing the importance of assessing reference gene stability under specific environmental conditions, our study focused on screening for reference genes with stable expression in bermudagrass roots across different genotypes and levels of alkaline salt stress. We selected seven reference genes (EF1α, PP2A, TIP41, GAPDH, Actin, β-tubulin, and CACS) that were previously stable under other abiotic stresses. Utilizing full-length and next-generation transcriptome databases, we identified 12 new candidate genes and systematically evaluated their stability to determine the most suitable reference gene for qRT-PCR analysis, ultimately enhancing our understanding of gene expression quantification in bermudagrass.
In our assessment of primer specificity and amplification efficiency, we rigorously followed standard protocols to ensure experimental accuracy (Zhu et al. 2022). Agarose gel electrophoresis revealed single, intense bands for all primers, and qRT-PCR analysis further validated their specificity through the observation of single melting peaks for each amplification product (Fig. 2). The amplification efficiencies spanned from 85.76% to 119.50%, with correlation coefficients ranging between 0.942 and 0.996 (Table 1), thereby reinforcing the reliability of our subsequent analyses. These results are in accordance with previous research that underscores the significance of primer validation in qRT-PCR studies (Yun et al. 2022).
Next, we evaluated the stability of candidate reference genes. Based on ΔCt values, EF1α, TIP41, and GAPDH emerged as the most stable reference genes (Fig. 3). A significant increase in ΔCt values was observed in the alkali-salt-treated group compared to the control, indicating heightened instability in reference gene expression under salt stress. This underscores the necessity of considering stress conditions during reference gene selection (Dong et al. 2022). For a more comprehensive evaluation, we utilized four widely recognized software programs: geNorm, NormFinder, BestKeeper, and RefFinder (Li et al. 2020). geNorm analysis showed that all genes exhibited M values below 1.5, suggesting high stability, with EF1α and PP2A displaying the lowest M values (Fig. 4). NormFinder analysis echoed these findings, identifying EF1α as the most stable gene with the lowest S value. BestKeeper analysis, on the other hand, revealed genotype-specific variations in reference gene stability. RefFinder, which synthesizes results from multiple software, confirmed EF1α as the most stable reference gene under the examined conditions (Figs. 3, 4; Table 3). This finding is in line with reports from other plant species, where EF1α has been established as a reliable reference gene across various stress conditions (Rutley et al. 2021).
We further validated the stability of EF1α by comparing its expression levels obtained via qRT-PCR with transcriptome data. The consistency between these datasets provided additional support for EF1α’s stability (Fig. 5). Next, we randomly selected 12 core genes with significant differential expression and performed qRT-PCR analysis using EF1α as the reference gene. Our results demonstrated a high consistency between the fold differences determined by qRT-PCR and the differential expression patterns revealed by transcriptome data based on FPKM values (Fig. 5). This not only corroborated the reliability of the transcriptome data but also reinforced EF1α’s suitability as a reference gene for gene expression analysis in bermudagrass roots under alkaline salt stress. Our findings offer a comprehensive assessment of potential reference genes for qRT-PCR analysis in this context and enhance our understanding of gene expression quantification in bermudagrass. The identification of EF1α as the most stable reference gene underscores the importance of rigorous reference gene selection in qRT-PCR studies and will facilitate future research on stress-response genes and molecular mechanisms in bermudagrass and related species.
5-Conclusions
In this study, we conducted a comprehensive evaluation of seven candidate reference genes for their stability in qRT-PCR analysis of gene expression in the roots of bermudagrass under alkaline salt stress. Through rigorous assessment of primer specificity, amplification efficiency, and gene expression stability using multiple algorithms (geNorm, NormFinder, BestKeeper, and RefFinder), EF1α emerged as the most stable reference gene across different genotypes and stress conditions. The validation of EF1α’s stability was further supported by consistent expression patterns between qRT-PCR and transcriptome data, as well as through the analysis of randomly selected core genes. This study not only provides a reliable reference gene for future qRT-PCR studies in bermudagrass under alkaline salt stress but also highlights the importance of systematic reference gene selection in ensuring the accuracy of gene expression quantification. The identified stable reference gene, EF1α, holds potential for broader application in gene expression studies of bermudagrass and other closely related species under various stress conditions, facilitating the exploration of stress-responsive genes and molecular mechanisms.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
This work was supported by the Natural Science Foundation of China (grant no. 32260341) and the Xinjiang Agricultural University Postgraduate Scientific Research Innovation Program (grant no. XJAUGRI2022023).
Author contributions
Lisi Tang and Peiying Li contributed to the study conception and design. Lisi Tang and Wen Li conducted the investigation and methodology. Data curation was carried out by Lisi Tang, Shirui Ma, Qikun Yu, and Wen Li. Peiying Li managed the project and provided resources. Lisi Tang, Guozhi Hu, Zhengfa Yu handled software applications, while Zongjiu Sun and Peiying Li supervised the research. The first draft of the manuscript was written by Lisi Tang. Validation was conducted by Lisi Tang, Chao Fu and Qikun Yu.The manuscript was reviewed and edited by Zongjiu Sun and Peiying Li. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
The authors declare no known conflicts of interest related to this publication.
Footnotes
Publisher's Note
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References
- Abbas A, Yu HY, Li XJ, Cui HL, Chen JC, Huang P (2021) Selection and validation of reference genes for RT-qPCR analysis in Aegilops tauschii (coss.) under different abiotic stresses. Int J Mol Sci 22(20):14. 10.3390/ijms222011017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen Y, Tan ZQ, Hu BY, Yang ZM, Xu B, Zhuang LL, Huang BR (2015) Selection and validation of reference genes for target gene analysis with quantitative RT-PCR in leaves and roots of bermudagrass under four different abiotic stresses. Physiol Plant 155(2):138–148. 10.1111/ppl.12302 [DOI] [PubMed] [Google Scholar]
- Chen XZ, Chen XG, Tan Q, He Y, Wang ZK, Zhou GY, Liu JA (2022) Selection of potential reference genes for RT-qPCR in the plant pathogenic fungus Colletotrichum fructicola. Front Microbiol 13:9. 10.3389/fmicb.2022.982748 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheng SS, Ku YS, Cheung MY, Lam HM (2022) Identification of stably expressed reference genes for expression studies in Arabidopsis thaliana using mass spectrometry-based label-free quantification. Front Plant Sci 13:14. 10.3389/fpls.2022.1001920 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dong XM, Zhang W, Zhang SB (2022) Selection and validation of reference genes for quantitative real-time PCR analysis of development and tissue-dependent flower color formation in Cymbidium lowianum. Int J Mol Sci 23(2):13. 10.3390/ijms23020738 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li ST, Hu ZL, Yang XW, Li Y (2020) Effect of nitrogen sources on omega-3 polyunsaturated fatty acid biosynthesis and gene expression in Thraustochytriidae sp. Mar Drugs 18(12):17. 10.3390/md18120612 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li G, Ma JW, Yin JL, Guo FL, Xi KY, Yang PH, Cai XD, Jia Q, Li L, Liu YQ, Zhu YX (2022) Identification of reference genes for reverse transcription-quantitative PCR analysis of ginger under abiotic stress and for postharvest biology studies. Front Plant Sci 13:12. 10.3389/fpls.2022.893495 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Niu LJ, Tao YB, Chen MS, Fu QT, Li CQ, Dong YL, Wang XL, He HY, Xu ZF (2015) Selection of reliable reference genes for gene expression studies of a promising oilseed crop, Plukenetia volubilis, by real-time quantitative PCR. Int J Mol Sci 16(6):12513–12530. 10.3390/ijms160612513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Qiu LY, Liang C, Zheng YD, Kang HY, Chen AY, Chen CL, Wang XL, Yang JL, Fang QF, Hui XJ, Hu YM, Chen ZW, Sha O, Zhu WG, Wang YJ (2022) Identification of human global, tissue and within-tissue cell-specific stably expressed genes at single-cell resolution. Int J Mol Sci 23(18):17. 10.3390/ijms231810214 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rutley N, Miller G, Wang FD, Harper JF, Miller G, Lieberman-Lazarovich M (2021) Enhanced reproductive thermotolerance of the tomato High pigment 2 mutant is associated with increased accumulation of flavonols in pollen. Front Plant Sci 12:16. 10.3389/fpls.2021.672368 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tang LS, Li W, Yu QK, Sun ZJ, Li PY (2024a) Assessing germplasm variation and tolerance thresholds of bermudagrass (Cynodon dactylon) to neutral and alkaline salt stress in ecological restoration. Agronomy-Basel 14(9):17. 10.3390/agronomy14092023 [Google Scholar]
- Tang LS, Yu QK, Li W, Sun ZJ, Li PY (2024b) Salt-alkali tolerance evaluation for bermudagrass and critical indicator screening at the seedling stage. Horticulturae 10(8):15. 10.3390/horticulturae10080825 [Google Scholar]
- Wang J, Xu J, Yang XH, Xu S, Zhang M, Lu F (2022) Boosting the power of transcriptomics by developing an efficient gene expression profiling approach. Plant Biotechnol J 20(1):201–210. 10.1111/pbi.13706 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ye TT, Shi HT, Wang YP, Yang F, Chan ZL (2016) Contrasting proteomic and metabolomic responses of bermudagrass to drought and salt stresses. Front Plant Sci 7:15. 10.3389/fpls.2016.01694 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yun B, Ryu S, Kang M, Lee J, Yoo J, Kim Y, Oh S (2022) Probiotic Lacticaseibacillus rhamnosus gg increased longevity and resistance against foodborne pathogens in Caenorhabditis elegans by regulating microrna Mir-34. Front Cell Infect Microbiol 11:12. 10.3389/fcimb.2021.819328 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhou XN, Zheng YY, Cai ZB, Wang XY, Liu Y, Yu AZ, Chen XL, Liu JY, Zhang Y, Wang AX (2021) Identification and functional analysis of tomato Tpr gene family. Int J Mol Sci 22(2):16. 10.3390/ijms22020758 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhu LY, Han ZY, He YF, Sun HX (2022) Caspase-1-dependent pyroptosis mediates adjuvant activity of platycodin D as an adjuvant for intramuscular vaccines. Cells 11(1):24. 10.3390/cells11010134 [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.