Selection and Validation of Reference Genes for RT-qPCR Analysis in Aegilops tauschii (Coss.) under Different Abiotic Stresses
1
Key Laboratory of Weed Biology and Management, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
2
Institute of Environment and Ecology, School of Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2021, 22(20), 11017; https://doi.org/10.3390/ijms222011017
Submission received: 13 August 2021
/
Revised: 18 September 2021
/
Accepted: 19 September 2021
/
Published: 13 October 2021
(This article belongs to the Section Molecular Genetics and Genomics)
Abstract
:Aegilops tauschii (Coss.) is an aggressive and serious annual grass weed in China. Its DD genome is a rich source of genetic material and performs better under different abiotic stress conditions (salinity, drought, temperature, etc.). Reverse-transcribed quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for reference gene selection and validation. This work aimed to evaluate the stability of reference gene expression in Ae. tauschii under different abiotic stresses (salinity, drought, hot, and cold) and developmental stages (seedling and development). The results show that the ubiquitin-conjugating enzyme E2 36-like (UBC36) and protein microrchidia 2-like (HSP) are the most stable genes under control and salinity conditions, respectively. Under drought stress conditions, UBC36 is more stable as compared with others. Glyceraldehyde-3-phosphate dehydrogenase (GADPH) is the most stable reference gene during heat stress conditions and thioredoxin-like protein (YLS) under cold stress condition. Phosphate2A serine/threonine-protein phosphatase 2A (PP2A) and eukaryotic translation initiation factor 3 (ETIF3) are the most stable genes at seedling and developmental stages. Intracellular transport protein (CAC) is recommended as the most stable gene under different abiotic stresses and at developmental stages. Furthermore, the relative expression levels of NHX1 and DREB under different levels of salinity and drought stress conditions varied with the most (HSP and UBC36) and least (YLS and ACT) stable genes. This study provides reliable reference genes for understanding the tolerance mechanisms in Ae. tauschii under different abiotic stress conditions.
1. Introduction
Aegilops tauschii Coss. (2n = 2x = 14) is a wild species and reported as a donor D genome in cultivate wheat (Triticum aestivum L.=AABBDD) [1,2]. It has the widest geographic distribution in many wheat-growing countries from Turkey to west Afghanistan and Central Asia, and it has adapted to different diversified conditions, performing better against heat, drought, and salinity stresses [3]. This weed spreads in China’s main wheat production areas and competes for light, water, and resources [4]. D genome of Ae. tauschii is a rich source of genetic material, and many traits can be used for wheat improvement programs, including drought, heat, and salinity [5,6]. Researchers are trying to evaluate these traits and have identified the genes for wheat crop improvement. Gene expression analysis is important in crop improvement programs and helps understand plants’ molecular mechanisms against different abiotic stresses [7].
Reverse-transcribed quantitative PCR (RT-qPCR) is deemed a highly suitable strategy for gene expression studies and can detect target genes’ expression. The RT-qPCR method is the most important technique and a common way to examine genes’ relative expression patterns in many crops and weeds [8]. Different challenges in gene expression studies through RT-qPCR relate to reference gene expression stability and to RNA and complementary deoxyribonucleic acid (cDNA) quality [9]. The selection and the use of an unstable reference gene create problems and unpredictable changes in gene expression studies in RT-qPCR [10]. Different kinds of reference genes, such as glyceraldehyde 3-phosphate dehydrogenase (GADPH), actin (ACT), elongation factor-1 alpha (EF1α), ubiquitin-conjugating enzyme E2 36-like (UBC36), and tubulin (TUB), have been used in many experiments but have not been uniformly expressed in the critical conditions [11]. An ideal and appropriate reference gene presents stability in different plant developmental stages and under different biotic and abiotic stress conditions [12]. Many studies reported in the past demonstrated that there is not a universal reference gene for all plant species and all experiments [13]. ACT has been reported as the most stable in tomato plants but proved least stable in cucumber under salinity stress, while 18S rRNA has been reported as a stable reference gene in rice, though unstable in some other crops [14,15]. Similarly, many other reference genes have been tested in various species. In general, it is necessary to select appropriate reference genes for different species, different treatments, and different experimental conditions [16].
NormFinder, RefFinder, BestKeeper, and geNorm have been developed and used for reference genes evaluation [17]. Because of their different algorithms, geNorm, NormFinder, and BestKeeper produce different results, while RefFinder provides a comprehensive ranking of reference genes [18]. Reference genes for many crops, including wheat, maize, soybean, poplar, eucalyptus, and peanut, have been reported, but the number is small for weeds [19]. Although several molecular studies on abiotic mechanisms in Ae. tauschii have been conducted [20,21], validation of reference genes in this weed have been rarely reported. This study evaluated the stability of several reference genes and validated them under different abiotic conditions and developmental stages. In the current study, we evaluated gene stability and its validation under different abiotic stresses and at developmental stages in Ae. tauschii. We evaluated 12 reference genes—actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), thioredoxin-like protein (YLS), polypyrimidine tract-binding protein homolog 1-like (PTB), protein microrchidia 2-like (HSP), S-adenosyl methionine decarboxylase proenzyme (SAMDC), ubiquitin-conjugating enzyme E2 36-like (UBC36), elongation factor 1 alpha (EF1α), serine/threonine-protein phosphatase 2A (PP2A), eukaryotic translation initiation factor 3 (ET1F3), intracellular transport protein (CAC), and peptidyl-prolyl cis-trans isomerase CYP19-4-like (CYP)—under different abiotic stresses (salinity, drought, heat, and cold stress) and developmental stages (seedling and development).
2. Results
2.1. Reference Genes Selection
Twelve reference genes were selected, including ACT, GAPDH, YLS, PTB, HSP, SAMDC, UBC36, EF1-alpha, PP2A, ET1F3, CAC, and CYP (Table 1). All primers showed amplification specificity by gel electrophoresis (Figure S1). Then, the candidate primer pairs were detected by RT-qPCR based on a standard curve and efficiency. Genes’ R2 were stably greater than 0.98, RT-qPCR efficiency (E = −1 + 10(−1/slope)) between 104% and 117% (Table 1) and annealing temperature of PCR was 48–57. The results under different abiotic stresses and developmental stages in Ae. tauschii was based on the estimation of Cq. The Cq value of each gene was indicated by the cycle at which the PCR amplifications entered the logarithmic phase. The reference genes with lower Cq values were considered to have higher expression than the other genes. The 12 candidate reference genes under different abiotic conditions and developmental stages showed diverse values in their Cq, ranging from 20.11 to 37.59 (Figure 1). Under salinity, the PP2A gene showed significantly higher expression, with Cq values ranging from 23.45 to 26.65 (Figure 1). On the other hand, ACT showed lower expression, with values ranging from 31.90 to 37.05. Under control conditions, Cq values ranged from 23.72 to 25.05, ETIF3 showed high expression, while CYP showed Cq ranging from 35.43 to 37.59. Similarly, under different abiotic stresses and developmental stages (Figure 1a–g), all reference genes showed different Cq values.
2.2. geNorm Analysis
geNorm analysis was used to calculate reference genes’ expression stability (M) under different abiotic stresses and developmental stages. Genes with the lowest M value are considered the most stable genes, while the least stable genes have higher M values. Reference genes with M value < 1.5 can be utilized in further studies (Figure 2). PP2A and CAC were denoted as the most stable genes with an M value of 0.10, while CYP was the least stable gene under control conditions. Under salinity stress conditions, PP2A and HSP were the most stable genes, with M values of 0.06, while YLS was the least stable gene, with an M value of 1.20. UBC36 was recorded as the most stable gene with an M value of 0.15 under drought stress conditions, while the ACT was considered the least stable gene, with an M value of 1.05. Under heat stress conditions, UBC36 was detected as the most stable gene, with an M value of 0.10, while HSP was considered the least stable gene, with a high M value of 1.37. Under cold stress conditions, GADPH and CAC were recorded as the most stable genes, with the lowest M value, while ETIF3 was considered the least stable gene, with a high M value. In the seedling stage, PP2A and ETIF3 were considered the most stable genes, with the lowest M value of 0.01, respectively. On the other hand, ACT was denoted as the least stable gene, with the highest M value of 0.52. At the developmental stage, PP2A and PTB were the most stable genes, with M values of 0.69, respectively, while UBC36 was the least stable gene with an M value of 2.53. Overall, CAC and HSP proved to be the most stable genes, with an M value of 1.61. The optimal number of reference genes required for accurate normalization was computed. All values were obtained for the pairwise variation between consecutive normalization factors.
Pairwise variation (Vn/Vn + 1) in genes to minimize the implementation of qPCR expression analysis with a cutoff value of < 0.15 (Figure 3). Seven to nine reference genes were valid for normalization under different abiotic stresses in Ae. tauschii, while under drought stress treatment and development stages genes showed variations.
2.3. NormFinder Analysis
NormFinder measured the stability value (M) of reference genes using evaluations under different abiotic stresses and developmental stages in Ae. tauschii. (Figure 4). UBC36, PP2A, UBC36, CAC, YLS, PP2A, ETIF3, and PTB were the most stable reference genes under control, salinity, drought, heat and cold, and under both growing (seedling and developmental) stages.
2.4. BestKeeper Analysis
BestKeeper analysis base on standard deviation (SD) and coefficient of variance (CV) was conducted to measure and evaluate the stability of reference genes. The lower SD and CV values are considered more stable. In addition, an SD value of less than 1 is acceptable for reference gene selection. The values with lower SD had higher expression levels and were the most stable, while a higher SD value indicated the least stable reference gene (Table 2). Under different abiotic stresses (control, salinity, drought, heat, cold) and developmental stages, UBC36, CAC, EFI-alpha, SAMDC, GADPH, UBC36, CYP, and HSP had lower SD values and were considered the most stable reference genes, respectively.
2.5. RefFinder Analysis
RefFinder is a web tool that is used to calculate the heterogeneity in the results of geNorm, NormFinder, and BestKeeper. A comprehensive ranking showed that UBC36, HSP, UBC36, UBC36, YLS, PP2A, and ETIF3 were the most stable genes, with lower M values in Ae. tauschii under different abiotic stresses and different developmental stages (Table 3).
2.6. Reference Gene Validation
The expression level of target genes (NHXI and DREB) was determined under different levels of salinity and drought stress conditions. Both most and least stable genes were chosen under salinity and drought stress conditions. Viewing the superiority in stability, HSP was considered further for the expression profiling of NHXI which, compared with expression at 50 Mm and 150 NaCl, the expression level appeared 1.6 times and 5.7 times at 50 and 150 mM NaCl, respectively (Figure 5a), while the expression level of the least stable gene, YLS, was only 1.4 times and 3.1 times, respectively. Under different drought stress conditions, the expression level of the target gene DREB, with most stable reference gene UBC36 under drought C1 = (control, C2 = 75 g L−1 PEG-6000 and C3 = 100 g L−1 PEG-6000) conditions was 4.97, 2.73, and 1.66 times and 1.07 times under different water field capacity treatment, respectively (Figure 5b). However, a difference was presented between the results for the most and least stable reference genes. The expression level of DREB with the least stable gene, ACT, showed 2.16, 1.29, and 0.85 times under different water field capacity treatment.
3. Discussion
Abiotic stresses, such assalinity, drought, heat, and cold, significantly affect plant survival, growth, and biomass production [22,23,24]. Plants have developed various mechanisms against various stresses and significantly alter the gene expression profile [25]. In the qRT-PCR analysis, the evaluation and selection of reference genes and PCR conditions are crucial and important prerequisites of gene expression profile analysis [26]. In the present study, we used 12 reference genes (ACT, GAPDH, YLS, PTB, HSP, SAMDC, UBC36, EF1-alpha PP2A, EF1F3, CAC, and CYP) and examined stability and validation under different abiotic stress conditions and developmental stages. The RT-qPCR efficiency of our study was between 104% and 117%; a similar range was used for ACT and GADPH, with 91% and 118% [27]. These reference genes were also used in different plants for gene expression profiling. Expression stability evaluated by the geNorm, NormFinder, and BestKeeper was not constant, as has been reported in previous studies [28]. In our study, PP2A and HSP were reported to be the most stable reference genes under salinity stress by geNorm analysis, but with NormFinder and geNorm they performed unsatisfactorily. Therefore, the comprehensive ranking through RefFinder results provides help in selecting appropriate reference genes. Through geNorm, reference genes were determined based on the pairwise variation between sequentially ranked genes (Vn/Vn+1), with the cutoff value of 0.15. When the Vn/n+1 value was below 0.15, no additional genes were required for accurate normalization [29].
UBC36 and GADPH were chosen for normalization and UBC36 showing high stability in Arabidopsis thaliana, on other hand, its expression profile in soybean and rice varied under different developmental stages [30,31]. HSP and PP2A showed more stability in our study, and GADPH was found to be the least stable reference gene under salinity stress conditions. In Cajanus cajan, HSP90 and UBC showed stability under salinity stress conditions, and several studies recognized them as stable stress candidate genes specifically used as internal controls [32]. Many studies have focused on investigating the resistance mechanism in different plants under abiotic stress conditions by using RT-qPCR. However, a very small number of studies have reported reference gene selection in weeds under abiotic conditions [33]. UBC36 appeared to be more stable under drought conditions in Ae. tauschii, with low M values according to NormFinder. Similarly, in chickpea plants, EF1α and UBC36 were reported to be the most stable reference genes under drought stress conditions. Our analyses also robustly suggested that ACT and YLS are the least stable reference genes under drought stress conditions in Ae. tauschii. The housekeeping gene GADPH and UBC36 was reported to be the most stable gene in Triticum durum under different abiotic stress conditions (including drought) [34]. Under cold stress conditions, HEL was reported to be the most stable reference gene in Robinia pseudoacacia [35]. Considering the results in our study under cold stress conditions, YLS was found to be the most stable gene in Ae. tauschii. GAPDH plays a vital role in carbohydrate metabolism and DNA repair [36]. GADPH showed more stability under cold stress and had ranked first in the BestKeeper analysis, which showed that our results precisely agreed with prior results.
Similarly, in Robinia pseudoacacia and Camellia sinensis, GADPH showed the most stability under various abiotic stresses [35,37]. PP2A and ETIF3 played important roles and had the most stable genes under the Ae. tauschii seedling and development stages. Likewise, UBC36 and EF1α retained the most stable genes in Avena sativa under various abiotic stresses [38]. In total, CAC and HSP were the most important and showed more stability in Ae. tauschii under various abiotic stress conditions. Likewise, in goosegrass, eIF-4 and ALS showed stability under different herbicide stresses [19]. We also observed such apparent effects in our research, although previous studies showed differing results. Considering the importance of the validation of reference genes, we selected the target gene NHX1 to evaluate the expression level under salinity stress conditions [39]. Comparison between most and least stable genes showed differences in fold change expression in the target genes. With HSP as a reference gene, NHX1 showed significantly higher expression under 50 mM NaCl and 150 mM NaCl. Similarly, HSP showed more stability in pigeon pea under salt stress conditions [32]. The expression level of DREB was evaluated with the most and least stable genes UBC36 and ACT, respectively, under drought stress conditions. The expression level of DREB showed a higher fold change with the most stable reference gene, which showed that selection of a suitable reference gene is essential for the accuracy of the RT-qPCR results. Similarly, validations of reference genes were analyzed by the expression level of the internal control target gene [40]. These results showed that HSP and UBC36 were the most stable reference genes under salinity and drought stress conditions. This study is important and will aid in obtaining accurate results at the gene expression level in Ae. tauschii. Similarly, reference gene validation was analyzed by the expression level of the internal control target gene [41]. Our results show that HSP is the most stable reference gene under salinity stress conditions. This study will aid in attaining accurate results by using the most appropriate reference genes to find the relative gene expression levels under different kinds of stresses in Ae. tauschii. Moreover, our study is limited to the selection of some known genes for the validation of reference genes in Ae. tauschii. It needs further study to deeply analyze the reference genes using RNA-Sequencing, which can further verify and can provide a variety of reference genes under abiotic stress and developmental stages in Ae. tauschii.
4. Materials and Methods
4.1. Plant Materials
Ae. tauschii genotypes were collected from five different provinces (Shandong, Shanxi, Shaanxi, Hebei, and Henan) of China [41]. Seeds were sown in plastic pots containing sandy clay under greenhouse conditions, and plants were selected based on their performance on abiotic stress. When the seedlings reached the two- to three-leaf stages, different stresses were applied, including salinity stress (50 mM and 150 mM NaCl) and drought stress (75 g L−1 PEG-6000 and 100 g L−1 PEG-6000) applied in Ae. tauschii with four replications [42,43]. For heat and cold stress, plants were exposed in the chamber at −4 °C and 40 °C for five days and then transferred to an environmental growth chamber maintained at 27 ± 1 °C with four replications. For every stress application, a controlled environment was maintained throughout the experiments. Leaf samples were harvested after 10 days of stress application and stored at −80 °C. For developmental stages, the leaves were harvested at the seedling (at the two-leaf stage) and the development stage (at the four- to five-leaf stages) and stored at −80 °C.
4.2. RNA Isolation and cDNA Synthesis
RNA was extracted by RNA prep Pure Plant Kit (Cat. #DP432, Tiangen Biotech Beijing CO., LTD, Beijing, China) by using 1 g of leaf samples. Genomic DNA was completely eliminated using RNase-Free DNase I (Tiangen Biotech Beijing CO., LTD, Beijing, China), according to the manufacturer’s instructions. The integrity was checked by 1.0% agarose gel electrophoresis. The purity and concentration of RNA was estimated with a NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The cDNA was synthesized by using the Fast Quant RT kit (Cat#KR116-02, Tiangen Biotech Beijing CO., LTD, Beijing, China) with 1 μg of total RNA. All samples were stored at −80 °C.
4.3. Reference Genes Selection, Amplification, and Primer Design
Twelve reference genes were tested, including ACT, GAPDH, YLS, PTB, HSP, SAMDC, UBC36, EF1-alpha, PP2A, ET1F3, CAC, and CYP. Reference gene sequences were obtained from the National Center for Biotechnology (NCBI) database for primer design. Forward and reverse primers (5′-3′) of reference genes were designed by Beacon Designer software and used within the exon region because primers are specific for the amplification of cDNA that contains intron between exon-exon junctions. Primer sequence, amplicon length, melting temp, and RT-qPCR efficiency are presented in Table 1.
4.4. RT-qPCR Analysis
An ABI 7500 RT-qPCR machine was used to detect reference genes’ expression level by utilizing SYBR green (Applied Biosystems, Foster City, CA, USA). The total volume of 20 μL of PCR mix containing 10 μL of SYBR Green PCR Master Mix, 1 μL of cDNA, 0.6 μL of each primer, 0.6 μL dye, and 7.2 μL of ddH2O was used. The cycling conditions of the RT-qPCR were used to obtain the melt curve temperature: 10 min at 95 °C, 40 cycles of 95 °C for 15s, 57–58 °C for 32 s, with an increase of 0.5 °C every 5 s conditions and extended at 72 °C for 30 s. Three technical and four biological replicates were used.
4.5. Data Analysis
BestKeeper, NormFinder, and geNorm were used to evaluate reference gene stability. RefFinder was used as a web tool to calculate the comprehensive ranking of reference genes under different abiotic stresses and at developmental stages. The Cq value of RT-qPCR was used in geNorm, BestKeeper, and NormFinder. GeNorm renewed the Cq values into relative ones by the formula 2−ΔCt (ΔCt = the corresponding Cq value—minimum Cq). GeNorm also calculates the pairwise variation (Vn/Vn+1) with a recommended cut-off value of 0.15.
4.6. NHX1 and DREB Expression under Salinity and Drought Stress Condition
For reference gene stability, the expression level of the NHX1 (Na+ transporters involved in plant salt tolerance) and DREB (dehydration-responsive element-binding protein 1E) genes was determined under different salinity (50 and 150 mM NaCl) and drought stress (75 g L−1 PEG-6000 and 100 g L−1 PEG-6000) conditions. Primers for NHX1 were designed in RT-qPCR with Beacon Designer software. NHX1 was amplified using the primer pair NHX1-F: CCGCAACCAAGTAGAGAAG / NHX1-R: GAGCATCATAAGAGCAACCT, while DREB was amplified with the pair DREB-F: CGAGTCTGTTGATGAGTCT / DREB-R 5′ TTCTGTAGTAAGTGCTTGCTA, respectively. The expression levels of NHX1 and DREB were normalized by using the most and least stable genes under salinity and drought stress conditions.5. Conclusions
We determined the reference genes’ stability under abiotic stresses and at developmental stages of Ae. tauschii. Results show that HSP, UBC36, YLS, UBC36, PP2A, and ETIF3 were the most stable genes under the salinity, drought, cold, heat, seedling, and development stages of Ae. tauschii, respectively. This study will promote future studies in gene expression and will help better understand the responses of Ae. tauschii under different abiotic stresses and developmental stages. Results of this study will be helpful in Ae. tauschii gene expression studies, which will lead to a wheat crop improvement program.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/ijms222011017/s1.
Author Contributions
Conceptualization, X.L.; methodology, A.A., H.Y., and H.C.; software, H.Y. and Adeel Abbas; writing—original draft, A.A.; H.Y.; J.C.; and P.H.; writing—review and editing, X.L. All authors have read and agreed to the published version of the manuscript.
Funding
This study was funded by the National Key Research and Development Program of China (2016YFD0300701) and the Earmarked Fund for China Agriculture Research System (CARS-25).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available in manuscript and Supplementary Materials.
Acknowledgments
We thank the Plant Protection and Quarantine Stations of each county in Henan, Shandong, Hebei, Shaanxi, Shanxi, Hubei, Anhui, Jiangsu, Sichuan, and Xinjiang provinces as well as in Beijing and Tianjin for providing a random countryside survey of Aegilops tauschii.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Ling, H.-Q.; Ma, B.; Shi, X.; Liu, H.; Dong, L.; Sun, H.; Cao, Y.; Gao, Q.; Zheng, S.; Li, Y. Genome sequence of the progenitor of wheat A subgenome Triticum urartu. Nature 2018, 557, 424–428. [Google Scholar] [CrossRef] [Green Version]
- Miki, Y.; Yoshida, K.; Mizuno, N.; Nasuda, S.; Sato, K.; Takumi, S. Origin of wheat B-genome chromosomes inferred from RNA sequencing analysis of leaf transcripts from section Sitopsis species of Aegilops. DNA Res. 2019, 26, 171–182. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kalia, B.; Wilson, D.L.; Bowden, R.L.; Singh, R.P.; Gill, B.S. Adult plant resistance to Puccinia triticina in a geographically diverse collection of Aegilops tauschii. Genet. Resour. Crop. Evol. 2017, 64, 913–926. [Google Scholar] [CrossRef]
- Yu, H.; Li, X. Distribution of Aegilops tauschii Coss. China and its research progress. J. Weed Sci. 2017, 36, 1–7. [Google Scholar]
- Kishii, M. An update of recent use of Aegilops species in wheat breeding. Front. Plant Sci. 2019, 10, 585. [Google Scholar] [CrossRef] [Green Version]
- Sohail, Q.; Inoue, T.; Tanaka, H.; Eltayeb, A.E.; Matsuoka, Y.; Tsujimoto, H. Applicability of Aegilops tauschii drought tolerance traits to breeding of hexaploid wheat. Breed. Sci. 2011, 61, 347–357. [Google Scholar] [CrossRef] [Green Version]
- Brozynska, M.; Furtado, A.; Henry, R.J. Genomics of crop wild relatives: Expanding the gene pool for crop improvement. Plant Biotechnol. J. 2016, 14, 1070–1085. [Google Scholar] [CrossRef]
- Li, T.; Wang, J.; Lu, M.; Zhang, T.; Qu, X.; Wang, Z. Selection and Validation of Appropriate Reference Genes for qRT-PCR Analysis in Isatis indigotica Fort. Front. Plant Sci. 2017, 8, 1139. [Google Scholar] [CrossRef] [Green Version]
- Xie, J.; Liu, T.; Khashaveh, A.; Yi, C.; Liu, X.; Zhang, Y. Identification and Evaluation of Suitable Reference Genes for RT-qPCR Analysis in Hippodamia variegata (Coleoptera: Coccinellidae) Under Different Biotic and Abiotic Conditions. Front. Physiol. 2021, 12. [Google Scholar] [CrossRef] [PubMed]
- Kozera, B.; Rapacz, M. Reference genes in real-time PCR. J. Appl. Genet. 2013, 54, 391–406. [Google Scholar] [CrossRef] [Green Version]
- Kundu, A.; Patel, A.; Pal, A. Defining reference genes for qPCR normalization to study biotic and abiotic stress responses in Vigna mungo. Plant Cell Rep. 2013, 32, 1647–1658. [Google Scholar] [CrossRef]
- Schmidt, G.W.; Delaney, S.K. Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress. Mol. Genet. Genom. 2010, 283, 233–241. [Google Scholar] [CrossRef]
- Bakir, Y.; Eldem, V.; Zararsiz, G.; Unver, T. Global transcriptome analysis reveals differences in gene expression patterns between nonhyperhydric and hyperhydric peach leaves. Plant Genome 2016, 9. [Google Scholar] [CrossRef] [Green Version]
- Wan, H.; Zhao, Z.; Qian, C.; Sui, Y.; Malik, A.A.; Chen, J. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal. Biochem. 2010, 399, 257–261. [Google Scholar] [CrossRef]
- Zhu, X.; Li, X.; Chen, W.; Chen, J.; Lu, W.; Chen, L.; Fu, D. Evaluation of new reference genes in papaya for accurate transcript normalization under different experimental conditions. PLoS ONE 2012, 7, e44405. [Google Scholar] [CrossRef]
- Zhao, J.; Zhou, M.; Meng, Y. Identification and validation of reference genes for RT-qPCR analysis in switchgrass under heavy metal stresses. Genes 2020, 11, 502. [Google Scholar] [CrossRef] [PubMed]
- Li, L.; Li, N.; Fang, H.; Qi, X.; Zhou, Y. Selection and validation of reference genes for normalisation of gene expression in Glehnia littoralis. Sci. Rep. 2020, 10, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Garrido, J.; Aguilar, M.; Prieto, P. Identification and validation of reference genes for RT-qPCR normalization in wheat meiosis. Sci. Rep. 2020, 10, 1–12. [Google Scholar] [CrossRef]
- Chen, J.; Huang, Z.; Huang, H.; Wei, S.; Liu, Y.; Jiang, C.; Zhang, J.; Zhang, C. Selection of relatively exact reference genes for gene expression studies in goosegrass (Eleusine indica) under herbicide stress. Sci. Rep. 2017, 7, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Suneja, Y.; Gupta, A.K.; Bains, N.S. Stress adaptive plasticity: Aegilops tauschii and Triticum dicoccoides as potential donors of drought associated morpho-physiological traits in wheat. Front. Plant Sci. 2019, 10, 211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, X.; Bai, S.; Li, L.; Han, X.; Li, J.; Zhu, Y.; Fang, Y.; Zhang, D.; Li, S. Comparative transcriptome analysis of two Aegilops tauschii with contrasting drought tolerance by RNA-Seq. Int. J. Mol. Sci. 2020, 21, 3595. [Google Scholar] [CrossRef]
- Fahad, S.; Hussain, S.; Bano, A.; Saud, S.; Hassan, S.; Shan, D.; Khan, F.A.; Khan, F.; Chen, Y.; Wu, C. Potential role of phytohormones and plant growth-promoting rhizobacteria in abiotic stresses: Consequences for changing environment. Environ. Sci. Pollut. Res. 2015, 22, 4907–4921. [Google Scholar] [CrossRef] [PubMed]
- Mahajan, S.; Tuteja, N. Cold, salinity and drought stresses: An overview. Arch. Biochem. Biophys. 2005, 444, 139–158. [Google Scholar] [CrossRef] [PubMed]
- Parihar, P.; Singh, S.; Singh, R.; Singh, V.P.; Prasad, S.M. Effect of salinity stress on plants and its tolerance strategies: A review. Environ. Sci. Pollut. Res. 2015, 22, 4056–4075. [Google Scholar] [CrossRef] [PubMed]
- Baillo, E.H.; Kimotho, R.N.; Zhang, Z.; Xu, P. Transcription factors associated with abiotic and biotic stress tolerance and their potential for crops improvement. Genes 2019, 10, 771. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, Y.; Zhang, G.; Chen, Y.; Bai, Q.; Gao, C.; Zeng, L.; Li, Z.; Cheng, Y.; Chen, J.; Sun, X. Selection of reference genes for qPCR analyses of gene expression in ramie leaves and roots across eleven abiotic/biotic treatments. Sci. Rep. 2019, 9, 1–13. [Google Scholar] [CrossRef]
- Akbarabadi, A.; Ismaili, A.; Kahrizi, D.; Firouzabadi, F.N. Validation of expression stability of reference genes in response to herbicide stress in wild oat (Avena ludoviciana). Cell. Mol. Biol. 2018, 64, 113–118. [Google Scholar] [CrossRef]
- Wang, M.; Lu, S. Validation of suitable reference genes for quantitative gene expression analysis in Panax ginseng. Front. Plant Sci. 2016, 6, 1259. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Le, D.T.; Nishiyama, R.; Watanabe, Y.; Tanaka, M.; Seki, M.; Ham, L.H.; Yamaguchi-Shinozaki, K.; Shinozaki, K.; Tran, L.-S.P. Differential gene expression in soybean leaf tissues at late developmental stages under drought stress revealed by genome-wide transcriptome analysis. PLoS ONE 2012, 7, e49522. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Li, J.; Liu, A.; Zou, J.; Zhou, X.; Xiang, J.; Rerksiri, W.; Peng, Y.; Xiong, X.; Chen, X. Expression profile in rice panicle: Insights into heat response mechanism at reproductive stage. PLoS ONE 2012, 7, e49652. [Google Scholar] [CrossRef] [Green Version]
- Sinha, P.; Saxena, R.K.; Singh, V.K.; Krishnamurthy, L.; Varshney, R.K. Selection and validation of housekeeping genes as reference for gene expression studies in pigeonpea (Cajanus cajan) under heat and salt stress conditions. Front. Plant Sci. 2015, 6, 1071. [Google Scholar] [CrossRef] [Green Version]
- Malone, J.M.; Morran, S.; Shirley, N.; Boutsalis, P.; Preston, C. EPSPS gene amplification in glyphosate-resistant Bromus diandrus. Pest Manag. Sci. 2016, 72, 81–88. [Google Scholar] [CrossRef] [PubMed]
- Kiarash, J.G.; Wilde, H.D.; Amirmahani, F.; Moemeni, M.M.; Zaboli, M.; Nazari, M.; Moosavi, S.S.; Jamalvandi, M. Selection and validation of reference genes for normalization of qRT-PCR gene expression in wheat (Triticum durum L.) under drought and salt stresses. J. Genet. 2018, 97, 1433–1444. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Abbas, M.; Wen, Y.; Niu, D.; Wang, L.; Sun, Y.; Li, Y. Selection and validation of reference genes for quantitative gene expression analyses in black locust (Robinia pseudoacacia L.) using real-time quantitative PCR. PLoS ONE 2018, 13, e0193076. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ray, S.; Maunsell, J.H. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 2011, 9, e1000610. [Google Scholar] [CrossRef] [Green Version]
- Hao, X.; Horvath, D.P.; Chao, W.S.; Yang, Y.; Wang, X.; Xiao, B. Identification and evaluation of reliable reference genes for quantitative real-time PCR analysis in tea plant (Camellia sinensis (L.) O. Kuntze). Int. J. Mol. Sci. 2014, 15, 22155–22172. [Google Scholar] [CrossRef]
- Tajti, J.; Pál, M.; Janda, T. Validation of reference genes for studying different abiotic stresses in oat (Avena sativa L.) by RT-qPCR. Plants 2021, 10, 1272. [Google Scholar] [CrossRef]
- Kulkarni, M.; Soolanayakanahally, R.; Ogawa, S.; Uga, Y.; Selvaraj, M.G.; Kagale, S. Drought response in wheat: Key genes and regulatory mechanisms controlling root system architecture and transpiration efficiency. Front. Chem. 2017, 5, 106. [Google Scholar] [CrossRef] [Green Version]
- Hossain, M.S.; Ahmed, R.; Haque, M.S.; Alam, M.M.; Islam, M.S. Identification and validation of reference genes for real-time quantitative RT-PCR analysis in jute. BMC Mol. Biol. 2019, 20, 1–13. [Google Scholar] [CrossRef]
- Zhang, S.; Zeng, Y.; Yi, X.; Zhang, Y. Selection of suitable reference genes for quantitative RT-PCR normalization in the halophyte Halostachys caspica under salt and drought stress. Sci. Rep. 2016, 6, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Abbas, A.; Yu, H.; Cui, H.; Li, X. Effect of drought stress on chlorophyll fluorescence, and biomass portioning of Aegilops tauschii L. Appl. Ecol. Environ. Res. 2019, 17, 1071–1082. [Google Scholar] [CrossRef]
- Abbas, A.; Yu, H.; Cui, H.; Li, X. Genetic diversity and synergistic modulation of salinity tolerance genes in Aegilops tauschii Coss. Plants 2021, 10, 1393. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Expression profile of reference genes under control (a), salinity (b), drought (c), heat (d), cold (e), seedling (f), and development stage (g). Boxes indicate the 5th/95th percentiles, lines across the boxes depict the medians, squares. ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation factor 1 alpha, SAMDC = S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation factor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Figure 1.
Expression profile of reference genes under control (a), salinity (b), drought (c), heat (d), cold (e), seedling (f), and development stage (g). Boxes indicate the 5th/95th percentiles, lines across the boxes depict the medians, squares. ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation factor 1 alpha, SAMDC = S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation factor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Figure 2.
Average expression stability (M) calculated by geNorm under control, abiotic stress conditions, and different developmental stages in Ae. tauschii. (a) Control, (b) salinity, (c) drought, (d) heat, (e) cold, (f) seedling (g) development, and (h) total. ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation factor 1 alpha, SAMDC= S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation factor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Figure 2.
Average expression stability (M) calculated by geNorm under control, abiotic stress conditions, and different developmental stages in Ae. tauschii. (a) Control, (b) salinity, (c) drought, (d) heat, (e) cold, (f) seedling (g) development, and (h) total. ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation factor 1 alpha, SAMDC= S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation factor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Figure 3.
Pairwise variation (V) of reference genes required under control, salinity, drought, cold, heat, seedling stage, and at the developmental stage.
Figure 3.
Pairwise variation (V) of reference genes required under control, salinity, drought, cold, heat, seedling stage, and at the developmental stage.
Figure 4.
Average expression stability (M) calculated by NormFinder under control, abiotic stress conditions and different developmental stages in Ae. tauschii. (a) control, (b) salinity, (c) drought, (d) heat, (e) cold, (f) seedling (g) development and (h) total. ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation factor 1 alpha, SAMDC= S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation factor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Figure 4.
Average expression stability (M) calculated by NormFinder under control, abiotic stress conditions and different developmental stages in Ae. tauschii. (a) control, (b) salinity, (c) drought, (d) heat, (e) cold, (f) seedling (g) development and (h) total. ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation factor 1 alpha, SAMDC= S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation factor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Figure 5.
Relative expression level of target genes (NHX1 and DREB) under salinity and drought stress, whereas salinity (S1 = 50 mM NaCl and S2 = 150 mM NaCl) (a) and drought (C1 = control, C2 = 75 g L−1 PEG-6000 and C3 = 100 g L−1 PEG-6000) (b) conditions.
Figure 5.
Relative expression level of target genes (NHX1 and DREB) under salinity and drought stress, whereas salinity (S1 = 50 mM NaCl and S2 = 150 mM NaCl) (a) and drought (C1 = control, C2 = 75 g L−1 PEG-6000 and C3 = 100 g L−1 PEG-6000) (b) conditions.
Table 1.
Primer sequence (forward and reverse 5′-3′), amplified characteristics, and 12 reference gene details for RT-qPCR.
Table 1.
Primer sequence (forward and reverse 5′-3′), amplified characteristics, and 12 reference gene details for RT-qPCR.
| Symbol | Gene Description | Gene Symbol in NCBI | Forward Primer (5′-3′) | Reverse Primer (5′-3′) | Amplicon Length (bp) | Correlation Coefficient | RT-qPCR Efficiency |
|---|---|---|---|---|---|---|---|
| ACT | Actin | LOC109759322 | TTGCCTTGGATTATGAACA3′ | GATGGCTGGAACAGAACTT3′ | 109 | 0.98 | 106% |
| GAPDH | Glyceraldehyde-3-phosphate dehydrogenase | LOC109783816 | TACTTCACAGCCGATAGC | CCGATTGTTGGACGATACT | 129 | 0.99 | 107.85% |
| EF1-alpha | Elongation factor 1 alpha | LOC109759403 | ATTGGTGGCATTGGAACT | TCTCAACAGACTTAACCTCAG | 115 | 0.99 | 111.60% |
| SAMDC | Phosphate2Aserine/threonine-protein phosphatase 2A | LOC109775250 | TACTTCACAGCCGATAGC | CCGATTGTTGGACGATACT | 114 | 0.98 | 117.12% |
| UBC36 | Ubiquitin-conjugating enzyme E2 36-like | LOC109787298 | GTATCACATAGACAGCATCATC | AGCATCCAGGACATTCAA | 124 | 0.99 | 109.07% |
| CYP | Peptidyl-prolyl cis-trans isomerase CYP19-4-like | LOC109782168 | GGTTAAGCCATCCTCCTT | CTCGTAGTTACAGTGGTGAT | 116 | 0.98 | 111.44% |
| PTB | Polypyrimidine tract-binding protein homolog 1-like | LOC109755882 | GACGATTCAAGCACGATTCT | TTGTTCCAGCCGATTGTTG | 112 | 0.98 | 107.76% |
| CAC | Intracellular transport protein | LOC109744258 | CACGCTGCTCACTGTTAC | AATCTGGCATCCTTCTACTTCA | 92 | 0.98 | 112.24% |
| PP2A | Serine/threonine protein phosphatase 2A | LOC109772007 | CTGTCACATAGGTAGAGTAGAT | CACGCAAGATGGAGTAAC | 128 | 0.99 | 113.60% |
| ETIF3 | Eukaryotic translation initiation factor 3 | LOC109750994 | AGCCTCTTCATCTTCTTCA | TTGGTCTGGATAGCATTGA | 92 | 0.99 | 117.75% |
| HSP | Protein microrchidia 2-like | LOC109787360 | CTGTATCTGAGGTTCTTGGT | CTACTCGGTGGTCAAACT | 101 | 0.97 | 111.19% |
| YLS | Thioredoxin-like protein | LOC109773417 | GTCAACGGTGATGTTCTTCT | ACAGTCTCCACAATGTCAAC | 103 | 0.99 | 104.74% |
Table 2.
Reference genes expression stability ranked by BestKeeper under different abiotic stresses and at developmental stages in Ae. tauschii.
Table 2.
Reference genes expression stability ranked by BestKeeper under different abiotic stresses and at developmental stages in Ae. tauschii.
| Gene | Control | Salinity | Drought | Heat | Cold | Seedling Stage | Development Stage | Total |
|---|---|---|---|---|---|---|---|---|
| PP2 | 0.5 | 0.29 | 0.54 | 2.76 | 1.87 | 0.42 | 2.09 | 2.55 |
| CAC | 0.46 | 0.06 | 0.49 | 1.85 | 1.49 | 0.6 | 1.97 | 1.43 |
| ETIF3 | 0.52 | 0.45 | 0.65 | 3.68 | 1.63 | 0.43 | 1.55 | 3.37 |
| SAMDC | 0.24 | 0.22 | 0.71 | 0.18 | 1.16 | 0.17 | 1.54 | 2.67 |
| UBC36 | 0.2 | 0.18 | 0.59 | 1.44 | 1.17 | 0.04 | 2.5 | 1.52 |
| EF1-alpha | 0.22 | 0.48 | 0.32 | 1.33 | 1.08 | 0.46 | 1.71 | 2.25 |
| HSP | 0.24 | 0.26 | 0.62 | 2.26 | 1.56 | 0.09 | 1.4 | 1.23 |
| GADPH | 0.42 | 1.16 | 0.46 | 1.78 | 0.73 | 0.13 | 1.1 | 1.53 |
| PTB | 0.39 | 0.51 | 1.04 | 1.71 | 1.32 | 0.27 | 2.1 | 1.51 |
| ACT | 0.52 | 0.38 | 2.27 | 2 | 1.17 | 0.97 | 1 | 1.54 |
| YLS | 0.64 | 2.21 | 1.03 | 0.58 | 1.04 | 0.45 | 1.85 | 1.75 |
| CYP | 0.9 | 0.17 | 0.38 | 1.66 | 1.27 | 0.97 | 0.81 | 2.24 |
ACT = actin, GAPDH = glyceraldehyde-3-phosphate dehydrogenase, EF1-alpha = elongation fac-tor 1 alpha, SAMDC= S-adenosylmethionine decarboxylase proenzyme, UBC36 = ubiquitin-conjugating enzyme E2 36-like, CYP = peptidyl-prolyl cis-trans isomerase CYP19-4-like, PTB = polypyrimidine tract-binding protein homolog 1-like, CAC = intracellular transport protein, PP2A = serine/threonine protein phosphatase 2A, ETIF3 = eukaryotic translation initiation fac-tor3, HSP = protein microrchidia 2-like, YLS = thioredoxin-like protein.
Table 3.
Reference gene expression stability ranked by RefFinder under different abiotic stresses and developmental stages in Ae. tauschii.
Table 3.
Reference gene expression stability ranked by RefFinder under different abiotic stresses and developmental stages in Ae. tauschii.
| Rank | Control | Salinity | Drought | Heat | Cold | Seedling Stage | Development Stage | Total |
|---|---|---|---|---|---|---|---|---|
| 1 | UBC36 1.78 | HSP 1.78 | UBC36 1.57 | UBC36 1.78 | YLS 1.78 | PP2A 1.57 | ETIF3 2.06 | CAC 1.68 |
| 2 | SAMDC 2.38 | UBC36 2.28 | SAMDC 3.22 | SAMDC 2.38 | SAMDC 2.83 | ETIF3 2.3 | PTB 2.58 | PTB 1.73 |
| 3 | EF1-alpha 3.46 | PP2A 2.45 | EF1-alpha 3.36 | EF1-alpha 3.46 | ACT 3.41 | YLS 4.12 | PP2A 3.98 | HSP 2.24 |
| 4 | HSP 3.98 | CAC 3.50 | HSP 3.98 | HSP 3.98 | UBC36 3.66 | EF1-alpha 4.56 | EF1-alpha 3.98 | GADPH 3.87 |
| 5 | PP2A 4.28 | ACT 4.60 | GADPH 4.28 | PP2A 4.28 | GADPH 4.41 | PTB 5 | CYP 4.41 | UBC36 4.43 |
| 6 | CAC 4.41 | CYP 4.74 | CYP 4.36 | CAC 4.41 | PTB 4.60 | UBC36 5.62 | SAMDC 4.68 | YLS 5.63 |
| 7 | ETIF 5.89 | SAMDC 6.29 | CAC 4.53 | ETIF 5.89 | EF1-alpha 5.18 | SAMDC 5.63 | GADPH 6.4 | ACT 6.74 |
| 8 | GADPH 6.62 | PTB 7.52 | ETIF3 7.09 | GADPH 6.62 | CAC 5.63 | HSP 6 | CAC 6.92 | EF1-alpha 8.24 |
| 9 | PTB 7.3 | ETIF3 8.74 | PP2A 7.54 | PTB 7.3 | CYP 8.21 | GADPH 6.05 | HSP 6.9 | CYP 8.97 |
| 10 | ACT 9.74 | EF1-alpha 9.74 | PTB 10.24 | ACT 9.74 | ETIF3 10.24 | CAC 7.84 | ACT 7.18 | SAMDC 9.97 |
| 11 | YLS 11 | GADPH 11 | YLS 10.74 | YLS 11 | PP2A 11.24 | CYP 11 | YLS 8.94 | PP2A 10.74 |
| 12 | CYP 12 | YLS 12 | ACT 12 | ETIF3 12 | HSP 11.47 | ACT 12 | UBC36 12 | ETIF3 12 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Abbas, A.; Yu, H.; Li, X.; Cui, H.; Chen, J.; Huang, P. Selection and Validation of Reference Genes for RT-qPCR Analysis in Aegilops tauschii (Coss.) under Different Abiotic Stresses. Int. J. Mol. Sci. 2021, 22, 11017. https://doi.org/10.3390/ijms222011017
AMA Style
Abbas A, Yu H, Li X, Cui H, Chen J, Huang P. Selection and Validation of Reference Genes for RT-qPCR Analysis in Aegilops tauschii (Coss.) under Different Abiotic Stresses. International Journal of Molecular Sciences. 2021; 22(20):11017. https://doi.org/10.3390/ijms222011017
Chicago/Turabian StyleAbbas, Adeel, Haiyan Yu, Xiangju Li, Hailan Cui, Jingchao Chen, and Ping Huang. 2021. "Selection and Validation of Reference Genes for RT-qPCR Analysis in Aegilops tauschii (Coss.) under Different Abiotic Stresses" International Journal of Molecular Sciences 22, no. 20: 11017. https://doi.org/10.3390/ijms222011017
APA StyleAbbas, A., Yu, H., Li, X., Cui, H., Chen, J., & Huang, P. (2021). Selection and Validation of Reference Genes for RT-qPCR Analysis in Aegilops tauschii (Coss.) under Different Abiotic Stresses. International Journal of Molecular Sciences, 22(20), 11017. https://doi.org/10.3390/ijms222011017
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
