Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions.

Selecting appropriate reference genes is vital to normalize gene expression analysis in birch (Betula platyphylla) under different abiotic stress conditions using quantitative real-time reverse transcription PCR (qRT-PCR). In this study, 11 candidate birch reference genes (ACT, TUA, TUB, TEF, 18S rRNA, EF1α, GAPDH, UBC, YLS8, SAND, and CDPK) were selected to evaluate the stability of their expression in different tissues and under different abiotic stress conditions. Three statistical algorithms (GeNorm, NormFinder, and BestKeeper) were used to analyze the stability of the 11 candidate reference genes to identify the most appropriate one. The results indicated that EF-1α was the most stable reference gene in different birch tissues, ACT was the most stable reference gene for normal conditions, ACT and TEF were the most stable reference genes for salt stress treatment, TUB was the most stable reference gene for osmotic stress treatment, and ACT was the most appropriate choice in all samples of birch. In conclusion, the most appropriate reference genes varied among different experimental conditions. However, in this study, ACT was the optimum reference gene in all experimental groups, except in the different tissues group. GAPDH was the least stable candidate reference gene in all experimental conditions. In addition, three stress-induced genes (BpGRAS1, BpGRAS16, and BpGRAS19) were chosen to verify the stability of the selected reference genes in different tissues and under salt stress. This study laid the foundation for the selection of appropriate reference gene(s) for future gene expression pattern studies in birch.

Introduction

Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) has become a common technique to detect gene expression levels in the field of molecular biology because of its speed, high efficiency, labor-saving, and sensitivity. It has been widely used in basic biological research [1], food spoilage detection [2], environmental monitoring [3], and diagnosis and treatment of disease [4]. QRT-PCR has strict requirements on the purity and quality of RNA, reverse transcription efficiency, the specificity of the primers, PCR amplification efficiency, and the selection of internal reference genes [5]. Reference genes can have a large influence on the results of relative expression level determination. Thus, selecting ideal reference genes is vital to obtain reliable results. One or more internal reference genes are typically required to normalize qRT-PCR data to obtain more accurate results.

Over the course of an experiment, a reliable internal control should show a minimal change in expression, whereas the expression of a gene of interest may change greatly. Housekeeping genes are often selected because they encode proteins essential to cell viability and therefore are assumed to be expressed similarly in different cell types [6]. Commonly used internal controls include actin (ACT), beta-tubulin (TUB), elongation factor 1 alpha (EF1-α), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) [7-8]. However, increasing numbers of reports have confirmed that the expression of housekeeping genes can vary in different tissues and may be affected by the experimental conditions. For instance, TUB is a wildly used reference gene; a study showed that it was unsuitable as a reference gene in different tissues of Pennisetum glaucum [9]. The TUA (α-tubulin) gene was selected as reference gene in Salicornia europaea under salt stress [10-11]; whereas it was not considered as an ideal reference gene in S. europaea under drought treatment [12]. ACT was selected as a reference gene in wheat and Kosteletzkya virginica under salt stress, but was not suitable as a reference gene in papaya under many experimental conditions or in Salicornia europaea and cucumber under salt stress [12-16]. Therefore, selecting the appropriate reference genes according to different tissues and different experimental conditions is essential.

Studies on the evaluation and validation of reference genes have been conducted in many plant species, especially in herbaceous plants, such as Arabidopsis thaliana [17], Glycine max [18]. Similar studies have been carried out in certain woody plants, such as Morus alba L. [19], Carex rigescens [20]. However, the systematic evaluation and validation of appropriate reference genes in birch (Betula platyphylla) under different abiotic stress conditions has not been reported.

Betula platyphylla, which is a species of deciduous hardwood, is widely distributed in the mid-high mountains of warm temperate regions in the world, including northern China, Russian Far East, Siberia, Mongolia, Northern Korea, and Japan [21]. With the continuous expansion of drought and saline areas worldwide, it is of great significance to cultivate new salt tolerant and drought resistant varieties of birch. Therefore, selection of internal reference genes in birch with stable expression under different abiotic stress conditions plays a key role in studies on stress-related gene expression.

The analysis results of qRT-PCR were affected to varying degrees of influence in reference genes choosing. Some studies show how to choose genes as reference genes to normalized expression data. [22]. Therefore, in this study, eleven candidate reference genes (ACT, TUA, TUB, TEF (Translation elongation factor)), 18S rRNA (18S ribosomal RNA), EF1α, GAPDH, UBC (Ubiquitin-conjugating enzyme E2), YLS8 (Thioredoxin-like protein YLS8), SAND (S-adenosyl methionine decarboxylase), and CDPK (CDPK-related kinase) were selected to evaluate their expression stability in different tissues and under abiotic stress conditions in birch. Three different statistical tools (GeNorm, NormFinder, and BestKeeper) were used to analyze the stability of 11 candidate reference genes and identify the most appropriate reference gene. Our results will facilitate of appropriate reference genes selection for the development of gene expression pattern studies in birch.

Materials and methods

Plant material and stress treatments

In this experiment, open pollinated northeast white birch (B. platyphylla) seeds from the Northeast Forestry University were planted in plastic pots with mixture of turf peat and sand (v/v 3:1), in a growth house where the conditions kept at 70–75% relative humidity, 400 μmol·m-2s-1 light intensity, a 16 h light/8 h dark photocycle at 25±2°C. The 8-week-old birch plants grown in soil were watered with a solution of 2 L 200 mM NaCl or 2 L 300 mM mannitol (distilled water served as the control) for 3, 6, 12, 24, and 48 h, respectively. The root, stem, and leaf tissues from 6 birch seedlings were collected after each treatment. Three independent replications were performed. All the samples were placed at −80°C after freezing in liquid nitrogen for further study.

Total RNA isolation and cDNA synthesis

The total RNA of all samples was isolated using a Universal Plant Total RNA Extraction Kit (Bioteke, Beijing, China) following the manufacturer’s instructions. RNA integrity was checked using 1.5% agarose gel electrophoresis, and the RNA concentration and purity were determined using a NanoDrop 2000 Spectrophotometer (NanoDrop, Thermo Scientific, Waltham, MA, USA). All the RNA samples with A260/A280 ratios = 1.9–2.1 and A260/A230 ratios > 2.0 were used for further study. Then RNA (1 μg) was reversely transcribed into cDNA using a TransScript One-Step gDNA Removal and cDNA Synthesis SuperMIX (TransGen Biotech, Beijing, China) following the manufacturer’s instructions. The synthesized cDNA was stored at −20°C for further study.

Candidate reference gene selection and primer design

Eleven candidate reference genes that are commonly used in qRT-PCR were chosen for this study ACT, TUA, TUB, TEF, 18S rRNA, EF1α, GAPDH, UBC, YLS8, SAND, and CDPK [22-23]. The GenBank accession numbers for the 11 genes, which were obtained from the birch genome data (unpublished), are listed in Table 1. Specific primers for the reference genes were designed using Primer premier 5.0 (Premier Biosoft International, Palo Alto, CA, USA). The primer sequences for qRT-PCR are listed in Table 1.

Table 1

Primer sequences and amplification parameters of 11 candidate reference genes from birch.

Gene symbol	Gene name	Accessionno.	Primers(5′-3′)	Tm	E	Ampliconlength(bp)	Mean Ct	SD	CV (%)
18S rRNA	18S ribosomal RNA	MK388236	F: AACGAACGAGACCTCAGCCTR: ACTCGTTGAATACATCAGTG	61.1853.09	1.97	171	17.39	1.36	14.01
GAPDH	Glyceraldehyde-3-phosphate dehydrogenase	MK388226	F: AAGCTCAATGGCATTGCACTR: TGGAAGAAACATCAGTGCAC	58.7456.26	1.89	205	26.87	2.33	15.64
ACT	Actin	MK388227	F: TGAGAAGAGCTATGAGTTGCR: GTAGATCCACCACTAAGCAC	54.8955.27	1.98	204	22.49	1.03	8.26
TUA	Alpha-tubulin	MK388228	F: ATATCATCCTTGACAACTTCR: GATGCCACATTTGAAGCCAG	50.1857.71	1.94	210	22.80	1.00	7.92
TUB	beta-tubulin	MK388229	F: GTTAGCGAGCAGTTTACAGCR: ACCAACAACCTCTTCTTCTT	57.2154.39	2.01	195	23.22	1.05	8.10
UBC	Ubiquitin-conjugating enzyme E2	MK388230	F: CAAATGACAGTCCCTATGCTR:GGTCAGTAAGCAATGAACAT	55.1253.58	1.99	219	26.05	1.81	12.48
EF-1α	Elongation factor 1-alpha	MK388231	F: GACAACGTTGGCTTCAACGTR: GCAAACTTCACAGCAATGTG	59.6256.43	1.92	203	23.09	1.96	15.27
TEF	Translation elongation factor	MK388232	F: GTACAATGATGAGAACACTGR: TCCATGTCATAGCACTCATC	51.6454.64	1.96	201	22.28	1.41	11.38
SAND	Sadenosyl methionine decarboxylase	MK388233	F: GCATCTAGGACAACCTAGAGR: CCACTATCATGCATAGAAGC	54.3853.65	1.86	213	26.19	1.66	11.40
YLS8	Thioredoxin-like protein YLS8	MK388234	F: GCATCTGTTGCTGAGACAATR: CTCCACAATATCAATGAACT	56.4250.48	1.90	216	23.47	1.53	11.71
CDPK	CDPK-related kinase	MK388235	F: GAAGATGAGCTCATCTACCTR: TAAGTACTGATTGCAGCAGC	53.6655.85	1.93	212	29.80	2.06	12.45

Tm, Melting Temperature; E, PCR amplification efficiency; Ct, cycle threshold; CV, coefficient of variation; SD, standard deviation

Quantitative real time PCR

A TransStart Top Green qPCR SuperMix kit (TransGen Biotech, Beijing, China) was used for qRT-PCR. All qRT-PCR reactions were carried out on Qtower3G (Analytik, Jena, Germany). The qRT-PCR reaction system included: 10 μL of 2 × TransStart Top Green Realtime qPCR SuperMix, 0.4 μL upstream primers, and 0.4 μL downstream primers (10 μM), 2 μL of cDNA templates (100 ng), and 7.2 μL of RNAase-free ddH2O with a volume of 20 μL. The qRT-PCR reaction conditions were as follows: 94°C for 30 s; 45 cycles at 94°C for 12 s, 60°C for 30 s, and 72°C for 40 s; and 79°C for 1 s for plate reading. The melting curve was constructed to verify the specificity of each sequence-specific primer. The PCR amplification efficiency (E) was calculated as E = −1 + 10 (−1/slope) [24], where the slope was derived from a standard curve generated by 10-fold serial dilutions of the mixed cDNA for each primer pair. Three independent replications were performed.

Data analysis

SigmaPlot 10 software (Systat Software, Inc., San Jose, CA, USA) was used to show the cycle threshold (Ct) value distribution of all samples, which can reflect clearly the average expression level of the genes in the samples. Threshold for the Ct values is the machine setting. The average Ct value was calculated using three biological replications. GeNorm [25] and NormFinder [26] software were used to analyze the stabilities of the candidate reference genes’ expression. The mean Ct value must be transformed into relative expression level based on the formula E-ΔCt (-ΔCt = Ct value of each sample-the minimum Ct value) [27]. The E-ΔCt value can be analyzed by GeNorm and NormFinder to obtain the candidate reference gene’s expression stability. GeNorm can determine the stability of each candidate reference gene through calculating the M value according to its expression. When the M value is less than 1.5, the gene can be used as an appropriate reference gene. Moreover, the lower the M value, the more stable the expression of the candidate reference gene. NormFinder can rank the stability of candidate reference genes expression. Moreover, it can identify the best reference gene among all candidate reference genes. The BestKeeper [28] program analyzed the pairing correlation under a given set of experimental conditions using the Ct values of the reference genes in each group. According to the standard deviation (SD) and coefficient of correlation of the candidate genes R value, the expression stability of each reference gene was evaluated in the BestKeeper program. A reference gene with a lower SD value and an R value closer to 1 would have stable expression. Correlation analysis (R2) using SPSS (19.0) between M values vs SV values, SV value vs SD values, M values vs SD values for determine its data robustness. A simple T test using SPSS to illustrate the significance of data differences. Differences were considered to be significant if P < 0.05. * represented 0.01 < P < 0.05.

Validation of reference genes

The identified optimum reference genes and the least stable reference genes in salt or in different tissues were used to normalize the relative expression levels of BpGRAS1 (GenBank number:MN117546), BpGRAS16 (GenBank number:MN117547) and BpGRAS19 (GenBank number:MN117548). ACT and TEF were used as the most stable reference genes in the control and 200 mM NaCl-treated samples, while GAPDH was used as the least stable reference gene. EF-1α, TUB, and their combinations were used as the most stable reference genes in different birch tissues, while GAPDH was used as the least stable reference gene. The same qRT-PCR reaction systems and conditions were used as stated above. Three parallel total RNA samples were applied to analyze the relative expression. Data processing as descried in 2-ΔΔCt [29].

Results

Specificity and amplification efficiency of qRT-PCR primers

Agarose gel electrophoresis showed that single PCR products could be amplified from the 11 candidate reference genes (S1 Fig), indicating that the primers designed by Primer 5.0 software had strong specificity and fulfilled the requirements of qRT-PCR primers. The melting curves for all 11 candidate reference genes had a distinct single peak, and the three replications showed perfect repeatability (S2 Fig). The amplification efficiency of these reference genes ranged from 1.86 to 2.01 (Table 1), which met the requirements of the qRT-PCR experiment. Therefore, these subjects reference genes (ACT, TUA, TUB, TEF, 18S rRNA, EF1α, GAPDH, UBC, YLS8, SAND, and CDPK) could serve as the research subjects.

Analysis of the expression level of the 11 candidate reference genes

Ct value is one of the criteria used to measure gene expression level. A gene with a higher Ct value would have a lower gene expression level [29]. Ct values of the 11 candidate reference genes were unevenly distributed in the different treatments and tissues of birch (Fig 1). However, the mean Ct value can illustrate the abundance of the transcripts from the 11 candidate reference genes (Table 1), which can be used to analyze the stability of reference genes in different experimental samples.

Fig 1

Cycle threshold (Ct) value distribution of eleven candidate reference genes in different tissues and abiotic stress conditions in birch.

Boxes contain the 5th and 95th quartiles of the Ct values of the different genes tested, median (central horizontal line) and minimal/maximal value (vertical bar).

The mean Ct values of all the samples varied from 17.39 to 29.80. Among the 11 candidate reference genes in all samples, 18S rRNA had the highest expression level, with an average Ct ± SD of 17.39 ± 1.36. ACT, TUA, TUB, TEF, EF-1α, and YLS8 had relatively high expression level, with average Ct ± SD values of 22.49 ± 1.03, 22.80 ± 1.00, 23.23 ± 1.05, 22.28 ± 1.41, 23.09 ±1.96, and 23.47 ± 1.53, respectively. CDPK had the highest average Ct value, indicating that it had the lowest expression level (Fig 1).

The lower the coefficient of variation (CV) value, the higher the stability of the candidate reference gene [30]. Among the 11 candidate reference genes in all samples, the CV value of TUA was the lowest, followed by TUB and ACT. GAPDH had a high CV value, which indicated that its expression was least stable (Table 1). Thus, the results indicated that the expression levels of 11 candidate reference genes were different among the different experimental conditions.

GeNorm analysis

The geNorm software can identify reference genes with better stabilities by calculating the average expression stability index (M value). The software will rank candidate reference genes expression stabilities according to their M values. The geNorm algorithm reveals the appropriate number of reference genes required for normalization to calculate the pairwise variation between the normalization factors NFn and NFn+1 of the two sequences (Vn/n+1). When Vn/n+1 is less than 0.15, n is the number of genes used in the normalization [31-32]. The lower the M value, the more stable the reference gene. Conversely, a higher M value indicates worse stability. Genes with an M value less than 1.5 could be used as reference genes [33]. To obtain optimum reference genes for the different experimental conditions, total samples were divided into four groups including different tissues, normal, salt, and osmosis, as described in the section describing plant material and treatments. The results are shown in Fig 2. Ten of the eleven candidate reference genes in this study could be considered as appropriate reference genes with the M value less than 1.5, except for GAPDH in salt, different tissues, and total samples.

Fig 2

Expression stability and ranking of eleven candidate reference genes as analyzed by geNorm.

The black spots represent the average expression stability (M). The highest M value indicates the most unstable gene, while the lowest represents the most stable. The order from left to right indicates the stability ranking of the eleven candidate reference genes. The most variable is on the left while the most stable is on the right.

In the different tissues of birch, TUB and EF-1α were the most stable reference genes. In normal conditions, ACT and TUA were the most stable. Under salt stress treatment, ACT and EF-1α were optimum reference genes. Under osmotic stress treatment, ACT and TUB were the most ideal reference genes. In total samples, ACT and TUA were the most ideal choices (Fig 2). In conclusion, the most appropriate reference genes were different in the different experimental conditions. However, ACT could be the best reference gene in all experimental conditions except in different tissues; GAPDH was the least stable reference gene in all the groups in this study.

NormFinder analysis

NormFinder software can select the most appropriate reference gene according to candidate reference genes expression steady values [34]. A reference gene with the least steady value (SV value) has the most stable expression. NormFinder can not only compare the expression differences of candidate reference genes, but also compares the variation between samples. The results of gene stability ranking of candidate reference genes were generated and are shown in Table 2. TUB was the most stable reference gene in total group and in the normal conditions group. TEF was optimum reference gene under salt and osmotic stress. GAPDH was the least stable reference gene in all experimental groups. 18S rRNA was unstable in most groups, while it was optimum in the different tissues group. The above results are basically consistent with the geNorm analysis.

Table 2

Expression stability values (SV value) of candidate reference genes under different treatments of birch, as calculated using Normfinder.

Rank	Different tissues		Normal conditions		Salt stress		Osmotic stress		Total
	Gene	SV	Gene	SV	Gene	SV	Gene	SV	Gene	SV
1	18S rRNA	0.038	TUB	0.356	TEF	0.272	TEF	0.225	TUB	0.302
2	EF1α	0.083	EF1α	0.416	ACT	0.276	TUB	0.289	ACT	0.340
3	SAND	0.097	SAND	0.418	TUB	0.286	EF1α	0.387	TUA	0.341
4	ACT	0.099	ACT	0.503	EF1α	0.316	ACT	0.428	YLS8	0.562
5	TUB	0.211	YLS8	0.526	TUA	0.430	YLS8	0.526	UBC	0.741
6	YLS8	0.458	TUA	0.557	UBC	0.473	SAND	0.547	SAND	0.748
7	TEF	0.483	18S rRNA	0.694	SAND	0.501	TUA	0.624	TEF	0.806
8	TUA	0.532	CDPK	0.695	18S rRNA	0.777	CDPK	0.732	EF1α	0.948
9	UBC	1.117	TEF	0.745	YLS8	0.889	UBC	0.799	CDPK	1.091
10	CDPK	1.458	UBC	0.968	CDPK	0.907	18S rRNA	1.137	18S rRNA	1.097
11	GAPDH	2.226	GAPDH	1.685	GAPDH	2.328	GAPDH	1.438	GAPDH	1.399

Bestkeeper analysis

Bestkeeper software is commonly used to choose appropriate reference genes; however, the number of candidate reference genes cannot exceed 10. Thus, we chose the 10 best candidate reference genes according to the results of geNorm and NormFinder. In BestKeeper, SD and R values are used to select ideal reference genes, however, the mean standard deviation (mSD) can be used to assess whether the expression of a gene is stable [35]. A gene with an SD value less than 1.0 can be considered as ideal candidate reference genes. At the same time, the closer the R value is to 1, the better the stability of the reference gene. The results of ranking the candidate reference genes according to their SD values are shown in Table 3.

Table 3

Expression stability values (standard deviation (SD) value and R value) of candidate reference genes under different treatments of birch, as determined by BestKeeper.

Rank	Different tissues			Normal conditions			Salt stress			Osmotic stress			Total
	Gene	SD	R value	Gene	SD	R value	Gene	SD	R value	Gene	SD	R value	Gene	SD	R value
1	TEF	0.070	0.704	CDPK	0.490	0.776	TEF	0.310	0.572	TUB	0.310	0.955	ACT	0.550	0.888
2	TUA	0.090	0.414	ACT	0.630	0.912	TUA	0.350	0.753	ACT	0.350	0.773	TUA	0.550	0.891
3	TUB	0.230	0.895	18S rRNA	0.650	0.694	ACT	0.420	0.952	TUA	0.440	0.697	TUB	0.550	0.881
4	EF1α	0.250	0.997	TUA	0.710	0.909	TUB	0.420	0.869	TEF	0.480	0.950	18S rRNA	0.690	0.356
5	ACT	0.380	0.903	TUB	0.740	0.950	EF1α	0.510	0.943	YLS8	0.570	0.755	TEF	0.710	0.662
6	18S rRNA	0.570	0.941	TEF	0.950	0.901	UBC	0.510	0.750	EF1α	0.630	0.944	YLS8	0.810	0.877
7	SAND	0.630	0.993	EF1α	0.970	0.957	SAND	0.630	0.873	SAND	0.640	0.856	SAND	0.880	0.793
8	UBC	0.710	0.149	YLS8	0.980	0.914	18S rRNA	0.730	0.348	CDPK	0.710	0.630	EF1α	0.930	0.776
9	YLS8	0.780	0.997	SAND	1.020	0.947	CDPK	0.760	0.879	UBC	1.030	0.632	UBC	0.970	0.822
10	CDPK	0.780	-0.174	UBC	1.120	0.848	YLS8	0.860	0.853	18S rRNA	1.230	0.765	CDPK	1.090	0.711

In the different tissues group, it was obvious that TEF was the best reference gene, although its R value was far from satisfactory. In normal conditions, CDPK had the lowest SD value, followed by ACT, and the R value of ACT was closer to 1 than that of CDPK. On the whole, CDPK was more appropriate to serve as a candidate reference gene under the normal experimental conditions used. Under salt stress conditions, SD values of all the candidate reference genes were less than 1.0. Thus, the 10 tested candidate reference genes could be used as reference genes, however, the R values of ACT and EF1α are closer to 1, so we consider choosing them. TEF was the best candidate reference gene, which agreed with the results of NormFinder. ACT and EF1α had R values that closer to 1. Under osmotic stress conditions, TUB was the best choice because of its ideal SD and R values. Ultimately, ACT, TUA, and TUB had similar stabilities according to the results in all samples. These three candidate reference genes were suitable as candidate reference genes, which agreed with the results of geNorm.

To verify the selected reference genes, the most and least stable reference genes were used to assess the relative expression level of target BpGRAS genes after normalization under salt stress and different tissues. The GRAS transcription factor family is one of the largest families of transcription factors and are involved in regulating the expression of several target function genes in plants biotic and abiotic responses, including that to salt. High-salt and drought stress could induce the expression of SCL7 in Populus euphratica, which belongs to the GRAS transcription factor family [36]. Eight GRAS transcription factor genes were upregulated in different tissue of Dendrobium catenatum following exposure to heat and salt stresses [37]. Our previous research showed that transiently overexpressed BpGRASs had significantly increased salt tolerance in birch (unpublished).

We used the most stable and least stable candidate reference genes according to our results to normalize the expression level of BpGRAS genes. The expression patterns of the BpGRAS genes were markedly different when the most and least stable reference genes were used to normalize their expression in salt stress and different tissues (Fig 3).

Fig 3

Relative quantification of targeted BpGRAS gene expression levels in birch using validated reference genes.

Samples of leaves collected at 0, 3, 6, 12, 24, and 48 h under salt stress, respectively. A1-C1: Samples treated with 200 mM NaCl using ACT as internal the reference gene to normalize the expression of BpGRAS genes. A2-C2: Samples treated with 200 mM NaCl using TEF as the internal reference gene to normalize the expression of BpGRAS genes. A3-C3: Samples treated with 200 mM NaCl using GAPDH as the internal reference gene to normalize the expression of BpGRAS gene. * represented 0.01 < P < 0.05.

When the ideal reference genes ACT and TEF were used for normalization under treatment with 200 mM NaCl, the expression level of the BpGRAS genes were significantly enhanced compared with that in the control group at most time points, and their expression patterns were similar. However, we could not draw the same conclusion when the least stable reference gene was used (Figs 3 and 4). When the ideal reference genes ACT and TEF were used as internal controls for samples treated with 200 mM NaCl, BpGRAS1 had at the highest transcript level at 48 h after salt treatment. However, when the least stable reference gene GAPDH was used to normalize the expression level of BpGRAS1, BpGRAS1 showed it was hardly expressed at 48 h. The results are the same as follows, when the ideal reference genes ACT and TEF were used for normalization after treatment of 200 mM NaCl, BpGRAS16 and BpGRAS19 showed higher transcript levels at 48 h after salt treatment; however, it was hardly expressed at 48 h when the least stable reference gene, GAPDH, was used for normalization. Simultaneously, T-test showed that the expression level of NaCl treatment group was significantly higher than that of the control.

Fig 4

Expression levels in birch using the validated reference genes.

Samples of leaves, stems, and roots in normal conditions assessed using qRT-PCR with different internal reference gene to normalize the expression of BpGRAS genes. * represented 0.01 < P < 0.05.

The most stable reference genes for use in different tissues were EF1α and TUB and the least stable reference gene was GAPDH. These three genes were used to normalize the expressions of the BpGRAS genes. The combination of EF1α and TUB was also used for normalization. The results are shown in Fig 4. When the appropriate reference genes or their combination were used, the expression of BpGRAS1 in root was the highest, followed by that in the stem, with the lowest expression in the leaf. The expression of BpGRAS16 was highest in the stem, followed by leaf and then the root. The expression of BpGRAS19 in the stem and root were the essentially the same. However, when the least stable reference gene was used for normalization, the BpGRAS genes were hardly expressed in the stem and root. Obviously, the expression patterns of the BpGRAS genes were similar after using the appropriate reference genes and their combinations for normalization. However, the expression patterns of BpGRAS genes showed large differences when the least stable reference gene was used.

In summary, using different reference genes for normalization could produce different experimental results. Therefore, selecting the ideal reference genes according to different experimental conditions and different tissues is very importance.

Discussion

Determining a gene’s expression level is an important step to understand the function of the gene product in biological processes during environmental stress, and in developmental and cellular processes [38]. To ensure the accuracy of relative RT-PCR, specific PCR conditions and an appropriate internal controls must be determined. Thus, the selection of internal reference genes that show stable expression in all tissues and under the experimental conditions being investigated plays a key role in determining stress-related gene expression under different abiotic stress conditions. Identifying suitable internal reference genes in birch will encourage gene expression studies in this tree species.

There are many reports concerning reference gene selection in plant, including in Salicornia europaea [12], cucumber [15]. However, until now, there have been no systematic reports about reference genes selection in birch. Therefore, the present study was designed to identify and validate suitable reference genes for gene expression analysis in birch.

In this study, 11 candidate reference genes that have been frequently used in previous studies were evaluated, and three data analysis tools (geNorm, NormFinder, BestKeeper), which are specialized for reference gene selection, were used. The expression levels of the 11 candidate reference genes were significantly different among the different samples (Fig 1). The three analysis tools have different algorithms; therefore, the ranking of these 11 candidate reference genes according to their expression stability varied. The differences in the statistical theories used by the three pieces of software would explain the different results. Using correlation analysis (R2) 11 candidate genes between M values vs SV values (ranged from 0.0022 to 0.8169), SV values vs SD values (ranged from 0.0048 to 0.8232), M values vs SD values (ranged from 0.0068 to 0.9159), which indicated the data could not establish the expression stability. We should select the reference genes with better expression stability as identified by all three tools. Therefore, the rankings of candidate reference genes calculated by these three algorithms are listed (S1 Table). In general, EF1α was the best reference gene in different tissues. ACT and TEF are the best for salt-stressed sample and TUB alone is the best for osmotic-stressed sample. Meanwhile, ACT was also the most appropriate reference gene in normal conditions and in all the samples. Previously GAPDH was identified as the optimum reference gene in salt-treated leaves, Cd-treated roots, cold-treated leaves and roots, and PEG-treated leaf samples in Carex rigescens [20]. However, GAPDH was the least stable reference gene in all the experimental conditions in the present study, despite being one of the most commonly used reference genes in previous studies.

Suitable reference genes have been confirmed in many plant species in different tissues and under abiotic stress. In Salicornia europaea, ACT (Actin) and GAPDH were the optimum combination of internal reference genes to study gene expression under drought stress [12]. In rice, UBQ5 and eEF1α was most stable as reference genes in different tissues [39]. In tomato, the expression stabilities of GAPDH and phosphoglycerate kinase (PGK) were ranked as the top during light stress but were poorly ranked during N and cold stress [40]. In cucumber, TUA was considered as an appropriate reference gene in different tissues. EF1α was identified as a suitable reference gene for abiotic stress treatment [15]. Thus, appropriate reference genes differ among different plants and according to the experimental conditions. In conclusion, different reference genes should be selected according to different experimental conditions to obtain accurate results.

Conclusion

In this study, ACT was identified as the optimum reference gene in all experimental groups, except in the different tissues group. GAPDH was the least stable candidate reference gene in all experimental conditions. This study provided appropriate reference genes for expression studies in birch, which will be beneficial for more accurate relative quantification of mRNA expression in birch for different tissues, in normal conditions, and under salt and osmotic stress conditions.

The results of 1.5% agarose gel electrophoresis after 30 cycles PCR.

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Click here for additional data file.

Dissociation curves of the qRT-PCR amplicons.

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Click here for additional data file.

Comprehensive rankings of the stability of the reference genes by three algorithms.

GN: Ranking of candidate reference genes calculated by geNorm. NF: Ranking of candidate reference genes calculated by NormFinder. BK: Ranking of candidate reference genes calculated by Bestkeeper

(DOCX)

Click here for additional data file.

7 Oct 2019

PONE-D-19-25334

​Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions

PLOS ONE

Dear Dr Xiaoyu Ji,

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3. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed:

-http://hsianglab.000webhostapp.com/pubs/pdf/02rtpcr_pmbr.pdf

-https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4217110/

-https://www.frontiersin.org/articles/10.3389/fpls.2017.01659/full

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: An impressive body of work is presented in this manuscript to select appropriate reference genes in birch. Overall, the manuscript is reasonably well written. However, there are some points that the authors need to consider.

Critical points:

1. Multiple parameters need to consider before choosing an ideal reference gene for qPCR analysis. Among them, one of the very important criteria is qPCR efficiency, and it should be close to 2 (within 80–100%). The authors used E-ΔCt formula for data analysis. I assume ‘E’ stands for qPCR efficiency. Therefore, it is necessary to measure the qPCR efficiency of each gene and mention in the manuscript. Apart from that, all other criteria are satisfactory: single pick dissociation curve, single band in agarose gel, and amplicon size.

2. Authors tried to summarize the analysis of three statistical algorithms (GeNorm, NormFinder, and BestKeeper) by calculating the ‘sum of rankings’ in table 4. Before that, it needs to establish the robustness of data by correlation analysis (R2). Example: correlation analysis between M Value vs Stability Value. After that, authors can perform a sum of rankings analysis using the best-correlated algorithms or all algorithms.

3. Statistical analysis is missing in figure 3 and 4. I would suggest performing a simple T-test.

4. Sampling and calculation procedure is not clear. I assume authors harvested root, stem and leaf tissues from normal and stressed samples after the given time points. How many samples were used per experiments? Are they biological or technical replication? Also, maintain uniformity in the text; either use “Normal” or “Control”. In addition, authors should clarify how they calculate average for each groups (i.e., tissue, normal, salt and osmotic).

Minor points:

Line 65: All are not woody plants. Example. Musa paradisiaca

Line 100: Mention cycle no in the Fig.A1 legend.

Line 113: Range of the mean Ct values (17.34 to 28.27) is not matching with Table 1 (17.39 to 29.80).

Line 138: To follow the result easily, mention the “four groups” here as well. Also, describe “total” sample.

Line 142-145: Some data points are missing in Figure 2. Ex: EF-1α in different tissues and ACT in salt stress.

Line 145: Use uniform terminology in the manuscript. Either “all samples” or “total samples”.

Line 151: Authors should explain the calculation procedures in details in Figure 2 legend. How they calculated “the average expression stability (M)” values for normal and stressed samples? I assume the authors averaged the data from different time points as well as different tissues. Also, they should mention the replication no. I would recommend this suggestion for other figures and tables as well.

Line 132 & 155: Numbers are same for both sections.

Line 172: Are not “M values” stands for geNorm analysis? If so, correct Table 2 as it is showing values from NormFinder analysis.

Line 183: I think authors should explain why they choose SD value over the R-value for the ranking.

Line 186-187: “On the whole…. experimental conditions used”; Do you mean normal condition?

Line 188 -189: “Under salt stress conditions…. used as reference genes”; In this situation authors can consider the R-value to choose best one.

Line 192: “Ultimately, ACT…. to the results”; Mention the sample name.

Line 202: The authors validated well the best reference genes through expression analysis of GRAS TFs in salt-stressed samples. To do that, the authors cited multiple references that showed stressed-mediated expression of GRAS homologs. However, they did not cite any reference for tissue-specificity. I would recommend adding some references to show that the GRAS TFs can be expressed tissue-specific manner.

Line 255: I think it will be “A2-C2” instead of “B2-C2”.

Line 293: Authors mentioned about cloning. However, no such section available in the materials and method.

Line 303. Why not TUB for osmotic stress. It showed lowest sum of rankings.

Line 342-349: Mention RNA amount that were used for cDNA synthesis.

Line 351-356: Mention Tm of the primers.

Line 367-368: Mention cut-off/ threshold for the Ct values (if manually adjusted).

Line 370: How many biological and/or technical replications were used for mean Ct value calculation?

Reviewer #2: The authors have clearly established standard reference gene(s) that can be used for qRT-PCR evaluation in the widely used timber tree, Birch (Betula platyphylla). The design of experiments and the candidate genes chosen for the study follows the standard studies that has been performed in several plant and animal species. The results are clearly expressed.

General comments:

1. The description of results and discussion are jittered and need to be rephrased to get a seamless flow.

2. Excessive references (65) that doesn’t add more value to the paper may be restricted to max.40. More of recent and references of woody species should be included eg.:

a. Wang JJ, Han S, Yin W, Xia X, Liu C. Comparison of Reliable Reference Genes Following Different Hormone Treatments by Various Algorithms for qRT-PCR Analysis of Metasequoia. International Journal of Molecular Sciences. 2018. 20(1):34

b. Wei Y, Liu Q, Dong H, et al. Selection of Reference Genes for Real-Time Quantitative PCR in Pinus massoniana Post Nematode Inoculation. PLoS One. 2016;11(1):e0147224.

c. Wang J, Abbas M, Wen Y, Niu D, Wang L, Sun Y, et al. (2018) Selection and validation of reference genes for quantitative gene expression analyses in black locust (Robinia pseudoacacia L.) using real-time quantitative PCR. PLoS ONE 13(3): e0193076. https://doi.org/10.1371/journal.pone.0193076

d. Chen X, Mao Y, Huang S, et al. Selection of Suitable Reference Genes for Quantitative Real-time PCR in Sapium sebiferum. Front Plant Sci. 2017;8:637.

3. The results, few sentences verbatim is repeated in the abstract, results and discussion – which might be rephrased.

However a few points, listed below, when rectified in the paper would be perfect.

Major :

Line 65 – Ref 29 is either misplaced/ missing

Line 66 – Triticum is not a woody plant – and including this example doesn’t add any new information.

In the - ‘Validation of reference genes’ results section paragraphs starting from line 220 to 235 is not clear and the reader gets lost. Consider rephrasing the paras.

Line 287 – Too many references may be avoided, to keep it concise. The given literature has been previously cited in the introduction section also. Eg.: Line 288 – reference 52 – the original paper has No PCR studies reported in this paper and hence irrelevant.

Line 317 – wrong reference cited (44) instead it should be 62.

Line 321 – ref. Wan et al., 2010 – not in reference.

Line 323 – wrong reference – referring to animal sciences paper not Magnolia denudate

Line 334 – the source of birch seeds should be mentioned.

Line 337 – the quantity (in ml or L) used each time may be mentioned. Whether tap water or distilled water was used?

Line 424 – ref.10 – non relevant animal studies paper may be omitted.

Line 475 – ref.29 is NOT a ‘validation’ paper

Line 536 – ref.52 - no RT-PCR studies reported in the paper, hence irrelevant.

Line 567 – ref.63 – irrelevant reference

Minor:

Line 39 – maybe modified as “food spoilage detection” to be specific.

Line 40 – just TWO important references would suffice to support.

Line 74 and 78 – not clear – consider rephrasing.

Line 80 – a couple of recent literature would suffice.

Line 86 – The sentence is repeated in the abstract verbatim, which may be avoided.

Line 92 – “reached” may be replaced with “fulfilled”

Line 94 – “repeats” may be replaced with “replications”

Line 96 – “subjects” may be replaced with “candidates”

Superfluous ‘the’ may be omitted – eg. Line 106 and few other paragraphs.

Line 118 – The result is repeated elsewhere and also in the same para line 114.

Line 340 – ‘cryotheraphy’ may be replaced with ‘after freezing in liquid nitrogen’

Line 341 – ‘experiments’ may be replaced with ‘replications’

Line 344 – ‘detected’ may be replaced with ‘checked’

Line 350 – ‘synthetic’ may be replaced with ‘synthesized’

Line 353 – too many references, repeated.

Line 365 – ‘experiments’ may be replaced with ‘replications’

Line 393 – the non-standard phrase ‘depended on the way of’ may be replaced with ‘as described in’

**********

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Reviewer #1: No

Reviewer #2: Yes: Rajagopal Bala

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29 Oct 2019

Dear reviewers,

We are very grateful to your and the editors’ valuable suggestions. Based on these suggestions, we have made careful modification on the original manuscript. We also responded point by point to the reviewer’s comments as listed below. Hope these will make it more acceptable for publication. If there are further issues to be clarified, please contact us without hesitation. Thank you.

With best wishes,

Xiaoyu Ji

Reviewer 1

Critical points:

1. Multiple parameters need to consider before choosing an ideal reference gene for qPCR analysis. Among them, one of the very important criteria is qPCR efficiency, and it should be close to 2 (within 80–100%). The authors used E-ΔCt formula for data analysis. I assume ‘E’ stands for qPCR efficiency. Therefore, it is necessary to measure the qPCR efficiency of each gene and mention in the manuscript. Apart from that, all other criteria are satisfactory: single pick dissociation curve, single band in agarose gel, and amplicon size.

Response: We have added PCR efficiency (E) in Table 1.(see line: 152-154, 233-234.)

2. Authors tried to summarize the analysis of three statistical algorithms (GeNorm, NormFinder, and BestKeeper) by calculating the ‘sum of rankings’ in table 4. Before that, it needs to establish the robustness of data by correlation analysis (R2). Example: correlation analysis between M Value vs Stability Value. After that, authors can perform a sum of rankings analysis using the best-correlated algorithms or all algorithms.

Response: We have established the robustness of data by correlation analysis (R2) between M Value vs Stability Value, M Value vs Stability Value. (see line: 176-177, 400-402.)

3. Statistical analysis is missing in figure 3 and 4. I would suggest performing a simple T-test.

Response: We have added it according to the suggestion. T-test was performed to analyze its significanced, and the results were shown in Figure 3 and Figure 4. (see line: 357-371.)

4. Sampling and calculation procedure is not clear. I assume authors harvested root, stem and leaf tissues from normal and stressed samples after the given time points. How many samples were used per experiments? Are they biological or technical replication? Also, maintain uniformity in the text; either use “Normal” or “Control”. In addition, authors should clarify how they calculate average for each groups (i.e., tissue, normal, salt and osmotic).

Response: We have explained it according to the suggestion. Biologically repeated sampling from 6 birch seedlings in three independent experiments. Three independent replications were performed for technical repetition in qRT-PCR. The mean value is arithmetic mean.(see line: 120, 154.)

Minor points:

1. Line 65: All are not woody plants. Example. Musa paradisiaca.

Response: We have deleted the relevant reference according to the suggestion. (see line: 87.)

2. Line 100: Mention cycle no in the Fig.A1 legend.

Response: We have added mentioned cycle in the Figure S1 according to the suggestion. (see line: 202.)

3. Line 113: Range of the mean Ct values (17.34 to 28.27) is not matching with Table 1 (17.39 to 29.80).

Response: We have corrected it according to the suggestion. The range of the mean Ct values is 17.39~29.80. (see line: 214.)

4. Line 138: To follow the result easily, mention the “four groups” here as well. Also, describe “total” sample.

Response: Four groups means ‘different tissues, normal, salt, and osmosis’. Total number of samples called ‘total sample’, and we have corrected it according to the suggestion. (see line: 245-246.)

5. Line 142-145: Some data points are missing in Figure 2. Ex: EF-1α in different tissues and ACT in salt stress.

Response: Missing data points (EF-1α in different tissues, ACT in salt stress, ACT in normal condition) have been completed. We have reconstructed the Figure 2 according to the suggestion.

6. Line 145: Use uniform terminology in the manuscript. Either “all samples” or “total samples”.

Response: We have corrected it according to the suggestion. Use uniform terminology ‘total sample’. (see line :253.)

7. Line 151: Authors should explain the calculation procedures in details in Figure 2 legend. How they calculated “the average expression stability (M)” values for normal and stressed samples? I assume the authors averaged the data from different time points as well as different tissues. Also, they should mention the replication no. I would recommend this suggestion for other figures and tables as well.

Response: We have added it according to the suggestion. Detailed calculation process has been demonstrated. (see line :238-242.)

8. Line 132 & 155: Numbers are same for both sections.

Response: We have corrected number according to the suggestion.(see line: 206, 235.)

9. Line 172: Are not “M values” stands for geNorm analysis? If so, correct Table 2 as it is showing values from NormFinder analysis.

Response: The Normfinder software processing results are SV values. We have corrected it according to the suggestion. (see line :274.)

10. Line 183: I think authors should explain why they choose SD value over the R-value for the ranking.

Response: More references were ranked with SD values, which was more convincing, however R values can also be used as ranking criteria. We have explained it in the revised manuscript. (see line :288-290.)

11. Line 186-187: “On the whole…. experimental conditions used”; Do you mean normal condition?

Response: Yes, it was normal condition. We have corrected it according to the suggestion. (see line :298.)

12. Line 188-189: “Under salt stress conditions…. used as reference genes”; In this situation authors can consider the R-value to choose best one.

Response: We have taken this method according to the suggestion. Compared R values in this case and find that the R values of ACT and EF1α were closer to 1, so we choosed them. (see line :300-301.)

13. Line 192: “Ultimately, ACT…. to the results”; Mention the sample name.

Response: We have corrected it according to the suggestion. (see line :304.)

14. Line 202: The authors validated well the best reference genes through expression analysis of GRAS TFs in salt-stressed samples. To do that, the authors cited multiple references that showed stressed-mediated expression of GRAS homologs. However, they did not cite any reference for tissue-specificity. I would recommend adding some references to show that the GRAS TFs can be expressed tissue-specific manner.

Response: We have cited relevant reference to illustrate the tissue specificity of GRAS TFs expression according to the suggestion. (see line :318-320.)

15. Line 255: I think it will be “A2-C2” instead of “B2-C2”.

Response: We have corrected it according to the suggestion. (see line :362.)

16. Line 293: Authors mentioned about cloning. However, no such section available in the materials and method.

Response: In the previous study, we have obtained the sequence of these 11 genes. We have mentioned it according to the suggestion. (see line :394.)

17. Line 303: Why not TUB for osmotic stress. It showed lowest sum of rankings.

Response: TUB ranks better than TEF. We have corrected it according to the suggestion. (see line :45, 405.)

18. Line 342-349: Mention RNA amount that were used for cDNA synthesis.

Response: We have added it in the material method according to the suggestion. (see line :130-131.)

19. Line 351-356: Mention Tm of the primers.

Response: We have added it in Table 1 according to the suggestion. (see line :233.)

20. Line 367-368: Mention cut-off/ threshold for the Ct values (if manually adjusted).

Response: The cycle threshold (Ct) values is the machine setting. We have explained it in the revised manuscript. (see line :159-160.)

21. Line 370: How many biological and/or technical replications were used for mean Ct value calculation?

Response: The mean Ct value was calculated using three biological replications, we have added it according to the suggestion. (see line :160.)

Reviewer 2

General comments:

1. The description of results and discussion are jittered and need to be rephrased to get a seamless flow.

Response: We have reworded inappropriate and redundant parts according to the suggestion.

2. Excessive references (65) that doesn’t add more value to the paper may be restricted to max.40. More of recent and references of woody species should be included eg.:

a. Wang JJ, Han S, Yin W, Xia X, Liu C. Comparison of Reliable Reference Genes Following Different Hormone Treatments by Various Algorithms for qRT-PCR Analysis of Metasequoia. International Journal of Molecular Sciences. 2018. 20(1):34.

b. Wei Y, Liu Q, Dong H, et al. Selection of Reference Genes for Real-Time Quantitative PCR in Pinus massoniana Post Nematode Inoculation. PLoS One. 2016;11(1):e0147224.

c. Wang J, Abbas M, Wen Y, Niu D, Wang L, Sun Y, et al. (2018) Selection and validation of reference genes for quantitative gene expression analyses in black locust (Robinia pseudoacacia L.) using real-time quantitative PCR. PLoS ONE 13(3): e0193076. https://doi.org/10.1371/journal.pone.0193076

d. Chen X, Mao Y, Huang S, et al. Selection of Suitable Reference Genes for Quantitative Real-time PCR in Sapium sebiferum. Front Plant Sci. 2017;8:637.

Response: We have read these four articles in detail and cited them, while ensuring that the references are not redundant, within 40 articles according to the suggestion.

3. The results, few sentences verbatim is repeated in the abstract, results and discussion – which might be rephrased.

Response: We have corrected them according to the suggestion, eg.: see line: 108.

Major:

1. Line 65 – Ref 29 is either misplaced/ missing

Response: We have deleted it according to the suggestion. (see line :87.)

2. Line 66 – Triticum is not a woody plant – and including this example doesn’t add any new information.

Response: We have deleted it according to the suggestion. (see line :89.)

3. In the - ‘Validation of reference genes’ results section paragraphs starting from line 220 to 235 is not clear and the reader gets lost. Consider rephrasing the paras.

Response: We have corrected it and deleted corresponding unclear segment according to the suggestion. (see line :334-336.)

4. Line 287 – Too many references may be avoided, to keep it concise. The given literature has been previously cited in the introduction section also. Eg.: Line 288 – reference 52 – the original paper has No PCR studies reported in this paper and hence irrelevant.

Redundant reference has been.

Response: We have deleted it according to the suggestion.(see line :388-389.)

5. Line 317 – wrong reference cited (44) instead it should be 62.

Response: We have corrected it according to the suggestion. (see line : 416.)

6. Line 321 – ref. Wan et al., 2010 – not in reference.

Response: We have cited this reference according to the suggestion. (see line : 419.)

7. Line 323 – wrong reference – referring to animal sciences paper not Magnolia denudate.

Response: We have deleted it according to the suggestion. (see line : 420.)

8. Line 334 – the source of birch seeds should be mentioned.

Response: We have mentioned birch seeds source in the revised manuscript. (see line : 113-114.)

9. Line 337 – the quantity (in ml or L) used each time may be mentioned. Whether tap water or distilled water was used?

Response: We have mentioned volume of distilled water according to the suggestion. (see line : 117-118.)

10. Line 424 – ref.10 – non relevant animal studies paper may be omitted.

Response: We have deleted it according to the suggestion.

11. Line 475 – ref.29 is NOT a ‘validation’ paper.

Response: We have deleted it according to the suggestion.

12. Line 536 – ref.52 - no RT-PCR studies reported in the paper, hence irrelevant.

Response: We have deleted it according to the suggestion.

13. Line 567 – ref.63 – irrelevant reference.

Response: We have deleted it according to the suggestion.

Minor:

1. Line 39 – maybe modified as “food spoilage detection” to be specific.

Response: We have corrected it according to the suggestion. (see line : 61.)

2. Line 40 – just TWO important references would suffice to support.

Response: We have deleted redundant references according to the suggestion. (see line : 62.)

3. Line 74 and 78 – not clear – consider rephrasing.

Response: We have rephrased it according to the suggestion.(see line : 94-98.)

4. Line 80 – a couple of recent literature would suffice.

Response: We have deleted redundant references according to the suggestion.(see line : 101.)

5. Line 86 – The sentence is repeated in the abstract verbatim, which may be avoided.

Response: We have rephrased repeated words according to the suggestion. (see line : 108.)

6. Line 92 – “reached” may be replaced with “fulfilled”

Response: We have corrected it according to the suggestion. (see line : 196.)

7. Line 94 – “repeats” may be replaced with “replications”

Response: repeats --> replications. We have corrected it according to the suggestion. (see line : 198.)

8. Line 96 – “subjects” may be replaced with “candidates”

Response: We have corrected it according to the suggestion. (see line : 199.)

9. Superfluous ‘the’ may be omitted – eg. Line 106 and few other paragraphs.

Response: We have corrected it according to the suggestion. (see line : 208, 209.)

10. Line 118 – The result is repeated elsewhere and also in the same para line 114.

Response: We have removed repeated results according to the suggestion. (see line : 218-219.)

11. Line 340 – ‘cryotheraphy’ may be replaced with ‘after freezing in liquid nitrogen’

Response: We have corrected it according to the suggestion. (see line : 121.)

12. Line 341 – ‘experiments’ may be replaced with ‘replications’

Response: experiments --> replications.We have corrected it according to the suggestion. (see line : 120.)

13. Line 344 – ‘detected’ may be replaced with ‘checked’

Response: We have corrected it according to the suggestion. (see line : 127.)

14. Line 350 – ‘synthetic’ may be replaced with ‘synthesized’

Response: We have corrected it according to the suggestion. (see line : 133.)

15. Line 353 – too many references, repeated.

Response: We have deleted redundant references according to the suggestion. (see line : 138.)

16. Line 365 – ‘experiments’ may be replaced with ‘replications’

Response: experiments --> replications. We have corrected it according to the suggestion. (see line : 154.)

17. Line 393 – the non-standard phrase ‘depended on the way of’ may be replaced with ‘as described in’

Response: We have corrected it according to the suggestion. (see line : 190.)

Submitted filename: Cover letter and Response to reviewers.docx

Click here for additional data file.

5 Nov 2019

PONE-D-19-25334R1

​Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions

PLOS ONE

Dear Dr. Ji

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by 15th Nov. 2019. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

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An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Mayank Gururani

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: A greatly improved manuscript from the first version. The text is now mostly a coherent and well-written piece of work. I would be happy to accept this work, with some minor revisions as indicated here:

1. Line 175-176: Author should add the graph for correlation analysis or at least mention the R2 value in the manuscript.

2. Line 337-339: “Simultaneously, T-test showed that the expression of GAPDH was significantly lower than other reference genes” – I think this is an incorrect statement. T-test result is showing the significant difference of GRAS expression between control vs salt-treated sample. Thus, only the second part of this sentence is appropriate – “the expression level of NaCl treatment group was significantly higher than that of the control”.

3. Author should mention the P-value threshold for T-test in Figure 3 and 4 legends.

4. Line 403-404: “ACT and TUB were the optimum reference genes under salt and osmotic stress.” However, according to Table 4, ACT and TEF are the best for salt-stressed sample and TUB alone is the best for osmotic-stressed sample. Author should cross-check it and the Abstract as well (Line 44-45).

5. According to authors (line 117-119), there are three treatment groups (i.e., Control, Salt, and Osmotic), five time points (3, 6, 12, 24, and 48 h), and three tissue samples (i.e., root, stem, and leaf). Which tissue samples were used for figure 3? Similarly, which treatment groups and what time points were used for Figure 4? Author should clarify it in the respective figure legend.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: Yes: Ritesh Ghosh

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14 Nov 2019

Reviewer 1

1. Line 175-176: Author should add the graph for correlation analysis or at least mention the R2 value in the manuscript.

Response: We have analyzed the data of 11 candidate genes between M values vs SV values, SV values vs SD values , M values vs SD values, which indicated the data could not establish the expression stability. We have been added the scope of R2 to the discussion section. (see line: 176-177, 406-409) The data of R2 in detail as follows.

Gene R2 (M vs SV) R2 (SD vs SV) R2 (M vs SD)

CDPK 0.5082 0.2565 0.8657

YSL8 0.2269 0.0534 0.0068

SAND 0.7552 0.1032 0.4931

UBC 0.3972 0.1771 0.9159

TEF 0.8121 0.4036 0.8018

18S 0.8169 0.416 0.1001

EF1α 0.8158 0.6027 0.93

ACT 0.068 0.2959 0.0611

TUA 0.0022 0.0048 0.0315

TUB 0.2061 0.8232 0.0996

We have calculated the ranking of candidate reference genes by three algorithms according to the following references:

1.Wang J, Abbas M, Wen Y, Niu D, Wang L, Sun Y, et al. (2018) Selection and validation of reference genes for quantitative gene expression analyses in black locust (Robinia pseudoacacia L.) using real-time quantitative PCR. PLoS ONE 13(3): e0193076.

2.He M, Cui S, Yang X, Mu G, Chen H and Liu L. (2017) Selection of suitable reference genes for abiotic stress-responsive gene expression studies in peanut by real-time quantitative PCR. Electronic Journal of Biotechnology 28: 76–86.

3.Wei Y, Liu Q, Dong H, Zhou Z, Hao Y, Chen X, et al. (2016) Selection of Reference Genes for Real-Time Quantitative PCR in Pinus massoniana. PLoS One 11(1): e0147224.

4.Zhu J, Zhang L, Li W, Han S, Yang W and Qi L (2013) Reference Gene Selection for Quantitative Real-time PCR Normalization in Caragana intermedia under Different Abiotic Stress Conditions. PLoS One 8(1): e53196.

2. Line 337-339: “Simultaneously, T-test showed that the expression of GAPDH was significantly lower than other reference genes” – I think this is an incorrect statement. T-test result is showing the significant difference of GRAS expression between control vs salt-treated sample. Thus, only the second part of this sentence is appropriate – “the expression level of NaCl treatment group was significantly higher than that of the control”.

Response: We have deleted this inappropriate sentence according to the suggestion. (see line: 342-344)

3. Author should mention the P-value threshold for T-test in Figure 3 and 4 legends.

Response: We have added P-value threshold for T-test in the Figure 3 and 4 legends according to the suggestion. (see line: 178-179, 368, 373-374)

4. Line 403-404: “ACT and TUB were the optimum reference genes under salt and osmotic stress.” However, according to Table 4, ACT and TEF are the best for salt-stressed sample and TUB alone is the best for osmotic-stressed sample. Author should cross-check it and the Abstract as well (Line 44-45).

Response: Yes, TUB alone is the best for osmotic-stressed sample. We have cross-checked the manuscript and corrected it according to the suggestion. (see line: 44-45, 412-413)

5. According to authors (line 117-119), there are three treatment groups (i.e., Control, Salt, and Osmotic), five time points (3, 6, 12, 24, and 48 h), and three tissue samples (i.e., root, stem, and leaf). Which tissue samples were used for figure 3? Similarly, which treatment groups and what time points were used for Figure 4? Author should clarify it in the respective figure legend.

Response: Leaf tissue samples were used in the Figure 3, and leaves, stems, and roots in normal conditions samples were used in the Figure 4. We have added it in the Figure 3 and Figure 4 legends according to the suggestion. (see line: 363, 372)

18 Nov 2019

Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions

PONE-D-19-25334R2

Dear Dr. Ji,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Mayank Gururani

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Ritesh Ghosh

22 Nov 2019

PONE-D-19-25334R2

Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions

Dear Dr. Ji:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

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on behalf of

Dr. Mayank Gururani

Academic Editor

PLOS ONE