Selection of reference genes for RT‐qPCR normalization in blueberry (Vaccinium corymbosum × angustifolium) under various abiotic stresses

Abstract

As a small fruit rich in anthocyanins, blueberry (Vaccinium corymbosum × angustifolium) has become a focus of research in recent years for identifying genes related to anthocyanin transport and stress resistance mechanisms based on transcriptome sequencing. However, the lack of validated, stably expressed reference genes greatly limits the functional study of blueberry genes. Therefore, in this study, we selected 14 candidate reference genes from a blueberry transcriptome database and used three algorithms (geNorm, NormFinder and BestKeeper) to evaluate the expression stability of these genes in various organs at different fruit developmental stages under five abiotic stress conditions. EF1α, EIF and TBP were observed to be the most stable and were thus chosen as reference genes for quantitative real‐time PCR. Measurement of the relative expression of VcMATE1 (European Nucleotide Archive accession number KF875433) in blueberry further verified the reliability of these reference genes, which may have great utility for determining the accuracy of gene expression analyses in future research on blueberry.

A stably expressed reference gene is required for us to understand gene function and stress resistance mechanisms of blueberry. In this study, 14 candidate reference genes were evaluated and validated under natural conditions and various abiotic stresses by three algorithms. Our results may lay the foundation for accurate analysis of blueberry gene expression.

Abbreviations

ABA

abscisic acid

ACE

actin

Com

Comprehensive ranking

Ct

cycle threshold

CTAB

cetyl trimethyl ammonium Bromide

CYP

cyclophilin

EF1α

elongbation factor 1‐alpha 3

EIF

eukaryotic initiation factor 4A

Fbox

F‐box family protein

FLD

flowering locus D

GAPDH

glyceraldehyde‐3‐phosphate dehydrogenase

HIS

histone

HKG

housekeeping gene

Nr database

RefSeq nonredundant proteins database

PEG

poly(ethylene glycol)

PP2A

protein phosphatase 2A regulatory subunit

RP

RNA polymerase subunit

RPKM

reads per kilobase per million mapped reads

RT‐qPCR

quantitative real‐time PCR

S

stability

SAND

SAND family protein

SD

standard deviation

TARAAPE

transcripts more prevalent in the sarcocarp library

TBP

TATA‐box binding protein

TBRAAPE

transcripts more prevalent in the exocarp library

TUB

β‐Tubulin

UBCE

ubiquitin conjugating enzyme2

Plants are continuously exposed to various ambient conditions that can cause detrimental effects during all developmental stages [1, 2]. Abiotic stresses are nonbiological factors that influence living organisms in a specific environment that negatively affects the growth and yields of crop [3]. High salinity [2, 4], drought [4, 5], flooding [6, 7], chilling [8, 9, 10], heat [4, 10] and heavy metal stresses [11, 12, 13, 14, 15, 16] are adverse natural factors that affect fruit tree growth and yield that are undesirable to be encountered during the whole process. Shielding plants from high salinity, drought and other abiotic stresses is difficult. Plants have evolved strategies to cope with such stresses, and elucidation of the underlying mechanisms is thus increasingly useful [2, 5]. Researchers have found that abiotic stress‐induced gene expression is an efficient, valuable tool for perceiving slight changes in the expression levels of worthwhile target genes [5].

In conjunction with advances in scientific research in multiple disciplines, a variety of methods have been developed for use in gene expression analysis, such as RNA sequencing, serial analysis of gene expression, RNase protection assays, microarrays and northern blotting. Three main techniques are currently used to detect differential gene expression induced by various abiotic stress conditions: northern blotting, microarrays and quantitative real‐time PCR (RT‐qPCR) [17]. But the uncontrollability of the quality of RNA and the low‐throughput nature of northern blotting limit the accuracy of the determination of expression levels. Because northern blotting is time‐consuming and labor intensive, it has been largely superseded by microarrays and RT‐qPCR [17]. Although the ability of microarray platforms to handle thousands of gene probes is attractive, this method does not have the sensitivity needed to detect variation in gene expression or the specificity exhibited by probe hybridization [18, 19, 20].

RT‐qPCR, which has the highest sensitivity and accuracy of different methods for detecting gene expression changes, uses probes or fluorescent dyes to determine the quantity of the initial template [21]. Similar to DNA microarray hybridization, RT‐qPCR can be used to screen multiple target genes simultaneously. Furthermore, the nature of the PCR leads to a high detection sensitivity and high specificity, thereby allowing gene expression levels to be measured. Such characteristics make RT‐qPCR an attractive tool for better studying gene expression changes and regulatory mechanisms induced by abiotic stresses in plants [22].

Because of the earlier‐mentioned benefits, we used RT‐qPCR based on the SYBR Green I method in this study. Although RT‐qPCR is powerful and sensitive, another indispensable factor, reference genes, must also be considered. Reference genes are internal controls whose expressions in various species, organs, cells and environments are relatively constant. During detection of gene expression level changes, reference genes can be used to correct for sample size and experimental errors. Although both functional and nonfunctional genes are feasible choices as reference genes, housekeeping genes (HKGs) are the most frequently used because their expression levels are less influenced by ambient factors, and they are consistently and stably expressed in almost all organs and during all growth phases. HKGs have minimal influence on RNA quality and the efficiency of reverse transcription, and normalization to HKGs increases the reliability of RT‐qPCR results. Recent studies have shown that a suitable HKG should be carefully selected because no HKG is applicable to all conditions. In this study, we therefore aimed to select a suitable reference gene for the standardization of gene expression in blueberry [17].

Blueberry is a deciduous or evergreen, perennial shrub in Vaccinium (Ericaceae), a genus that also includes cranberry and lingonberry [23]. Blueberry is of interest because of its valuable, succulent, attractive fruits, which contain a high level of anthocyanins [24]. In addition, commercial blueberry cultivation is becoming more popular among growers. To better understand blueberry physiology and gene functions, we chose a transcriptomic approach, which requires precise quantification of expression abundance. RT‐qPCR is an accurate, stable and efficient method for the detection of transcript abundance. Gene expression data obtained by RT‐qPCR must be normalized using validated, stably expressed reference genes. The evaluation and identification of suitable reference genes were thus important for our study of blueberry. Various abiotic stresses seriously affect fruit yield [4, 10, 15, 25]. We therefore aimed to study gene expression in blueberry under abiotic stress, which required the identification of a stable reference gene. On the basis of literature reports, we chose the following 14 reference genes as candidates for standardization of a target gene and tested their expression stability: actin (ACT), cyclophilin (CYP), elongation factor 1‐alpha 3 (EF1α), eukaryotic initiation factor 4A (EIF), F‐box family protein (Fbox), flowering locus D (FLD), glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), histone (HIS), protein phosphatase 2A regulatory subunit (PP2A), RNA polymerase subunit (RP), SAND family protein (SAND), TATA‐box binding protein (TBP), β‐Tubulin (TUB) and ubiquitin conjugating enzyme2 (UBCE) [17]. Our search for a stable internal reference gene was aimed to lay a foundation for future studies of gene expression in blueberry.

Materials and methods

Plant materials, growth conditions and abiotic treatments

The half‐high blueberry cultivar ‘Northland’ was used during the 2018 season in this study. We collected roots, stems, leaves, leaf buds and flower buds from fresh, tender parts. Flowers were gathered at the full‐bloom phase, and green, pink and blue fruits were, respectively, collected 24, 42 and 54 days after flowering. Seeds, exocarps and sarcocarps were separated from blue fruits after collection. Samples collection location was Engineering Center of Genetic Breeding and Innovative Utilization of Small Fruits of Jilin Province, Changchun, China.

For abiotic stress treatments, samples were collected from ~1000 two‐year‐old seedlings of ‘Northland’ grown at the farm of Tonghua Heyun Modern Agricultural Co. in Tonghua, China, during the 2018 season. The plants were then cultured in 4 L Hoagland’s nutrient solution (pH 4.5–5.0) and grown in a controlled climate chamber (25 °C/22 °C day/night temperature, 16‐h/8‐h photoperiod, 100 μmoL·m⁻²·s⁻¹ photon flux density and 40–60% relative humidity). The roots of the blueberry were cleaned before placing into Hoagland’s nutrient solution. All samples in nonstress and stress treatments were oxygenated for 2 h twice daily, with the Hoagland’s nutrient solution replaced at regular intervals. After treatment, plant materials were immediately collected in pre‐prepared aluminum foil parcels, quickly frozen in liquid nitrogen and then stored in an ultracold storage freezer at −80 °C until milling for total RNA isolation. The whole process was performed rapidly to prevent sample thawing.

Prior to abiotic treatments, we precultured the plants in the climate chamber for 10 days. The following stress treatments were applied: salt treatment (110 mm NaCl), alkaline treatment (110 mm NaHCO₃), saline–alkaline treatment (50 mm NaCl and 70 mm NaHCO₃), drought treatment [8% poly(ethylene glycol) (PEG) 8000] and AlCl₃ treatment (100 μm AlCl₃). At various time points during the different stress treatments (0, 2, 6, 12 and 24 h), root and leaf samples were separately collected, with at least three biological repeats, and frozen in liquid nitrogen for expression analyses (Fig. S1; Table S1).

RNA extraction, DNase treatment and cDNA synthesis

Total RNA was extracted using a modified cetyl trimethyl ammonium bromide method [26, 27]. The quantity and quality of extracted RNA were determined on an IMPLEN P330 instrument. Only RNA samples meeting the following criteria were used in this study: (a) absorbance (A) ratios within a certain range, namely, 1.8 ≤ A ₂₆₀/A ₂₈₀ ≤ 2.0; (b) A ₂₆₀/A ₂₃₀ approximately equal to 2.0; and (c) 28S/18S ribosomal RNA bands clear and distinct, with no smearing on 1.2% (w/v) agarose gels. To ensure the consistency of each individual reaction, we synthesized cDNA in 20‐µL volumes containing 1000 ng template RNA using a PrimeScript RT reagent kit with gDNA Eraser (Perfect Real Time, Takara, Japan) according to the kit protocol. The gDNA Eraser in the reagent kit was able to effectively remove DNA in the total RNA. All cDNAs were stored at −20 °C until use.

Selection of candidate reference genes

The 14 candidate reference genes were evaluated. These genes were chosen based on their previous use in blueberry and other popular species, including ACT, CYP, EF1α, EIF, Fbox, FLD, GAPDH, HIS, PP2A, RP, SAND, TBP, TUB and UBCE. Because blueberry genomic information is lacking, we had previously generated a transcriptome from blueberry exocarps and sarcocarps by Illumina sequencing technology. After assembly and annotation using SOAPdenovo, expression profile data of each organ were mapped to the transcriptome. The Genome Analyzer IIX platform was used to convert unigene reads per kilobase per million mapped reads (FPKM values). The following statistics were obtained for differentially expressed genes: gene ID, gene expression level, gene description and the differential expression relationship, log₂ (TBRAAPE_RPKM/TARAAPE_RPKM). Values of false discovery rate ≤0.001 and |log₂ (TBRAAPE_RPKM/TARAAPE_RPKM)| ≥ 1 were used as the criteria for judging the significance of gene expression differences [28].

Using the earlier transcriptome, we also selected several HKGs as candidate genes according to the results of previous studies. Genes meeting the following criteria were considered to be candidate reference genes: protein annotated in the RefSeq non‐redundant proteins database and |log₂ (TBRAAPE_RPKM/TARAAPE_RPKM) | < 0.6.

Primer design and validation of candidate genes

Primers were designed with primer premier 5 software (PREMIER, North York, ON, USA) and the Primer‐BLAST online tool according to the following criteria: primer length of ~18–30 bp, GC content 40–60%, melting temperature 58–62 °C and amplicon length of 100–150 bp. All RT‐qPCR primers were synthesized by Suzhou Genewiz Bio‐Technology Services Co. (Suzhou, China).

High‐quality amplification efficiency is a prerequisite for reliable RT‐qPCR results. After even mixing, cDNA was diluted by 5‐fold gradient dilution (5⁰, 5⁻¹, 5⁻², 5⁻³ and 5⁻⁴). Calibration curves were automatically generated by the StepOne Plus system software, and PCR amplification efficiency was automatically calculated according to the formula E = 10^(−1/slope) − 1. To ensure high specificity and efficiency of primers during RT‐qPCR amplification, we used only primers with an amplification efficiency near 100% and a correlation coefficient (R ²) >0.99. The presence of a single peak in the melting curve was required to further confirm the amplification specificity of the mRNA of a candidate reference gene.

RT‐qPCR and data analyses

RT‐qPCR was performed in 96‐well plates on an Applied Biosystems StepOne Plus Realtime PCR system (Thermo Fisher, Waltham, MA, USA), with the ratio of components in each 20‐µL reaction mixture conforming to the specifications of a TB Green Premix Ex Taq II kit (Tli RNaseH Plus; Takara). The following cycling protocol was used: 40 cycles of 95 °C for 30 s, 95 °C for 5 s and 60 °C for 30 s, followed by 95 °C for 10 s, 60 °C for 60 s and 95 °C for 15 s to generate the melting curve. After program completion, background‐corrected fluorescence data and cycle threshold (C_t) values were immediately calculated, as well as output by the instrument software. To confirm primer specificity, we checked the RT‐qPCR products by 1% (w/v) agarose gel electrophoresis.

Analysis of candidate reference‐gene expression stability

C_t values of three replicates, output by the earlier‐mentioned software, were averaged, and the relative expression level (Q) of each analyzed gene was calculated using the formulas $Q=2^{-\mathrm{\Delta }{C}_t}$ and $Q=2^{-\mathrm{\Delta }\mathrm{\Delta }{C}_t}$.

To assess the feasibility of candidate reference genes, we analyzed the generated data in geNorm [29], NormFinder [30] and BestKeeper [31]. geNorm was used to obtain M‐values for each candidate gene, as well as the optimal number of reference genes, the latter based on the average pairwise variation (V_{n / n +1}). Using geNorm, we ranked candidate genes according to their expression stability by calculating their M‐values, which are inversely proportional to their stability. The default value of V_{n / n +1}, which was slightly adjustable, was 0.15. If V_{n / n +1} was no more than 0.15, the optimal number of reference genes was n. If the value of V_{n / n +1} was greater than 0.15, the optimal number was expected to be n + 1. NormFinder was used to obtain stability (S) values and the optimal intergroup gene combination. BestKeeper was also used to analyze the stability of candidate reference genes and additionally used to directly calculate average C_t values. Because BestKeeper can analyze only 10 genes at a time, we removed the four worst genes as determined by NormFinder and geNorm. Finally, geometric means of the results of the three algorithms were combined to obtain a consensus ranking of candidate reference genes.

Validation of reference genes

VcMATE1 (European Nucleotide Archive accession number KF875433) was previously cloned in our laboratory [32]. The forward and reverse primer sequences used for RT‐qPCR were 5′‐TGCTTCCATGGCTACCTCCTT‐3′ and 5′‐TTTTGCTCCATAGGACTGCCC‐3′, respectively. Several abiotic stress conditions, mentioned earlier, were chosen for validation of stable and unstable reference genes. To normalize the expression level of VcMATE1, we used the reference genes most and least stably expressed under various conditions, namely, the most stable in organs and colored fruits at different periods of maturity (EIF, EF1α), leaves and roots under conditions of salinity (PP2A, TBP), alkaline stress (EIF, UBCE), saline–alkaline conditions (PP2A, HIS), simulated drought (TBP, GAPDH) and exposed to AlCl₃ (TBP, EF1α), and the least stable under nonstress conditions (ACT, CYP) and exposure to salinity (CYP, SAND), alkalinity (GAPDH, CYP), saline–alkaline (CYP, SAND), AlCl₃ (SAND, UBCE) and simulated drought (SAND, ACT).

Results

Screening for universal candidate reference genes

As shown in Table S2, we evaluated the expression stabilities of all transcripts and removed those transcripts lacking a credible function annotation. Using combined information from the transcriptome database and previous reports, we ultimately selected 14 candidate genes: ACT (unigene 2464), CYP (unigene 13197), EF1α (unigene 12271), EIF (unigene 19256), Fbox (unigene 7226), FLD (unigene 28351), GAPDH (unigene 17625), HIS (unigene 8208), PP2A (unigene 14576), RP (unigene 15023), SAND (unigene 12206), TBP (unigene 11381), TUB (unigene 4780) and UBCE (unigene 4251) (Table 1).

Table 1.

Description of the candidate reference genes. KEGG, Kyoto Encyclopedia of Genes and Genomes.

Gene symbol	Target sequence	Nr Description	Nr ID	KEGG Orthology	Gene length (bp)	TARAAPE_RPKM	TBRAAPE_RPKM	Log2 ratio
ACT	Unigene 2464	Actin (Populus trichocarpa)	gi|224088196|ref|XP_002308365.1|	K10355	585	59.346	41.9122	−0.50178
CYP	Unigene 13197	Cyclophilin (Ziziphus jujuba)	gi|196166898|gb|ACG70968.1|	K01802	495	40.5789	40.4744	−0.00371
EF1α	Unigene 12271	Elongation factor 1‐alpha 3 (Lilium longiflorum)	gi|5917747|gb|AAD56020.1|AF181492_1	K03231	258	685.0639	830.3061	0.27740
EIF	Unigene 19256	Eukaryotic initiation factor 4A‐14 (Nicotiana tabacum)	gi|2500520|sp|Q40467.1|IF414_TOBAC	K03257	123	250.9837	239.3225	−0.06918
Fbox	Unigene 7226	F‐box protein family (Arabidopsis lyrata subsp. lyrata)	gi|297806791|ref|XP_002871279.1|	K10102	609	9.1991	8.8816	−0.05067
FLD	Unigene 28351	Flowering locus D (Arabidopsis thaliana)	gi|240255318|ref|NP_187650.4|	K11450	453	3.4496	3.3721	−0.03278
GAPDH	Unigene 17625	Glyceraldehyde‐3‐phosphate dehydrogenase (Magnolia quinquepeta)	gi|120669|sp|P26518.1|G3PC_MAGLI	K00134	582	596.6472	656.4584	0.13783
HIS	Unigene 8208	Histone H3.2 (Arabidopsis thaliana)	gi|153799895|gb|ABS50666.1|	K11253	468	153.1042	143.9564	0.28304
PP2A	Unigene 14576	Protein phosphatase 2A regulatory subunit B (Arabidopsis thaliana)	gi|75274192|sp|Q9LU89.1|2A5N_ARATH	K11584	1518	30.1756	31.5648	0.06493
RP	Unigene 15023	RNA polymerase subunit (Medicago truncatula)	gi|124359979|gb|ABN07995.1|	K03013	723	75.1492	51.1261	−0.55570
SAND	Unigene 12206	SAND family protein (Arabidopsis lyrata subsp. lyrata)	gi|297822433|ref|XP_002879099.1|	K20195	1890	29.2467	27.7054	−0.18620
TBP	Unigene 11381	TATA‐box binding protein (Phaseolus vulgaris)	gi|4102725|gb|AAD10238.1|	K03120	606	37.4796	41.8497	0.15911
TUB	Unigene 4780	beta‐Tubulin (Eucalyptus grandis)	gi|153799895|gb|ABS50666.1|	K07375	1125	15.5758	15.5318	−0.00408
UBCE	Unigene 4251	Ubiquitin conjugating enzyme2‐like (Solanum tuberosum)	gi|213494485|gb|ACJ48964.1|	K06689	444	236.2412	245.37	0.05470

RNA was quantified, and we verified the integrity of the RNA. The 28S RNA band on the agarose gel was approximately two times brighter than that of 18S RNA (Fig. S2; Table S3).

RT‐qPCR using a 5‐fold serially diluted template yielded amplification products with high efficiency and specificity. The locations of primer pairs on transcript sequences were shown in Data S1. Primer specificity was confirmed by the presence of a single peak in melting curves (Table S4). Amplification efficiencies of candidate reference gene primers varied between 94.312% and 103.908%, and standard curve correlation coefficients ranged from 0.990 to 0.999 (Table 2). RT‐qPCR products were evaluated by 1.0% (w/v) agarose gel electrophoresis and sequencing. Each lane on the agarose gels contained only one band, and the sequencing results confirmed that the expected products were generated (Fig. S3).

Table 2.

List of primer sequences and related information for 14 candidate reference genes.

Gene symbol	Target sequence	Gene description	Primer sequence (5′–3′) (forward/reverse)	R 2	Amplification efficiency (%)	Amplicon Tm (°C)	Amplicon length (bp)
ACT	Unigene 2464	Actin	GAAATAACAGCGTTGGCCCC	0.997	96.014	83.92	112
			GGAAGGTACTGAGGGATGCG
CYP	Unigene 13197	Cyclophilin	TATTTGCTGATACCACGCCCA	0.990	100.106	84.07	101
			CCCTTTGTAGTGCAATGGCTTC
EF1α	Unigene 12271	Elongation factor‐1 alpha 3	TGGAAATGGGTATGCCCCAG	0.999	97.624	83.47	147
			ACCATACCGGCATCTCCATTC
EIF	Unigene 19256	Eukaryotic initiation factor 4A	GGAGGAAAGGTGTTGCCATCA	0.999	99.905	81.99	117
			GGAGATCAGCAACGTTTGCTG
Fbox	Unigene 7226	F‐box family protein	CGATTCAAGAGCGTGTCAAAGC	0.997	96.744	83.19	109
			AATGCAAACCTGAGACGGTGG
FLD	Unigene 28351	Flowering locus D	GAGTGAAGCTGGTTGGGAGAA	0.997	94.312	83.77	100
			GAAGTTGAAGCAGACTTGCGG
GAPDH	Unigene 17625	Glyceraldehyde‐3‐phosphate dehydrogenase	CCGGAGCTGAGTTTGTTGTT	0.998	95.161	82.58	105
			GACCACCTTCTTTGCACCAC
HIS	Unigene 8208	Histone	AGGAGTCAAGAAGCCCCACA	0.999	96.045	81.70	127
			AGCAATCTCACGAACAAGCC
PP2A	Unigene 14576	Protein phosphatase 2A regulatory subunit	TTCCTGAGATTCGTGGCATCA	0.992	98.785	81.25	103
			CCTCGGAATCGAAAAGATCCA
RP	Unigene 15023	RNA polymerase subunit	GACGAAGGTAGCACCGAGAG	0.996	95.203	79.9	142
			GTGTTTGGCCGTGAATGGAC
SAND	Unigene 12206	SAND family protein	CACCCGAATTCCACTTCAATTG	0.993	97.808	83.63	101
			GGATTATCGGATGCAAGGTCG
TBP	Unigene 11381	TATA‐box binding protein	GCCAACCGGTGGATCTTTCTA	0.991	103.908	80.79	108
			GTGCAATGGCCTTAAGTTCCAA
TUB	Unigene 4780	Beta‐tubulin	CCCCGATAACTTCGTGTTTGG	0.995	100.983	83.48	101
			CGACATCGAGAACCGAATCAAT
UBCE	Unigene 4251	Ubiquitin conjugating enzyme2	CAAACCCCGATGATCCTCTTG	0.990	101.35	83.63	101

Expression profiles of candidate reference genes

Under a nonstress condition, C_t values of candidate genes varied from 19.88 to 32.34 in all test samples, with most values ranging between 22.54 and 29.41. The average C_t values of CYP, Fbox and FLD were 31.68, 32.25 and 30.86, respectively, which indicates that their expressions were weak and their transcript abundances were low. Among the studied reference genes, genes with high expression variation (>6 cycles) were ACT and CYP (6.10 and 7.20 cycles, respectively). The remaining candidate reference genes had low expression variation (<4 cycles), which ranged from 2.04 to 3.34 cycles (Fig. 1A; Data S2).

Fig. 1.

RT‐qPCR C_t values of the candidate reference genes. (A) Candidate reference genes were analyzed in all organ samples. (B) Candidate reference genes were analyzed in leaf and root samples under five abiotic stresses. The box indicates the 25th and 75th percentiles. A line in the box represents the median. Whiskers represent the maximum and minimum values.

Under the different abiotic stress conditions used in this study, C_t values of the 14 genes ranged from 20.80 to 36.36. The majority of C_t values were between 22.23 and 29.55. C_t values of CYP, Fbox and FLD were 35.18, 36.36 and 34.89, respectively. Fbox, PP2A, TUB, ACT, FLD, CYP and SAND exhibited high expression variation (>6 cycles, namely, 6.11, 6.22, 6.45, 7.46, 7.49, 8.15 and 9.99, respectively), whereas UBCE, EF1α, TBP, EIF, GAPDH, HIS and RP had low expression variation (<6 cycles): 3.56, 5.04, 5.19, 5.27, 5.36, 5.52 and 5.66, respectively (Fig. 1B; Data S3).

The length of the box also provided information about deviations: the shorter the box, the smaller the deviations. Screening for reliable reference genes by various scientific methods was thus necessary to standardize gene expressions under specific conditions in blueberry.

Analysis of candidate reference‐gene expression stability

geNorm analysis

According to the geNorm analysis, the genes with the smallest M‐value (0.485), and thus highest stability, in all organ samples without treatment were EF1α and EIF. Although V ₂/V ₃ and V ₃/V ₄ were both greater than 0.15, V ₄/V ₅ was smaller than 0.15, which indicated that four genes were needed for normalization of gene expression. The third‐ and fourth‐most stable genes were RP (M = 0.583) and SAND (M = 0.667), respectively, and the least stable gene was CYP (M = 1.101) (Figs 2A and 3A; Table 3).

Fig. 2.

Expression stability and ranking of the candidate reference genes as determined by geNorm. (A) All organ samples without abiotic stresses. (B) All tissue samples under five abiotic stresses. (C) NaCl treatment. (D) NaHCO₃ treatment. (E) NaCl + NaHCO₃ treatment. (F) Simulated drought. (G) AlCl₃ treatment. Average expression stability values (M) of the reference genes measured by geNorm. The lower M‐value indicated more stable expression level.

Fig. 3.

Pairwise variation (V_n/V_{n +1}) values calculated by geNorm. (A) All organ samples without abiotic stresses. (B) All tissue samples under different abiotic stresses. V_n/V_{n +1} > 0.15 means an additional (n + 1) reference was required, whereas V_n/V_{n +1} ≤ 0.15 means only n reference was required.

Table 3.

Expression stability ranking of 14 candidate reference genes in all organ samples of blueberry without abiotic stresses.

Symbol	All organ samples in blueberry
	GeNorm		NormFinder		BestKeeper			Com
	M a	Rank	S b	Rank	r c	SD d	Rank
ACT	0.965	13	0.878	13	– e	–	(11)	12
CYP	1.101	14	1.256	14	–	–	(12)	14
EF1α	0.485	1	0.328	3	0.773	0.634	2	2
EIF	0.485	1	0.271	2	0.855	0.599	1	1
Fbox	0.856	11	0.634	12	–	–	(14)	12
FLD	0.875	12	0.617	11	–	–	(13)	11
GAPDH	0.739	7	0.408	6	0.768	0.707	4	5
HIS	0.699	5	0.427	7	0.558	0.555	(10)	7
PP2A	0.791	9	0.479	9	0.742	0.673	6	9
RP	0.583	3	0.377	5	0.773	0.743	2	3
SAND	0.667	4	0.245	1	0.765	0.376	5	3
TBP	0.830	10	0.604	10	0.647	0.843	9	10
TUB	0.766	8	0.435	8	0.715	0.748	7	8
UBCE	0.713	6	0.365	4	0.705	0.501	8	6
Best gene	EF1α/EIF		SAND			EIF		EIF
Worst gene	CYP		CYP			Fbox		CYP
Best combination	EF1α/EIF/RP/SAND

Results without statistical significance were put in parentheses and not used at last.

^a M: stability values were calculated by geNorm. The lower the M‐value is, the more stable is the gene.

^b S: stability values were calculated by NormFinder. The lower the S‐value is, the more stable is the gene.

^c r: Pearson’s correlation coefficient was calculated by BestKeeper. The higher the r‐value is, the more stable is the gene.

^dSD: the SD was calculated by BestKeeper. The value of SD should be <1.

^eThe dashes indicate that the Pearson’s correlation coefficient that was P > 0.05 or SD > 1 was deleted.

In both leaf and root samples under all abiotic stresses, the most stable genes were EIF and UBCE (M = 0.487), and the least stable gene was SAND (M = 1.054). Because V ₂/V ₃ > 0.15 and V ₃/V ₄ < 0.15, we needed to add a third gene, HIS (M = 0.530), to the normalization. The candidate reference genes assessed in our study did not exhibit consistent stability across different sample sets and all situations (Figs 2B and 3B; Table 4). Under high salinity, PP2A + TBP (M = 0.292, V ₂/V ₃ = 0.129) were ranked as the most stable, whereas SAND (M = 0.967) was the least stable (Figs 2C and 3B; Table 5). Under high alkalinity, EIF + SAND (M = 0.422, V ₂/V ₃ = 0.129) and GAPDH (M = 0.881) were the best and worst reference genes, respectively (Figs 2D and 3B; Table 5). In leaf and root samples subjected to combined salinity and alkalinity stress, HIS + UBCE (M = 0.285, V ₂/V ₃ = 0.102) and SAND (M = 1.022) had the highest and lowest stabilities, respectively (Figs 2E and 3B; Table 5). GAPDH + HIS (V ₂/V ₃ = 0.143), with an M‐value of 0.367, and ACT, with an M‐value of 0.970, were, respectively, the most and least suitable genes in leaf and root samples under PEG‐simulated drought (Figs 2F and 3B; Table 5). EF1α + TBP (M = 0.359, V ₂/V ₃ = 0.124) performed best in root samples subjected to the AlCl₃ treatment, whereas UBCE (M = 0.933) performed the worst (Figs 2G and 3B; Table 5). SAND and CYP were the least stably expressed candidate reference genes under the different abiotic stresses in this study (Fig. 2; Tables 4 and 5). These results underscore the importance of screening for the reference genes that are most appropriate for a given set of experimental conditions.

Table 4.

Expression stability ranking of 14 candidate reference genes in all samples of blueberry under abiotic stresses.

Symbol	All samples under five abiotic stresses
	GeNorm		NormFinder		BestKeeper			Com
	M a	Rank	S b	Rank	r c	SD d	Rank
ACT	0.903	12	0.205	5	– e	–	(14)	11
CYP	0.958	13	0.307	12	–	–	(11)	13
EF1α	0.779	8	0.159	3	0.919	0.997	2	3
EIF	0.487	1	0.194	4	0.883	0.909	4	1
Fbox	0.833	10	0.243	8	–	–	(8)	9
FLD	0.861	11	0.409	14	–	–	(12)	14
GAPDH	0.677	6	0.300	10	0.834	0.729	5	7
HIS	0.530	3	0.290	9	0.952	0.965	1	3
PP2A	0.722	7	0.207	6	–	–	(9)	8
RP	0.571	4	0.133	2	–	–	(10)	5
SAND	1.054	14	0.230	7	–	–	(13)	12
TBP	0.614	5	0.082	1	0.907	0.943	3	1
TUB	0.812	9	0.310	13	–	–	(7)	10
UBCE	0.487	1	0.304	11	0.746	0.488	6	6
Best gene	EIF/UBCE		TBP			HIS		TBP/EIF
Worst gene	SAND		FLD			ACT		FLD
Best combination	EIF/UBCE/HIS		EIF/PP2A (0.080)

^a M: stability values were calculated by geNorm. The lower the M‐value is, the more stable is the gene.

^b S: stability values were calculated by NormFinder. The lower the S‐value is, the more stable is the gene.

^c r: Pearson’s correlation coefficient was calculated by BestKeeper. The higher the r‐value is, the more stable is the gene.

^dSD: the SD was calculated by BestKeeper. The SD should be <1.

^eThe dashes indicate that the Pearson’s correlation coefficient that was P > 0.05 or SD > 1 was deleted.

Table 5.

Expression stability comprehensive ranking of 14 candidate reference genes in all organs under common conditions or in both leaves and roots under various abiotic stresses.

Method	1	2	3	4	5	6	7	8	9	10	11	12	13	14
(A) Ranking order under different organs (better‐good‐average)
geNorm	EF1α/EIF		RP	SAND	HIS	UBCE	GAPDH	TUB	PP2A	TBP	Fbox	FLD	ACT	CYP
NormFinder	SAND	EIF	EF1α	UBCE	RP	GAPDH	HIS	TUB	PP2A	TBP	FLD	Fbox	ACT	CYP
BestKeeper	EIF	EF1α	RP	GAPDH	SAND	PP2A	TUB	UBCE	TBP	HIS	ACT	CYP	FLD	Fbox
Comprehensive ranking	EIF	EF1α	RP	SAND	GAPDH	UBCE	HIS	TUB	PP2A	TBP	FLD	Fbox	ACT	CYP
(B) Ranking order under all stresses (better‐good‐average)
geNorm	EIF/UBCE		HIS	RP	TBP	GAPDH	PP2A	EF1α	TUB	Fbox	FLD	ACT	CYP	SAND
NormFinder	TBP	RP	EF1α	EIF	ACT	PP2A	SAND	Fbox	HIS	GAPDH	UBCE	CYP	TUB	FLD
BestKeeper	HIS	EF1α	TBP	EIF	GAPDH	UBCE	TUB	Fbox	PP2A	RP	CYP	FLD	SAND	ACT
Comprehensive ranking	EIF	TBP	EF1α	HIS	RP	UBCE	GAPDH	PP2A	Fbox	TUB	ACT	SAND	CYP	FLD
(C) Ranking order under NaCl stress (better‐good‐average)
geNorm	PP2A/TBP		HIS	RP	EIF	GAPDH	ACT	TUB	EF1α	UBCE	FLD	Fbox	CYP	SAND
NormFinder	PP2A	GAPDH	TBP	EIF	EF1α	RP	ACT	HIS	TUB	UBCE	FLD	Fbox	CYP	SAND
BestKeeper	PP2A	FLD	GAPDH	HIS	TBP	EF1α	EIF	Fbox	ACT	TUB	RP	CYP	SAND	UBCE
Comprehensive ranking	PP2A	TBP	GAPDH	HIS	EIF	EF1α	RP	ACT	FLD	TUB	Fbox	UBCE	CYP	SAND
(D) Ranking order under NaHCO3 stress (better‐good‐average)
geNorm	EF1α/SAND		ACT	TUB	Fbox	EIF	RP	UBCE	PP2A	TBP	FLD	HIS	CYP	GAPDH
NormFinder	EIF	UBCE	Fbox	TUB	EF1α	RP	TBP	ACT	PP2A	SAND	FLD	HIS	CYP	GAPDH
BestKeeper	UBCE	EIF	SAND	TBP	Fbox	TUB	EF1α	HIS	GAPDH	FLD	ACT	RP	PP2A	CYP
Comprehensive ranking	EIF	UBCE	EF1α	Fbox	SAND	TUB	TBP	ACT	RP	PP2A	FLD	HIS	GAPDH	CYP
(E) Ranking order under NaCl + NaHCO3 stress (better‐good‐average)
geNorm	HIS/UBCE		PP2A	TBP	RP	Fbox	EIF	TUB	FLD	ACT	EF1α	GAPDH	CYP	SAND
NormFinder	PP2A	RP	UBCE	HIS	Fbox	EF1α	EIF	TBP	ACT	TUB	GAPDH	FLD	CYP	SAND
BestKeeper	TUB	PP2A	FLD	Fbox	EIF	HIS	UBCE	EF1α	GAPDH	RP	ACT	TBP	CYP	SAND
Comprehensive ranking	PP2A/HIS		UBCE	Fbox	RP	EIF	TUB	FLD	TBP	EF1α	ACT	GAPDH	CYP	SAND
(F) Ranking order under drought stress (better‐good‐average)
geNorm	GAPDH/HIS		EIF	TBP	PP2A	TUB	UBCE	RP	Fbox	CYP	EF1α	SAND	FLD	ACT
NormFinder	TBP	PP2A	TUB	CYP	Fbox	GAPDH	RP	EIF	HIS	EF1α	UBCE	FLD	SAND	ACT
BestKeeper	TBP	EIF	GAPDH	RP	UBCE	TUB	Fbox	FLD	SAND	PP2A	EF1α	HIS	CYP	ACT
Comprehensive ranking	TBP	GAPDH	EIF	TUB	PP2A	RP	Fbox	HIS	UBCE	CYP	EF1α	FLD	SAND	ACT
(G) Ranking order under AlCl3 stresses (better‐good‐average)
geNorm	EF1α/TBP		GAPDH	CYP	TUB	PP2A	FLD	Fbox	SAND	ACT	RP	HIS	EIF	UBCE
NormFinder	GAPDH	EF1α	TBP	RP	Fbox	CYP	ACT	PP2A	TUB	FLD	EIF	HIS	UBCE	SAND
BestKeeper	ACT	TBP	HIS	EF1α	RP	EIF	GAPDH	UBCE	TUB	PP2A	FLD	SAND	CYP	Fbox
Comprehensive ranking	TBP	EF1α	GAPDH	ACT	RP	TUB	CYP	PP2A	Fbox	HIS	FLD	EIF	SAND	UBCE

NormFinder analysis

The most suitable reference gene is the one with the smallest S‐value. Calculated S‐values and rankings of candidate reference genes are summarized in Tables 3 and 4.

According to NormFinder, the most stably expressed reference gene in all organ samples under nonstress conditions was SAND (S = 0.245). The most unstable gene was CYP (S = 1.256; Table 3). CYP was calculated to be the worst gene in both geNorm and NormFinder.

In leaves and roots under the five abiotic stresses, TBP (S = 0.082) and EIF + PP2A (S = 0.080) had the best performance. The worst gene was FLD (S = 0.409) (Table 4). In leaf and root samples subjected to the NaCl treatment, the best reference genes were PP2A (S = 0.111) and GAPDH + PP2A (S = 0.072). The most unstable one was SAND (0.673) (Table 5). Under NaHCO₃ treatment conditions, EIF (S = 0.150) and Fbox + UBCE (S = 0.111) were found to be the most stable for analysis of leaf and root samples. GAPDH (0.759) performed the worst (Table 5). In root and leaf samples subjected to combined NaCl–NaHCO₃ treatment, PP2A (S = 0.188) and PP2A + RP (S = 0.130) were the best choices, and SAND (0.984) was the worst (Table 5). In leaf and root samples under PEG treatment conditions, TBP and EIF + TUB exhibited the most stable expression, with S‐values of 0.141 and 0.106, respectively, and the most unstable gene was ACT (0.551) (Table 5). In AlCl₃‐stressed leaf and root samples, GAPDH (S = 0.130) and EF1α + GAPDH (S = 0.082) displayed the highest stability. The unstable gene was SAND (0.661; Table 5). The stability rankings of leaves and roots separately under different stresses analyzed by NormFinder were shown in Table S5.

BestKeeper analysis

BestKeeper, designed by Pfaffl et al. [31], can be used to analyze both reference and relevant target genes. Expression levels can be analyzed for only 10 HKGs and 10 target genes in 100 samples at a time. After using BestKeeper to calculate the Pearson’s correlation coefficient (r), standard deviation (SD) and coefficient of variation between each pair of genes, the magnitudes of these values can be compared to determine the most stable reference genes. In particular, the larger the value of r and the smaller the values of SD and the coefficient of variation, the higher is the expression stability. If the SD is >1, the expression of the candidate reference gene is not considered to be stable.

We first excluded candidate genes with SD values greater than 1. In plant organ samples not subjected to any stress treatments, the most stable reference gene was EIF (r = 0.855), followed by EF1α (r = 0.773) and RP (r = 0.773), which were equally good choices, and then GAPDH (r = 0.765) and SAND (r = 0.768; Table 3). In leaves and roots under all abiotic stresses, the five most stable genes in descending order were HIS (r = 0.952), EF1α (r = 0.919), TBP (r = 0.907), EIF (r = 0.883) and GAPDH (r = 0.834; Table 4). The stability ranking of 14 candidate reference genes for leaves and roots taken separately of blueberry under all five abiotic stresses by BestKeeper were shown in Table S6. The most stable gene in leaves under all stresses was HIS (r = 0.880). In roots, TBP (r = 0.970) performed the best under five treatments. When leaves and roots were calculated separately under individual stress conditions, the P‐values of all candidate genes were not <0.05, so the results were not included in it.

Comprehensive ranking

According to the results of three software programs, we ranked all of the 14 candidate reference genes comprehensively. For all organs under common conditions, EIF and EF1α was the most stable combination. On the contrary, CYP and ACT were the least stable genes. Under all five kinds of stresses, EIF and TBP were the most stable genes, and CYP and FLD were the worst ones in leaves and roots of blueberry. Under NaCl stress, PP2A/TBP and CYP/SAND were the best and worst reference genes, respectively. EIF/UBCE was the most stable combination, and GAPDH and CYP were the least stable genes under NaHCO₃ stress. PP2A/HIS ranked the best order, and CYP and SAND ranked the worst order under saline–alkaline condition. The most stable reference genes under drought stress were TBP/GAPDH, and the most unstable genes were SAND and ACT. The best performed reference genes under AlCl₃ treatment were TBP and EF1α. SAND and UBCE performed the worst (Table 5). The comprehensive ranking for leaves and root taken separately was shown in Table S7.

To validate the candidate reference genes, we chose them from the comprehensive rankings to analyze the relative expression of VcMATE1 (Table 5).

Validation of selected reference genes

To confirm the reliability of our results, we selected the two most stable and two least stable reference genes under different experimental conditions and used them to analyze the relative expression of VcMATE1 under specific conditions (Fig. 4; Data S4). When the combination of EIF + EF1α was used as reference genes, the relative expression profiles of VcMATE1 in different organs and fruit developmental stages were extremely similar to those obtained using EIF or EF1α as the reference (Fig. 4C). VcMATE1 expression trends normalized using ACT and CYP, the two least stable genes, differed from those based on EIF and EF1α, and the levels of relative expression of VcMATE1 were extremely high; this was especially true when CYP was the reference gene (Fig. 4D,E).

Fig. 4.

Relative quantification of VcMATE1 expression using validated reference genes for normalization in different organs. (A) VcMATE1 normalized by stable reference gene EF1α + EIF. (B) VcMATE1 normalized by stable reference gene EIF. (C) VcMATE1 normalized by stable reference gene EF1α. (D) VcMATE1 normalized by unstable reference gene ACT. (E) VcMATE1 normalized by unstable reference gene CYP. The error bars represent the SD of three biological replicates. Asterisks indicate that the difference is significant at *P < 0.05 and extremely significant at **P < 0.01, t‐test.

Under conditions of salinity, the expression trend of VcMATE1 in leaves calculated by stable reference genes PP2A + TBP, PP2A and TBP was similar (12 h > 24 h > 0 h ≈ 2 h > 6 h). When CYP or SAND was used as a reference gene, the expression levels of VcMATE1 were quite different (Fig. 5A). The relative expression levels of VcMATE1 in roots based on PP2A + TBP followed the same trend as those obtained using TBP or PP2A as the reference gene. A trend similar to the one based on these two stable reference genes (0 h > 24 h > 2 h > 12 h > 6 h) was observed when VcMATE1 was normalized relative to SAND. As shown in Fig. 5B, in contrast, the trend obtained using CYP as the reference gene was 24 h > 0 h > 12 h > 2 h > 6 h.

Fig. 5.

Relative quantification of VcMATE1 expression using validated reference genes for normalization under different stress conditions. (A) Leaves treated with 110 mm NaCl. (B) Roots treated with 110 mm NaCl. (C) Leaves treated with 110 mm NaHCO₃. (D) Roots treated with 110 mm NaHCO₃. (E) Leaves treated with 50 mm NaCl + 70 mm NaHCO₃. (F) Roots treated with 50 mm NaCl + 70 mm NaHCO₃. (G) Leaves treated with 8% PEG 8000. (H) Roots treated with 8% PEG 8000. (I) Leaves treated with 100 μm AlCl₃. (J) Roots treated with 100 μm AlCl₃. The error bars represent the SD of three biological replicates. Asterisks indicate that the difference is significant at the level of *P < 0.05 and extremely significant at the level of **P < 0.01, t‐test.

When the leaves of blueberry were treated by NaHCO₃, VcMATE1 expressed similarly based on stable genes (EIF + UBCE, EIF, UBCE) and an unstable one (CYP), but the expression level of VcMATE1 treated for 24 h was extremely high, which was different from the stable reference genes. The expression trend of VcMATE1 based on an unstable gene, GAPDH, was almost flat. Therefore, CYP and GAPDH were testified not suitable as reference genes under this condition (Fig. 5C). Roots under alkaline conditions followed a decreasing trend when the least stable genes, CYP and GAPDH, were used as internal controls. In contrast, a fluctuating pattern of expression was observed for VcMATE1 based on the most stable reference genes (EIF + UBCE, EIF and UBCE, respectively; Fig. 5D).

Under the treatment of 50 mm NaCl + 70 mm NaHCO₃, stable candidate genes (PP2A + HIS, PP2A and HIS) were used as reference genes, and the expression of VcMATE1 in the leaves showed a trend of decreasing first and then increasing. Taking unstable candidate genes CYP and SAND as internal controls, the expression of VcMATE1 in leaves generally showed an upward trend. Also, the relative expression was extremely low (SAND as reference gene, treated for 12 h) or high (CYP as reference gene, treated for 24 h), which was not conducive to studying the expression pattern of the target gene (Fig. 5E). We observed that normalization of the relative expression of VcMATE1 in blueberry roots under saline–alkaline conditions yielded similar results when the best genes (PP2A + HIS, PP2A and HIS) were used as calibrators, with major discrepancies obtained upon normalization using the worst reference genes, CYP and SAND. The relative expression level of VcMATE1 normalized using SAND was nearly 0 at 12 h after stress treatment; compared with this value, 37.22‐, 2.09‐ and 2.32‐fold higher VcMATE1 expression levels were observed at the same time point based on CYP, PP2A and HIS, respectively (Fig. 5F).

Under drought conditions, the relative expression of VcMATE1 in blueberry leaves showed a similar expression trend based on the selected stable (TBP + GAPDH, TBP and GAPDH) and unstable (ACT and SAND) reference genes. However, treated for 24 h, the expression level of the target gene with ACT as reference gene significantly increased, which would lead to the unreliable expression pattern (Fig. 5G). VcMATE1 expression levels in roots under simulated drought conditions decreased regularly (0 h > 6 h > 2 h > 12 h > 24 h) based on TBP + GAPDH, TBP and GAPDH as internal reference genes. Obviously different expression trends were obtained when the least stable reference genes were used as internal controls (SAND: 6 h > 12 h > 0 h > 6 h > 24 h; ACT: 0 h > 24 h > 12 h > 6 h > 2 h; Fig. 5H).

Expression levels of VcMATE1 in leaves under AlCl₃ treatment conditions followed the trend of 24 h > 12 h > 0 h > 2 h > 6 h when TBP + EF1α, TBP and EF1α were used as internal controls. In contrast, the patterns of VcMATE1 expression normalized according to the least stable genes, UBCE and SAND, were 24 h > 12 h > 2 h > 6 h > 0 h and 24 h > 6 h > 12 h > 2 h > 0 h, respectively (Fig. 5I). In roots, when the most stable genes (TBP + EF1α, TBP and EF1α) were selected as reference genes, the relative expression trends of VcMATE1 were 0 h > 24 h > 12 h > 6 h > 2 h. When the unstable gene, UBCE, was used as reference gene, the expression trend of the target gene decreased first and then increased, but after 2 h of treatment, the relative expression was almost zero. A different expression trend was observed when the least stable reference gene was used as internal control (SAND: 0 h > 12 h > 24 h > 2 h > 6 h; Fig. 5J). All of this indicated that unstable candidate genes were not reliable as reference genes.

These results confirm the feasibility and reliability of the selected reference genes.

Discussion

With the development of biotechnology, RT‐qPCR has been applied widely for analysis of gene expression, and the selection of appropriate internal reference genes is recognized as the primary prerequisite for reliable and accurate real‐time results. Nevertheless, previous studies on reference gene standardization have demonstrated that reference gene stability is not absolutely constant in diverse species and organs, and under different abiotic/biotic conditions and developmental stages. Even reference genes that work well in model plants may barely be applicable to other species. For instance, ACT is invariably considered to be the best choice in a variety of model species, including Arabidopsis thaliana under abiotic stresses (salt, drought and cold) [33, 34, 35, 36, 37], Nicotiana tabacum under stress treatment (heat, cold, drought, salt and UV) [38] and Oryza sativa subjected to NaCl and abscisic acid (ABA) treatments [39]. Moreover, the same reference gene is often not applicable across closely related species. ACT has been found to be the most stable reference gene for Vitis vinifera under salt and osmotic stresses [40, 41]. In blueberry under salt stress, ACT ranked eighth out of 14 candidate genes in our study, thus demonstrating that it was not stably expressed under our experimental conditions. UBC, another reference gene used in the genus Vaccinium, has been found to be suitable as an internal control in different organs of both rabbiteye and southern highbush blueberry [42], but did not perform well in the half‐high blueberry cultivar ‘Northland’ in our study.

Several factors may be responsible for the earlier‐mentioned variation and observed differences in the expression stability of candidate reference genes. First, RNA expression levels are not constant under all conditions, with those of internal reference genes varying because of differences in factors such as cell‐cycle stage, species, materials and sequencing libraries. Second, we used three main algorithms, NormFinder, geNorm and BestKeeper, to analyze the data obtained in this study [17, 43, 44]. These three methods are the ones currently used by researchers to assess the stability of candidate genes for use as reference genes in RT‐qPCR analyses. NormFinder can generate the best reference gene or best combination, whereas geNorm can select a combination of reference genes and rank them by suitability. Unlike NormFinder and geNorm, BestKeeper does not require preprocessing of data and can directly make use of C_t values obtained by RT‐qPCR for calculations [44]. It is not an exaggeration to say that a comprehensive analysis using multiple methods is the best way to obtain the optimal reference gene.

To date, no reference gene has been found to be suitable in all types of cells or organs [17, 43, 45]. Researchers should therefore conduct preliminary experiments to identify stably expressed reference genes based on the type of cells and organs to be studied and their experimental requirements. At the same time, the expressions of two or more internal reference genes, chosen using an algorithm that selects multiple reference genes, can be averaged and used to normalize the specific target gene expression data to obtain more reliable results.

In this study, we evaluated genes that have been frequently used as internal controls in a large number of species. The most stably expressed genes in various organs and the five abiotic stress conditions were EIF/EF1α and EIF/TBP, respectively. In a previous investigation, EF1α was found to be the most suitable reference gene in O. sativa [46] and Solanum tuberosum [44] during different development stages and under hormone, salt and drought treatments. EF1α and EIF4A were determined to be the most stable genes for use in different organ and abiotic stress subsets (ABA, drought, salt and high/low temperature) in Pennisetum glaucum [47]. Some, although not all, findings in other species are consistent with those of our study, thus indicating that our results are also credible. Regardless of whether our results are consistent with the conclusions of other studies, however, our observations demonstrate that the reference gene most suitable for a set of experimental conditions and a specific analysis should be selected and further evaluated prior to measurements of gene expression levels.

Finally, to further confirm the accuracy of the results of this study, we selected the VcMATE1 gene, a member of the MATE (multidrug and toxic compound extrusion transporter) family, which phylogenetic analysis has clustered with genes involved in the detoxification of xenobiotics or export of toxic cations [32 ]. On the basis of its predicted function, we expected VcMATE1 to respond to diverse abiotic stress conditions and thus be of interest in future studies of blueberry stress resistance. We therefore normalized expression levels of VcMATE1 using the two most stable and two least stable reference genes in each treatment subgroup. Relative expression levels of the VcMATE1 gene normalized using the most stable reference gene were the most consistent. Moreover, the selected genes were stable under normal conditions over time, which indicates that our study results are significant and valuable.

Conclusions

To ensure the accuracy of gene expression analyses, we selected 14 candidate reference genes from a blueberry fruit transcriptome and analyzed them by RT‐qPCR to identify the most appropriate ones for the normalization of potential functional gene expression data. In this study, we determined the optimal set of reference genes for different organs of blueberry under normal and abiotic stress conditions. In all organs under nonstress conditions, EIF + EF1α was the best choice, whereas EIF + TBP was the best combination under all five abiotic stresses. We provided more specific reference gene recommendations for analyses of expression under individual stresses: PP2A/TBP (salinity), EIF/UBCE (alkalinity), PP2A/HIS (salinity–alkalinity) and TBP/GAPDH (drought) and TBP/EF1α (AlCl₃). The use of these reference genes should aid future studies of molecular mechanisms of stress resistance and molecular breeding in blueberry.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

HS conceived and designed the project. YD performed the experiments, analyzed the data and wrote the manuscript. YL provided the blueberry samples. HS and YL contributed to the manuscript writing review and editing.

Supporting information

Fig. S1. Abiotic treatments of 2‐year‐old cutting plants of blueberry. (A) 110 mm NaCl treatment. (B) 110 mm NaHCO₃ treatment. (C) 50 mm NaCl + 70 mm NaHCO₃ treatment. (D) 8% PEG8000 treatment. (E) 100 μm AlCl₃ treatment.

Fig. S2. Agarose gel electrophoresis for total RNA of blueberry.

Fig. S3. Products of RT‐qPCR of 14 candidate reference genes.

Table S1. Description of the samples under abiotic stresses used for RT‐qPCR.

Table S2. Selection of candidate reference genes based on blueberry fruit transcriptome.

Table S3. RNA quantification of blueberry under different experimental conditions. (A) RNA quantification of different tissues of blueberry under common condition. (B) RNA quantification of tissues of blueberry in different abiotic conditions.

Table S4. The amplification specificity of 14 candidate reference mRNA genes.

Table S5. (A) Expression stability ranking of 14 candidate reference genes in leaves of blueberry under abiotic stresses by NormFinder. (B) Expression stability ranking of 14 candidate reference genes in leaves of blueberry under abiotic stresses by NormFinder.

Table S6. Expression stability ranking of 14 candidate reference genes in leaves and roots of blueberry under abiotic stresses by BestKeeper.

Table S7. Expression stability comprehensive ranking of 14 candidate reference genes in leaves and roots under abiotic stresses. (A) Expression stability comprehensive ranking of 14 candidate reference genes in leaves under abiotic stresses. (B) Expression stability comprehensive ranking of 14 candidate reference genes in roots under abiotic stresses.

Data S1. Primer pair annealing locations on their respective transcripts.

Data S2. The C_t values of 14 candidate genes in different tissues of blueberry.

Data S3. The C_t values of 14 candidate genes in leafs and roots of blueberry under different abiotic stresses.

Data S4. (A) The C_t values of VcMATE1 in different tissues of blueberry (three replicates). (B) The C_t values of VcMATE1 of blueberry under different abiotic stresses (three replicates).

Data accessibility

Model data are available in the European Nucleotide Archive under accession number KF875433.