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Article

Screening of Reference miRNA of Different Early- and Late-Flowering Tree Peony Varieties

by
Jiajia Shen
1,
Xiaohui Wang
2,
Yuying Li
1,
Lili Guo
1,* and
Xiaogai Hou
1,*
1
College of Agriculture, Henan University of Science and Technology, Luoyang 471023, China
2
Luoyang Academy of Agricultural and Forestry Sciences, Luoyang 471002, China
*
Authors to whom correspondence should be addressed.
Plants 2023, 12(14), 2629; https://doi.org/10.3390/plants12142629
Submission received: 7 June 2023 / Revised: 7 July 2023 / Accepted: 10 July 2023 / Published: 12 July 2023
(This article belongs to the Special Issue Flower Germplasm Resource and Genetic Breeding)
Browse Figures
Versions Notes

Abstract

:
miRNA plays an important role in plant growth and development and in response to various stresses. Quantitative real-time PCR (qRT-PCR) technology is often used to detect the expression level of miRNAs and genes by comparing with reference genes. In order to screen out the optimal reference miRNAs in different tree peony varieties, the petals of 42 different early- and late-flowering tree peony varieties were used as experimental materials, and geNorm, NormFinder, Bestkeeper, and RefFinder software were used to evaluate the stability of 16 candidate reference miRNAs. The results showed that the average Ct values of all candidate reference miRNAs were between 15.34 ± 0.29 and 32.64 ± 0.38. The optimal number of reference miRNAs was four, which were PsPC-5p-19095, PsPC-3p-51259, PsmiR159a, and PsPC-3p-6660 in geNorm. The stability of PsPC-3p-6660 was the highest in the analysis results of NormFinder software. Among the analysis results of Bestkeeper software, PsMIR319-p5 has the highest stability. Among the results of comprehensive evaluation and analysis of several software using RefFinder, the candidate reference miRNA with the highest stability was PsPC-3p-6660. When PsPC-3p-6660 was used as the reference miRNA, the expression of PomiR171 and PomiR414 in response to different flowering times of tree peony was relatively stable in 42 tree peony varieties, indicating that PsPC-3p-6660 was stable and reliable. The results of this study provide a reference miRNA for studying the expression changes of miRNA in different tree peony varieties and further exploring the regulatory mechanism of miRNA in different peony varieties.

1. Introduction

Tree peony (Paeonia suffruticosa Andrews.) is a perennial woody plant, which belongs to Paeoniaceae, Paeonia, section Moutan. As a kind of multi-purpose plant with economic value, tree peony has high ornamental value because of its rich varieties, gorgeous colors, and diverse flowers [1,2]. In addition, it also has high nutritional and medicinal values because its root can be used as medicine and the seeds can be used to extract oil, which is beneficial to human health [3,4,5,6]. The natural flowering period of this tree is around April, which is a one-flower-a-year plant, and a few varieties have the phenomenon of second flowering [7,8]. Among them, middle-flowering tree peony varieties dominate, and early-flowering and late-flowering varieties are few. The characteristics of a relatively short and concentrated flowering period seriously affect the ornamental value of tree peony and limit the development of tree peony industry. Therefore, understanding the internal molecular mechanism of tree peony flowering may be helpful to discover new genes related to flowering traits, which can be used to regulate the flowering time of tree peony, prolong the group flowering period, and increase economic benefits.
At present, the research on tree peony mainly focuses on the construction of tissue culture rapid propagation system [9], cultivation and domestication [10], genetic diversity [11], seed oil analysis [12], and gene cloning [13]. In recent years, with the rapid development of biotechnology, transcriptome analysis [14], miRNA identification [15], and other related molecular biology studies have been gradually carried out, and gene expression correlation studies have gradually become the focus of research.
Gene expression analysis is an important tool to elucidate complex regulatory processes, such as genetics, signal transduction, and metabolic pathways in the plant life cycle [16]. Real-time quantification PCR can be used to verify gene expression levels and the reliability of sequencing results [17,18]. Compared with conventional real-time PCR (RT-PCR), quantitative real-time PCR (qRT-PCR) has the characteristics of high sensitivity, strong specificity, and low cost and has been widely used in gene expression level analysis [19,20]. However, the stability and accuracy of qRT-PCR results are affected by many factors, among which the reference gene plays a role in the correction and standardization of the results and is one of the important factors affecting the reliability of qRT-PCR results. Transcription levels of reference genes may change in different species, tissues, treatments, and developmental stages [21]. Therefore, it is necessary to select the most suitable reference gene according to the test requirements. At present, some software and algorithms for evaluating the stability of reference genes have been developed, such as geNorm, NormFinder, and Bestkeeper, and these algorithms have been successfully used to screen out the reference genes related to biotic and abiotic stress, growth, and development in plants, such as Zea mays [22], Hylocereus sp. [23], Allium sativum [24], and Itoh peony [25].
With the application of high-throughput sequencing and other technologies, a large number of microRNAs (miRNAs) have been found in many higher plants. miRNA is a class of small single-stranded non-coding RNA molecules with a length of about 20–24 nucleotides that regulate gene expression at the post-transcriptional level through sequence complementation [26]. The first plant miRNA was first discovered in Arabidopsis in 2002 [27]. Studies have shown that miRNAs have been reported to exist widely in a variety of plants, and miRNAs are involved in regulating plant growth and development, morphogenesis, and environmental stress response, such as drought, salt, and temperature [28,29]. It has been found that miRNAs play an important role in the flowering mechanism by regulating the expression of flowering genes [30]. For example, miR172 induced rice flowering by inhibiting the expression of OsIDS1 and SNB of the target gene AP2 family [31]. miR159 could delay the flowering time by reducing the expression of the target gene MYB transcription factor in Arabidopsis [32]. Overexpression of miR171 could inhibit the expression of the target gene Scarecrow-like (SCL), which in turn leads to a late-flowering phenotype in Hordeum vulgare transgenic plants, accompanied by a smaller and translucent anther phenotype [33]. Therefore, it is also of great significance to explore miRNA related to tree peony flowering so as to realize the regulation of tree peony flowering. However, there was only one report on reference miRNA in tree peony for different bud development processes, flower development processes, and different tissues [34], but the screening of reference miRNA among different early- and late-flowering tree peony varieties has not been reported.
In this study, the petals of 42 different early- and late-flowering tree peony varieties were used as experimental materials. Primers of U6 (snRNA) and 15 miRNAs from small RNA sequencing were designed, resulting in a total of 16 candidate genes selected as candidate reference genes for miRNA expression normalization. qRT-PCR was used to detect and analyze the expression stability of 16 candidate reference miRNAs in 42 different tree peony varieties. The stability of 16 candidate genes was evaluated by geNorm, NormFinder, Bestkeeper, and RefFinder, and the optimal reference miRNA was verified. The results of this study will provide reference miRNAs for the expression of other target miRNAs in different tree peony varieties.

2. Results

2.1. miRNAs Quality Analysis

The miRNAs of 42 different early- and late-flowering tree peony varieties were extracted from the petals. The integrity of miRNAs was detected by 3% agarose gel electrophoresis, and the concentration and purity of miRNAs were detected by NanoDrop 1000 spectrophotometer (Implen, Munchen, Germany). The miRNA of each sample had a single bright and clear band at about 100 bp, indicating that the integrity of miRNAs was good. The NanoDrop 1000 spectrophotometer detection results showed that the A260/280 nm of each miRNA sample was 1.8–2.1, indicating that the purity of miRNAs was good, which met the requirements of subsequent tests.

2.2. Primers Specificity Analysis of 16 Candidate miRNAs from Different Tree Peony Varieties

The primer melting curves of 16 candidate reference miRNAs were analyzed by qRT-PCR, and the results showed that the melting curves of all primers showed a single peak, indicating that the primers of the 16 candidate reference miRNAs had good specificity and could be used for the evaluation of the reference miRNAs (Figure 1).

2.3. Ct Value Analysis of 16 Candidate miRNAs of Different Tree Peony Varieties

The expression levels of miRNAs in 16 candidate reference miRNAs were analyzed by qRT-PCR. The results showed that the average Ct values of 16 candidate reference miRNAs in the petals of 42 tree peony varieties ranged from 15.34 ± 0.29 to 32.64 ± 0.38. The average Ct values in descending order were PsmiR159a, U6, PsmiR11607, PsmiR171k-3p, PsPC-3p-51259, PsmiR858-3p, PsPC-3p-23386, PsPC-5p-9292, PsPC-5p-19095, PsPC-3p-13662, PsPC-3p-6660, PsMIR319-p5, PsPC-3p-15676, PsPC-3p-18408, PsPC-3p-70893, and PsMIR11609-p5. PsmiR159a had the lowest average Ct value, indicating that it had the highest transcription level. PsmiR11609-p5 had the highest average Ct value, indicating that it had the lowest transcription level. PsmiR171k-3p had the largest difference in the Ct value of a single gene among different samples, while PsPC-3p-6660 had the smallest difference in the Ct value (Figure 2).

2.4. The Expression Stability of 16 Candidate Reference miRNAs Analyzed by geNorm

The geNorm software mainly analyzes the expression stability of candidate reference genes through the expression stability M. The larger the M value, the more unstable the gene, and the smaller the M value, the more stable the gene. The analysis results showed that the stability order from high to low of 16 candidate reference miRNAs was PsPC-5p-19095 = PsPC-3p-51259 > PsmiR159a > PsPC-3p-6660 > PsMIR319-p5 > PsPC-5p-9292 > PsMIR11609-p5 > PsPC-3p-18408 > PsPC-3p-23386 > PsPC-3p-70893 > PsPC-3p-13662 > PsmiR858-3p > U6 > PsmiR11607 > PsPC-3p-15676 > PsmiR171k-3p, indicating that PsPC-5p-19095 and PsPC-3p-51259 had the best stability, and the most unstable expression was PsmiR171k-3p (M = 1.09) (Figure 3). Pairwise variations can be used to determine the optimal number of reference genes. The results of geNorm histogram analysis showed that V4/5 = 0.13 < 0.15, indicating that the optimal number of references for miRNA quantitative expression was 4, which were PsPC-5p-19095, PsPC-3p-51259, PsmiR159a, and PsPC-3p-6660 (Figure 4).

2.5. The Expression Stability of 16 Candidate Reference miRNAs Analyzed by NormFinder

NormFinder can rank the stability of candidate reference genes according to the stability value S of gene expression. NormFinder can not only compare differences between candidate genes but also calculate variations between sample groups. The smaller the S value, the more stable the gene, otherwise the worse the stability of the reference gene. According to the evaluation results of NormFinder software, the most stable expression of the 16 candidate reference miRNAs was PsPC-3p-6660 (S = 0.37), followed by PsPC-5p-19095 (S = 0.39), and the most unstable was PsmiR171k-3p (S = 0.79) (Table 1). The expression stability of 16 candidate reference genes in different early- and late-flowering tree peony varieties was ranked from high to low as follows: PsPC-3p-6660, PsPC-5p-19095, PsMIR319-p5, PsPC-3p-51259, PsPC-5p-9292, PsMIR11609-p5, PsPC-3p-18408, PsmiR159a, PsPC-3p-23386, PsPC-3p-70893, PsPC-3p-13662, U6, PsmiR858-3p, PsmiR11607, PsPC-3p-15676, and PsmiR171k-3p. Because NormFinder software can only screen out one best reference gene, PsPC-3p-6660 was selected as the best reference base in the analysis using NormFinder software. The results were similar to geNorm software but with slight differences.

2.6. The Expression Stability of 16 Candidate Reference miRNAs Analyzed by Bestkeeper

The Bestkeeper software mainly evaluates the stability of the reference genes by calculating the standard deviation (SD) and the covariance (CV). The smaller the SD value, the better the gene stability. The analysis results showed that the SD values of most candidate miRNAs were less than 1, among which PsMIR319-p5 had the best stability, followed by PsPC-33-6660. Only one candidate miRNA had an SD value greater than 1, namely, PsmiR171k-3p, indicating that it had the lowest stability (Table 2).

2.7. The Expression Stability of 16 Candidate Reference miRNAs Analyzed by RefFinder

Since the analysis results of different software have certain differences, the RefFinder software can be used for the final comprehensive analysis. RefFinder analysis is a comprehensive evaluation and ranking of the stability of reference genes obtained by geNorm, NormFinder, and Bestkeeper software. The analysis results showed that there were great similarities between the results obtained by RefFinder and the other three software, but there were also some differences. Among the petals of 42 different early- and late-flowering tree peony varieties, PsPC-3p-6660 was the most stable and consistent with the results of NormFinder, followed by PsPC-5p-19095. PsmiR171k-3p was the most unstable miRNA, and the stability order from high to low is as follows: PsPC-3p-6660, PsPC-5p-19095, PsMIR319-p5, PsPC-3p-51259, PsPC-5p-9292, PsMIR11609-p5, PsmiR159a, PsPC-3p-23386, PsPC-3p-18408, PsPC-3p-70893, PsmiR11607, PsPC-3p-13662, PsmiR858-3p, U6, PsPC-3p-15676, and PsmiR171k-3p. There was consistency with the most unstable miRNA obtained by the other three software, indicating that the results were reliable (Table 3).

2.8. Validation of Reference miRNAs

In order to verify the reliability of the selected reference miRNAs, we selected the most stable reference, PsPC-3p-6660, and the most unstable reference, PsmiR171k-3p, as reference miRNAs to analyze the expression patterns of PomiR171 and PomiR414 in response to different flowering times in different tree peony varieties. The results showed that when PsPC-3p-6660 was used as the reference miRNA, the relative expression levels of PomiR171 and PomiR414 in different tree peony varieties were relatively stable, and the average Ct values of 42 petal samples were 0.24 ± 0.03~2.91 ± 0.55 and 0.08 ± 0.03~2.06 ± 0.68, respectively. When PsmiR171k-3p was used as the reference miRNA, the relative expression levels of PomiR171 and PomiR414 in different tree peony varieties were quite different. The average Ct values of 42 petal samples were 0.60 ± 0.03~26.30 ± 2.49 and 0.56 ± 0.31~32.18 ± 3.40, respectively (Figure 5). Since PomiR171 and PomiR414 are differentially expressed miRNAs in response to different flowering stages of tree peony, when PsPC-3p-6660 with high stability was used as the reference miRNA, the results of relatively stable expression of PomiR171 and PomiR414 among the samples were more reliable. When PsmiR171k-3p with the lowest stability was used as the reference miRNA, the results of relatively stable expression of PomiR171 and PomiR414 among the samples were not reliable. Therefore, PsPC-3p-6660 is the most suitable reference miRNA of different early- and late-flowering tree peony varieties.

3. Discussion

Because the differences between species, materials, and tissues will affect the stability and reliability of quantitative results, it is necessary to select appropriate reference genes for data standardization during qRT-PCR. In this study, 42 early- and late-flowering tree peony varieties were used as materials, and the best reference miRNA was successfully screened out, which provided suitable reference miRNA for miRNA research of different tree peony varieties.
In general, the same reference does not have stability all the time. Under drought stress, the most suitable reference miRNAs in roots and leaves of Glycine max were miR156a and miR167a [35]. In Allium sativum, the most stable reference miRNA for different explants was AsmiR168a-5p and for different genotypes, the most stable reference miRNA was AsmiR159a-1 [36]. In Juglans regia, the most suitable reference miRNAs for flower buds at different differentiation stages were jre-miR394a, jre-miR159a, and jre-miR159c, and the most suitable reference miRNAs for leaf buds at different differentiation stages were 5.8S rRNA and jre-miRn3 [37]. The most stable reference miRNA combinations during seed development in Brassica napus were miR167-1_2, miR11-1, and miR159-1 [38].
In this study, the stability analysis of 16 candidate reference miRNAs in 42 different tree peony varieties showed that PsPC-5p-19095 and PsPC-3p-51259 had the highest stability in the geNorm software. PsPC-3p-6660 had the highest stability in the NormFinder software. PsMIR319-p5 has the highest stability in the Bestkeeper software. In the results of comprehensive evaluation and analysis using RefFinder, the candidate reference miRNA with the highest stability was PsPC-3p-6660, which was consistent with the results of NormFinder and slightly different from the analysis results of geNorm and Bestkeeper software. The difference between the analysis results of each software has also appeared in the previous research results [39]. The reason for the difference in the stability of each candidate reference miRNA may be due to the large number of candidate reference miRNAs selected. Secondly, the difference in mathematical algorithms between different software will also affect the stability ranking of the test results to a certain extent. The results of several software showed that the stability of PsmiR171k-3p was the lowest, indicating that the candidate miRNA was not suitable as the reference gene in different tree peony varieties.
At present, U6 is one of the most common reference genes, which is used as the reference gene in miRNA quantitative expression analysis of various plants, such as Vitis vinifera [40], Brassica oleracea [41], and Jatropha curcas [42]. Studies have shown that U6 is not stable in all cases [43]. U6 is a suitable reference miRNA for different tissues and stem tissues under drought stress in Hylocereus polyrhizus [44]. However, in the evaluation of the expression stability of walnut flower bud and leaf bud differentiation, tissue parts, and varieties, U6 had the worst stability and was not suitable as the reference miRNA. This phenomenon also exists in plants, such as wheat and longan [45]. In this study, U6 was used as a candidate reference miRNA, and its stability was low in the four software, which was not suitable as the reference miRNA for different tree peony varieties.

4. Materials and Methods

4.1. Plant Materials

A total of 42, i.e., 21 early-flowering and 21 late-flowering, tree peony varieties were selected from the experimental farm of Henan University of Science and Technology (112°24′52.05″ E, 34°35′45.91″ N) as experimental material for the evaluation of the expression stability of candidate reference genes from April to May 2021. The samples were frozen with liquid nitrogen and then stored in a −80 °C refrigerator for later use (Table 4).

4.2. Primers Design of Candidate Reference miRNAs

In this study, U6 and 15 miRNAs with relatively stable expression levels were selected from the small RNA sequencing data of Paeonia ostii ‘Fengdan’, Mutant plants of Paeonia ostii ‘Fengdan’, and Paeonia suffruticosa ‘Lianhe’ (CNGBdb, accession number CNP0002984) at the blooming stage (BS), initial flowering stage (IF), full blooming stage (FB), and decay stage (DE) in our laboratory, including 6 known miRNAs (PsmiR159a, PsmiR858-3p, PsMIR11609-p5, PsmiR171k-3p, PsmiR11607, and PsMIR319-p5) and 9 novel miRNAs (PsPC-3p-70893, PsPC-3p-18408, PsPC-5p-19095, PsPC-3p-51259, PsPC-3p-23386, PsPC-3p-13662, PsPC-3p-15676, PsPC-3p-6660, and PsPC-5p-9292).
qRT-PCR primers were designed using poly (A) by Primer Premier 5.0 software. Primers should avoid special structures such as dimers and hairpin structures to prevent adverse effects on test results. Then, they were sent to Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China) for synthesis (Table 5).

4.3. Isolation of miRNA and Synthesis of cDNA

miRNAs were isolated from the petals of 42 tree peony varieties using the miRcute Plant miRNA Isolation Kit (Tiangen, Beijing, China). Then, miRcute Plus miRNA First-Strand cDNA Synthesis Kit (Tiangen, China) was used to synthesize the first strand. Reaction system (20 μL): Total miRNA 2 μL, 2× miRNA RT Reaction Buffer 10 μL, miRNA RT Enzyme Mix 2 μL, and RNase-free ddH2O 6 μL. The reaction procedure was as follows: 42 °C for 60 min and 95 °C for 3 min.

4.4. Primers Specificity Analysis

Using cDNA as a template, qRT-PCR primers were used for PCR amplification using a 2× PCR Taq Master Mix (Blue Dye) (Nobelab Biotech, Beijing, China). Reaction system (25 μL): 2× PCR Taq Master Mix 12.5 μL, Primer F (10 μM) 1 μL, Primer R (10 μM) 1 μL, cDNA 2 μL, ddH2O 8.5 μL. The reaction procedure was as follows: 95 °C for 2 min; 95 °C 30 s, 60 °C 30 s, 72 °C 10 s, 35 cycles; 72 °C for 2 min.
cDNAs were diluted in a gradient of 51, 52, 53, 54, and 55. An SYBR® Green Premix Pro Taq HS qPCR Kit (Accurate Biology, Changsha, China) was used for qRT-PCR. Reaction system (20 μL): 2× SYBR® Green Pro Taq HS Premix 10 μL, Primer F (10 μM) 0.4 μL, Universal Primer R (10 μM) 0.4 μL, cDNA 2 μL, ddH2O 7.2 μL. The reaction procedure was as follows: 95 °C 30 s; 95 °C 5 s, 60 °C 30 s, 40 cycles. Three technique replicates were set for each sample.

4.5. Expression Stability Analysis of 16 Candidate Reference miRNAs

The stability of 16 candidate reference miRNAs was evaluated and ranked using geNorm, NormFinder, and Bestkeeper software, respectively. And then the online tool RefFinder (http://blooge.cn/RefFinder/, accessed on 13 February 2023) [46,47] was used to comprehensively evaluate and analyze the results obtained by the above three software. Finally, the most suitable reference miRNA among different tree peony varieties was screened.

4.6. Validation of Candidate Reference miRNAs

The most stable and the unstable miRNAs were selected as the reference miRNAs, and the expression patterns of PomiR171 and PomiR414 (CNGBdb, accession number CNP0002984) in response to different flowering times of tree peony in different tree peony varieties were analyzed. Three technique replicates were set for each sample, and the qRT-PCR primers were given in Table 6. The expression levels of the miRNA were calculated using the formula of 2−ΔΔCt, and statistical analysis and mapping were performed using SPSS 22.1 (one-way ANOVA, Duncan’s test, p < 0.05) and Origin 2018.

5. Conclusions

Using petals of 42 different early- and late-flowering tree peony varieties as experimental materials, the expression stability of 16 candidate reference genes was evaluated and analyzed by geNorm, NormFinder, Bestkeeper, and RefFinder software using qRT-PCR. The results showed that the average Ct values of all candidate reference miRNAs were between 15.34 ± 0.29 and 32.64 ± 0.38. In the geNorm software, PsPC-5p-19095 and PsPC-3p-51259 had the highest stability, and the optimal number of reference miRNAs was four, which were PsPC-5p-19095, PsPC-3p-51259, PsmiR159a, and PsPC-3p-6660. The stability of PsPC-3p-6660 was the highest in the analysis results of the NormFinder software. Among the analysis results of the Bestkeeper software, PsMIR319-p5 had the highest stability. Among the results of comprehensive evaluation and analysis of several software using RefFinder, the candidate reference miRNA with the highest stability was PsPC-3p-6660. Therefore, PsPC-3p-6660 can be used as the reference miRNA for miRNA studies of different tree peony varieties. This provides a reference miRNA for the study of other miRNAs in different tree peony varieties.

Author Contributions

Conceptualization, L.G. and X.H.; methodology, software, data curation, and writing—original draft preparation, J.S.; formal analysis, investigation, and data curation, X.W. and Y.L.; writing—review and editing, supervision, project administration, and funding acquisition, L.G. and X.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Outstanding Youth Fund Project of the Natural Science Foundation of Henan Province, grant number 202300410119; the Innovation Scientists and Technicians Troop Construction Projects of Henan Province, grant number 212101510003; and the Colleges and Universities Science and Technology Innovation Talent Support Plan of Henan Province, grant number 22HASTIT036.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are listed in the text and its additional files.

Conflicts of Interest

The authors declare no conflict of interest.

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Plants 12 02629 g001 550
Figure 1. Primer melting curves analysis of 16 candidate reference miRNAs in different tree peony varieties. A single peak indicates that the primer specificity is good.
Figure 1. Primer melting curves analysis of 16 candidate reference miRNAs in different tree peony varieties. A single peak indicates that the primer specificity is good.
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Plants 12 02629 g002 550
Figure 2. Expression levels of 16 candidate reference miRNAs in different tree peony varieties. The part of the box is the concentrated range of the Ct value, the ‘□’ in the box is the average, the ‘◈’ in the box is the outliers, the horizontal line in the box is the median, and the upper and lower side lines of the box are the upper four digits and the lower four digits, respectively.
Figure 2. Expression levels of 16 candidate reference miRNAs in different tree peony varieties. The part of the box is the concentrated range of the Ct value, the ‘□’ in the box is the average, the ‘◈’ in the box is the outliers, the horizontal line in the box is the median, and the upper and lower side lines of the box are the upper four digits and the lower four digits, respectively.
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Figure 3. Expression stability values M of 16 candidate reference miRNAs analyzed by geNorm.
Figure 3. Expression stability values M of 16 candidate reference miRNAs analyzed by geNorm.
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Figure 4. Pairwise variations of 16 candidate reference miRNAs.
Figure 4. Pairwise variations of 16 candidate reference miRNAs.
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Plants 12 02629 g005 550
Figure 5. Expression analysis of PomiR171 (A) and PomiR414 (B) in different tree peony varieties using reference miRNAs (PsPC-3p-6660 and PsmiR171k-3p). The numbers 1–42 represent 42 tree peony varieties.
Figure 5. Expression analysis of PomiR171 (A) and PomiR414 (B) in different tree peony varieties using reference miRNAs (PsPC-3p-6660 and PsmiR171k-3p). The numbers 1–42 represent 42 tree peony varieties.
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Table
Table 1. NormFinder analysis of 16 candidate reference miRNAs ranking.
Table 1. NormFinder analysis of 16 candidate reference miRNAs ranking.
miRNAStability ValueStandard ErrorRank
PsPC-3p-66600.370.031
PsPC-5p-190950.390.032
PsMIR319-p50.400.033
PsPC-3p-512590.420.034
PsPC-5p-92920.430.035
PsMIR11609-p50.440.036
PsPC-3p-184080.460.037
PsmiR159a0.480.038
PsPC-3p-233860.540.049
PsPC-3p-708930.540.0410
PsPC-3p-136620.600.0411
U60.620.0412
PsmiR858-3p0.640.0413
PsmiR116070.660.0414
PsPC-3p-156760.720.0515
PsmiR171k-3p0.790.0516
The smaller the Stability Value and Standard Error, the more stable the gene is.
Table
Table 2. Bestkeeper analysis of 16 candidate reference miRNAs ranking.
Table 2. Bestkeeper analysis of 16 candidate reference miRNAs ranking.
miRNAsSDCVRank
PsMIR319-p50.592.151
PsPC-3p-66600.672.432
PsmiR116070.753.913
PsPC-5p-190950.762.764
PsMIR11609-p50.772.555
PsPC-3p-233860.773.046
PsPC-3p-708930.782.647
PsPC-5p-92920.803.098
PsPC-3p-512590.803.219
PsmiR858-3p0.823.2610
PsPC-3p-184080.842.9711
PsPC-3p-136620.843.0612
PsmiR159a0.895.1413
U60.955.1114
PsPC-3p-156760.983.5015
PsmiR171k-3p1.104.9916
The smaller the SD and CV values, the more stable the gene is.
Table
Table 3. RefFinder analysis and ranking of 16 candidate reference miRNAs.
Table 3. RefFinder analysis and ranking of 16 candidate reference miRNAs.
RankmiRNAsGeomean of Ranking Values
1PsPC-3p-66601.68
2PsPC-5p-190952.00
3PsMIR319-p52.59
4PsPC-3p-512593.36
5PsPC-5p-92926.06
6PsMIR11609-p56.24
7PsmiR159a7.07
8PsPC-3p-233867.98
9PsPC-3p-184088.10
10PsPC-3p-708938.91
11PsmiR116079.53
12PsPC-3p-1366211.24
13PsmiR858-3p11.93
14U612.72
15PsPC-3p-1567615.00
16PsmiR171k-3p16.00
The smaller the Geomean of Ranking Values, the more stable the gene is.
Table
Table 4. 42 tree peony varieties.
Table 4. 42 tree peony varieties.
Varieties (Number)
Early-flowering tree peony varietiesPaeonia suffruticosa ‘Taoyanhong’ (1)‘Huolianjindan’ (2)‘Pinghuqiuyue’ (3)
‘Jiaohong’ (4)‘Dapengzhanchi’ (5)‘Cangjiao’ (6)
‘Lanju’ (7)‘Haibo’ (8)‘Lanhudie’ (9)
‘Lanyueliang’ (10)‘Zhaofen’ (11)‘Yuncuicaidie’ (12)
‘Jianshifen’ (13)‘Xishifen’ (14)‘Mantianxing’ (15)
‘Jingyu’ (16)‘Suxinbai’ (17)‘Xueyuanhongxing’ (18)
Paeonia rockii ‘Zibanbai’ (19)‘Erqiao’ (20)Paeonia ostii ‘Fengdan’ (21)
Late-flowering tree peony varieties‘Feiyanhongzhuang’ (22)‘Shouanhong’ (23)‘Haitangzhengrun’ (24)
‘Daduolan’ (25)‘Doulv’ (26)‘Shenheizi’ (27)
‘Zihongzhengyan’ (28)Paeonia rockii ‘Mingmou’ (29)Paeonia rockii ‘Xianemao’ (30)
Paeonia rockii ‘Baizhangbing’ (31)Paeonia rockii ‘Baiyanwei’ (32)Paeonia rockii ‘Yubanxiuqiu’ (33)
‘Shuixinfenhe’ (34)Paeonia rockii ‘Fenzouchou’ (35)‘Xiuqiuhong’ (36)
‘Zilouxiangcui’ (37)‘Ziyan’ (38)‘Jinge’ (39)
‘Jinzhi’ (40)‘Baiwangshizi’ (41)‘Lianhe’ (42)
Table
Table 5. Primers for qRT-PCR.
Table 5. Primers for qRT-PCR.
miRNAsmiRNA Sequence (5′-3′)Primer F Sequence (5′-3′)
PsPC-3p-70893TTCAACCCAACTTCGTCTCTTCGCCTTCAACCCAACTTCGTC
PsPC-3p-18408TTACGTTGCCTTTCTTCCTCTGGGCGTTGCCTTTCTTCCTCTG
PsmiR159aTTTGGATTTAAGGGAGCTCTAGCGGGTTTGGATTGAAGGGAG
PsPC-5p-19095AAAAGTCGGATCGCCAGCAACATCCGTCGGATCGCCAGCAACAT
PsPC-3p-51259AAATCTCGCCCAGACCCATGCCAAATCTCGCCCAGACCCAT
PsmiR858-3pCTCGTTGTCTGTTCGACCTTGCCGTGTTGTCTGTCCGACCTTG
PsMIR11609-p5TGAACCCTTTTTCCTACACTCCGCCTGAACCCTTTTTCCTAC
PsPC-3p-23386TGTGCTCTCCCTCTTCGTCAAGCCTTGTGCTCTCCCTCTTCGT
PsPC-3p-13662CGCCTCTTCCCTTGATTAAACGCCTTCGCCTCTTCCCTTGATT
PsmiR171k-3pTTGAGCCGCGCCAATATCACTCTCAGCCGCGCCAATATCAC
PsmiR11607ACTCGGTTGTCTGACAGACGCTTGGCACTCGGTTGTCTGAC
PsMIR319-p5CTGCCATCTCATGCATAAGGTCGCCTGCCATCTCATCCATAAG
PsPC-3p-15676AATCTCGTCCAGACCTATGGCCGCCAATCTCGTCCAGACCTAT
PsPC-3p-6660AACACGGGAAGTAGGCATTGCAGCGGAACACGGGAAGTAGGCATTG
PsPC-5p-9292CAACATCTTCGGCATCTAATCAGAGCTGGCAACATCTTCGGCATCT
U6 ACAGAGAAGATTAGCATGGCC
Table
Table 6. Primers for qRT-PCR.
Table 6. Primers for qRT-PCR.
miRNAsmiRNA Sequence (5′-3′)Primer F Sequence (5′-3′)
PomiR171TTGAGCCGCGTCAATATCTCTGGTCAGCCGCGTCAATATCTCT
PomiR414TCATCATCGTCATCATCTTCCCCGCATCATCGTCATCATCTTCC
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Shen, J.; Wang, X.; Li, Y.; Guo, L.; Hou, X. Screening of Reference miRNA of Different Early- and Late-Flowering Tree Peony Varieties. Plants 2023, 12, 2629. https://doi.org/10.3390/plants12142629

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Shen J, Wang X, Li Y, Guo L, Hou X. Screening of Reference miRNA of Different Early- and Late-Flowering Tree Peony Varieties. Plants. 2023; 12(14):2629. https://doi.org/10.3390/plants12142629

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Shen, Jiajia, Xiaohui Wang, Yuying Li, Lili Guo, and Xiaogai Hou. 2023. "Screening of Reference miRNA of Different Early- and Late-Flowering Tree Peony Varieties" Plants 12, no. 14: 2629. https://doi.org/10.3390/plants12142629

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