Reference genes selection for Calotropis procera under different salt stress conditions

Maria R. V. Coêlho; Rebeca Rivas; José Ribamar C. Ferreira-Neto; Valesca Pandolfi; João P. Bezerra-Neto; Ana Maria Benko-Iseppon; Mauro G. Santos

doi:10.1371/journal.pone.0215729

Abstract

Calotropis procera is a perennial Asian shrub with significant adaptation to adverse climate conditions and poor soils. Given its increased salt and drought stress tolerance, C. procera stands out as a powerful candidate to provide alternative genetic resources for biotechnological approaches. The qPCR (real-time quantitative polymerase chain reaction), widely recognized among the most accurate methods for quantifying gene expression, demands suitable reference genes (RGs) to avoid over- or underestimations of the relative expression and incorrect interpretation. This study aimed at evaluating the stability of ten RGs for normalization of gene expression of root and leaf of C. procera under different salt stress conditions and different collection times. The selected RGs were used on expression analysis of three target genes. Three independent experiments were carried out in greenhouse with young plants: i) Leaf₁₀₀ = leaf samples collected 30 min, 2 h, 8 h and 45 days after NaCl-stress (100 mM NaCl); ii) Root₅₀ and iii) Root₂₀₀ = root samples collected 30 min, 2 h, 8 h and 1day after NaCl-stress (50 and 200 mM NaCl, respectively). Stability rank among the three algorithms used showed high agreement for the four most stable RGs. The four most stable RGs showed high congruence among all combination of collection time, for each software studied, with minor disagreements. CYP23 was the best RG (rank of top four) for all experimental conditions (Leaf₁₀₀, Root_50, and Root₂₀₀). Using appropriated RGs, we validated the relative expression level of three differentially expressed target genes (NAC78, CNBL4, and ND1) in Leaf₁₀₀ and Root₂₀₀ samples. This study provides the first selection of stable reference genes for C. procera under salinity. Our results emphasize the need for caution when evaluating the stability RGs under different amplitude of variable factors.

Citation: Coêlho MRV, Rivas R, Ferreira-Neto JRC, Pandolfi V, Bezerra-Neto JP, Benko-Iseppon AM, et al. (2019) Reference genes selection for Calotropis procera under different salt stress conditions. PLoS ONE 14(4): e0215729. https://doi.org/10.1371/journal.pone.0215729

Editor: Haibing Yang, Purdue University, UNITED STATES

Received: July 11, 2018; Accepted: April 8, 2019; Published: April 18, 2019

Copyright: © 2019 Coêlho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available from PRJNA508417 (SRA), SAMN10522464 (BioSample accession), PRJNA508417 (BioProject ID).

Funding: This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [grant numbers CNPq-470247/2013-4 and 310871/2014-0]. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) to M.R.V.C through a scholarship granted. M.G.S. and A.M.B.I. acknowledge CNPq for fellowships and financial support, and the CAPES Program BioComputacional [grant number 88882.160046/2013-01] for financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: ACT, Actin/actin-like conserved site-containing protein; ACT104, Actin 104; CNBL4, calcineurin B-like protein 4; CYP23, Peptidyl-prolyl cis-trans isomerase 23; FBOX, F-box protein PP2-A12; MAPK2, Mitogen-activated protein kinase kinase kinase 2; NAC78, NAC domain-containing protein 78-like; ND1, NADH dehydrogenase subunit 1; PPR, Putative pentatricopeptide repeat-containing protein; r40S, 40S ribosomal protein S3a; TBB4, Tubulin beta-4-chain; UBQ11, Polyubiquitin 11- like; UBP25, Ubiquitin carboxyl-terminal hydrolase 25; RG, reference gene; TG, target gene; Cq, quantification cycle

Introduction

Calotropis procera (Aiton) W. T. Aiton (Apocynaceae) is an evergreen shrub highly tolerant to drought and salt stresses with remarkable invasive ability in arid and semiarid regions [1]. Due to its pharmacognostic features, this shrub has been used in traditional medicine for the treatment of various diseases [1]. Ecophysiological studies have emphasized the superior physiology of C. procera, which show reduced stomatal conductance with high photosynthetic rate under water deficit [2,3]. These characteristics point this species as rich and attractive source of genes to be used in plant breeding programs for enhancing drought and salinity tolerance. In this sense, gene expression analysis can be used to evaluate the molecular mechanisms involved in plant response to different stresses. In the past years, advances in next-generation sequencing techniques have revolutionized transcriptomics and quickly established RNA-Seq as a robust methodology for gene expression analysis [4–7].

Efforts have focused on transcriptome and/or metabolomics of C. procera to study biosynthetic pathways of genes associated to the production of pharmacological compounds [8] and those involved in responses to heat, drought and salt stresses [9–11]. Because of its sensitivity, accuracy, reproducibility and rapid execution, qPCR has become a routine and robust approach for monitoring differential gene expression and validating data obtained by other methods, including RNA-Seq [12,13]. However, the accuracy of the qPCR results is largely influenced by RNA quality, cDNA preparation method and qPCR efficiency [14]. Such variables can cause quantitative and qualitative differences between the analyzed samples. Thus, a normalization step using endogenous controls [also called reference genes (RGs)] is essential [14–16]. RGs should ideally be constitutively expressed in the studied tissue or cell type and should not be affected by the treatments performed. Additionally, the uniform distribution of their transcripts across different treatments is required, functioning as a calibrator to compare different samples at the same quantitative level. The use of suitable RGs ensures the observed variation in target transcripts quantification is due to changes in expression, avoiding false positives or negatives in the process of gene expression analysis.

The most common RGs used in plants are those involved in fundamental cellular processes such as actin (ACT), ubiquitin (UBQ), α-tubulin (TUA), β-tubulin (TUB), 18S ribosomal RNA (18S rRNA), elongation factor 1-α (EF1α), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) [17–19]. Nevertheless, there is evidence that the transcription level of commonly used RGs can vary considerably depending on the species, tissue type, developmental stage and, physiological and experimental conditions [16,20]. In this context, statistical algorithms such as geNorm [21], NormFinder [22], and BestKeeper [23] have been effectively employed to evaluate the best RGs for normalization of qPCR data.

Regarding C. procera, there are no reports to date on the selection of RGs previously submitted to a careful statistical analysis to determine their stability. In addition, considering the available reports, only one RG GAPDH [24] or ACT [8] has been used for qPCR assays in C. procera. This action reduces the statistical robustness the results. According to MIQE guidelines (Minimum Information for Publication of Quantitative real-time PCR Experiments) [25], the normalization step should be carried out against multiple RGs chosen from a variety of candidate RGs tested with the application of at least one algorithm.

This study verified the expression stability of 10 candidates RGs of C. procera in two tissue types (root and leaf) under different salt (NaCl) concentrations and different collect time combinations. Statistical algorithms, including geNorm, NormFinder, and BestKeeper, were used. In this sense, the present study provides the most stable and reliable RGs for each experimental condition. We also tested the selected RGs in the study of the three target genes expression in two tissue types (root and leaf), under different salt concentrations and different imposition of times.

Materials and methods

Plant material and salt stress assays

Calotropis procera seeds were collected on the seacoast of Pernambuco state, Brazil (7°50'32.9" S, 34°50'21.2" W, and 160 m away from the sea). Their surface was disinfected by immersion in 0.5% (v/v) sodium hypochlorite solution for 5 min. Seeds were germinated in Petri dishes with wet filter paper and kept in a growth chamber (at 25°C, 12 h photoperiod, and 70% relative humidity). After radicle emergence, seedlings were transferred to pots containing 7 kg of sandy soil and maintained in a greenhouse for three months. Plants were distributed in three independent experiments (S1 Table): i) Leaf₁₀₀—young plants watered every day with NaCl (100 mM), during 45 days. At 30 min, 2 h, 8 h and 45 days of salt stress, youngest fully expanded leaves were collected; ii) Root₅₀ and iii) Root200—young plants watered every day with NaCl (50 or 200 mM for Root₅₀ and Root₂₀₀, respectively). At 30 min, 2 h, 8 h and 1 day of salt stress, root tissue samples were collected. Control samples were watered daily with distilled water and collected for each salt stress time, respectively in each experiment. All samples were collected from three plant replicates. Samples were frozen in liquid nitrogen and stored at -80°C until RNA isolation.

Total RNA isolation and cDNA synthesis

Total RNA was isolated from samples (leaf and root tissues) using the SV Total RNA Isolation System (Promega, Fitchburg WI, USA) by following the manufacturer’s instructions. RNA integrity was checked in 1.5% (w/v) agarose gel electrophoresis, stained with blue-green loading dye I (LGC Biotecnologia, SP, Brazil) and the quantity and quality of RNA samples were evaluated by fluorometry (Qubit, Oregon, USA). Reverse transcription reaction was carried out with 1 μg of total RNA, using the GoScript Reverse Transcription System Kit by (Promega, Fitchburg WI, USA) according to manufacturer’s instructions (Promega) and stored at -20°C.

RNA-Seq libraries: Synthesis, sequencing, and analysis

We also performed transcriptome sequencing of C. procera leaves samples (NCBI Sequence Read Archive identification: PRJNA508417) exposed to NaCl (100 mM) at 30 min, 2 h, 8 h and 45 days after salt stress, including not stressed control samples (0 h and 45 days), according to the Leaf₁₀₀ experiment description (S1 Table and S1 Fig). Each of the six RNA-Seq libraries was composed by a bulk combining equimolar RNA amounts of the three biological replicates were sequenced using Illumina paired-end sequencing technology on Illumina Hi-Seq TM 2500 platform (S1 Fig). After cleaning the raw reads and discarding low-quality reads, we ran Trinity [26] to assemble the clean reads into transcripts as described in Haas et al. [27].

Transcript quantification for RNA-Seq reads was performed with RSEM based on mapping the RNA-Seq reads of each experimental library (treatments 30 min, 2 h, 8 h compared to 0 h control and 45 days after stress imposition x 45 days control), against the assembled transcriptome [28] (S1 Fig). To estimate differential gene expression between our libraries, we used the edgeR tool [29], implemented in the Bioconductor package [30], requiring R software for statistical computing. The differentially expressed transcripts [log₂Fold-Change (FC) > 2.0 or < - 2.0, and P-value < 0.05] were identified based on comparisons between experimental libraries and respective controls, using the normalized number of fragments mapping on each library. The ‘Fold-Change’ (FC) term afore-mentioned is a measure describing how much a quantity changes as compared with an initial (control) to a final value (treatment).

Selection of target and candidate reference genes in the C. procera RNA-Seq libraries

Ten RGs were selected based on promising candidate genes according to previously published papers for other plant species [31–33], besides Log₂FC between +1.0 and -1.0 and P-value > 0.05 for all the RNA-Seq expression contrasts (Table 1).

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Table 1. Statistical parameters [Log₂Fold-change (FC) and P-value] of the candidate reference genes (RGs) and target genes (TGs) selected from Calotropis procera leaf transcriptome (Illumina HiSeq 2500) under salt stress.

https://doi.org/10.1371/journal.pone.0215729.t001

We selected, additionally, three target genes (TGs) related to salt stress response from the C. procera leaf transcriptome (RNA-Seq) to be used in qPCR gene expression analyses. TGs choice was based on two factors: (i) on their up-regulation (Log₂FC > 2.0 and P-value < 0.05), at least one RNA-Seq expression contrast; and (ii) reported participation in the plant response to saline stress. The following TGs were scrutinized: ND1 (NADH dehydrogenase subunit 1 [34], CNBL4 (Calcineurin B-like protein 4 [35,36], and NAC78 (NAC domain-containing protein 78-like [37,38] (Table 1).

Both RGs and TGs were submitted to the BLASTx (cut-off: e-value ≤ 3e -20) at NCBI [Non- redundant protein sequences (nr)] for annotation (Table 2).

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Table 2. Primer pair of the candidate reference genes and target genes used in this study.

https://doi.org/10.1371/journal.pone.0215729.t002

Primer design parameters

Transcript-specific primers were designed using the Primer3 web tool (http://bioinfo.ut.ee/primer3-0.4.0/) with the following parameter settings: length 18–22 bp, GC content of 45% - 55% (ideal content of 50%), annealing temperature (T_a) of 58°C– 62°C (ideal of 60°C) and amplified products of 65–200 bp (Table 2).

qPCR setup

The qPCR reactions were performed on PCR LineGene 9600 (Bioer, Hangzhou, China) using GoTaq qPCR Master Mix (Promega, Fitchburg WI, USA). Briefly, a 10 μL reaction mixture consisted of 5 μL SYBR Green Super Mix (Applied Biosystems, Foster City CA, USA), 2 μL of diluted cDNA (1/10), 0.3 μL for each primer (5 μM) and 2.4 μL ddH₂O. Non-template controls were also included for each primer pair. Reactions were carried out under the following conditions: 95°C for 2 min, followed by 40 cycles of 95°C for 15 s and 62°C for 1 min. The melting curve was generated by varying the amplification temperature from 65–95°C. All qPCR reactions were carried out in triplicate (biological and technical) [25]. The amplification efficiency (E) was determined from a standard curve generated by serial dilutions of cDNA (1/10, 1/100, 1/1000, and 1/10000) for each primer, in triplicate, and calculated by using the equation: E = 10 ^{(-1/slope of the standard curve)} -1 [39]. Slopes in the range of -3.58 to -3.10 were considered acceptable for the qPCR assay [40]. These slope values correlated to amplification efficiencies between 90% (E = 1.9) and 110% (E = 2.1).

Analysis of the reference genes expression stability

Three of the most notorious softwares available–geNorm v 3.5 [21], NormFinder v. 0.953 [22], and BestKeeper [23]–were used to evaluate the expression stability of ten candidate RGs: ACT104, ACT, CYP23, FBOX, MAPK2, UBQ11, UBP25, PPR, r40S and TBB4 (Table 2). For geNorm and NormFinder, the raw Cq-values were transformed into relative quantities–Q = E^ΔCq, where E represents the average efficiency for each gene, ΔCq is the difference between the lowest quantification cycle (Cq-value) of a sample of a particular gene and the Cq-value of each sample in a dataset [41].

In geNorm, the expression stability value (M) was calculated based on the average of the pairwise variation (V) for a candidate RG with all other genes tested, the default limit M ≤ 1.5. Genes with the lowest M-value have the most stable expression [21]. The average M of all genes together is then calculated by stepwise exclusion of the least stable gene until the two most stable genes in the remaining set cannot be ranked any further. Besides, geNorm also allows estimating the optimal number of RGs that must be used for normalization process. Normalization factor (NF) is calculated based on the geometric mean of the expression of the two most stable RGs and then the NF_n+1 with the next most stable gene. To determine the number of genes to be used for accurate normalization, the pairwise variation (V_n/n+1) was determined out of two sequential NFs (NF_n and NF_n+1) [21]. Vandesompele et al. proposed V ≤ 0.15 as a cut-off, below which the inclusion of an additional RG is not required [21].

NormFinder calculates the stability value using mathematical modeling algorithm to consider the intra- and inter-group variation of the candidate RGs. The lower stability value represents the highest stability. The fundamental principle is that a stable RG should have minimal variation across experimental groups and subgroups [22].

In BestKeeper, the raw Cq-values were used to calculate the Pearson correlation coefficient (r), which was obtained by the pairwise comparison between the BestKeeper index generated by the algorithm and the candidate RGs. Pearson correlation was determined as an indicator of expression stability, in which genes with higher r-value and P-value < 0.05 were more stable [23]. Samples with SD-value (standard deviation) > 1 were excluded from analysis [23]. Data from geNorm (M-values), NormFinder (stability values) and BestKeeper (r-value and SD) were used to generate rankings.

The expression stability of the candidate RGs was evaluated in all time combinations together: 30 min, 2 h, 8 h and 45 days, for Leaf₁₀₀; 30 min, 2 h, 8 h and 1 day, for Root₅₀ and Root_200. Additionally, we also analyzed expression stability in a factorial time combination for each experiment, totaling 15 time combinations per experiment.

Evaluation of target genes expression by qPCR

The expression pattern of three TGs (Table 1) was performed on Leaf₁₀₀ (2 h, 8 h and 45 days) and Root₂₀₀ (2 h, 8 h and 1 day) using the most stable candidate RGs suggested by the software applied. The Rest2009 software package (REST Standard mode) was used to calculate and analyze the relative expression of the TGs. Relative expression was calculated using the formula: E ^{(ΔCq Target)}/ E ^{(ΔCq RG)}, where E represents the average efficiency for each gene, ΔCq is the difference between mean Cq-value of a control sample and the mean Cq-value of treated sample. The REST bases its performance on pairwise comparisons (between RGs and TGs, control and treatment samples) using randomization and bootstrapping techniques (Pairwise Fixed Reallocation Randomization Test [42]. Hypothesis testing (P < 0.05) was used to determine whether the difference in expression between the control and treatment conditions was significant.

Results

Reference genes (RGs) and target genes (TGs) qPCR amplification

Ten candidates RGs were selected across C. procera RNA-Seq data, evaluated by qPCR and used to study the transcriptional modulation of three TGs. Products of these genes were associated with known functions involved in basal or vital cellular processes (Table 2). The specificity of PCR products was confirmed by the presence of a single amplicon with the expected size, with no amplicon visualized in non-template controls, as confirmed by 2% agarose gel electrophoresis (S2 Fig). The specificity of qPCR products was also confirmed by melting curves, each showing a single peak (S3 Fig).

All RGs and TGs showed suitable amplification E-values, ranging from 93% (ND1 and NAC78) to 109% (CYP23) (Table 2). The Cq-values provided by qPCR assay allowed us an overview of the gene expression levels (i.e., lower Cq-values correspond to higher expression levels and vice-versa). As shown in Fig 1, the mean Cq-values of ten RGs varied from 18.1 (UBQ11 in Leaf₁₀₀ samples) to 25.8 (FBOX, in Root₅₀ samples) in all experiments. For Leaf₁₀₀, the mean Cq-values ranged from 18.1–24.6 (UBQ11 < UBP25 < ACT104 < PPR < ACT < TBB4 < CYP23 < MAPK2 < r40S < FBOX, lower to higher Cq) (Fig 1A). The Cq-values of root samples was very similar in both experiments, with variation from ranging from 19.6–25.8 in Root₅₀ (ACT104 < TBB4 < UBQ11 < MAPK2 < UBP25 < PPR < CYP23 < ACT < r40S < FBOX) (Fig 1B) and from 19.9–25.7 in Root₂₀₀ (ACT104< UBQ11 < MAPK2 < TBB4 < UBP25 < PPR < CYP23 < ACT < FBOX < r40S) (Fig 1C).

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Fig 1.

Quantification cycle (C_q-value) of 10 candidate reference genes in leaf and root samples of Calotropis procera under different salt stress (A) Leaf₁₀₀ (100 mM NaCl), (B) Root₅₀ (50 mM NaCl) and (C) Root₂₀₀ (200 mM NaCl). The Boxplot indicates the interquartile range. The horizontal dashed line represents the mean and the solid line the median. The upper and lower dashes represent the maximum and minimum values. Dots indicate the lowest and highest Cq value.

https://doi.org/10.1371/journal.pone.0215729.g001

Global analysis of expression stability

Considering all collection times together (global analysis) (30 min, 2 h, 8 h and 45 days for Leaf₁₀₀; 30 min, 2 h, 8 h and 1 day for Root₅₀ and Root₂₀₀), the expression stability of each RG was analyzed to rank the most stable RGs for each experimental condition, using geNorm, NormFinder and BestKeeper algorithms (Tables 3 and S2).

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Table 3. Ranking of the four most stable reference genes, according to geNorm, NormFinder and BestKeeper softwares, considering global in time combination of leaf and root samples of Calotropis procera under different salt stress: Leaf₁₀₀ (100mM NaCl), Root₅₀ (50mM NaCl) and Root₂₀₀ (200mM NaCl).

https://doi.org/10.1371/journal.pone.0215729.t003

The geNorm algorithm showed M-value < 1.5 for all candidate RGs in all treatments (S2 Table). The four most stable RGs for NaCl-stressed leaves (Leaf₁₀₀) were CYP23, ACT, PPR, and r40S, while CYP23, UBP25, ACT104, and ACT were most stable for NaCl-stressed roots (both Root₅₀ and Root₂₀₀) (Table 3). On the other hand, the less stable RGs were UBP25 and UBQ11 for Leaf₁₀₀; FBOX and TBB4 for Root₅₀; FBOX and TBB4 for Root₂₀₀ were the less stable RGs (S2 Table).

According to the NormFinder algorithm, the four most stable RGs were ACT, TBB4, PPR and r40S in Leaf₁₀₀; CYP23, UBP25, ACT104 and UBQ11 in Root₅₀ and UBP25, CYP23, ACT104 and r40S in Root₂₀₀ (Table 3). The less stable RGs in Leaf₁₀₀ were UBQ11 and UBP25; for Root₅₀ were FBOX and TBB4, and for Root₂₀₀ were FBOX and TBB4 (S2 Table).

For BestKeeper algorithm, the four most stable RGs were TBB4, ACT104, r40S and ACT in Leaf₁₀₀; ACT104, CYP23, UBP25 and ACT in Root₅₀ and ACT104, ACT, UBP25 and CYP23 in Root₂₀₀ (Table 3). The less stable RGs were FBOX and UBP25 for Leaf₁₀₀; FBOX and r40S for Root₅₀; FBOX and TBB4 for Root₂₀₀ (S2 Table).

Although each software has its own statistical method to provide a stability rank, there is a certain degree of congruence among their results. In the current study, the congruence among geNorm, NormFinder, and BestKeeper is presented, concerning the four top-ranked RGs using all collect times together (global analysis) (Fig 2). For Leaf₁₀₀ samples, we observed 75%, 75% and 50% congruence between geNorm vs. NormFinder, NormFinder vs. BestKeeper and geNorm vs. BestKeeper, respectively (Fig 2A). In turn, we had congruence for root samples (Root₅₀ and Root₂₀₀) between geNorm vs. NormFinder, NormFinder vs. BestKeeper and geNorm vs. BestKeeper, corresponding to 75%, 75%, and 100%, respectively (Fig 2B and 2C).

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Fig 2.

Comparison among geNorm, NormFinder and BestKeeper concerning to four top-ranked shared reference genes using all sampling times together (global analysis) of leaf and root samples of Calotropis procera under different salt stress: A) Leaf₁₀₀ (100mM NaCl); B) Root₅₀ (50mM NaCl); and C) Root₂₀₀ (200mM NaCl).

https://doi.org/10.1371/journal.pone.0215729.g002

The RGs choice to use in qPCR analysis was determined according to geNorm, which also provided high congruence between other softwares studied and presented the optimal number of RGs required for reliable normalization according to V-value ≤ 0.15. In this context, for Leaf₁₀₀ (30min, 2 h, 8 h, and 45 d), geNorm determined two RGs (CYP23 and ACT) as the best pair (Table 3). On the other hand, geNorm determined three RGs (CYP23, UBP25, and ACT104; see V_3/4 ≤ 0.15; Table 3) as most suitable RGs for Root₅₀ and Root₂₀₀ (30 min, 2 h, 8 h, and 1 d).

Analysis of the expression stability considering factorial time combination

We also evaluated expression stability of the RGs per factorial combination from all collection times for each experiment, totaling 15-time combinations (S2 Table). Comparing all 15-time combinations in each algorithm revealed that the four most stable RGs are not strictly preserved for Leaf₁₀₀, Root_50, and Root₂₀₀ (Fig 3 and S2 Table). In this context, we averaged the congruence of all different collection times compared to global collection time (30 min, 2 h, 8 h and 45 days for Leaf₁₀₀; 30 min, 2 h, 8 h and 1 day for Root₅₀ and Root₂₀₀). For Leaf₁₀₀ samples, we observed on average 84%, 70% and 54% of congruence concerning global time combinations for geNorm, NormFinder and BestKeeper, respectively (Fig 3A–3C and S2 Table). On the other hand, concerning to global time combinations for Root₅₀, average congruence for geNorm, NormFinder and BestKeeper was 71%, 77%, and 77%, respectively (Fig 3D–3F and S2 Table). Moreover, we observed average congruence of 73%, 73%, and 79% for geNorm, NormFinder, and BestKeeper, respectively, concerning to global time combinations for Root₂₀₀ (Fig 3G–3I and S2 Table).

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Fig 3.

Comparison among 14 time combinations (2–15 for leaf samples and 2’-15’ for root samples) concerning the global time combination (1) of four top-ranked reference genes in geNorm (A, D, G), NormFinder (B, E, H) and BestKeeper (C, F, I) of leaf and root samples of Calotropis procera under different salt stress: Leaf₁₀₀ (100 mM NaCl), Root₅₀ (50 mM NaCl) and Root₂₀₀ (200 mM NaCl). Numbers represent time combinations: Leaf₁₀₀: 1 (30min-2h-8h-45d), 2 (30min-2h-8h) 3 (30min-2h-45d), 4 (30min-8h-45d), 5 (2h-8h-45d), 6 (30min-2h), 7 (30mim-8h), 8 (30min-45d), 9 (2h-8h), 10 (2h-45d), 11 (8h-45d), 12 (30min), 13 (2h), 14 (8h), 15 (45d). Root₅₀ and Root₂₀₀: 1’ (30min-2h-8h-1d), 2’ (30min-2h-8h) 3’ (30min-2h-1d), 4’ (30min-8h-1d), 5’ (2h-8h-1d), 6’ (30min-2h), 7’ (30mim-8h), 8’ (30min-1d), 9’ (2h-8h), 10’ (2h-1d), 11’ (8h-1d), 12’ (30min), 13’ (2h), 14’ (8h), 15’ (1d).

https://doi.org/10.1371/journal.pone.0215729.g003

Isolating the first hours (30 min, 2 h, 8 h) for each experiment and their factorial combinations (totaling seven time combinations), revealed the more frequent RGs among the rank top four out of seven time combinations (Fig 4 and S2 Table). For Leaf₁₀₀, the more frequent RG was PPR, according to geNorm and NormFinder. PPR, TBB4 and MAPK2, according to BestKeeper (Fig 4A and S2 Table). For Root_50, the more frequent RGs was CYP23, according to geNorm; UBP25, according to NormFinder; CYP23, UBP25 and ACT104, according to BestKeeper (Fig 4B and S2 Table). For Root₂₀₀, the more frequent RGs were CYP23, UBP25 and PPR according to geNorm; ACT104 according to NormFinder; ACT104 and UBP25 according to BestKeeper (Fig 4C and S2 Table).

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Fig 4. Frequency of the four top-ranked reference genes (RGs) among seven time combinations concerning the first hours (30 min, 2 h, 8 h) of salt stress in geNorm, NormFinder and BestKeeper.

Tissues regard Calotropis procera leaf and root samples under different salt stress time points: A) Leaf₁₀₀ (100 mM NaCl), B) Root₅₀ (50 mM NaCl) and C) Root₂₀₀ (200 mM NaCl).

https://doi.org/10.1371/journal.pone.0215729.g004

Target genes expression in different experimental conditions by qPCR

The transcriptional patterns of three TGs (ND1, CNBL4, and NAC78) under Leaf₁₀₀ (2 h, 8 h, and 45 days) and Root₂₀₀ (2 h, 8 h and 1 day) were analyzed using the most suitable reference genes for Leaf₁₀₀ (CYP23 and ACT) and Root₂₀₀ (CYP23, UBP25 and ACT104) as recommended by geNorm (S2 Table).

In short-term salt stress (2 h), gene expression analysis via qPCR revealed that most target genes exhibited constitutive expression in both salt-stressed tissues (Leaf₁₀₀ and Root₂₀₀) (Fig 5). The only exception was NAC78, which was up-regulated in Leaf₁₀₀ (Fig 5C). Interestingly, the gene expression modulation occurred, preferentially, at 8 h of salt-stress, with up-regulation in Leaf₁₀₀ (Fig 5A–5C) and down-regulation in Root₂₀₀ (Fig 5D–5F) of all TGs tested. On the other hand, in the last treatment times after salt stress (i.e., 45 days and 1 day, in Leaf₁₀₀ and Root₂₀₀, respectively) we observed constitutive expression for three target genes. The exception occurred for ND1 at 1 day (in Root₂₀₀), in which the expression was down-regulated (Fig 5D).

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Fig 5.

Relative expression of the target genes ND1, CNBL4 and NAC78 in Calotropis procera Leaf₂₀₀ (A, B, C) and Root₂₀₀ experiments (D, E, F). The references genes used were CYP23 and ACT (in Leaf₁₀₀) CYP23, UBP25 and ACT104 (in Root₂₀₀). Leaf₁₀₀ and Root_200: salt stress by concentrations of NaCl 100 and 200 mM, respectively. Values followed by * means P < 0.05. Up-regulation of gene expression (up); down-regulation of gene expression (down); ns (not significant at p < 0.05, or constitutive expression); relative expression values below or above the red line, associated with ‘*’, indicate up- and down-regulation, respectively.

https://doi.org/10.1371/journal.pone.0215729.g005

Additionally, we compared the qPCR/ Leaf₁₀₀ relative expression and Leaf₁₀₀ RNA-Seq data. Our results revealed that CNBL4, NAC78 qPCR results in Leaf₁₀₀ 2 h, 8 h, and 45 d (Fig 5B and 5C) were according to the respective RNA-Seq data (Table 1). For ND1, the qPCR (Fig 5A) and RNA-Seq (Table 1) gene expression results converged in the 2 h and 45 days treatments.

Discussion

The advent of high-throughput next-generation DNA sequencing (NGS) platforms has provided more comprehensive and maximized studies on diverse genomes, including non-model plant species [7,43,44]. At the same time, advances in RNA sequencing (RNA-Seq) methods have effectively aided in characterization and quantification of transcriptomes (even without a reference genome). They contributed to the understanding of genes expression regulation under different experimental conditions [6,45,46]. However, due to the existence of potential errors during the preparation, synthesis, sequencing and analysis of gene expression libraries (including RNA-Seq), a second method is required to validate the results indicated by the first. The qPCR is currently the most appropriated method for such purpose [12,47], and quality control measures are necessary to mitigate potential errors in qPCR results. Thus, the selection of suitable reference genes is a fundamental requisite. The use of inappropriate RGs may overestimate or underestimate the relative expression of the target genes and lead to [18].

In this study, transcripts of C. procera (RNA-Seq), identified statistically as constitutively expressed (considering log₂FC and P-value), were used as a source for candidate reference genes screening. The expression levels and stability analysis of ten RGs were evaluated in leaf and root samples of C. procera under different salt concentrations (NaCl). Using geNorm, NormFinder and BestKeeper software allowed us to analyze the expression stability of RGs in salt concentrations individually and factorial of time combinations.

According to the Cq-value and stability expression analysis, discrepancies were observed among candidate RGs under all conditions studied (including different tissues, salt concentrations and collection time combinations), indicating the importance of studies on RGs stability under different experimental conditions. Although several works have reported the use of traditional RGs as suitable in qPCR assays [48–50], recent studies have shown expression stability for many of these genes may be affected in different plant species under experimental conditions [16,20]. These reports, consistent with our results, support the careful evaluation of candidate RGs under given experimental conditions [16,51].

The RGs stability rankings suggested by different softwares were not often entirely identical for the same experimental conditions, as distinct statistical algorithms and analytical procedures are applied [52]. Despite the high degree of similarities, we found less congruence between results of geNorm vs. BestKeeper, for Leaf₁₀₀ (30 min, 2 h, 8 h, and 45 days) (Fig 2A). Such a relative divergence between BestKeeper and other softwares was also reported by other authors. According to Zhang et al.[33], in an experiment conducted on Halostachys caspica under salt stress, 25% congruence between BestKeeper vs. geNorm and 100% between geNorm vs. NormFinder were found. Similarly, de Andrade et al. [53] found high correlation among geNorm vs. NormFinder. However, geNorm vs. BestKeeper showed the lowest correlation. In this context, the choice of RGs to use in the qPCR analysis was determined by geNorm and confirmed by other softwares. The geNorm is one of the most widely used for gene expression stability analysis, besides informing the optimal number of RGs necessary to validate the TGs [16,17].

In spite of RGs specificity for each time combination, we found CYP23, a Peptidyl-prolyl cis-trans isomerase involved in key processes of protein folding [54], as the most frequent among the four most stable RGs, considering all experimental conditions studied. Similarly, Singh et al. [32] found CYP as the most stable RGs for wounding, heat, methyl jasmonate and biotic stress, for different tissues and combined stress samples. Based on this scenario, CYP23 is a powerful RG candidate to be further tested on expression analysis of C. procera under different experimental conditions, especially under salinity.

Analyzing all time combinations on Leaf₁₀₀ experiment, we found ACT, a cytoskeletal protein associated with plant cell growth [55], as the most frequent RG among the four most stable RGs. Previously, actins were identified as stable RGs in salt, drought, cold and heat stress [31,52]. Furthermore, UBP25 was most frequent RG among the four most stable for Root₅₀ and Root₂₀₀ experiments. On the other hand, UBP25 was the less stable for Leaf₁₀₀ submitted to prolonged period of salinity (45 days). Interestingly, all time combinations containing time 45 days for Leaf₁₀₀ showed UBP25 as one of the less stable RGs, considering all softwares studied. However, UBP25 was among four most stable RGs for most of the first hours combinations (excluding 45 days) on Leaf₁₀₀. UBP25 participates in ubiquitin-proteasome system (UPS) for maintenance of homeostasis and modulation of the stability proteins under salinity and other abiotic stresses [56,57], inducing the less stability under the high salt stress (45 days, Leaf₁₀₀) compared to the other candidate RGs.

The following target genes, related to salt-stress response, had their expression analyzed by RNA-Seq and qPCR: ND1 (NADH dehydrogenase subunit 1), which acts on the mitochondrial electron transport chain and is involved with rapid systemic signaling triggered by salinity and other abiotic stresses [34]; CNBL4 (Calcineurin B-like protein) involved on SOS pathway as calcium sensors, working in combination with kinases and ion channels to exclude cytosolic salt [35,36]; and NAC78 (NAC domain-containing protein 78-like) that belongs to NAC transcription factor family (NAC-TFs) involved in regulating plant growth, development processes and abiotic stress responses, including drought and salinity [37,38]. The qPCR data of ND1 (exception for 8 h treatment), CNBL4, and NAC78 for Leaf₁₀₀ experiment are in agreement with RNA-Seq expression results. The convergence of the results between these two approaches (that is, data validation) increases the robustness of our gene expression data, since the qPCR is considered a gold standard validation method for expression analysis. The up-regulation of the gene expression in response to salt stress as CNBL4, NAC78 and ND1 in leaf tissue, contribute to the establishment in Calotropis procera to high salinity adverse environments.

Regarding the root expression of target genes (qPCR / Root₂₀₀ experiment), they were not up-regulated in any of the treatments (showing up down-regulation or constitutive expression). When compared to Leaf₁₀₀ experiment results, this suggests: CNBL4, NAC78 and ND1 participate, more actively, in leaf response to salt stress (that is, tissue-specific transcriptional modulation); and /or the transcriptional modulation of the referred targets is dependent on the NaCl concentration. To determine the cause associated with those results, further inquiries are required. However, this gene sample already suggests the complexity of the molecular physiology of C. procera under stress, highlighting the capacity of adaptation of its transcriptome to different conditions and/or to the demand of different organs.

Our study provides, powerful background about ten candidate RGs for the first time, which can be used in C. procera studies under salt stress and can provide great potential to be tested in other experimental conditions. We indicate the most reliable RGs for 15-time combinations under three different experimental conditions, including two plant tissues and three NaCl concentrations. The CYP23 is a powerful RG candidate for expression normalization of C. procera under different experimental conditions. In addition, UBP25 should be avoided as RG for long-lasting salt stress in C. procera’s leaf. Finally, our findings emphasize the need for caution when evaluating the RGs stability in a set of samples under high amplitude of variant factors. The use of more than one software supported a reliable way to select the best RGs to validate TGs on qPCR.

Supporting information

S1 Table. Experimental conditions/ samples collected for Calotropis procera RNA-Seq libraries and qPCR assays.

https://doi.org/10.1371/journal.pone.0215729.s001

(DOCX)

S2 Table. Expression stability rank and pairwise variation of candidate reference genes in 15 time combinations in leaf and root samples of Calotropis procera under different salinity conditions [Leaf₁₀₀ (100 mM NaCl), Root₅₀ (50 mM NaCl), Root₂₀₀ (200 mM NaCl)] after geNorm, NormFinder and BestKeeper analysis.

† SD above 1, genes excluded from the rank of BestKeeper. * Values followed by * variables do not depend linearly on each other are according to the Pearson's correlation test (p < 0.05).

https://doi.org/10.1371/journal.pone.0215729.s002

(DOCX)

S1 Fig. Experimental designs for stress application and RNA-Seq libraries sequencing performed in the present work.

Legend: RB: biological replicate.

https://doi.org/10.1371/journal.pone.0215729.s003

(TIF)

S2 Fig. Amplification products of 10 candidate reference genes and three target genes in agarose gel (2%) from Calotropis procera by PCR.

M: marker 100 bp; 1–2 ND1; 3–4 CNBL4; 5–6 NAC78; 7–8 MAPK2; 9–10 CYP23; 11–12 ACT104; 13–14 TBB4; 15–16 UBQ11; 17–18 ACT; 19–20 r40S; 21–22 PPR; 23–24 UBP25; 25–26 FBOX. Even numbers mean no template control.

https://doi.org/10.1371/journal.pone.0215729.s004

(TIF)

S3 Fig. Melting temperature (°C) of 10 candidate reference genes and three target genes from Calotropis procera by qPCR.

Each line represents the melting curve for each individual replicate.

https://doi.org/10.1371/journal.pone.0215729.s005

(TIF)

Acknowledgments

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [grant numbers CNPq-470247/2013-4 and 310871/2014-0]. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. M.R.V.C. thanks the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the scholarship granted. M.G.S. and A.M.B.I. acknowledges to CNPq for fellowships, financial support, and CAPES Program BioComputacional [grant number 88882.160046/2013-01] for financial support.

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Figures

Abstract

Introduction

Materials and methods

Plant material and salt stress assays

Total RNA isolation and cDNA synthesis

RNA-Seq libraries: Synthesis, sequencing, and analysis

Selection of target and candidate reference genes in the C. procera RNA-Seq libraries

Primer design parameters

qPCR setup

Analysis of the reference genes expression stability

Evaluation of target genes expression by qPCR

Results

Reference genes (RGs) and target genes (TGs) qPCR amplification

Global analysis of expression stability

Analysis of the expression stability considering factorial time combination

Target genes expression in different experimental conditions by qPCR

Discussion

Supporting information

S1 Table. Experimental conditions/ samples collected for Calotropis procera RNA-Seq libraries and qPCR assays.

S1 Fig. Experimental designs for stress application and RNA-Seq libraries sequencing performed in the present work.

S2 Fig. Amplification products of 10 candidate reference genes and three target genes in agarose gel (2%) from Calotropis procera by PCR.

S3 Fig. Melting temperature (°C) of 10 candidate reference genes and three target genes from Calotropis procera by qPCR.

Acknowledgments

References