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. 2025 Jan 12;13(1):e4765. doi: 10.1002/fsn3.4765

Selection and Validation of Appropriate Reference Genes for qRT‐PCR Analysis of Iris germanica L. Under Various Abiotic Stresses

Yuan Yuan 1, Chungui Liu 1, Jianzhong Bao 1, Fengtong Li 1,✉
PMCID: PMC11725058  PMID: 39803220

ABSTRACT

Choosing the appropriate reference genes for quantitative real‐time PCR (qRT‐PCR) is very important for accurately evaluating expression of target genes. Iris germanica L. is a widely used horticultural plant with high ornamental value, which also shows a strong ability to tolerate abiotic stresses. No comprehensive research has been carried out on optimal reference genes in Iris germanica L. under abiotic stress. In this study, nine reference genes were selected as candidates based on the transcriptome sequencing data of Iris germanica L. The assessment of expression stability under various abiotic stress was conducted using four distinct methods (GeNorm, NormFinder, BestKeeper, and RefFinder). It was found that the optimal reference genes were ACT and F3H for drought and different temperature stresses. EF1α and ACT exhibited superior performance under salt stress. The expression of the IgP5CS gene was evaluated to provide additional validation for the accuracy of the selected optimal reference genes, indicating that inappropriate may lead to significant deviations in the results. This research identified reliable reference genes in I. germanica L. across various abiotic stress conditions, thereby facilitating the investigation into the molecular mechanisms responsible for stress tolerance in I. germanica L.

Keywords: abiotic stress, Iris germanica L., quantitative real‐time PCR, reference gene

In this research, we evaluated the expression stability of 9 candidate reference genes by four different algorithms (GeNorm, Normfinder, BestKeeper, and RefFinder). ACT and F3H appeared to be the most reliable reference genes under different abiotic stresses. Furthermore, the expression patterns of IgP5CS‐validated ACT and F3H genes can serve as appropriate reference genes.

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1. Introduction

Quantitative real‐time PCR (qRT‐PCR) is a widely employed technique for revealing gene function, due to its high sensitivity and precision (Bustin 2002; Gachon, Mingam, and Charrier 2004). Nevertheless, the accuracy of gene expression can be significantly influenced by various factors including the sample quality, and the amplification efficiency of qRT‐PCR (Derveaux, Vandesompele, and Hellemans 2010; Hao et al. 2014). To mitigate the impact of these confounding factors, internal reference genes are commonly employed in experimental settings to ensure precise and biologically relevant expression measurements (Schmidt and Delaney 2010; Zhao et al. 2022). Housekeeping genes are frequently employed as standardized reference genes (Jain et al. 2006; Souček et al. 2017), nevertheless, their expression levels exhibit significant variations across diverse tissues and species (Wang et al. 2021). Furthermore, several reports have demonstrated that environmental factors such as salinity, temperature, and hormones, along with particular experimental conditions, can also affect their expression levels (Podevin et al. 2012; Xu et al. 2015; Silva et al. 2021). Unstable reference genes may lead to substantial distortions and misrepresentations of the transcript data (Remans et al. 2014). To ensure the accuracy of expression data, it is crucial to choose appropriate reference genes according to different plant species and various experimental conditions.

Iris germanica L., which is a widely used perennial flower, has high ornamental value with big and colorful flowers. It was also applied in the cosmetics, pharmaceutics, and perfumes industries (Khatib, Faraloni, and Bouissane 2022). Furthermore, the rhizome extract from I. germanica L. exhibited a strong effect on pesticidal and anti‐cancer activities (Iranzadasl et al. 2021). I. germanica L. also showed a higher tolerance to drought and cold compared with the other species of the genus Iris (Zhang et al. 2021). The regulatory mechanism responsible for the adaptation to abiotic stresses in I. germanica L. is currently unknown. The impact of abiotic stresses on gene expression levels can be complicated and diverse (Zhang et al. 2021). To date, although a report has demonstrated that IgUBC, IgGAPDH, and IgTUB were appropriate reference genes for I. germanica L. across different flowering stages (Wang et al. 2021), the optimal reference genes for various abiotic stresses have not been elucidated.

This research aimed to discover highly stable reference genes in I. germanica L. under various abiotic stress conditions. 9 candidate reference genes, TUA, TUB, ACT, EF1α, PGK, UBQ, GAPDH, UBC, and F3H, generated from the transcriptome database were investigated using qRT‐PCR under drought, salt, heat, and cold treatments. Four statistical algorithms (GeNorm, NormFinder, BestKeeper, and RefFinder) have been applied to evaluate the optimal reference genes needed for normalization. To confirm the reliability and validity of the reference genes screened, the expression of IgP5CS (△1‐Pyrroline‐5‐Carboxylate Synthetase) gene encoding the crucial regulatory enzymes in the proline biosynthesis pathway which participates in stress tolerance (Yang et al. 2021) was analyzed under the above conditions. Our research will contribute to gene expression and help accelerate the comprehension of abiotic stress mechanisms in I. germanica L.

2. Materials and Methods

2.1. Plant Materials and Treatments

The plant materials used for the experiments were I. germanica cultivar ‘October sunshine’, which were collected from the Iris Resource Nursery (32°25'N; 119°23'E) at Jiangsu Lixiahe District Institute of Agricultural Sciences, China. Seedlings (Figure 1) were selected from tissue‐cultural plantlets at the age of 6 months and planted in an artificial climate incubator under normal conditions (25°C, 16 h light/8 h dark). For cold and heat treatments, plants were moved into the artificial climate incubator at 6°C and 40°C, respectively. For drought and salt stress treatments, the roots of plants were watered with full‐strength Murashige and Skoog medium solution supplemented with 250 mM NaCl or 20% PEG 6000, respectively. Leaves were then collected at 0, 4, 12, 24, 48, and 72 h after various stress treatments, and then placed in liquid nitrogen immediately. All samples were stored at −80°C for subsequent experiments. Each experiment included three biological replicates.

FIGURE 1.

FIGURE 1

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The phenotype of the abiotic stress treated plants. The phenotype of the plants treated with different abiotic stresses for 72 h. (A) WT, (B) drought stress, (C) salt stress, (D) heat stress, and (E) cold stress.

2.2. Reference Gene Selection

The sequences of reference genes reported from Arabidopsis thaliana and Oryza sativa were obtained from The Arabidopsis Information Resource (TAIR) and Rice Genome Annotation Project (RGAP). These sequences were used to perform a blast search their homologues from the transcriptome assemblies in I. germanica L. 9 candidate reference genes (EF1α, TUA, TUB, UBQ, UBC, GAPDH, PGK, ACT, F3H) and one target gene (P5CS) were obtained from the I. germanica L. transcriptome data from our laboratory (unpublished). Their identity to known amino acid sequences were ranging from 54.91% to 98.94% (Table 1). Primer designs were conducted using Primer 5.0 software. Parameters were set as follows: melting temperatures (Tm) 55°C–65°C, primer lengths 17–25 bp, and PCR product lengths 107–295 bp (Table 2). All PCR products were verified by 1.5% agarose gel electrophoresis.

TABLE 1.

Similarity to genes from Arabidopsis thaliana and Oryza sativa .

Gene Arabidopsis thaliana locus Amino acid identity with I. germanica (%) Oryza sativa locus Amino acid identity with I. germanica (%)
IgEF1α At1G07940 95.77 Os03g08050 97.09
IgGAPDH AT3G04120 64.40 Os02g07490 85.55
IgACT At5g09810 98.94 Os01g64630 96.83
IgUBQ At4G05320 84.92 Os02g06640 85.10
IgUBC At5G53300 97.97 Os06g30970 95.95
IgPGK At1G56190 90.20 Os05g41640 87.40
IgTUA At4G14960 97.35 Os03g51600 97.35
IgTUB At1g75780 97.54 Os06g46000 96.89
IgF3H At3G51240 85.33 Os03g03034 54.91
IgP5CS AT2G39800 86.45 Os05g38150 87.95
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TABLE 2.

Primer sequences and amplification parameters.

Gene Description Primer sequences (forward/reverse) Product length (bp) R 2 Amplification efficiency (%)
IgEF1α Elongation factor 1 alpha CCCCTCCGTCTTCCTCTTC 284 0.999 97.96
TTGGCAGCCTCCTTTGC
IgGAPDH Glyceraldehyde3‐phosphate dehydrogenase CCCGTCTTGCCTGTCATTAG 152 0.991 103.54
ATCTGAACTGTTCCCGTCTCC
IgACT Actin TGGTGGTTGAGAAGACTGGG 177 0.997 99.05
TGGCACGGATTCGGGAG
IgUBQ Ubiquitin CTCCGGCTTAGAGGTGGTATG 119 0.993 91.89
TCTTTGTCCTGGATCTTTGCTT
IgUBC Ubiquitin‐protein ligase GCGGCGTCTTCCTCGTTA 235 0.992 103.09
TGAGCAATCTCAGGCACCAA
IgPGK Phosphoglycerate kinase TGGACGACGCCCAGAAC 156 0.993 102.52
GCACAAGAGGAGCCAAACTAA
IgTUA Alpha‐tubulin GACCTACACTAACCTCAACCGTCT 139 0.991 101.26
CATGAAGTGGATTCTCGGGTAC
IgTUB Beta‐tubulin TGGATTCCCAACAATGTCAAGTC 201 0.994 96.33
CCGCCTCTGTAAACTCCATCTCA
IgF3H Flavanone 3‐hydroxylase AGCAGAGGCAGCGTGTC 149 0.999 108.34
CAGGTCCTCCGCCACA
IgP5CS Pyrroline‐5‐carboxylate synthase GATTCCAAGAGGCAGTAACAA 277
AGGTCCACCACAAAGAGCA
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2.3. RNA Extraction and cDNA Synthesis

The total RNA of leaf samples was extracted using RNA simple Total Kit (TakaRa Dalian, China) following the product manual. The extracted RNA was assessed for quality and purity by NanoDrop 2000 spectrophotometer (Thermo, Wilmington, USA). RNA samples with an A260/A280 ratio of 1.9–2.1 and A260/A230 ratio > 2.0 were used for subsequent cDNA synthesis. cDNA was synthesized using 5 μg of total RNA in a volume of 20 μL with the PrimeScript RT cDNA Synthesis Kit (TaKaRa, Dalian, China). The initial reaction containing: 5 μg total RNA, 1 μL oligo (dt) primers, 1 μL dNTPs, and RNase‐free water. The mixture was incubated at 65°C for 5 min. The reverse transcription reaction: 10 μL RNA Mix, 4 μL 5 × PrimeScript buffer, 20 U RNase inhibitor, 200 U RT enzyme, and RNase‐free water. The mixture was incubated at 42°C for 30 min, followed by 70°C for 15 min. The cDNA was then diluted to a final volume of 60 μL with nuclease‐free water.

2.4. Quantitative Real‐Time PCR

The qRT‐PCR assay was implemented and analyzed with CFX96 system (Bio‐Rad). Each reaction assay was prepared as follows: 50 ng cDNA, 0.5 μL of each primer, 10 μL 2 × TransStart Tip Green aPCR SuperMix, and RNase‐free water. The amplification program was used: an initial step of 95°C for 30 s, 94°C for 5 s, and 60°C for 30 s (×40). The melting curves were measured after cycle 40 by heating from 60°C to 95°C at a rate of 0.5°C s−1. Each reaction was conducted for three replicates. The cycle threshold (Ct) value was collected automatically. Tenfold dilution cDNA series (10, 100, 1000, 10,000) were used to calculate the standard curve values. The amplification efficiency (E) and correlation coefficient (R 2 ) values were calculated by the formula E = [10−1/slope−1] × 100%.

2.5. Statistical Data Analysis

Three commonly used algorithms GeNorm, NormFinder, BestKeeper, and a web‐based tool, RefFinder (http://www.ciidirsinaloa.com.mx/RefFinder‐master/?type=refer%20ence#tabs‐1) were used to evaluate the stability of candidate reference genes under different conditions (Curis et al. 2019; Xie, Wang, and Zhang 2023; W. Xu et al. 2020). The GeNorm measures the stability of reference gene according to the M value. GeNorm also calculated the V n /V n+1 (pairwise variation) to determine the optimal number of reference genes needed. Reference genes are ranged with NormFinder according to their stability value (SV). BestKeeper program measures the expression stability of genes by assessing the coefficients of variation (CV) and the standard deviations (SD). The lower values of CV and SD are considered that the gene is more suitable to be used as references. Finally, the results obtained from the above three algorithms were integrated by the web‐based analysis tool RefFinder.

2.6. Validation of Selected Reference Genes

We quantified the expression of IgP5CS in all the tested samples to confirm the stabilities of the reference genes selected. Normalization of the gene IgP5CS was conducted using the two most stable and one most unstable reference genes as determined by RefFinder. The 2−∆∆Ct method was used to analyze the relative gene expression level. Three technical replicates were performed for each biological sample. The bar plot was generated by the platform ImageGP 2 (https://www.bic.ac.cn/BIC) (Chen et al. 2024).

3. Results

3.1. Assessment of Primer Specificity of Reference Genes

Only a specific product of the expected size was observed by 1.5% gel electrophoresis (Figure 2). The melt curves of all primers had a single amplification peak with no primer dimer appearing. The amplification efficiency of 9 reference genes ranged from 91.89% (UBQ) to 108.34% (F3H), which met the requirement of 90.00%–110.00%. The R 2 ranged from 0.991 to 0.999, which is consistent with R 2 > 0.980. These results suggest that the primers designed for the 9 reference genes have successfully met the standard for qRT‐PCR and are suitable for further experiments. The information regarding the candidate reference genes, as well as correlation coefficients, is shown in Table 2.

FIGURE 2.

FIGURE 2

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Primer specificity and amplicon size of 9 candidate reference genes. (A) melting curves of 9 candidate reference genes exhibiting single peaks. (B) agarose gel electrophoresis (1.5%) showing specific amplification products of expected size using qRT‐PCR. 1–9: EF1α, UBQ, ACT, TUA, PGK, F3H, GAPDH, UBC, and TUA. M: 2000 bp marker.

3.2. Expression Analysis of Reference Genes

Ct value is an important indicator of gene expression abundance, with genes exhibiting smaller Ct values demonstrating higher levels of expression. All Ct values ranged from 17.58 to 32.42 under different experimental treatments (Figure 3). The mean Ct ranged from 21.45 (EF1α) to 29.77 (TUB). The box plots illustrate the variation of Ct values, a decreased level of dispersion suggesting a higher degree of consistency in gene expression. ACT has the lowest variation trend, whereas PGK has the highest variation trend.

FIGURE 3.

FIGURE 3

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Expression levels of 9 candidate reference genes across all experimental samples. The boxes indicate the 25/75 percentiles and the lines across the box indicate the median values. The whisker caps indicate the maximum and minimum Ct values. The higher boxes and whiskers indicate the greater the variations.

3.3. Expression Stability Analysis

3.3.1. GeNorm Analysis

The default threshold of the M value was 1.5, whereas the smaller M value means greater stability. The M values of all reference genes were found to be less than 1.5, demonstrating that they met the fundamental criteria for serving as reference genes (Figure 4). For the drought treatment, the most stable reference genes were ACT and F3H, whereas the least stably expressed gene was UBC. EF1α and ACT (M = 0.50) showed the highest stability under salt stress, ACT and F3H (M = 0.19) showed the most stable expression levels under heat treatment. For the cold treatment, the expression levels were most stable for ACT and F3H (M = 0.18). For all samples, F3H and ACT (M = 0.49) were the most stably expressed genes.

FIGURE 4.

FIGURE 4

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Expression stability of reference genes under different conditions based on GeNorm analysis. The most stable genes are on the right, whereas the least stable genes are on the left. (A) drought stress, (B) salt stress, (C) heat stress, (D) cold stress, and (E) all samples.

GeNorm algorithm was employed to compute the V n/n+1 (pairwise variation), and the default V n/n+1 value is 0.15. For the drought stress samples, V 3/4 was below 0.15 (Figure 5), suggesting that three reference genes are needed for normalizing gene expression. For the heat treatment, V 8/9 was 0.14, showing that eight reference genes are required. For the cold treatment, V 2/3 was 0.13, indicating that two reference genes were needed. For the salt treatment, all the V n/n+1 exceeded 0.15, that indicates the optimal number of reference genes has not been determined. For all the samples, V 8/9 was 0.149, showing that eight reference genes are necessary.

FIGURE 5.

FIGURE 5

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GeNorm analysis of the V values for the reference genes under various conditions. The average pairwise variations (V n /V n  + 1) were analyzed to measure the effect of adding reference gene on the qRT‐PCR. V n /V n  + 1: The pairwise variation between normalization factors n and n + 1.

3.3.2. NormFinder Analysis

NormFinder is employed to compute the stable value for each reference gene, as lower variation means greater gene expression stability. The results are displayed in Figure 6. For the drought treatment, ACT (0.26) and GAPDH (0.26) exhibited the highest stability. Regarding the salt treatment, EF1α (0.07) and ACT (0.25) exhibited the highest stability. The most stably expressed genes were ACT and F3H for the heat treatment, cold treatment and all samples, which was consistent with the results obtained from the GeNorm analysis.

FIGURE 6.

FIGURE 6

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Ranging of reference genes using NormFinder software. (A) drought stress, (B) salt stress, (C) heat stress, (D) cold stress, and (E) all samples. The lower the stability value, the higher the expression stability.

3.3.3. BestKeeper Analysis

The standard deviation (SD) obtained from BestKeeper are showed in Figure 7. ACT and F3H had the most stable expression for the drought treatment, cold treatment and all samples. ACT and EF1α were the genes with the most stable expression under salt stress. For the heat treatment, TUB and UBQ were the most stable genes, which was discrepant with the results obtained from GeNorm and NormFinder analysis.

FIGURE 7.

FIGURE 7

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Stability evaluation of reference genes calculating by BestKeeper. (A) drought stress, (B) salt stress, (C) heat stress, (D) cold stress, and (E) all samples.

3.3.4. RefFinder Analysis

To avoid limitations caused by a single program, ReFinder was employed to analyze the results of the above three algorithms. The rankings of RefFinder are shown in Table 3. The ranking was as follows: ACT > F3H > EF1ɑ > TUA>TUB > UBQ>UBC > GAPDH>PGK. ACT and F3H showed the highest stability for the drought treatment, heat treatment, cold treatment, and all the samples. For the salt treatment, EF1ɑ and ACT exhibited the highest stability, whereas GAPDH exhibited the least stability.

TABLE 3.

Stability ranking of reference genes in RefFinder.

Method 1 2 3 4 5 6 7 8 9
Drought stress
GeNorm ACT|F3H GAPDH EF1α TUA UBQ TUB PGK UBC
NormFinder ACT GAPDH F3H EF1α TUA UBQ TUB PGK UBC
BestKeeper ACT F3H EF1α GADPH UBQ TUA TUB UBC PGK
Comprehensive ranking ACT F3H GAPDH EF1α TUA UBQ TUB PGK UBC
Salt stress
GeNorm EF1α|ACT TUA UBQ F3H TUB PGK UBC GAPDH
NormFinder EF1α ACT UBQ TUA F3H TUB PGK UBC GAPDH
BestKeeper ACT EF1α F3H TUB TUA UBQ UBC PGK GADPH
Comprehensive ranking EF1α ACT TUA UBQ F3H TUB PGK UBC GAPDH
Heat stress
GeNorm ACT|F3H GAPDH UBQ TUB EF1α UBC PGK TUA
NormFinder ACT F3H UBQ GAPDH EF1α TUB UBC TUA PGK
BestKeeper TUB UBQ UBC ACT F3H EF1α GADPH TUA PGK
Comprehensive ranking ACT F3H UBQ TUB GAPDH UBC EF1α TUA PGK
Cold stress
GeNorm ACT|F3H EF1α GAPDH TUA TUB PGK UBC UBQ
NormFinder ACT F3H EF1α GAPDH TUA TUB PGK UBC UBQ
BestKeeper ACT F3H EF1α PGK UBQ TUA GADPH UBC TUB
Comprehensive ranking ACT F3H EF1α GAPDH TUA PGK TUB UBQ UBC
All samples
GeNorm ACT|F3H EF1α TUA TUB UBQ UBC GAPDH PGK
NormFinder ACT F3H EF1α TUA TUB UBQ UBC GAPDH PGK
BestKeeper ACT F3H EF1α TUB UBQ TUA UBC GADPH PGK
Comprehensive ranking ACT F3H EF1α TUA TUB UBQ UBC GAPDH PGK
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3.4. Reference Gene Validation

To evaluate the precision of the selected reference gene, we examined the expression of the IgP5CS gene under various stress conditions. IgP5CS is a stress‐inducible gene and plays a crucial role in enhancing plants abiotic stress tolerance. The two most stable reference genes (ACT and F3H) and the most unstable reference gene (PGK) were selected for normalizing gene expression data according to the RefFinder rankings.

As shown in Figure 8, the expression of IgP5CS exhibited similar expression trends, when ACT and F3H were used for normalization. By comparison, the use of the unstable reference gene PGK led to a different expression pattern of IgP5CS. Therefore, unstable reference genes may lead to a deviation in the expression of the target gene and affect the accuracy of the experiment, which further verifies the reliability of using ACT and F3H as reference genes.

FIGURE 8.

FIGURE 8

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Expression of IgP5CS gene under different stresses using the selected reference genes for normalization. The most stable reference genes (ACT and F3H) and the most unstable gene (PGK) were selected as the normalization factors. Error bars show the standard error calculated from three biological replicates.

4. Discussion

qRT‐PCR is a highly accurate, simple, specific, and sensitive method for analyzing gene expression (Deng et al. 2016). The accuracy and stability of gene expression data mainly depend on the stability of reference genes (Xiao et al. 2014). In the case of plants, no reference gene was universally accepted under all test conditions or across various tissues (Hruz et al. 2011; Razavi et al. 2019). The expression levels of commonly used reference genes differs among different plant species, including TUB, EF1α, ACT, and GADPH (G. Wang et al. 2019; Zhang et al. 2022). However, there have been reports indicating that reference genes not only play a role in maintaining fundamental cellular functions but also actively contribute to various other cellular processes (De Vega‐Bartol et al. 2013). As a result, their expression levels exhibit significant variation across different experimental conditions. In Schima superba, AP‐2 exhibited the highest stability for both cold and drought stresses, and eIF‐5α exhibited the highest stability for salt stress (Yao et al. 2022). In Arabidopsis, Actin exhibited higher stability under biotic and abiotic stress but showed relatively lower stability during developmental stages (Czechowski et al. 2005). Therefore, it is essential to validate the most suitable reference genes for specific conditions before utilizing them for normalization.

I. germanica L., a highly ornamental species of the genus Iris, exhibits a strong abiotic stress resistance, including drought, high temperatures, and low temperatures (Zhang et al. 2021). The current research on I. germanica L. primarily focused on the following aspects: physiological and biochemical measurement (Zhao et al. 2021), genetic diversity (Li et al. 2020), phenotypic analysis (Fan et al. 2022), and investigation of the flowering mechanism (Fan et al. 2020). Due to limited information on reference gene stability, few studies have been conducted on stress‐resistant mechanisms in I. germanica L. More and more researchers have reported the stability of gene expression across different plant species and under different stress conditions (Renganathan et al. 2023; Zhang et al. 2018; Zhang et al. 2017). However, to date, no comprehensive investigation of reference genes in Iris species across different abiotic stresses has been reported.

This research identified the suitable reference genes in I. germanica L. under various abiotic stresses. 9 reference genes were chosen from I. germanica L. to evaluate their stability in expression under drought, salinity, heat, and cold treatment. The ranking order calculated by different algorithms showed slight differences, due to the different statistical methods for assessing reference genes (Duan et al. 2017; Dudziak et al. 2020). NormFinder computes the SV, whereas GeNorm calculates the M value through pairwise variation, with both methods aiming to achieve minimal values that indicate optimal expression stability. BestKeeper evaluates the reference genes by analyzing SD and CV of Ct values across various samples (Pfaffl et al. 2004). The results obtained from GeNorm and NormFinder exhibited minimal variations, as both methods employ similar principles to evaluate reference genes. For example, EF1α was the most stable reference gene for salt stress according to GeNorm and NormFinder, whereas the best gene from BestKeeper was ACT. RefFinder allocates suitable weights to individual genes and computes their geometric mean to derive the conclusive ranking (He et al. 2019). We also used RefFinder to comprehensively evaluate the results from these three algorithms. The results from RefFinder exhibited a high degree of consistency with those obtained from NormFinder and GeNorm methods. Therefore, the most stable gene can serve as an internal reference gene for accurately correcting the expression levels of target genes under abiotic stress conditions.

To further demonstrate the reliability of the selected reference genes, we employed the two most stable genes (ACT and F3H) and one least stable gene (PGK) to standardize the expression levels of the IgP5CS gene. The expression of IgP5CS was significantly upregulated under drought, salt, and cold stress, which showed similar expression patterns of PbP5CS, AtP5CS1, and SpP5CS under abiotic stress conditions (Ma et al. 2022; Yang et al. 2021; Yoshiba et al. 1995). The expression level of IgP5CS1 was not induced under heat stress, which is the same as the report that AtP5CS1 was not responsive to high temperatures (Yoshiba et al. 1995). However, the use of an unstable reference gene (PGK) for normalization resulted in a significant deviation in the expression pattern of IgP5CS. The findings indicated that a more reliable gene can improve the precision of the results, and unstable reference genes can result in faulty qRT‐PCR results.

5. Conclusion

In this research, the expression stability of 9 candidate reference genes was evaluated by four different algorithms (GeNorm, Normfinder, BestKeeper, and RefFinder). Through the analysis of gene stability, ACT and F3H were identified as the most reliable reference genes under drought, heat, and cold stress. For salt stress, EF1α and ACT were the most stable reference genes. Furthermore, the expression patterns of IgP5CS‐validated ACT and F3H genes can serve as appropriate reference genes. Our research will significantly enhance greatly facilitate the identification of stress tolerance genes in I. germanica L. and other Iris plants.

Author Contributions

Yuan Yuan: data curation (lead), formal analysis (lead), visualization (supporting), writing – original draft (lead). Chungui Liu: investigation (supporting), resources (supporting). Jianzhong Bao: data curation (supporting), formal analysis (supporting). Fengtong Li: conceptualization (equal), supervision (equal), visualization (equal).

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This work was supported by the Special Fund for Scientific Research and Development of Jiangsu Lixiahe District Institute of Agricultural Sciences (SJ(22)113), and Jiangsu Agricultural Science & Technology Innovation and Promotion Program in 2024 (Yangnongban(2024)101).

Funding: This study was supported by Jiangsu Provincial Department of Agriculture and Rural Affairs, Yangnongban (2024)101. Jiangsu Lixiahe District Institute of Agricultural Sciences, SJ(22)113.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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