Journal of Applied Microbiology ISSN 1364-5072

ORIGINAL ARTICLE

Effective qPCR methodology to quantify the expression of virulence genes in Aeromonas salmonicida subsp. salmonicida
pez-Patin ~ o2, D.L. Milton3,4, T.P. Nieto1 and R. Farto1 L. Rivera1, M.A. Lo 1 2 3 4

Laboratorio de Microbiolog
ıa Marina, Departamento de Biolog
ıa Funcional y Ciencias de la Salud, Universidad de Vigo, Vigo, Spain Laboratorio de Fisiolog
ıa Animal, Departamento de Biolog
ıa Funcional y Ciencias de la Salud, Universidad de Vigo, Vigo, Spain Department of Molecular Biology, Ume a Centre for Microbial Research, Ume a University, Ume a, Sweden Southern Research Institute, Birmingham, AL, USA

Keywords Aeromonas salmonicida subsp. salmonicida, gene expression, normalization, qPCR, reference genes. Correspondence Leticia Rivera, Laboratorio de Microbiolog
ıa Marina, Universidad de Vigo, As Lagoas, 36310 Vigo, Spain. E-mail: [email protected] 2014/2037: received 2 October 2014, revised 3 December 2014 and accepted 16 December 2014 doi:10.1111/jam.12740

Abstract Aims: This study aimed to select and validate different methodological strategies to quantify the expression of the virulence genes ascC and ascV by qPCR in Aeromonas salmonicida subsp. salmonicida (Aer. salmonicida). Methods and Results: Using the geNorm, Normfinder and BestKeeper algorithms, reference genes for the qPCR were selected based on their in vitro expression stabilities in three Aer. salmonicida strains. Gene amplification efficiency was calculated by Real-time PCR Miner and LinReg PCR programmes, which have not been used previously in the analysis of bacterial gene expression. The expression of the ascC and ascV virulence genes in a virulent Aer. salmonicida strain was evaluated by three quantification models, including single (least or most stable) or three most stable reference genes, combined with constant or specific gene amplification efficiency. The most stable reference genes were gyrB, proC and rpoC, while rpoD and fabD were the least stable. Quantification models showed different expression patterns. Conclusions: The optimal strategy to quantify mRNA expression was to use a combination of the three algorithms and the quantification model including the three most stable reference genes. Real-time PCR Miner or LinReg PCR were valuable tools to estimate amplification efficiency. Significance and Impact of the Study: The methods used in this study gave more reliable expression data using qPCR than previously published methods. The quantification and expression dynamics of virulence genes will contribute to a better understanding of how Aer. salmonicida interacts with its host and the environment, and therefore to the prevention of epizootics due to this pathogen.

Introduction In 2012, the global production of turbot was 77 117 tonnes. During this time, Spain was the largest producer of turbot with 7970 tonnes and 99
2% of this turbot production was in the Autonomous Community of Galicia, making it the leading producer of turbot in Spain. To prevent severe economic losses within this important industry due to disease, steps must be taken to protect the turbot production from potential risks of infection by 792

bacteria, such as Aeromonas salmonicida subsp. salmonicida (Aer. salmonicida). Aeromonas salmonicida is the aetiologic agent of furunculosis, a systemic disease in fish that is characterized by lethargy, multiple haemorrhages in the fins, anus, and muscle, kidney necrosis and bleeding in the liver and spleen. Aeromonas salmonicida affects mainly salmonid species (Toranzo et al. 2005; Austin and Austin 2007), but also causes disease in a wide variety of non-salmonid fish, including turbot (Scopthalmus maximus), Senegalese

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sole (Solea senegalensis), Atlantic cod (Gadus morhua), halibut (Hippoglosus hippoglosus), lamprey (Petromyzon marinus), carp (Cyprinus carpio), goldfish (Carassius auratus) and eel (Anguilla anguilla) (Farto et al. 2011; Beaz-Hidalgo and Figueras 2013; Coscelli et al. 2014). During infection of turbot, virulent and avirulent strains of Aer. salmonicida were shown to display differences in their distribution patterns in the tissues and in their persistence (Farto et al. 2011). Both strain types, however, possess known virulence genes typically associated with the different stages of infection in salmonids, such as the ascC or ascV genes, which encode components of the type III secretion system (T3SS) (Lago et al. 2012) and which are essential for the virulence of Aer. salmonicida in fish (Burr et al. 2005; Dacanay et al. 2006). These observations suggest that variation in virulence gene expression may be the cause of the strain differences during infection. Little is known about the expression or regulation of the ascC or ascV virulence genes. Quorum sensing (QS) is one such regulatory mechanism that likely regulates the expression of T3SS genes in Aer. salmonicida as it does in other Aeromonas species (Sha et al. 2005; Vilches et al. 2009); however, QS is not well understood in this strain. Aer. salmonicida possesses the quorum-sensing luxIR homologues asaI and asaR, respectively, which encode AsaI, an N-acylhomoserine lactone signal synthase, and AsaR, a transcriptional regulator that responds to the signal molecule to regulate expression of proteases (Swift et al. 1997; Rasch et al. 2007). Quantitative real-time reverse transcriptase polymerase chain reaction (qPCR) is one of the most commonly used molecular techniques for mRNA gene expression analysis. Sensitivity, specificity, good reproducibility and a wide dynamic range of concentrations in which to perform the protocols are the main characteristics of this technique (Bustin 2002; Bustin and Nolan 2004). There are two possible qPCR methods for quantification of gene expression: absolute quantification based on an internal or external calibration curve and relative quantification based on the relative expression of a target gene vs a reference gene. Of these two, relative quantification is the preferred method. In previous studies using relative quantification, various reference genes were used in qPCR analyses of gene expression in different bacterial species and were shown to be stably expressed (Savli et al. 2003; Nieto et al. 2009; Bergsveinson et al. 2012; Galisa et al. 2012; Sumby et al. 2012; Valihrach and Demnerova 2012). However, none of these studies identified an ideal universal reference gene for qPCR gene expression analysis in bacteria. Several algorithms, such as geNorm (Vandesompele et al. 2002), NormFinder (Andersen et al. 2004) and BestKeeper (Pfaffl et al. 2004), have been developed to

select stably expressed reference genes. Each algorithm, although, often shows a different stability ranking for the same data set (Nieto et al. 2009; Galisa et al. 2012). Candidate reference genes are first ranked according to their stability and then gene expression is quantified using a specific quantification model. The simplest model assumes a constant amplification efficiency value (100%), while the most complex model uses gene specific values. The conventional standard curve method is the most frequently used method to calculate gene specific amplification efficiency values for bacteria; however, this method relies heavily on the assumption that the PCR efficiency of each amplicon is constant in all samples. Other alternative estimation methods, such as Real-time PCR Miner (Zhao and Fernald 2005) or LinReg PCR (Ramakers et al. 2003), have been developed to provide more accurate data. To the best of our knowledge, no studies have compared the different normalization or quantification strategies to determine the best method for quantifying gene expression. This study aimed to identify effective tools to quantify by qPCR the in vitro expression of the virulence genes ascC and ascV in Aer. salmonicida. Methods were developed for the selection of reference genes and for the quantification of gene expression, including alternative PCR efficiency calculations for bacteria. In addition, we have identified stable reference genes that will aid quantification of gene expression in Aer. salmonicida. These data will contribute to a better understanding of the virulence mechanisms of Aer. salmonicida and will thus aid the control of epizootics due to this pathogen. Material and methods Bacterial strains The Aer. salmonicida strain ACRp 43.1, an isolate from turbot (Sc. maximus) that was previously shown to be virulent for this host (Lago et al. 2012), was used to quantify the in vitro expression of the virulence genes ascC and ascV, which encode components of the T3SS. Furthermore, this strain and two isogenic mutant strains carrying null mutations in the QS genes asaI and asaR were used to select reference genes for qPCR analyses. The wild-type Aer. salmonicida strains were incubated at 18°C for 72 h on Tryptone Soy agar (TSA); while the asaI and asaR mutants were grown on TSA containing 50 lg ml1 of kanamycin (TSA-Km50). Escherichia coli strains SY327 and S17-1 were used to construct mutant strains as previously described (Farto et al. 2011). Chromobacterium violaceum (CV206) was used as a biosensor for QS signal molecule production and was grown on TSA at 30°C for 24–48 h.

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Construction of the asaI and asaR mutant strains To clarify if QS is one such regulatory mechanism that likely regulates the expression of T3SS genes in further studies in Aer. salmonicida, the quantification of their expression in wild-type and mutant strains carrying null mutations in the QS genes asaI and asaR genes is required. In this study mutations were made in the QS genes in the Aer. salmonicida strain ACRp 43.1, and this strain and their two isogenic mutant strains were used to select the most stable reference genes for all strains. The Aer. salmonicida asaI and asaR mutants were constructed by inserting an R6K origin suicide plasmid that carries a small fragment from 50 -region either from asaI (pGP704-Km3-asaI) or from asaR (pGP704-Km3-asaR). To construct these plasmids, the suicide vector pGP704Km3 (courtesy of Victoria Shingler, Ume a University) was digested with BglII and KpnI and ligated to a similarly digested PCR amplified fragment from the 50 -end of either asaI or asaR. Amplification of the asaI or asaR fragments was done using the following primer sets for asaI, Forward-GAAGATCTTGGGCAGCATGTAAT and Reverse-CGGGGTACCGTAGAAAACGAGCTTTAT, and for asaR, Forward- GAAGATCTCGTTTGGCCGAGTTG AT and Reverse- CGGGGTACCGATCACAGGCCAGCAT AT. The cloned fragments were sequenced to ensure accuracy. Plasmids pGP704-Km3-asaI or pGP704-Km3asaR were mobilized into Aer. salmonicida strains by conjugation as described previously (Farto et al. 2011) and allowed to recombine into the chromosome creating asaI and asaR null mutations. The insertion mutations were confirmed by PCR and sequencing using total DNA from the mutant strains and primer sets both internal to and flanking the relevant gene. In addition, these mutations were shown to be stably maintained as no loss of kanamycin resistance was seen after 76 generation of growth of both mutant strains in the absence of kanamycin. No growth differences were seen between the wild type and the mutants. To determine if the asaI and asaR genes are involved in QS, the mutants were tested for loss of N-acyl-L-homoserine lactones (AHL) production using the AHL biosensor Ch. violaceum CV206, which detects AHLs with carbon chains from C4 to C8 (Throup et al. 1995; McClean et al. 1997). First, the Aer. salmonicida strains were grown in Tryptone Soy Broth (TSB; Cultimed) and incubated at 18°C for 24 h with shaking. All cultures were adjusted to an OD595 of 0
4, which corresponds to 108 cells ml1 and cell numbers were confirmed by performing colony forming unit (CFU) counts. For each culture, a 50 ll sample was spotted onto a TSA plate and incubated at 18°C for 48 h. Following incubation, the TSA plates were covered with a soft agar overlay 794

containing the biosensor strain and incubated at 18°C for 48 h. The soft agar overlay was prepared by mixing 100 ll of a Ch. violaceum culture, which was grown in TSB at 30°C for 24 h with shaking and adjusted to an OD595 of 0
4, with 5 ml of melted 0
6% agar. Following incubation, the production of AHL was determined by measuring the diameter violacein production by Ch. violaceum. For the asaI mutant, no AHLs were detected and for the asaR mutant, a significant decrease in AHL production (2
9  0
56 cm) was seen compared to the wild type (4
25  0
07 cm). Reference genes selection and primer design Five candidate reference genes (Table 1) were selected from commonly described housekeeping genes in other bacteria (Savli et al. 2003; Gil et al. 2004). These genes encode essential functions involved in the metabolism of amino acids, fatty acids, RNA and DNA. The selection of primer sequences was determined using the Primer3Plus primer design programme (Untergasser et al. 2007). Specificity of the amplification product for each primer pair was verified by the presence of a single band at the expected size in a 2% agarose gel electrophoresis. Total RNA extraction Bacteria were grown in TSB at 18°C for 24 h with shaking. After which, the culture was adjusted to an OD590 of 0
05 (107 cells ml1 as determined by CFU counts). A 100 ll sample was transferred to 15 ml TSB and incubated at 18°C for 72 h with shaking. A 1
5 ml aliquot of each culture was collected at different postinoculation times (0, 16, 24, 48 and 72 h) and bacteria were pelleted by centrifugation (16 667 g) at 4°C for 3 min. Total RNA was extracted from the bacterial cells using TRIzolâ (Life Technologies, Carlsbad, CA) and following manufacturer’s instructions. Following extraction, RNA was precipitated overnight at 20°C and collected by centrifugation. The RNA pellet was dissolved in 100 ll of 0
01% dimethyl pyrocarbonate-treated water and stored at 80°C until assayed. To remove any remaining genomic DNA, total RNA was treated with RNase-free, DNase I (Fermentas, Waltham, MA) following manufacturer’s instructions. Standard precautions were performed to avoid RNase contamination affecting RNA integrity. The RNA quantity and quality was checked spectrophotometrically using a NanoDrop ND1000 (Thermo SCIENTIFIC, Whaltam, MA) and all the samples showed an A260/280 ratios of approx. 2. RNA integrity was confirmed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and an RNA 6000 Nano Labchip kit according to manufacturer’s instructions. These experiments were

Journal of Applied Microbiology 118, 792--802 © 2014 The Society for Applied Microbiology

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Table 1 Gene names, gene products, primer sequences, amplicon characteristics and efficiency Efficiency (%)

Gene

Gene product

Primer sequences (50 –30 )

Tm* (°C)

proC

ascC

Pyrroline-5-carboxylate reductase Malonyl CoA-ACP transacylase RNA polymerase subunit beta’ RNA polymerase sigma factor DNA gyrase, subunit B T3SS component

ascV

T3SS component

F - AGATAAGCAGCTTGCCCTCA R - CCTTATCCAGGCGGGTTATT F – AACCACGCAGTTCAACCAGT R - GACCTGTTTGCCTTGGTCAT F – CGCGAACACCCGGTTCTGCT R - TAGGCGGCACAAACGAGCGG F - TGCGGACGGGATCGGTCGTA R - GAAAACGCCCCGGATGCCGA F – AGTGCTGACCCAGCTGATCT R - GGACTGCAGGAAGTCGTAGC F - CGTCTCATCAATCTGGAGCA R - TGAGGGGAAAGATCTCGATG F - TTTGCGCTGTCGGCCATCGT R - GCTGCCCGGTTTTGGCTTGC

60
10 60
20 60
60 60
00 59
98 60
04 59
77 60
04 60
00 60
00 59
90 60
10 59
97 59
98

fabD rpoC rpoD gyrB

Amplicon length (bp)

Stand. curve

Real-time PCR miner

LinReg PCR

227

104
6

98

90

229

111
7

98
5

92

122

170
8

109

106

133

136
8

101
8

238

118
6

110

104

235

–

98

–

150

–

96

–

96

F, forward; R, reverse. *Various annealing temperatures (55–65°C, data not shown) were previously tested.

performed in triplicate using three independent bacterial cultures grown on three separate occasions. Reverse transcription and real-time PCR The qPCR reactions were performed on a iCycler iQ5 Real-Time PCR Detection System (Bio-Rad, Hercules, CA) using an iScriptTM One-Step RT-PCR Kit with SYBRâ Green (Bio-Rad, Hercules, CA) as the fluorophore. Each reaction was performed in triplicate with a total reaction mixture of 15 ll containing 7
5 ll 29 SYBRâ Green RT-PCR Reaction Mix, 1 ll (10 lmol l1) of each gene’s primer set (Table 1), 0
3 ll iScript Reverse Transcriptase and 100 ng of a total RNA sample. The cycling conditions were as follows: 50°C for 10 min, 95°C for 5 min and 55 cycles of 95°C for 10 s followed by 60°C for 30 s. To confirm amplification of a single cDNA sequence for each gene, additional melting curve analysis was performed using 81 cycles of heating from 55 to 95°C for 30 s with temperature increases in steps of 0
5°C every 10 s. The cycle at which a significant increase in fluorescence was first detected as statistically significant above background, threshold cycle (Ct), was calculated automatically using the Bio-Rad iCycler iQ5 software V2.0. To ensure the absence of random and genomic DNA contaminations, negative controls, which included a reaction with no template and a reaction with no reverse transcriptase were included on each plate. The standard curve method, which was performed by ten-fold dilution series, and the alternative programmes Real-time PCR Miner (Zhao and Fernald 2005) and LinReg PCR (Ramakers et al. 2003) were used to determine

the efficiency of every PCR reaction. Real-time PCR Miner identified and fitted the exponential phase of the curve using a four parameter logistic model, which fitted the raw fluorescence data as a function of PCR cycles, and a three-parameter simple exponent model, which uses an interactive nonlinear regression algorithm. After which, candidate gene regression values were obtained using P-values from the regression data and an efficiency value was calculated from a weighted average. In addition to this, LinReg PCR was used to analyse the PCR product curves to determine the efficiency of the exponential section of the product curve by linear regression. Stability of gene expression and ranking of the candidate reference genes Stability of gene expression was evaluated using RNA isolated from Aer. salmonicida ACRp 43.1 and the isogenic asaI and asaR mutant strains. To obtain a more consistent analysis, geNorm (Vandesompele et al. 2002), NormFinder (Andersen et al. 2004) and BestKeeper (Pfaffl et al. 2004) algorithms were used. While BestKeeper analysis used Ct values directly, geNorm and NormFinder analysis first transformed raw Ct values to relative quantities and used the specific amplification efficiency (E) calculated by Real-time PCR Miner or LinReg PCR programmes. The average Ct values of the triplicate analyses were exported into Microsoft Excel and converted into relative quantities (RQ) by using the following equation, RQ ¼ EDCt , where E is the specific amplification efficiency and DCt = min Ct  sample Ct (min Ct is the lowest Ct value of each gene and sample

Journal of Applied Microbiology 118, 792--802 © 2014 The Society for Applied Microbiology

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Ct is the Ct value of the sample being transformed). The highest relative quantities for each gene were set to 1 (Wang et al. 2012; http://medgen.ugent.be/~jvdesomp/ genorm/). The geNorm software identified the most stably expressed gene or set of genes from a pool of genes based on the average expression stability, M-value, which is defined as the average pairwise variation in a particular gene with all other potential reference genes (Vandesompele et al. 2002). The minimum number of genes required to calculate a robust normalization factor or pairwise variation (V-value) was estimated. The NormFinder software (Model-based approach) allowed the identification of an optimal reference gene by determining both inter-group and intra-group variations and combining these results into a stability value for each candidate reference gene. Genes with the lowest stability value had the most stable expression. The BestKeeper software calculated Pearson’s correlation coefficients (r) of Ct values for each gene pair and combined all results into a weighted expression index (BestKeeper Index), which is the geometric mean of Ct values of candidate reference genes. To evaluate the results obtained from these three different algorithms, the comprehensive ranking order was calculated. Each gene was assigned a weight from one to five (one being the most stable) according to its ranking obtained with geNorm, NormFinder and BestKeeper, the geometric mean (GM) of each gene was then calculated and re-ranked based on the GM values. The RefFinder web-based tool (http:// www.leonxie.com/referencegene.php) assumes a constant efficiency of reaction (100%). The programme uses the Ct values of each gene to calculate the gene rankings using the geNorm, Normfinder and BestKeeper algorithms as well as the comprehensive ranking. Relative quantification of in vitro expression of the ascC and ascV virulence genes Relative quantification of the virulence genes ascC and ascV from Aer. salmonicida ACRp 43.1 was performed using the most and least stable single or several reference genes recommended by geNorm, Normfinder and BestKeeper algorithms. The following relative quantification models (RQM) were applied: (RQM1) a single reference gene model (with the most (a) and least stable (b) reference genes) that assumes 100% amplification efficiency (Livak and Schmittgen 2001); (RQM2) a single reference gene model (the most stable) that uses gene specific amplification efficiency (Pfaffl 2001); (RQM3) a three reference genes model that uses gene specific amplification efficiencies (Hellemans et al. 2007). Based on results of reference gene stability ranking obtained in this study, the Real-time PCR Miner programme was selected to cal796

culate the gene specific amplification efficiency. Ct values for each gene obtained using three independent bacterial cultures were exported to Microsoft Excel and the different quantification methods were applied. Gene expression was normalized to the reference gene according to each quantification model described above. Statistical analysis Data analyses were carried out using SIGMAPLOT software (ver. 12). Data were tested for normality and homogeneity of variance; Log transformations were applied when necessary. Gene expression levels were compared with Student0 s t-test and one-way ANOVA followed by Tukey’s post hoc test. For all statistical tests, differences were considered significant if the P-value is ˂ 0
05. Results Selection of candidate reference genes and amplification efficiency Gene names, primer sequences and amplicon characteristics including melting temperature (Tm), length and amplification efficiency of the five candidate reference genes are listed in Table 1. The Tm ranged from 59
7 to 60
6°C and DNA bands between 122 and 238 bp were obtained. Standard curve method provided overestimated efficiency values, whereas the values obtained with Realtime PCR Miner or LinReg PCR programmes ranged within recommended values (from 90 to 110%). Expression levels of candidate reference genes The Ct values of the genes evaluated are represented in box-and-whiskers plots (Fig. 1). The Ct values ranged from 15
92 to 25
98 indicating different expression levels among the genes. Low dispersion was shown by Ct values for each gene and the median and average Ct values were similar for all genes. According to the average Ct values, the highest expressed gene was gyrB, followed by rpoC, fabD, rpoD and proC. Data samples at 0 h were excluded for further analysis due to the high Ct values obtained, which is indicative of a very low expression at this specific sampled time. No signal was detected for the negative controls. Stability of gene expression and ranking of candidate reference genes The stability of expression of candidate reference genes was analysed using the geNorm, NormFinder and BestKeeper algorithms. For geNorm, the five genes were

Journal of Applied Microbiology 118, 792--802 © 2014 The Society for Applied Microbiology

Expression of virulence genes in Aeromonas

L. Rivera et al.

28 26

Ct values

24 22 20 18 16 14 proC

fabD

rpoC

rpoD

gyrB

Figure 1 Average cycle threshold (Ct) values for candidate reference genes. Boxes correspond to Ct values within the 25th and 75th percentiles; median is represented by a horizontal line. Whiskers (error bars) indicate the 90th and 10th percentiles, and Ct values outside this range (outliers) are represented as dots.

ranked according to their M-value, with the lowest M-value indicating the highest expression stability for each gene. All candidate reference genes presented M-values below 1
5, indicating good expression stability (Vandesompele et al. 2002). The pairwise variation (V-value) was performed to determine the minimum number of reference genes necessary for accurate data normalization. The most stable pairwise combination of genes was proC and rpoC, followed by gyrB and fabD. The least stable gene was rpoD (Table 2 and Fig. S1). Reference genes were added to the normalization factor until the pairwise variation dropped below a cut-off value 0
15. In our data sets, V-values were higher than 0
15, and therefore, the five genes should be included to calculate a robust normalization factor (Fig. 2). Using NormFinder, the most stable gene was gyrB; proC, rpoC and fabD presented medium values; and rpoD was the least stable (Table 2 and Fig. S2). For BestKeeper, the most stable genes with lowest SD and highest r values were rpoD, followed by gyrB and rpoC, while the least stable genes were proC and fabD (Table 2 and Fig. S3). According to the r values (r > 0
9), all genes were included in the BestKeeper

index. The comprehensive ranking results of previous algorithms showed that gyrB, proC and rpoC were the most stable reference genes, while the least stable were rpoD and fabD (Table 2 and Fig. S4). The above stability rankings remained the same when the specific efficiencies values were estimated by Real-time PCR Miner or LinReg PCR. However, with Real-time PCR Miner programme, less data were excluded from the final calculation compared to those of LinReg PCR. The results obtained with NormFinder and BestKeeper were confirmed by RefFinder. However, slight differences were found in the geNorm analysis, for which the most stable pairwise combination of genes were rpoC and gyrB followed by proC, fabD and rpoD. The comprehensive ranking was also slightly different as rpoC and proC alternated the second and third ranking position with respect to the results obtained, while the other genes kept the same position (data not shown). Relative quantification of in vitro expression of the ascC and ascV virulence genes The relative expression levels of both the ascC and ascV virulence genes of Aer. salmonicida ACRp 43.1 are shown in Figures 3–5. To estimate the expression level of the virulence genes, different relative quantification models were used. The magnitude of the relative expression values are different depending on the model used. However, both genes showed similar relative expression tendencies. When the most (Fig. 3) and the least (Fig. 4) stable reference genes were used, homogenous expression values were obtained for both virulence genes throughout the experiment. Furthermore, no statistical differences were found when the expression values from the two quantification models (RQM1a and RQM1b) were compared (Table 3). The same results occur when using a single gene regardless of the efficiency values as determined by RQM1 and RQM2 (data not shown). However, the combination of the three most stable reference genes (Fig. 5) resulted in higher and more variable expression values throughout the experiment. In fact, the ascC and ascV expression levels peaked at 16 h (2
7-fold and 2
1-fold

Table 2 Ranking of candidate reference genes according to their stability value using geNorm, NormFinder, BestKeeper and Comprehensive ranking order Normfinder (stability value)

geNorm (M-value) proC rpoC gyrB fabD rpoD

0
60 0
60 0
63 0
69 0
77

gyrB proC rpoC fabD rpoD

Comprenhensive ranking (GM)

Bestkeeper (SD) 0
12 0
15 0
15 0
24 0
26

Journal of Applied Microbiology 118, 792--802 © 2014 The Society for Applied Microbiology

rpoD gyrB rpoC proC fabD

0
88 1
04 1
18 1
53 1
55

gyrB proC rpoC rpoD fabD

1
81 2
00 2
08 2
92 4
30

797

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8

0·20

0·15

0·10

6

8 Log [CFU]

V- value

0·15

6

4

4

0·05

2 2

0·00

V2/3

V3/4 Pairwise variations

0

V4/5

Figure 2 Determination of the optimal number of reference genes. Pairwise variation analysis to determine the minimum number of reference genes for normalization. The cut-off value 0
15 is indicated with a dash line.

8

0

16

24 Time (h)

48

72

Relative expression level

10

0

Figure 4 In vitro relative expression of ascC and ascV in ACRp 43.1 ) Bacterial culture symbols. Relative expression was calculated ( according to relative quantification model RQM1b by using the least stable reference gene fabD. ( ) ascC gene; ( ) ascV gene. Error bars represent the standard deviation of three independent bacterial cultures (performed separately in time).

6

4

4 2 2

6

8 6

4

4 2

0

0

16

24 Time (h)

48

72

0

0

Figure 3 In vitro relative expression of ascC and ascV in ACRp 43.1 ( ) Bacterial culture symbols. Relative expression was calculated according to relative quantification model RQM1a by using the most stable reference gene gyrB. (□) ascC gene; ( ) ascV gene. Error bars represent the standard deviation of three independent bacterial cultures (performed separately in time).

for ascC and ascV respectively), then decreased to a minimum value at 48 h (1-fold for both genes), and increased again at 72 h. Similarly, significant differences in expression values at specific time points were found when comparing RQM1a or RQM1b with RQM3 for ascC or RQM1a and RQM3 for ascV (Table 3). Discussion In this study, different methodological strategies to quantify by qPCR the in vitro expression of the ascC and ascV virulence genes in a virulent Aer. salmonicida strain were evaluated. Several candidate reference genes were selected and the stability of their expression was evaluated for use 798

2

0

16

24

48

72

Relative expression level

Log [CFU]

8

8 10

Log [CFU]

6

Relative expression level

10

0

Time (h) Figure 5 In vitro relative expression of ascC and ascV in ACRp 43.1 ( ) Bacterial culture symbols. Relative expression was calculated according to relative quantification model RQM3 by using the three most stable reference genes gyrB, proC and rpoC. ( ) ascC gene; ( ) ascV gene. Error bars represent the standard deviation of three independent bacterial cultures (performed separately in time).

in relative qPCR quantification methods. Furthermore, different relative qPCR quantification models were evaluated to highlight the importance of choosing the most appropriate model. This study describes for the first time qPCR methods for the selection of reference genes and strategies for the quantification of gene expression in Aer. salmonicida. Due to its high specificity and sensitivity, qPCR has become one of the most used techniques for gene expression analysis (Derveaux et al. 2010). Quantification of gene expression is affected by several factors, such as RNA quality, primer efficiency, cDNA synthesis and the amount of amplified cDNA template.

Journal of Applied Microbiology 118, 792--802 © 2014 The Society for Applied Microbiology

Journal of Applied Microbiology ISSN 1364-5072

ORIGINAL ARTICLE

Effective qPCR methodology to quantify the expression of virulence genes in Aeromonas salmonicida subsp. salmonicida
pez-Patin ~ o2, D.L. Milton3,4, T.P. Nieto1 and R. Farto1 L. Rivera1, M.A. Lo 1 2 3 4

Laboratorio de Microbiolog
ıa Marina, Departamento de Biolog
ıa Funcional y Ciencias de la Salud, Universidad de Vigo, Vigo, Spain Laboratorio de Fisiolog
ıa Animal, Departamento de Biolog
ıa Funcional y Ciencias de la Salud, Universidad de Vigo, Vigo, Spain Department of Molecular Biology, Ume a Centre for Microbial Research, Ume a University, Ume a, Sweden Southern Research Institute, Birmingham, AL, USA

Keywords Aeromonas salmonicida subsp. salmonicida, gene expression, normalization, qPCR, reference genes. Correspondence Leticia Rivera, Laboratorio de Microbiolog
ıa Marina, Universidad de Vigo, As Lagoas, 36310 Vigo, Spain. E-mail: [email protected] 2014/2037: received 2 October 2014, revised 3 December 2014 and accepted 16 December 2014 doi:10.1111/jam.12740

Abstract Aims: This study aimed to select and validate different methodological strategies to quantify the expression of the virulence genes ascC and ascV by qPCR in Aeromonas salmonicida subsp. salmonicida (Aer. salmonicida). Methods and Results: Using the geNorm, Normfinder and BestKeeper algorithms, reference genes for the qPCR were selected based on their in vitro expression stabilities in three Aer. salmonicida strains. Gene amplification efficiency was calculated by Real-time PCR Miner and LinReg PCR programmes, which have not been used previously in the analysis of bacterial gene expression. The expression of the ascC and ascV virulence genes in a virulent Aer. salmonicida strain was evaluated by three quantification models, including single (least or most stable) or three most stable reference genes, combined with constant or specific gene amplification efficiency. The most stable reference genes were gyrB, proC and rpoC, while rpoD and fabD were the least stable. Quantification models showed different expression patterns. Conclusions: The optimal strategy to quantify mRNA expression was to use a combination of the three algorithms and the quantification model including the three most stable reference genes. Real-time PCR Miner or LinReg PCR were valuable tools to estimate amplification efficiency. Significance and Impact of the Study: The methods used in this study gave more reliable expression data using qPCR than previously published methods. The quantification and expression dynamics of virulence genes will contribute to a better understanding of how Aer. salmonicida interacts with its host and the environment, and therefore to the prevention of epizootics due to this pathogen.

Introduction In 2012, the global production of turbot was 77 117 tonnes. During this time, Spain was the largest producer of turbot with 7970 tonnes and 99
2% of this turbot production was in the Autonomous Community of Galicia, making it the leading producer of turbot in Spain. To prevent severe economic losses within this important industry due to disease, steps must be taken to protect the turbot production from potential risks of infection by 792

bacteria, such as Aeromonas salmonicida subsp. salmonicida (Aer. salmonicida). Aeromonas salmonicida is the aetiologic agent of furunculosis, a systemic disease in fish that is characterized by lethargy, multiple haemorrhages in the fins, anus, and muscle, kidney necrosis and bleeding in the liver and spleen. Aeromonas salmonicida affects mainly salmonid species (Toranzo et al. 2005; Austin and Austin 2007), but also causes disease in a wide variety of non-salmonid fish, including turbot (Scopthalmus maximus), Senegalese

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dent of the PCR instrument (Arikawa and Yang 2007; Graeber et al. 2011; Zhao and Fernald 2005). According to our results, the efficiency values calculated by these alternative methods gave values within the recommended range from 90 to 110% as well as similar stability rankings of reference genes indicating that of these two programmes can be used. However, Real-time PCR Miner was selected for the relative quantification of virulence genes as fewer errors were detected, which lead to less data exclusion from the final calculation compared to those of LinReg PCR and thus, a more accurate calculation. This study is the first example of the use of these alternative methods for the relative quantification of gene expression in bacteria. Although rarely achieved, several methods assume an ideal and constant amplification efficiency (100%) of the reaction for each sample (Gibson et al. 1996; Livak and Schmittgen 2001; Wong and Medrano 2005). This is the case for RefFinder, which was used for comparative purposes in our study. This web-based tool almost confirmed our previous selection of candidate reference genes for quantification of gene expression. It could be expected, as the specific amplification efficiency values were close to 100%. Although use of RefFinder would simplify the calculations, because it combines the use of the three algorithms, the gene specific efficiency is not taken into account by this programme. To determine the minimum number of reference genes for accurate data normalization, pairwise variation (Vvalue) was calculated. Our results showed that all genes should be included for data normalization as the obtained values were higher than the threshold (0
15) (Vandesompele et al. 2002). However, the threshold value should not be viewed as a strict cut-off value (Maroufi et al. 2010; http://medgen.ugent.be/~jvdesomp/genorm/). In fact, in our case, the use of three, four or five genes did not show significant changes in the average of the gene variance estimates. Therefore, the use of the three most stable reference genes (gyrB, proC, and rpoC) was considered sufficient for accurate data normalization, as previously suggested (Vandesompele et al. 2002). Interestingly, these genes are also stably expressed in other bacterial species. For instance, proC is stably expressed in Pediococcus claussenii (Bergsveinson et al. 2012) and Pseudomonas aeruginosa (Savli et al. 2003), while proC and rpoC are stably expressed in Acidithiobacillus ferrooxidans (Nieto et al. 2009). In contrast, gyrB had low stability in Gluconacetobacter diazotrophicus (Galisa et al. 2012) and Oenococcus oeni (Sumby et al. 2012), but showed high stability in Staphylococcus aureus (Valihrach and Demnerova 2012). Moreover, stable expression of these three genes occurred with different experimental conditions and samples and are thus, promising candidate genes for consideration as ideal reference genes following further analyses in other bacteria. 800

The use of unstable reference genes may lead to the inaccurate interpretation of the gene expression data, which emphasizes the importance of validating each reference gene under appropriate experimental conditions before it can be used for data normalization (Dheda et al. 2005). Under our experimental conditions, the use of one or more reference genes for data normalization resulted in variations in ascC or ascV expression during growth. Whereas similar expression values were seen using the most or the least stable reference gene, using a combination of the three most stable reference genes (gyrB, proC and rpoC) gave variable expression for both ascC or ascV throughout growth indicating that this quantification model is an accurate strategy for quantifying in vitro gene expression in Aer. salmonicida. Characterizing gene expression in Aer. salmonicida could contribute to a better understanding of how this pathogen interacts with its host and its environment and could aid the control of epizootics due to this pathogen. In summary, this study provides evidence that the combinatorial use of the geNorm, NormFinder and BestKeeper algorithms and a quantification model that uses three stable reference genes will give a more accurate qPCR quantification of gene expression in Aer. salmonicida. Moreover, Real-time PCR Miner or LinReg PCR programmes were shown to be effective alternatives to estimate amplification efficiency. Thus, these methods improve on existing methods for selecting reference genes and quantification models for obtaining accurate qPCR gene expression data. Acknowledgements This study was supported by grants PGIDIT06RMA26101PR, 10MMA312017PR and 2008/080 of Xunta de Galicia (Regional Government of Galicia) and 09VIA11 from Vigo University. The authors thank M. Lemos (University of Santiago de Compostela) for kindly providing the wild-type isolate of Aer. salmonicida. M.A. L
opez-Pati~ no thanks the Xunta de Galicia (Spain) for providing his postdoctoral research contract (Isidro Parga Pondal program, P.P. 0000 300S 14008). The authors thank Dr. J.L. Soengas for helping to perform qPCR analysis. Conflict of Interest None declared. References Andersen, C.L., Jensen, J.L. and Orntoft, T.F. (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization,

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Supporting Information Additional Supporting Information may be found in the online version of this article: Figure S1. Gene expression stability of candidate reference genes calculated by geNorm. Average expression stability values (M). Figure S2. Gene expression stability of candidate reference genes calculated by NormFinder. Figure S3. Gene expression stability of candidate reference genes calculated by BestKeeper. Figure S4. Comprehensive ranking order of candidate reference genes.

Journal of Applied Microbiology 118, 792--802 © 2014 The Society for Applied Microbiology