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Alternaria sp. MG1, an endophytic fungus isolated from Vitis vinifera, can independently produce resveratrol, indicat‑ ing that this species contains the key genes for resveratrol biosynthesis. Identification of these key genes is essential to understand the resveratrol biosynthesis pathway in this strain, which is currently unknown in microorganisms. qRT‑ PCR is an efficient and widely used method to identify the key genes related to unknown pathways at the level of gene expression. Verification of stable reference genes in this strain is essential for qRT ‑ PCR data normalization, although results have been reported for other Alternaria sp. strains. In this study, nine candidate reference genes including TUBA, EF1, EF2, UBC, UFD, RPS5, RPS24, ACTB and 18S were evaluated for expression stability in a diverse set of six samples representing different growth periods. We compared cell culture conditions and an optimized condition for resveratrol production. The comparison of the results was performed using four statistical softwares. A combination of TUBA and EF1 was found to be suitable for normalization of Alternaria sp. MG1 in different develop ‑ mental stages, and 18S was found to be the least stable. The reference genes verified in this study will facilitate further research to explore gene expression and molecular mechanisms as well as the improvement of secondary metabolite yields in Alternaria sp. MG1. To our knowledge, this is the first validation of reference genes in Alternaria with the capa‑ bility to produce resveratrol. Additionally, these results provide useful guidelines for the selection of reference genes in other Alternaria species. Keywords: qRT‑ PCR, Reference genes, α‑ Tubulin, Elongation factor 1, Alternaria sp. been studied (Sessitsch et al. 2013). Several endophytic Introduction fungi (Alternaria sp.) were identified previously that are Many important bioactive compounds are widely used in capable of independent resveratrol production (Shi et al. medical services and health care (Khan 2016; Larsen and 2012). Although fundamental physiological research has Matchkov 2016; Morata et al. 2015). Many of these com- been performed (Zhang et al. 2013a, b), the metabolic path- pounds are either microbial metabolites or their semi-syn- ways and cellular processes remain to be elucidated. thetic derivatives (Golinska et al. 2015; Sessitsch et al. 2013; Gene expression profiling is an informative technique Stepniewska and Kuzniar 2013). In the microbial popula- to investigate biological systems (Li et al. 2015). The tion, endophytes are a large group which may contain mil- method of qRT-PCR (quantitative real time PCR) can lions of different species, but only a minority of them have measure gene expression across different sample popula - tions (Derveaux et al. 2010; Wong and Medrano 2005). *Correspondence: email@example.com; firstname.lastname@example.org However, there are many factors that can influence the College of Food Science and Engineering, Northwest A & F University, 28 Xinong Road, Yangling 712100, Shaanxi, People’s Republic of China accuracy of the results such as the quality and quantity Key Laboratory for Space Biosciences and Space Biotechnology, School of mRNA templates or amplification efficiency. Gener - of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, ally, normalizing expression of the target genes to one or Shaanxi, People’s Republic of China © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Che et al. AMB Expr (2016) 6:106 Page 2 of 10 several reference genes provide an efficient way to reduce China). For preparation of Alternaria sp. MG1 cells, the these effects and increase the relevance of the results strain was inoculated into a 250-mL flask containing (Huggett et al. 2005; Marabita et al. 2016). However, the 100 mL PDB (liquid potato-dextrose broth, pot,ato 200 g use of inappropriate reference genes that change expres- with 20 g glucose in 1000 mL tap-water). The cultivation sion levels under different conditions can cause interpre - was carried out at 28 °C and 120 rpm in a rotary shaker. tation errors. Thus, the choice of appropriate reference According to the growth curve analysis (Additional file 1: genes for normalization is a prerequisite for qRT-PCR Figure S1), the cells were collected at points through- assay. out lag phase, logarithmic growth phase, and station- In recent years, validation of reliable reference genes ary phase after a cultivation of 2, 3, 4, 5 and 6 days by before their use for normalization has been performed centrifugation at 1136×g for 10 min at 4 °C (HC-3018R, for many species, such as Talaromyces marneffei (Dankai Anhui USTC Zonkia Scientific Instruments Co., Ltd., et al. 2015), Staphylococcus aureus (Sihto et al. 2014), Anhui, China). Next, the collected cells were washed Beauveria bassiana (Zhou et al. 2012), Oenococcus oeni twice with sterile water and immediately stored in liquid (Sumby et al. 2012) and others. Commonly used reference nitrogen until further analysis. The resting cells were col - genes for these fungi include the genes encoding the 18S lected using the method reported by Zhang et al. (2013a) ribosomal RNA (18S), ubiquitin fusion degradation pro- as follows: after cultivation for 4 days, the rinsed cells tein (UFD), ribosomal protein (RPS), elongation factor were resuspended in 0.2 mol/L pH 7.0 phosphate buffer (EF), β-actin (ACTB), α-tubulin (TUBA), ubiquitin-con- containing 0.1 g/L MgSO , 0.2 g/L CaSO and 4 mmol/L 4 4 jugating enzyme (UBC), and glyceraldehyde-3-phosphate phenylalanine for 21 h. After that, cells were washed dehydrogenase (GAPDH) (Kozera and Rapacz 2013). twice with sterile water and stored for further analysis. The previously reported reference genes for normaliz - ing qRT-PCR data in fungi, especially in Alternaria sp. Total RNA extraction and cDNA synthesis are β-tubulin (TUB) for A. alternata (Baez-Flores et al. Extraction of the total RNA from the cells was performed 2011; Buzina et al. 2008) and A. brassicicola (Sellam et al. using a Spin Column Fungal total RNA Purification Kit 2006), benA for A. alternata (Saha et al. 2012), 18S for (Sangon Biotech (Shanghai) Co., Ltd., China). Quality A. infectoria (Fernandes et al. 2014), and elongation fac- and quantity of the RNA extraction were analyzed using tor 1 (EF1) for A. brassicicola (Cho et al. 2014). Genes a NanoDrop 2000 Spectrophotometer (Thermo Scien - that show stable expression under many conditions may tific, Waltham, Massachusetts, USA) gel electrophore - differ in microorganisms due to different organization sis, and Agilent 2100 Bioanalyzer (Agilent Technologies, structures and when different genes are expressed. There - PaloAlto, California, USA). First-strand cDNA was syn- fore, it is necessary to identify reliable reference genes in thesized using a PrimeScript RT reagent Kit with gDNA Alternaria sp. MG1 for use in qRT-PCR assay. Eraser (Perfect Real Time) in strict accordance with the The aim of this study was to identify the most stable manufacturer’s operation manual (TAKARA Biotech- reference genes in Alternaria sp. under different growth nology (Dalian) CO., LTD., China). The cDNA products conditions and resveratrol production conditions. Genes were diluted fivefold and stored at −20 °C before further that show relatively similar levels of expression under analysis. all conditions could serve as reference genes that would be appropriate for comparison in the qRT-PCR assay Primer design of reference genes and qRT‑PCR of genes whose expression may vary during changes in amplification conditions metabolism or during resveratrol biosynthesis. Several The nine candidate genes (Table 1), ACTB, EF1, EF2, software applications were used for analysis of candidate RPS5, RPS24, TUBA, UBC, UFD and 18S were selected reference genes. These programs allowed evaluation of based on the transcriptome database of Alternaria sp. appropriate reference genes under given experimental MG1 (available through NCBI, SRA study accession conditions using statistical methods, such as Bestkeeper number SRP060338) (Che et al. 2016b). The internal ref - (Pfaffl et al. 2004), geNorm (Vandesompele et al. 2002), erence genes had highly similar sequences with reported and Normfinder (Andersen et al. 2004). genes from previous studies (DiGuistini et al. 2011; Fara- jalla and Gulick 2007; Goodwin et al. 2011; Skora et al. Materials and methods 2015). The primer pairs of candidate genes were designed Microorganism using the software Primer Premier (version 5.00) (http:// Alternaria sp. MG1 (code: CCTCC M 2011348), a strain www.premierbiosoft.com/primerdesign/index.html) with previously isolated from the cob of Merlot grape (Shi an amplicon length ranging from 100 to 300 bp. et al. 2012), was used in the study. It was maintained at The real-time quantitative PCR amplification and the China Center for Type Culture Collection (Wuhan, analysis were performed in the Bio-Rad iQ 5 Multicolor Che et al. AMB Expr (2016) 6:106 Page 3 of 10 Table 1 Relation of primers for the candidate genes to internal control Internal gene Gene name Primer sequence (5′–3′) Amplicon Amplification Regression Accession number Forward/reverse length (bp) efficiency (%) coefficient (R ) at GenBank ACTB β‑Actin CAAGACGGAAGGCTGG 195 100.4 0.997 GEMY01018051 AA/ CACTGCCGAGCGAGAAAT EF1 Elongation factor 1 CACTGGTTTTGCCTT 186 127.3 0.995 GEMY01015044 TTCCT/ TGTGGGCACCGTCAAAGT EF2 Elongation factor 2 ATAACAGCCTGGAAG 207 98.3 0.996 GEMY01001243 ATGC/ CTTTCACCATCCGTCAGTT RPS5 Ribosomal protein ACACCCATACAAAGAACG/ 131 104.1 0.985 GEMY01011888 S5 CCGAGTGCCTTGCTGA RPS24 Ribosomal protein CCGTCTTGTCGTTCCC/ 133 104.8 0997 GEMY01015522 S24 CGATTGGCGGTTTCTC TUBA α‑ Tubulin CAAGCGAGTCAGAAGC/ 101 106.9 0.984 GEMY01012167 GGTATGTTGGTGAGGGTAT UBC Ubiquitin‑ conjugat‑ GGCTCAAGAAACAGGAA/ 123 100.4 0.984 GEMY01016137 ing enzyme AGATTTACCACCCGAAC UFD Ubiquitin fusion TCCTCCTTGCCCTTGA/ 108 123.6 0.996 GEMY01001986 degradation CGAATCCGCCTCCTAC protein 18S 18S ribosomal RNA TCTTGTTTCCTTGGTGGGT/ 144 106.2 0.980 JN102357.1 GCATTTCGCTGCGTTCT Real-Time PCR Detection System (Bio-Rad Laboratories, bioinformatics .gene-quantification.info/b estke ep er. Inc., Hercules, California, USA) with the iQ 5 Optical html)using pair-wise correlations (Pfaffl et al. 2004), system Software Version 2.1. (http://www.bio-rad.com/ Genorm software (version 3.4) (https://genorm.cmgg. zh-cn/sku/1709753-iq5-optical-system-software?pare be/) (Vandesompele et al. 2002), NormFinder software ntCategoryGUID=2). A total reaction system of 25 μL (version 0.953) (http://moma.dk/normfinder-software/) contained SYBR Premix Ex TaqII (Tli RNase Plus) (2× (Andersen et al. 2004), and the comparative ∆Ct method Conc.), 12.5 μL; PCR primer mix (10 μM), 2 μL; cDNA (Pfaffl 2001; Silver et al. 2006). To comprehensively ana- template, 1 μL; and DNase-free water, 9.5 μL. The qRT- lyze the stability of these candidate genes, the web tool PCR amplification program was 95 °C for 5 min, followed RefFinfer (http://fulxie.0fees.us/?type=reference) (Xie by 40 cycles of 94 °C for 30 s, the ideal annealing temper- et al. 2012) was used to compare and rank the outcomes ature for each primer pair for 30 s, and 72 °C for 1 min, of the results using the different analysis programs. and then 72 °C for 10 min. All reactions were conducted in triplicate and melting curve analysis was performed. Results The correlation coefficients (R ) and slope values of the RNA purity and concentration standard curve and efficiency (E) were calculated using The mean values of quantity and quality of the RNA sam - the iQ 5 Optical system Software Version 2.1. ples are shown in Table 2. The concentrations of RNA To confirm the accuracy of the amplified products, samples ranged from 324.00 to 1329.56 ng/μL. The mean all the PCR products were analyzed by agarose gel elec- values of 260/280 were close to 2.00. trophoresis using 2% agarose gels in Tris-borate-EDTA (TBE) buffer stained with ethidium bromide. Verification of primer specificity of selected reference genes Determination of reference gene expression stability using A total of nine candidate genes (Table 1) were selected data analysis software for this study by referring to previous studies (DiGu- The transcript abundance of the reference genes was istini et al. 2011; Farajalla and Gulick 2007; Goodwin determined by the Ct value. The expression stability of et al. 2011; Skora et al. 2015) and the Alternaria sp. these candidate reference genes were evaluated using the MG1 transcriptome database. Agarose gel electropho- four methods described below. The methods are Excel- resis for preliminary PCR and melting curve analysis based tool—Bestkeeper software (version 1) (http:// was performed and the results are shown in Fig. 1 and Che et al. AMB Expr (2016) 6:106 Page 4 of 10 Table 2 The quantity and quality of RNA samples isolated from Alternaria sp. MG1 during different growth stages Sample 2 days 3 days 4 days 5 days 6 days Resting cell 260/280 2.18 2.16 2.13 1.97 2.09 2.00 260/230 1.61 1.57 1.65 1.41 1.60 1.52 Conc. (ng/μL) 1329.56 762.96 563.76 324.00 336.84 594.04 Fig. 1 Amplification of the candidate reference genes from cDNA templates. Agarose gel electrophoresis shows amplification of a specific PCR product of the expected size for each gene Additional file 1: Figure S1, respectively. We observed 20.24 and 23.38. Genes that showed different ranges of that the lengths of amplified fragment were consist - expression (Ct Ct ) were ACTB (1.52), EF1 (0.91), max− min ent with the expected size, and no primer dimers were EF2 (2.55), RPS5 (2.19), RPS24 (3.48), TUBA (1.33), UBC detected except for candidate gene RPS24. These results (2.52), UFD (3.13), and 18S (5.64). indicated the primers were specific and suitable for ref - erence gene validation. Stability evaluation of candidate genes using different PCR efficiency analysis was performed to validate analysis programs the optimal of the reference gene. The regression coef - To validate the stability of these nine candidate genes, we ficient (R value) and PCR amplification efficiency were used four evaluation methods. The Bestkeeper software calculated by a standard curve generated using tenfold was employed to validate and rank the stability evalu- serial dilutions of pooled cDNA. The PCR amplification ation of these candidate genes based on the standard efficiency of these candidate genes ranged from 98.3 to deviation (SD) of Ct values and the coefficient of variance 127.3%, and the regression coefficient (R value) of the (CV) expressed as a percentage of the Ct values. In this standard curve ranged from 0.980 to 0.997, well within approach, the most stable reference gene was identified the acceptable range of qRT-PCR (Table 1). by the comparison of SD value and CV value of these selected genes. The lowest SD and CV values represent Expression profiling of the candidate reference genes the genes with a highest stability, and vice versa. Here, Six samples were chosen for each candidate reference the descriptive statistics of these nine candidate genes gene in this study. were calculated based on the Ct values, and the statistical The average expression of the candidate genes during outcome is listed in Table 3. different growth stages was investigated by compari - The geometric mean (GM), arithmetic mean (AM), son of Ct values and the results are shown as a box-plot extremum (min and max) value, standard deviation (SD), (Fig. 2). In the figure, the interquartile values are shown and coefficient of variation (CV), were calculated. Sorted in boxes. The median expression level and the total by SD values, the tested genes were in the order of EF1 expression level are shown as a line and whisker, respec- < TUBA < ACTB < RPS5 < UBC < EF2 < RPS24 < UFD < tively. The expression level of the nine reference genes 18S. The overall variation of EF1 and TUBA were lowest with Ct value ranged from 14.22 to 25.66. Lower Ct val- with a SD value of 0.25. The CV values of EF1 and TUBA ues indicate higher expression level and vice versa. The were lower than the others, 0.99 and 1.2%, respectively. 18S gene showed the highest expression level, and the Interrelated analysis provided by BestKeeper concluded EF1 was the lowest. There was little difference among the that the most stable reference gene was EF1, and TUBA other candidate reference genes with Ct values between was the second most stable. Che et al. AMB Expr (2016) 6:106 Page 5 of 10 Fig. 2 Expression profiling of nine reference genes in the experimental set of Alternaria sp. MG1. Box represents 25/75 percentiles, whisker cap represents 10/90, the line in the box shows the median, and the dot indicates outlier of min and max value Table 3 CT data of reference genes calculated using Bestkeeper Genes ACTB EF1 EF2 RPS5 RPS24 TUBA UBC UFD 18S Number of sample 6 6 6 6 6 6 6 6 6 GM (CT ) 20.35 25.66 21.56 20.72 20.24 21.13 22.7 23.38 16.9 AM (CT ) 20.35 25.66 21.57 20.73 20.27 21.13 22.72 23.4 16.98 Min (CT ) 19.57 25.19 19.95 19.86 18.19 20.52 21 22.42 14.42 Max (CT ) 21.09 26.1 22.5 22.05 21.67 21.85 23.52 25.55 20.06 SD (±CT ) 0.41 0.25 0.61 0.49 0.69 0.25 0.6 0.78 1.05 CV (% CT ) 2.04 0.99 2.8 2.38 3.42 1.2 2.66 3.35 6.2 Min (x‑fold) −1.71 −1.39 −3.04 −1.82 −4.15 −1.53 −3.25 −1.95 −5.59 Max (x‑fold) 1.67 1.36 1.92 2.51 2.69 1.65 1.76 4.5 8.93 SD (x‑fold) 1.33 1.19 1.52 1.41 1.62 1.19 1.52 1.72 2.08 Another program, geNorm (Vandesompele et al. researchers (Kong et al. 2015; Li et al. 2015) have indi- 2002), was used to calculate the average expression sta- cated that multiple reference genes could increase bility of M value and analyze the stability of the can- instability and experimental complexity. Thus, pair - didate reference genes. The calculated M values of the wise variation (Vn/Vn + 1) was selected for assessing nine candidate reference genes are plotted in Fig. 3. The the optimal number of reference genes. The pairwise most stable expression genes had the lowest M value, variation V value was calculated using geNorm, and a and vice versa. Previous studies (Vandesompele et al. threshold V value of 0.15 was recommended to identify 2002; Wu et al. 2014), suggested selection of stable ref- the number of the additional reference genes (Vandes- erence genes with M values below the threshold of 1.5. ompele et al. 2002). In Fig. 4, all the V values were below As shown in Fig. 3, all the M values of the tested genes the cutoff value of 0.15. Pairwise variation analysis were less than 1.5. In the growth stage and the resting showed that the V2/3 value was 0.0126, which indicated cells, the EF1 and TUBA showed the highest expression two reference genes was sufficient for gene expression stability with the lowest M values (0.025). Sometimes, normalization and the two stability reference genes normalization with a single reference may produce sig- selected were EF1 and TUBA. nificant errors and the more than one reference genes In an alternate approach, we used the Normfinder may be needed in some experiments. However, some software program to evaluate these candidate reference Che et al. AMB Expr (2016) 6:106 Page 6 of 10 Fig. 3 Average expression stability values (M) of the nine candidate reference genes as calculated by geNorm Overall ranking order and selection of optimal reference genes In the separate assessments, the most stable reference gene was the same, but the other genes were ranked dif- ferently in the different analyses. Next, we used the web tool (RefFinder) to arrange the comprehensive results by integration of the results of the four assessments to com- pare these potential reference genes. An overview of the expression stability of the nine candidate genes from dif- ferent growing stages and different treatment of Alter - naria sp. MG1 are shown in Fig. 6. The ranking of these candidate reference genes (from most stable to least sta- ble) were TUBA, EF1, EF2, ACTB, RPS5, RPS24, UBC, Fig. 4 Pairwise variation ( V ) calculated by geNorm to determine the UFD, and 18S. optimal number of reference genes Discussion As a bioactive polyphenol, resveratrol has a variety of genes. As a model-based variance estimation approach, functions, such as preventing or slowing the occur- Normfinder is used to calculate stability values and rence of cancer, acting as a powerful antioxidant, and evaluate the expression stabilities of the tested genes extending life span. Pharmaceutical production and (Andersen et al. 2004; Maroufi et al. 2010). A lower aver - functional food processing present a high demand of age expression stability indicated genes that were stably resveratrol. To date, resveratrol was provided by extrac- expressed gene. In Fig. 5, the stability value ranking of tion from plant materials. This method of production these candidate reference genes was slightly different is highly limited by plant growth times and low yields. from that calculated by geNorm software. However, the Many resveratrol-producing Escherichia coli or yeasts most stable reference gene was the same (TUBA), fol- have been constructed by genetic modification (Conrado lowed by EF1, EF2, RPS5, RPS24, ACTB, UFD, UBC and et al. 2012; Krivoruchko and Nielsen 2015). However, 18S. The comparative ΔCt method was used to assess these processes have low yield and have stability issues gene expression stability. The stability results were the during production due to the use of the plant-derived same as those calculated using Normfinder software, genes. The problem of low yield results from a compli - and these results are shown together in Fig. 5. Again, cated metabolic pathway and a rate-limited enzyme. In the most stable reference gene was TUBA. a previous study, resveratrol production was increased Che et al. AMB Expr (2016) 6:106 Page 7 of 10 Fig. 5 Stability values of the nine candidate reference genes as calculated using ΔCt and NormFinder Fig. 6 Ranking candidate reference genes estimated using RefFinder slightly by adding substrates or production in Alternaria With several advantages, including the ability to quan- sp. MG1 resting cell culture (Zhang et al. 2013b). Thus, tify, reproducibility, sensitivity and accuracy, qRT-PCR understanding the expression of the genes involved in is a preferred method to use for quantifying the gene resveratrol biosynthesis pathway is the key problem to expression, and assessing mRNA levels among differ - be solved. As a newly characterized biological resource, ent samples. Validation of appropriate reference genes Alternaria sp. MG1 is able to produce resveratrol with- is a prerequisite for accurate analysis of gene expression out limitation from plant resources (Che et al. 2016a; using qRT-PCR (Li et al. 2016). In the past research, the Shi et al. 2012). Study of this fungus may allow insight most traditional reference genes used in qRT-PCR assay into the necessary pathways allowing engineering for were genes such as ACT, TUB, and 18S. In the past, many increased production. studies showed that the expression of these traditional Che et al. AMB Expr (2016) 6:106 Page 8 of 10 reference genes was not always stable under all condi- validation results. The overall ranking of these results was tions (Dankai et al. 2015; Zhou et al. 2012). The identi - integrated using the RefFinder system. The comprehen - fication of the most stable reference genes in Alternaria sive results of this research demonstrated that TUBA and sp. MG1 has not been achieved until now, although some EF1 were the most stable reference genes, 18S was the traditional reference genes have been used for qRT-PCR least stable gene, and the other candidate reference genes data normalization in some other Alternaria sp. (Dankai were intermediate among all six sets of experiments. et al. 2015; Sihto et al. 2014). Interestingly, TUBA was shown to be a reliable reference Several methods have been recently used to deter- gene for Penicillium expansum (De Clercq et al. 2016) mine the stability of gene expression and to validate the and Valsa mali var. mali (Vmm) (Yin et al. 2013), and best reference genes (Tong et al. 2009). However, there EF1 was found to be suitable for Clonostachys rosea (Sun is no consensus on the ideal approach that should be et al. 2015) and Tuber melanosporum (Cesare et al. 2015). used to examine the stability of reference gene expres- However, TUBA and EF1 were unstable and unsuit- sion. The pairwise comparison strategy, accessible able for use as reference genes in Blumeria graminis through the geNorm software, is a very popular option (Pennington et al. 2016), C. rosea (Sun et al. 2015), and to verify the expression stability of candidate genes (Yu Pandora neoaphidis (Chen et al. 2016). Other candidate et al. 2016). However, co-regulated genes may confound reference genes showed different stability in different the geNorm software and his would lead to an errone- fungi. For example, the most stable reference gene for ous choice of optimum normalizer pair (Andersen et al. Talaromyces marneffei was GAPDH , followed by TUBA, 2004). To investigate whether the potential co-regulated and ACTB (Dankai et al. 2015). ACTB was identified as genes affected the outcome of the results, researchers the reliable reference gene in Penicillium echinulatum removed one of the co-regulated genes from analysis and (Zampieri et al. 2014). reported that co-regulation did not influence the rank - Additionally, normalization with the combination of ing of reference genes by stability (Tong et al. 2009); we more genes resulted in improved accuracy. Previous similarly found no effect on the ranking by the inclu - research indicated the application of individual or com- sion of co-regulated genes (Additional file 1: Figure S2). binations of 2, 3, and 4 reference genes would result in Additionally, the reference genes that belonged to the different levels of abundance, but qualitatively similar same functional class were not top-ranked and did not patterns (Hu et al. 2009). How many reference genes occupy closed positions by geNorm software in previ- should be used is dependent on the purpose of research. ous studies (Exposito-Rodriguez et al. 2008). Similarly, One reference gene would be enough to show a rough the co-regulated genes were not top-ranked and did not expression mode of genes, if the reference gene was iden- occupy closed positions in this research. As a result, use tified as a stable expressed gene (Cho et al. 2014). Nev - of these two pairs of co-regulated genes did not affect ertheless, if the research purpose is to compare gene the final ranking of the reference genes by using geNorm expression levels among different samples or to get an software. Other methods such as NormFinder and Best- accurate expression level, the more reference genes Keeper, were reported to be less sensitive to co-regula- used, the more accurate the result is. However, other tion, and might serve as appropriate statistical applets to researches have reported that multiple reference genes further assess the stability for reference gene expression could increase instability and experimental complexity (Huang et al. 2014). In an effort to ensure the accuracy (Kong et al. 2015; Li et al. 2015). Thus, pairwise variation of the reference gene stability ranking and minimize bias (Vn/Vn + 1) was selected to assess the optimal number introduced by the validation approach, four different of reference genes. The pairwise variation V value was statistical approaches, ∆Ct, geNorm, NormFinder, and calculated using geNorm, and a threshold V value of 0.15 BestKeeper, were used to identify the suitable reference was recommended to identify the number of the addi- genes for accurate normalization in this study. The over - tional reference genes (Sumby et al. 2012; Vandesompele all ranking of these four approaches was integrated using et al. 2002). In this study, pairwise variation analysis a web-based comprehensive tool (RefFinder) developed (Fig. 4) showed that the V2/3 value was 0.0126, which to identify the most reliable reference genes by integrat- indicated that two reference genes was sufficient for gene ing these four evaluation methods (Xie et al. 2012). expression normalization and the two stability reference Nine traditional reference genes (TUBA, UFD, RPS24, genes selected were EF1 and TUBA. RPS5, UBC, EF1, EF2, ACTB and 18S) were selected as In conclusion, the results obtained here and in previ- candidate reference genes, and these genes were tested ous studies indicate that validation of reference genes during different growth periods and during an optimized is crucial for accurate normalization of gene expression condition for resveratrol production. Different statisti - measurements under different experimental conditions. cal algorithms and analytical methods gave different The reference genes verified in this study will be useful Che et al. AMB Expr (2016) 6:106 Page 9 of 10 Chen C, Xie TN, Ye SD, Jensen AB, Eilenberg J (2016) Selection of reference for future research to explore gene expression, molecular genes for expression analysis in the entomophthoralean fungus Pandora mechanisms, and improvement of secondary metabolite neoaphidis. Braz J Microbiol 47(1):259–265 yields in Alternaria sp. MG1. To our knowledge, this was Cho Y, Ohm RA, Devappa R, Lee HB, Grigoriev IV, Kim BY, Ahn JS (2014) Tran‑ scriptional responses of the Bdtf1‑ deletion mutant to the phytoalexin the first validation of reliable reference genes in Alter- brassinin in the necrotrophic fungus Alternaria brassicicola. Molecules naria. The results of this study provide useful guidelines 19(8):10717–10732 for the selection of reference genes in other Alternaria Conrado RJ, Wu GC, Boock JT, Xu HS, Chen SY, Lebar T, Turnsek J, Tomsic N, Avbelj M, Gaber R, Koprivnjak T, Mori J, Glavnik V, Vovk I, Bencina M, species. Hodnik V, Anderluh G, Dueber JE, Jerala R, DeLisa MP (2012) DNA‑ guided assembly of biosynthetic pathways promotes improved catalytic effi‑ Additional file ciency. Nucleic Acids Res 40(4):1879–1889 Dankai W, Pongpom M, Vanittanakom N (2015) Validation of reference genes for real‑time quantitative RT ‑PCR studies in Talaromyces marneffei. J Additional file 1. Figure S1. The growth curve of Alternaria sp. MG1 in Microbiol Methods 118:42–50 liquid potato‑ dextrose broth (PDB) at 28 °C and 120 rpm. Figure S2. Aver‑ De Clercq N, Vlaemynck G, Van Pamel E, Van Weyenberg S, Herman L, Dev‑ age expression stability values (M) of reference genes removed one of the lieghere F, De Meulenaer B, Van Coillie E (2016) Isoepoxydon dehydroge‑ coregulated genes as calculated by geNorm. nase (idh) gene expression in relation to patulin production by Penicillium expansum under different temperature and atmosphere. Int J Food Microbiol 220:50–57 Authors’ contributions Derveaux S, Vandesompele J, Hellemans J (2010) How to do successful gene Planning and designing of study: JXC, JLS, YLL; experimentation: JXC, YL; result expression analysis using real‑time PCR. Methods 50(4):227–230 analysis: JXC, YL; manuscript drafting: JXC, JLS, YL. All authors contributed DiGuistini S, Wang Y, Liao NY, Taylor G, Tanguay P, Feau N, Henrissat B, Chan SK, in the final approval of manuscript. All authors read and approved the final Hesse‑ Orce U, Alamouti SM, Tsui CKM, Docking RT, Levasseur A, Haridas manuscript. S, Robertson G, Birol I, Holt RA, Marra MA, Hamelin RC, Hirst M, Jones SJM, Bohlmann J, Breuil C (2011) Genome and transcriptome analyses of the Acknowledgements mountain pine beetle‑fungal symbiont Grosmannia clavigera, a lodge ‑ A special thanks to Cheng‑ quan Yang and Yao‑hua You (College of Horticul‑ pole pine pathogen. Proc Natl Acad Sci USA 108(6):2504–2509 ture, Northwest A & F University) for their support and help with this research. Exposito‑Rodriguez M, Borges AA, Borges‑Perez A, Perez JA (2008) Selection of internal control genes for quantitative real‑time RT ‑PCR studies during Competing interests tomato development process. BMC Plant Biol 8:12 The authors declare that they have no competing interests. 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