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Essay

Selection and Validation of Reference Genes in Different Tissues of Okra (Abelmoschus esculentus L.) under Different Abiotic Stresses

by
Zhipeng Zhu
1,
Jianxiang Yu
1,
Xinhui Tang
1,
Aisheng Xiong
2,* and
Miao Sun
1,2,*
1
College of Marine and Biological Engineering, Yancheng Teachers University, Yancheng 224002, China
2
State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
*
Authors to whom correspondence should be addressed.
Genes 2023, 14(3), 603; https://doi.org/10.3390/genes14030603
Submission received: 2 December 2022 / Revised: 12 February 2023 / Accepted: 20 February 2023 / Published: 27 February 2023
(This article belongs to the Special Issue Abiotic Stress in Plants: Present and Future)
Browse Figures
Review Reports Versions Notes

Abstract

:
Okra (Abelmoschus esculentus L.) is a particular vegetable with both edible and medicinal values. However, the expression pattern of the okra reference genes in response to abiotic stress has not been explored. In the present study, 18 potential reference genes were selected from okra in various tissues and abiotic stress conditions, and their expression levels were detected by Real-Time quantitative PCR (RT-qPCR). Their expression stabilities were calculated by four algorithms (geNorm, NormFinder, BestKeeper, and RefFinder). Under cold stress, the most stable genes included GAPC1 and CYP (leaf), CYP and ACT7 (root), HIS6 and GAPC1 (stem), and HIS6 and 60s (different tissues). Under salt stress, EF-1α and UBQ (leaf), EF-1α and UBQ (root), TUA4 and Eif (stem), and HIS6 and Eif (different tissues) were the most stable genes. Under drought stress, UBQ and Eif in the leaf, HIS6 and Eif in the root, TUA4 and HIS6 in the stem, and UBQ and Eif in different tissues were most stably expressed in okra. In addition, complete sequencing results by RefFinder showed that HIS6 and ACT7 in the leaf, HIS6 and Eif in the root, UBC5B and 60s in the stem, and HIS6 and Eif in different tissues, were most the suitable reference genes for okra. Furthermore, AeMYB1R1 transcription factor was used to verify the reliability of RT-qPCR values. In summary, this study was carried out to demonstrate the potential reference genes of okra under abiotic stress, aiming to provide a molecular basis for functional gene analysis and regulatory mechanism research of okra.

1. Introduction

Okra (Abelmoschus esculentus L.) belongs to the Malvaceae family and is widely cultivated worldwide [1]. Okra has been reported to have excellent edible [2] and nutritional [3] values. Moreover, okra has an essential medicinal value [4]. It can be used for anti-inflammatory [5], antioxidant [6], treatment of kidney diseases [7], and liver protection [8]. However, as the environment deteriorates, various abiotic stresses have serious effects on okra production [9].
Drought stress, cold stress, and salt stress are serious and sweeping abiotic stress factors on plants, which directly lead to crop decline worldwide [10,11]. Drought stress can cause a series of morphological, physiological, and biochemical changes in plants such as loss of leaf water, inhibition of plant growth, destruction of cell membrane structure, and electrolyte extravasation [12]. The damage caused by cold stress to plants is mainly manifested in changes in membrane lipid phase, increase in membrane permeability, increase in reactive oxygen species (ROS) and free radicals, decrease in chlorophyll content, and the accumulation of secondary metabolites [13]. Salt stress will reduce soil water potential and severely hinder plant growth and development by causing osmotic stress and destruction of ion balance, leading to plant malnutrition and membrane lipid peroxidation [14]. Nowadays, benefiting from genetic technology, including CRISPR/Cas, RNAi, and VIGS, the expression pattern of plant functional genes under abiotic stress is fully understood [15]. RT-qPCR is a standard technology for determining gene expression levels under abiotic stress, characterized by strong specificity, high sensitivity, accurate quantification, good repeatability, rapid response, and high throughput. However, RNA and cDNA quality, PCR amplification efficiency, and primer specificity may affect the accuracy of RT-qPCR results [16]. Therefore, suitable reference genes should be screened, and the expression levels of functional genes could be accurately analyzed by RT-qPCR [17]. Plant reference genes are stable in all samples and conditions, such as ACT, TUB, and EF-1α [18]. To date, reference genes of maize [19], sorghum [20], tomato [21], and other plants have been recorded in the ICG knowledge base (http://icg.big.ac.cn, accessed on 10 May 2022). However, there are few reports on okra reference genes [22]. Therefore, it is vital to explore and identify the potential reference genes of okra under various abiotic stresses.
Under different species and treatments, commonly used reference genes show significant expression instability [23]. Therefore, to obtain accurate RT-qPCR data, appropriate reference genes should be screened under specific conditions [24]. When selecting and verifying candidate reference genes, algorithms such as geNorm [25], NormFinder [26], BestKeeper [27], and RefFinder [28] are widely used in further analysis of RT-qPCR data [29,30]. To date, only a small number of studies have verified several reference genes for transcription normalization [31]. In this study, 18 potential reference genes were screened out from our published okra transcriptomic database [1] (NCBI accession number: SRR13983395). Gene expression analysis was performed on different tissues under various abiotic stresses by RT-qPCR to screen for the most suitable reference genes. Experimental groups in this study included three types of tissue (root, stem, and leaf) and three abiotic stress conditions (drought, salt, and cold stress). The 18 genes selected are common reference genes in plants, including Toona Ciliata [23], Glycine max [32], and Gentiana Macrophylla [33]. The genes in this study were actin-7 (ACT7), histone acetyltransferase MCC1 (HIS1), histone deacetylase 6 (HIS6), ubiquitin-conjugating enzyme E2 5B (UBC5B), ubiquitin-conjugating enzyme E2–17 kDa (UBC17), tubulin α-3 chain (TUB-α), S-adenosylmethionine decarboxylase proenzyme (SAMDC), membrane-anchored ubiquitin-fold protein 1 (MUB1), cyclophilin (CYP), polyubiquitin (UBQ), eukaryotic translation initiation factor (Eif), elongation factor 1 α (EF-1α), ADP-ribosylation factor A1E (ARFA1E), Glyceraldehyde-3-phosphate dehydrogenase C subunit 1 (GAPC1), Tubulin α-4 chain (TUA4), actin-related protein 2 (ARP), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and 60s ribosomal protein l 36 (60s). In addition, AeMYB1R1 transcription factor was used to verify the expression level of 18 selected genes. MYB, one of the most prominent transcription factor families in plants, regulates a variety of biological processes, including specific development, metabolism, response to abiotic stresses [34,35] in Arabidopsis thaliana [36], Zea mays [37], and G. max [38]. MYB1R1 is involved in gene regulation under stress in plants [39,40]. To validate the best selected reference genes, AeMYB1R1 expression levels under different conditions were investigated using the most stable and least stable reference genes or combinations. This study aimed to provide suitable reference genes for okra under different abiotic stresses and lay a theoretical foundation for improving okra yield and quality.

2. Materials and Methods

2.1. Plant Materials, Treatments, and Sample Collecting of Okra

The plant molecular biology laboratory of Yancheng Teachers University (33°39′ N, 120°16′ E) preserved okra seeds (A. esculentus L.) (cv ‘Hokkaido No.1’). After germination, okra seeds were transferred to nutrient soil for further cultivation. The growth condition was maintained at 22 °C/20 °C (day/night), 16 h/8 h (day/night) supplemental illumination with 300 μmol·(m2·s)−1 light intensity. One-month-old seedlings were treated with cold (−7 °C treatment), drought (20% PEG6000 solution treatment), and salt (200 mM NaCl solution treatment) stress. Experimental materials were processed three times and sampled at 0 h (10 am), 6 h (4 pm), 12 h (10 pm), and 24 h (10 am). The leaf, root, and stem samples of okra were separated, washed, and stored in a −80 °C ultralow temperature freezer for further analysis (Table 1). In all cases, three or more independent biological replicates were obtained from each sampling point.

2.2. Total RNA Extraction and cDNA Synthesis of Okra Samples

Total RNA was extracted from okra samples via Tiangen EasyPure® RNA Kit (Catalog Number: ER101-01, purchased from Beijing Transgen Biotechnology Co., Ltd., Beijing, China). RNA quality and RNA quantity were measured in the Thermo Fisher Scientific Spectrophotometer (Catalog Number: 912A0959, purchased from Thermofisher Scientific (China) Co., Ltd., Shanghai, China). RNA integrity was assessed by electrophoresis on 1.0% agarose gel. The RNA concentration was measured using a Thermo Scientific™ NanoDrop Lite (Catalog Number: ND-LITE-PR, purchased from Thermofisher Scientific (China) Co., Ltd., Shanghai, China).
The cDNA of okra samples was synthesized according to the product manual of the PrimeScript™ RT Master Mix (Catalog Number: RR036Q, purchased from Beijing Takara Biomedical Technology Co., Ltd., Beijing, China).

2.3. The Screening of Potential Reference Genes and Primer Design for RT-qPCR

Based on previous research and our published A. esculentus transcriptomic database (NCBI accession number: SRR13983395), 18 potential reference genes were screened, including ACT7, HIS1, HIS6, UBC5B, UBC17, TUB-α, SAMDC, MUB1, CYP, UBQ, Eif, EF-1α, ARFA1E, GAPC1, TUA4, ARP, GAPDH, and 60s. The nucleotide sequences of these 18 genes were obtained from the transcriptomic database. The primers for RT-qPCR were designed by NCBI Primer-Blast tool and DAMAN 6.0 software (Table 2) and synthesized by Anhui General Biological Systems Co., Ltd. (Chuzhou, China).

2.4. PCR and RT-qPCR Analysis

The PCR reaction was performed with TaKaRa PCR Amplification Kit (Catalog Number: R011, purchased from Beijing Takara Biomedical Technology Co., Ltd., Beijing, China) to test all primer pairs for specificity. The amplified products were analyzed by 1.0% agarose gel electrophoresis and visualized in a Gel Doc XR+ Gel Documentation System (Catalog Number: 1708195, purchased from Bio-Rad Laboratories, Hercules, CA, USA).
Three biological and three technical replicates were run for each RT-qPCR run. RT-qPCR was performed by SYBR® Premix Ex TaqTM II (Catalog Number: RR820Q, purchased from Beijing TaKaRa Biotechnology Co., Ltd., Beijing, China) and performed on a Thermal Cycler DiceTM Real-Time System Lite (Code No. TP700/TP760, purchased from TaKaRa Biotechnology (Dalian) Co., Ltd., Dalian, China). The PCR reaction procedure was as follows: 95.0 °C for 2.0 min and 40 cycles of 95.0 °C for 15.0 s, 60.0 °C for 15.0 s. The melting curve was drawn to confirm the specific RT-qPCR reaction product of three technical replicates and one template-free control. For each condition, a standard curve was generated using 3-fold serial dilutions of cDNA in triplicate. the amplification efficiency of each primer was calculated according to the formula
E = (10(−1/slope) − 1) × 100.
In addition, a standard curve was established and used to calculate PCR efficiency (E (%)) and correlation coefficient (R2) [41].

2.5. Ranking of Reference Gene Expression Stability Based on Four Algorithms

To evaluate and rank the expression stability of the 18 genes, three commonly used algorithms (geNorm, NormFinder, BestKeeper) were applied in multiple experimental groups. Using geNorm, an appropriate number of reference genes can be estimated by analyzing the pairing difference (Vn/n+1) of normalization factors. Vn/n+1 ≤ 0.15 means that the correct number of reference genes is n, and if the value is more than 0.15, another reference gene should be added to normalization factors until the value reaches 0.15. NormFinder determines the ranking of reference genes by calculating the stability of gene expression. The relative expressions of 2−ΔCt of each gene in each sample were used as the raw data to generate the stable value of gene expression, and then the most stable gene was selected according to the most stable value. BestKeeper determines gene expression stability by counting SD and CV values. After that, RefFinder is used to summarize the ranking and obtain the complete order of these 18 genes [28].

2.6. Accuracy Assessment of Reference Genes by the Transcription Factor AeMYB1R1

To evaluate the reliability of the stability of the expression of the reference gene with AeMYB1R1 transcription factor as the target gene, under cold, salt, and drought stress, and in different tissues, the relative expression levels of the top two and bottom two reference genes in the RefFinder stability ranking table (Table S1) were detected. In addition, three technical replicates were carried out. At the same time, relative gene expression levels were calculated and analyzed using the method described by Schmittgen and Livak [42].

3. Results

3.1. Amplification Specificity and Efficiency of Reference Genes in Okra

All reference genes had a single band in the gel, and RT-qPCR melting curves showed that each amplified candidate reference gene had a single peak (Figure S1). These results indicated that the primers had a high amplification specificity. The linear correlation coefficients of the melting curves of 18 genes were all greater than 0.99 (R2 > 0.99), indicating that the RT-qPCR data had a high degree of fitting. Amplification efficiency data of 18 genes showed that the lowest value was 90.82% (UBQ), and the highest value was 102.84% (TUA4), meeting the analysis requirements of plant reference genes (Table 2).

3.2. The Expression Profile Analysis of Reference Genes in Okra

The expression profiles of 18 genes were measured under the following conditions: different okra tissues (leaf, root, and stem), cold, salt, and drought stress. The data showed that the expression profiles of 18 genes varied significantly among different samples. Among them, the highest and lowest CT values were 33.89 and 16.64 (Figure 1). The most abundant gene was EF-1α, whose maximum, minimum, and median CT values were 32.61, 19.07, and 26.36, respectively. The least abundantly expressed gene was UBQ, with maximum, minimum, and median CT values of 22.16, 20.18, and 21.17, respectively.
Compared with other reference genes, the CT value ranges of UBQ, HIS6, and Eif were relatively narrow, indicating that their expression was more stable in different samples. In addition, the expression of 18 genes showed significant variability.

3.3. geNorm Analysis of Reference Genes in Okra

The expression stabilities of 18 genes from okra were analyzed by geNorm. In this algorithm, the M value represents the basis of the evaluation of gene expression stability (M < 1.0) [43]. Gene expression levels with lower M values are more stable, indicating more suitable as reference genes.
geNorm analyses for the 18 selected genes are shown in Figure S2. Under cold stress, the expression of CYP, UBC5B, and GAPC1 was the most stable in the leaf; the expression of UBQ, CYP, and ACT7 fluctuated least in the root; and the expression of HIS6, TUA4, and GAPC1 was the most stable in the stem. Meanwhile, in different okra tissues, the genes most consistently expressed were HIS6, CYP, and TUA4.
Under salt stress, the most stable expressed genes were EF-1α, UBQ, and ACT7 in leaf; UBC5B, EF-1α, and CYP in the root; TUA4, Eif, and 60s in stem; and HIS6, UBC5B, and 60s in different tissues. Under drought stress, UBQ, ACT7, and GAPC1 showed the most stable expression in leaf; UBQ, UBC5B, and ACT7 expressed most stably in the root; and TUA4, Eif, and HIS6 expressed most stably in the stem. Meanwhile, the most stable expressed genes were UBQ, Eif, and HIS6 in different tissues.
Under all treatments, HIS6, CYP, and HIS1 in leaf; ACT7, GAPC1, and HIS6 in the root; and HIS1, UBC5B, and GAPC1 in the stem had the highest expression stability. Moreover, HIS6, Eif, and UBQ were the most stable reference genes expressed in okra.
V2/3 values of paired variables were all less than 0.15 under abiotic stress and different tissues, indicating that the appropriate number of reference gene combinations in okra was 2. In addition, the data showed that, across all samples, paired variations of V2/3, V3/4, and V4/5 were 0.123, 0.152, and 0.141, respectively, which indicated that V values were altered by the addition of the third and fourth gene, 3 or 4 genes could be selected according to the trend of V value (Figure 2). Therefore, for all samples, the optimal reference gene combination was HIS6, Eif, UBQ, and ACT7.

3.4. NormFinder Analysis of Reference Genes in Okra

NormFinder’s analysis showed that under cold stress, the most stably expressed genes were GAPC1 (0.167), CYP (0.232), and ACT7 (0.3) in the leaf (Figure 3A); ACT7 (0.076), HIS6 (0.202), and CYP (0.233) in the root (Figure 3B); and GAPC1 (0.054), Eif (0.097), and ACT7 (0.125) in the stem (Figure 3C). Meanwhile, 60s (0.07), HIS6 (0.107), and EF-1α (0.191) (Figure 3D) had lower stability values across different tissues. Under salt stress, TUA4 (0.127), Eif (0.157), and UBQ (0.164) had the highest expression stability in the leaf (Figure 3E) and UBQ (0.117), ACT7 (0.144), and TUA4 (0.203) were the most stable genes expressed in the root (Figure 3F). The three most stable genes were CYP (0.128), ACT7 (0.131), and EF-1α (0.14) in the stem (Figure 3G), and in different okra tissues, Eif (0.072), GAPC1 (0.083), and ACT7 (0.125) (Figure 3H) were the most stably expressed genes. Under drought stress, ACT7 (0.095), Eif (0.106), and HIS6 (0.114) had the highest stability of expression in leaf (Figure 3I); HIS6 (0.056), UBQ (0.077), and Eif (0.102) were the three most stable genes in root (Figure 3J); HIS6 (0.022), GAPC1 (0.085), and UBC5B (0.122) were most stably expressed in the stem (Figure 3K); and NormFinder analysis identified that UBQ (0.059), GAPC1 (0.062), and HIS6 (0.062) (Figure 3L) were found to be the top-ranked gene with stable expression in different tissues.
Under different treatments, TUA4 (0.059), 60s (0.102), and HIS1 (0.143) were the most stable genes in leaf (Figure 3M); Eif (0.104), HIS6 (0.15), and TUA4 (0.172) were found to be suitable reference genes in the root (Figure 3N); and 60s (0.059), Eif (0.073), and GAPC1 (0.091) were among the most stable genes in stem (Figure 3O). Across all samples, the most stable genes included TUA4 (0.134), 60s (0.147), and GAPC1 (0.187) (Figure 3P).

3.5. BestKeeper Analysis of Reference Genes in Okra

BestKeeper analysis showed that under cold stress, the most stable genes included HIS6 (0.97 ± 0.29) and EF-1α (1.02 ± 0.30) in the leaf, GAPC1 (0.66 ± 0.11) and CYP (0.88 ± 0.11) in the root, and CYP (0.83 ± 0.07) and HIS6 (1.09 ± 0.17) in the stem. In different okra tissues, Eif (0.59 ± 0.18) and UBQ (1.04 ± 0.33) were the most stable genes. Under salt stress, the expressions of HIS6 (0.62 ± 0.03) and ACT7 (0.91 ± 0.22) were the most durable in the leaf, and the expressions of HIS6 (1.07 ± 0.08), EF-1α (1.15 ± 0.12) were the most durable in the root. Eif (0.59 ± 0.16) and UBQ (0.91 ± 0.27) were ranked as the two most stable reference genes in the stem, and in different okra tissues, Eif (1.25 ± 0.35) and HIS6 (1.25 ± 0.36) were the most stable genes. Under drought stress, the most stable genes included UBQ (0.99 ± 0.28) and GAPC1 (1.09 ± 0.29) in the leaf, GAPC1 (0.75 ± 0.23) and ACT7 (0.99 ± 0.28) in the root, Eif (0.81 ± 0.19) and UBQ (0.86 ± 0.23) in the stem, and the expressions of UBQ (1.37 ± 0.09) and Eif (1.45 ± 0.15) were the most stable in different tissues.
Under different treatments, ACT7 (0.59 ± 0.17) and HIS6 (1.14 ± 0.33) were the most stable genes in the leaf, HIS6 (1.33 ± 0.37) and 60s (1.52 ± 0.37) expressed stably in the root, and UBC5B (1.47 ± 0.47) and Eif (1.73 ± 0.51) were the most stable genes in the stem. Meanwhile, HIS6 (0.85 ± 0.14) and Eif (0.87 ± 0.19) were found to be the most stable genes in different okra samples (Table S2).

3.6. RefFinder Analysis of Reference Genes in Okra

RefFinder analysis showed that (Table S3), under cold stress, the most stable expression genes included GAPC1 in the leaf, CYP in the root, HIS6 in the stem, and in different okra tissues. HIS6 was the most stable reference gene. Under salt stress, the most stable genes included EF-1α in the leaf and root, TUA4 in the stem, and HIS6 expression was most persistent in different tissues. Under drought stress, UBQ in the leaf, HIS6 in the stem, and TUA4 in the root were calculated as the most stable genes, and in different okra tissues, the most stable gene was UBQ. Among all treatments, the most stable genes included HIS6 in the leaf and root and UBC5B in the stem. Moreover, HIS6 was stable in all samples, and the most unstable gene was TUB-α.

3.7. The Validation of Selected Reference Genes Based on AeMYB1R1

To verify the reliability of the above algorithm results, the top two and bottom two genes for expression stability were used to calculate the relative expression level of AeMYB1R1 transcription factor.
Under cold stress, compared with the most stable genes (HIS6, 60s) and their combination, the relative expression of AeMYB1R1 reached a peak (9.38, 8.23, and 8.80) at 12 h. However, compared with the most unstable reference genes (ARFA1E, TUB-α), the relative expression level of AeMYB1R1 exhibited a different trend (Figure 4A). In addition, under salt (Figure 4B) and drought stress (Figure 4C) and in other tissues (Figure 4D), compared with the most stable genes and their combinations, AeMYB1R1 expression levels showed similar trends in variation. Conversely, compared to the two most unstable reference genes, AeMYB1R1 expression levels and variation trends were inconsistent.

4. Discussion

Reference gene expression profiles vary with species, varieties, tissues, and stresses [17,44,45]. The selection of suitable reference genes is the key to the study of target gene expression in various experimental conditions and tissues. Therefore, it is crucial to screen for the most suitable reference genes for RT-qPCR analysis, and this study aimed to find the optimal reference genes for okra under abiotic stress. Few studies have evaluated reference genes for okra [31]. At present, this is the first specific report to verify okra reference genes under different abiotic stresses. In this study, 18 potential reference genes were selected from our published okra transcriptomic database, CYP and ACT7 were more suitable in the root under cold stress, EF-1α and HIS6 were more ideal in the root under salt stress. At the same time, TUA4 and Eif were more suitable in stem under salt stress (Table S3). The results showed that there were other optimal reference genes under different experimental conditions.
It was also evident in our findings that 18 genes showed differential expression in the root, leaf, and stem. Different reference genes should be selected even in various tissues of the same species, as is verified in most studies [46]. In Toona ciliata, PP2C59 and UBC5B expressed stably in leaf and HIS1 and ACT7 showed good stability in young stem [23]. In different tissues (root, leaf, and flower) of woodland strawberries, the most stable reference genes were FveMT1 and FveSAND, while FveCHC1 and FveEF1a were more suitable for fruit [47]. In apple, ACT and GAPDH in bud, EF-1, and PP2A in flower, and GAPDH and CYP in fruit were the reference genes stably expressed [48].
Under different abiotic stresses, reference genes showed showed differential expression characteristics [45]. Of the 12 selected reference genes in Peucedanum praeruptorum, PTBP1 and PP2A were stable under drought stress, and TIP41 was the gene with an unchanging expression under salt stress. At the same time, SAND and UBC9 exhibited the most stable expression under cold stress [49]. In parsley, under cold and salt stress, EF-1α and TUB were the most stable genes, and ACTIN expression was regular under drought stress [50]. In Polygonum cuspidatum, 60s expression was regular under cold stress, while under drought and salt stress, the study found ACTIN and TUB to be the most stable genes, respectively [30]. In this study, the most suitable reference genes under cold stress were HIS6 and 60s; UBQ and Eif were most suitable under drought stress. At the same time, the expressions of HIS6 and Eif were stable under salt stress (Table S3). Therefore, it is essential to determine the optimal reference genes under different experimental conditions by RT-qPCR data and algorithm analysis. Histone deacetylases are responsible for removing acetyl groups from histone lysines, thereby activating gene transcription and regulating plant physiology, development, and environmental response [51]. In the present study, HIS6 was expressed under different conditions, which may be a suitable reference gene under various abiotic stresses (Table S3). In contrast, TUB-α was a gene with lower expression stability, which has also been proven in Chrysoperla nipponensis [52]. However, as a commonly used reference gene, TUB expression was stable under stress in Betula platyphylla [53], Lymantria dispar [54], and animals [55]. This also indicated that a specific reference gene has various expression stability in various species under different stresses. In addition, due to reasons such as primer design, extension time, false negative results, etc., RT-qPCR products that were too large or too small negatively affected data reliability, which should be considered in future research.
In this experiment, geNorm (Figure S2), NormFinder (Figure 3), BestKeeper (Table S2), and RefFinder (Table S3) were used for gene expression analysis. The first three algorithms were used to evaluate the stability of gene expression. However, due to differences in these software algorithms, the ranking results were inconsistent. For example, UBQ ranked first in the stability of gene expression in drought treated okra root by geNorm but ranked second and third in NormFinder and BestKeeper, respectively. In strawberry, DBP ranked first in different tissue and fruit development stages by geNorm but third by NormFinder and BestKeeper [56]. In P. cuspidatum, 60SrRNA ranked first under abiotic stress by geNorm, but third and second by NormFinder and BestKeeper [30]. geNorm ranking is based on the similarity of expression levels of each reference gene from different experimental samples [25]. NormFinder evaluates the expression stability of reference genes based on intra- and inter-group variation [26]. BestKeeper directly relies on the CT value pairwise correlation analysis to calculate the stability ranking of candidate genes [27]. To combine the results of the first three algorithms, an online ranking software RefFinder was used for the overall ranking, which calculated the average gene weight and obtained the final ranking result [28]. To date, RefFinder has been applied in Lilium regale [57], Brassica juncea [2], and Solanum rostratum [58] for reference gene selection. In our study, according to RefFinder’s ranking, HIS6 was the best reference gene in okra (Table S2).
AeMYB1R1 transcription factor is used as a reference for the control variables in this study, and AeMYB1R1 was used to verify the expression level of 18 candidate genes. MYB proteins play an important role in plant development, metabolism, and response to biotic and abiotic stresses [34]. MYB15 is involved in low temperature regulation and freezing tolerance processes in plants [36]. AtMYB2 is involved in ABA induction of the genes that control salt and dehydration [59]. MYB1R1 is involved in gene regulation under drought stress in plants [39].

5. Conclusions

This study first reported the selection and validation of reference genes in different okra tissues under other treatments. Under cold stress, the most stable genes in okra were GAPC1 and CYP (leaf), CYP and ACT7 (root), HIS6 and GAPC1 (stem), and HIS6 and 60s (different tissues). Under salt stress, the most stable genes included EF-1α and UBQ (leaf), EF-1α and HIS6 (root), TUA4 and Eif (stem), and HIS6 and Eif (different tissues). Under drought stress, UBQ and Eif (leaf), HIS6 and Eif (root), TUA4 and HIS6 (stem), and UBQ and Eif (different tissues) were the most stable genes. Furthermore, according to RefFinder’s analysis, the most stable genes were HIS6 and ACT7 in the leaf, HIS6 and Eif in the root, and UBC5B and 60s in the stem. Meanwhile, the most stable genes expressed in all samples were HIS6 and Eif. The expression analysis results of AeMYB1R1 also confirmed the validity of the results. These results provide suitable reference genes studying the molecular response mechanism of okra under abiotic stress and lay a foundation for the analysis of okra functional genes.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/genes14030603/s1, Figure S1: Melting curves of 18 candidate reference genes, Figure S2: Expression stability ranking of 18 selected reference genes under different stresses and in different tissues of okra, as analyzed by geNorm, Table S1: Relative expression levels of the top two and bottom two reference genes in the RefFinder stability ranking table, Table S2: The expression stability of 18 selected reference genes under different stresses and in different tissues of okra, as calculated by BestKeeper, Table S3: The expression stability of 18 selected reference genes under different stresses and in different tissues of okra, as calculated by RefFinder.

Author Contributions

Z.Z.: Conceptualization, Experiment, Software, Visualization, and Writing—original draft preparation. J.Y.: Experiment, Methodology, and Writing—original draft preparation. X.T.: Conceptualization, Methodology, and Resources. A.X.: Conceptualization, Methodology, and Resources. M.S.: Conceptualization, Methodology, Resources, Writing—reviewing and modifying, and Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed by Natural Science Foundation for Higher Education Institutions in Jiangsu Province (21KJB210007), Key Research and Development Program of Jiangsu (BE2022386), and Priority Academic Program Development of Jiangsu Higher Education Institutions Project (PAPD).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in Supplementary Materials.

Acknowledgments

The authors thank State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Horticulture, Nanjing Agricultural University for providing seeds of A. esculentus and material conservation.

Conflicts of Interest

The authors declare that they do not have any competing financial or commercial interest that represents a conflict of interest in connection with this paper.

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Genes 14 00603 g001 550
Figure 1. CT value distribution for 18 selected reference genes. The middle horizontal line represents the median.
Figure 1. CT value distribution for 18 selected reference genes. The middle horizontal line represents the median.
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Figure 2. Pairwise variation value of 18 selected reference genes analyzed by geNorm. The threshold was set at 0.15, and the pairwise variation value greater than 0.15 indicated that more reference genes should be added for standardization.
Figure 2. Pairwise variation value of 18 selected reference genes analyzed by geNorm. The threshold was set at 0.15, and the pairwise variation value greater than 0.15 indicated that more reference genes should be added for standardization.
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Figure 3. Expression stability ranking of 18 selected reference genes under different stresses and in different okra tissues, as analyzed by NormFinder. The stability value of the 18 reference genes in leaf (A), root (B), stem (C), different tissues (D) under cold stress, leaf (E), root (F), stem (G), different tissues (H) under salt stress, leaf (I), root (J); stem (K), different tissues(L) under drought stress, leaf (M), root (N), stem (O) under different treatments and all samples (P).
Figure 3. Expression stability ranking of 18 selected reference genes under different stresses and in different okra tissues, as analyzed by NormFinder. The stability value of the 18 reference genes in leaf (A), root (B), stem (C), different tissues (D) under cold stress, leaf (E), root (F), stem (G), different tissues (H) under salt stress, leaf (I), root (J); stem (K), different tissues(L) under drought stress, leaf (M), root (N), stem (O) under different treatments and all samples (P).
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Figure 4. The relative expression level of AeMYB1R1 determined by the most stable and unstable reference genes. Changes in AeMYB1R1 transcription factor were normalized by selected reference genes (HIS6, 60s, HIS6 + 60s, ARFA1E, TUB-α, Eif, HIS6 + Eif, MUB1, UBQ, UBQ + Eif, SAMDC) under cold (A), salt (B), and drought (C) stress, and in different tissues (D). Values are the means ± standard error of triplicate assays.
Figure 4. The relative expression level of AeMYB1R1 determined by the most stable and unstable reference genes. Changes in AeMYB1R1 transcription factor were normalized by selected reference genes (HIS6, 60s, HIS6 + 60s, ARFA1E, TUB-α, Eif, HIS6 + Eif, MUB1, UBQ, UBQ + Eif, SAMDC) under cold (A), salt (B), and drought (C) stress, and in different tissues (D). Values are the means ± standard error of triplicate assays.
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Table
Table 1. Experimental details of reference gene selection in okra.
Table 1. Experimental details of reference gene selection in okra.
Experimental DesignTreatmentTissueBiological RepetitionSampling PointsNumber of Samples
Cold stress−7 °Cleaves, stems, roots3436
Salt stress200 mM NaClleaves, stems, roots3436
Drought stress20% PEG6000leaves, stems, roots3436
Table
Table 2. The information of 18 selected reference genes and related primers, and PCR characteristics in okra.
Table 2. The information of 18 selected reference genes and related primers, and PCR characteristics in okra.
Gene SymbolGene NameAccession NumberPrimer: Forward/ReverseAmplification Product Size (bp)Standard CurveE (%)R2
ACT7Actin-7OP448607F:TCGCAGACCGTATGAGCAAG
R:GGTGCTGAGTGATGCCAAGA		127y = −3.5111x + 29.77792.67%0.9937
HIS1Histone acetyltransferase MCC1OP448608F:GCTTCGGCACTTATCCACGA
R:CCTCCGCACACACTTGAATG		138y = −3.5239x + 30.46992.21%0.9974
HIS6Histone deacetylase 6OP448609F:TGAGGCTTCTGGGTTTTGCT
R:TTTGCCATTACCCACCCCAA		226y = −3.4937x + 29.44293.30%0.9976
UBC17Ubiquitin-conjugating enzyme E2–17 kDaOP448610F:TGCCGAGCTATACCCGATTG
R:GGCCAACACCTTGCTTTCAC		190y = −3.4411x + 26.95495.26%0.9971
TUB-αTubulin α-3 chainOP448611F:CCGAGAGCTGTGTTTGTGGA
R:CGCCGACAGCATTAAACACC		241y = −3.3538x + 29.26298.69%0.9967
UBC5BUbiquitin-conjugating enzyme E2 5BOP448612F:AGTGTTCCTTCCGCAACTTC
R:CGCTTAGCTCTTCGTCGCT		188y = −3.3125x + 25.576100.39%0.9929
SAMDCS-adenosylmethionine decarboxylase proenzymeOP448613F:TGATGCCCTTGAGCCATGTT
R:ACTTGAGTCTTGCCATCGGG		106y = −3.3157x + 29.643100.26%0.9986
MUB1Membrane-anchoredubiquitin-foldprotein 1OP448614F:CTTTCCGGCGGCTACAAGT
R:ACATCACAAAGAGGGCTCTGG		173y = −3.3915x + 28.57697.18%0.9979
CYPcyclophilinOP448615F:CCGGAGGCGAATCCATCTAC
R:AGACCCAACTTTCTCGACGG		224y = −3.4893x + 30.28293.46%0.9968
UBQpolyubiquitinOP448616F:CTGCACTTGGTCCTTCGTTTG
R:GGAGCACCAAATGAAGAGTGG		244y = −3.5635x + 29.45990.82%0.9998
Eifeukaryotic translation initiation factorOP448617F:TGCTTGGCAATGGTCGATGT
R:CCAGCAGCAATCCAAACCTTC		100y = −3.5559x + 26.51991.08%0.9992
EF-1αElongation factor 1-αOP448618F:CCCGGTGACAATGTGGGATT
R:GGCATAGCCGTTTCCGATCT		165y = −3.4148x + 29.23996.27%0.9922
ARFA1EADP-ribosylation factor A1EOP448619F:CCCGGTGACAATGTGGGATT
R:GGCATAGCCGTTTCCGATCT		165y = −3.3776x + 25.79597.73%0.9997
GAPC1Glyceraldehyde-3-phosphatedehydrogenase C subunit 1OP448620F:CGTGTCCCTACACCCAATGT
R:TCGGTTGCTGTAACCCCATT		270y = −3.4221x + 29.24795.98%0.9932
TUA4Tubulin α-4 chainOP448621F:ATCTCAACCGCCTTGTCTCG
R:CTCGGTACATGAGGCAGCAA		285y = −3.2557x + 28.359102.84%0.9946
ARPactin-related protein 2OP448622F:GGAGACGCATGTGCTGATTTG
R:GTGACACCATCACCAGAGTCA		317y = −3.3273x + 25.98899.78%0.9913
GAPDHglyceraldehyde-3-phosphate dehydrogenaseOP448623F:TGGAAGGATCGGTCGTTTGG
R:CACCCCACGGAATTTCCTCA		239y = −3.4732x + 29.39194.05%0.9964
60s60s ribosomal protein l36OP448624F:CAACAAGGGCCACAAAACCA
R:TTGACGATCTCGCGAACGAA		102y = −3.5047x + 28.51492.90%0.9937
MYB1R1Transcription factor MYB1R1OM963000F:CGCACCCATAACAACTCCCA
R:TCTTTCACTTACTCCCTCTTCAGC		178y = −3.4389x + 32.57495.34%0.9926
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Zhu, Z.; Yu, J.; Tang, X.; Xiong, A.; Sun, M. Selection and Validation of Reference Genes in Different Tissues of Okra (Abelmoschus esculentus L.) under Different Abiotic Stresses. Genes 2023, 14, 603. https://doi.org/10.3390/genes14030603

AMA Style

Zhu Z, Yu J, Tang X, Xiong A, Sun M. Selection and Validation of Reference Genes in Different Tissues of Okra (Abelmoschus esculentus L.) under Different Abiotic Stresses. Genes. 2023; 14(3):603. https://doi.org/10.3390/genes14030603

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Zhu, Zhipeng, Jianxiang Yu, Xinhui Tang, Aisheng Xiong, and Miao Sun. 2023. "Selection and Validation of Reference Genes in Different Tissues of Okra (Abelmoschus esculentus L.) under Different Abiotic Stresses" Genes 14, no. 3: 603. https://doi.org/10.3390/genes14030603

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