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Gene 580 (2016) 67–72

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Research paper

Reference genes selection and relative expression analysis from Shiraia sp. SUPER-H168 productive of hypocrellin Huaxiang Deng, Ruijie Gao, Xiangru Liao ⁎, Yujie Cai ⁎ The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 LihuRoad, Wuxi, Jiangsu 214122, China

a r t i c l e

i n f o

Article history: Received 23 June 2015 Received in revised form 29 December 2015 Accepted 4 January 2016 Available online 15 January 2016 Keywords: Real time quantitative PCR Reference genes Normalization Hypocrellin biosynthesis Relative expression

a b s t r a c t Shiraia bambusicola is an essential pharmaceutical fungus due to its production of hypocrellin with antiviral, antidepressant, and antiretroviral properties. Based on suitable reference gene (RG) normalization, gene expression analysis enables the exploitation of significant genes relative to hypocrellin biosynthesis by quantitative real-time polymerase chain reaction. We selected and assessed nine candidate RGs in the presence and absence of hypocrellin biosynthesis using GeNorm and NormFinder algorithms. After stepwise exclusion of unstable genes, GeNorm analysis identified glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and cytochrome oxidase (CyO) as the most stable expression, while NormFinder determined 18S ribosomal RNA (18S rRNA) as the most appropriate candidate gene for normalization. Tubulin (Tub) was observed to be the least stable gene and should be avoided for relative expression analysis. We further analyzed relative expression levels of essential proteins correlative with hypocrellin biosynthesis, including polyketide synthase (PKS), O-methyltransferase (Omef), FAD/FMN-dependent oxidoreductase (FAD), and monooxygenase (Mono). Compared to PKS, Mono kept a similar expression pattern and simulated PKS expression, while FAD remained constantly expressed. Omef presented lower transcript levels and had no relation to PKS expression. These relative expression analyses will pave the way for further interpretation of the hypocrellin biosynthesis pathway. © 2016 Elsevier B.V. All rights reserved.

1. Introduction As a significant medicinal fungus for several centuries in southern China, Shiraia bambusicola produces a series of perylenequinoid compounds, including hypocrellin A and hypocrellin B. These compounds have been extensively used in the treatment of skin diseases, rheumatoid arthritis, gastric diseases, and some vascular diseases (Chen et al., 1981). Although scientists have studied the fermentation optimization, purification and application of hypocrellin for three decades (Cai et al., 2010; Cai et al., 2011), gene expression analysis of hypocrellin biosynthesis has not been considered. Moreover, with the appearance of high-output sequencing technology, the genome of Shiraia sp. slf14 (Yang et al., 2014) has been sequenced and genes involved in hypocrellin biosynthesis have been annotated. Therefore, experimental confirmation on expression analysis of the hypocrellin pathway was imminent. Abbreviations: Act, actin; CsA, Citrate synthase A; CsB, Citrate synthase B; CyO, Cytochrome oxidase; PhoK, Phosphoglycerate kinase; PyrD, Pyruvate decarboxylase; Tub, Tubulin; 18S rRNA, 18S ribosomal RNA; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; RGs, reference genes; Omef, methyltransferase,; FAD, FAD/FMNdependent oxidoreductase; Mono, monooxygenase; PKS, polyketide synthase. ⁎ Corresponding authors at: The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Lihu Road 1800, Wuxi, Jiangsu Province, China. E-mail addresses: [email protected] (X. Liao), [email protected] (Y. Cai).

http://dx.doi.org/10.1016/j.gene.2016.01.019 0378-1119/© 2016 Elsevier B.V. All rights reserved.

Due to its accurate quantification, high sensitivity and throughput, real time quantitative PCR (RT-qPCR) has been widely applied in gene expression analysis. Apart from the selection of suitable reference genes (RGs) regardless of experimental conditions, several variations could be controlled to obtain stable expression data, such as initial sample quantity, RNA purity and integrity, efficiency of cDNA synthesis, and primer specificities (Bustin et al., 2009; Taylor et al., 2010). To minimize effects of variability, the normalization of two or more RGs is necessary prior to gene expression analysis, because they may have different stability, even in the same strains, in different conditions. Also, unsuitable RGs used to analyze relative expression of genes of interest will cause frequent errors. Bustin et al. (2009) introduced the minimum information for publication of quantitative real-time PCR experiments (MIQE) to ensure integrity, consistency, and experimental transparency. Several methods, such as GeNorm (Vandesompele et al., 2002), NormFinder (Andersen et al., 2004) and BestKeeper (Pfaffl et al., 2004), have been widely developed for the normalization of RGs. As a pathogenic fungus, researches on validation of RGs for Shiraia sp. SUPER-H168 (Cai et al., 2010) were not systematically discussed, though there are many reports about RGs from other pathogenic fungi, such as Beauveria bassiana (Zhou et al., 2012), Fusarium verticillioides (Montis et al., 2013), and Leptosphaeria maculans (Soyer et al., 2014). In addition, few reports focus on RG selection from strains that produce perylenequinoids, such as Hypericum perforatum production of hypericin (Velada et al., 2014) or Cercospora kikuchii production

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of cercosporin (Upchurch and Ramirez, 2010). These perylenequinoid compounds are similar to hypocrellin. RGs, such as those that encoded β-actin (Act), glyceral-dehyde-3-phosphate dehydrogenase (GAPDH), and 18Sribosomal RNA (18S rRNA), have been used in H. perforatum. Actin and β-tubulin were chosen for C. kikuchii. In this study, we evaluated several candidate RGs (Table 1) for Shiraia sp. SUPER-H168 and identified the optimal gene or gene combinations for expression analysis. Polyketides, such as perylenequinone, lovastatin, and griseofulvin, were known for original biosynthesis from carbon backbones. These polyketides were biosynthesized through polymerization carboxylic acids including acetate and propionate by polyketide synthases (Chooi and Tang, 2012). To confirm the candidate RGs, polyketide synthase (PKS) from Shiraia sp. SUPER-H168 was selected because of an essential function in a step of original hypocrellin biosynthesis (Choquer et al., 2005). The relative expression levels of essential target genes from the hypocrellin pathway (GenBank: KM434884.1), including methyltransferase (Omef), FAD/FMN-dependent oxidoreductase (FAD), and monooxygenase (Mono) were also assessed with validated RGs, in the presence of hypocrellin biosynthesis. 2. Material and methods 2.1. Strains and culture conditions Shiraia sp. SUPER-H168, a high producer of hypocrellin, was cultured on potato dextrose agar (PDA) at 30 °C for 7 days. Spores were washed with 15 mL sterile water, 5 mL spore suspension were transformed to PDA medium until logarithmic growth phase, and 5 mL aliquots were then transferred to the following two media: (i) hypocrellin productive medium: glucose 5%, yeast extract 1%, K2HPO4 228 mg/L, K2SO4 174 mg/L, CaCl2 294 mg/L, MgSO4 492 mg/L, ZnSO4 0.1 mg/L, CuSO4 0.08 mg/L, Tween 80 0.06%; (ii) no hypocrellin productive medium: glucose 5%, NH4Cl 1%, K2HPO4 228 mg/L, K2SO4 174 mg/L, CaCl2 294 mg/L, MgSO4 492 mg/L, ZnSO4 0.1 mg/L, CuSO4 0.08 mg/L, Tween 80 0.06%.

Samples were collected at different time points from 1 to 4 days. 2.2. RNA isolation and first strand cDNA synthesis RNA was isolated using a Takara RNA Extraction Kit, (TaKaRa, Ōtsu, Japan) according to manuscript's protocol. To eliminate genomic DNA, RNA samples were treated with RNase-free DNase I (TaKaRa). The quantity and quality of the total RNA extracted was determined using

a NanoDrop-2000C spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and the integrity was evaluated by analyzing the ratio between rRNA subunits of 18S and 28S after electrophoresis. The first strand cDNA was synthesized by reverse transcribing 500 ng RNA with 5 × All-In-One RT MasterMix (Applied Biological Materials Inc., Richmond, Canada), and cDNA samples were stored at −20 °C. 2.3. Quantitative real-time PCR Shiraia sp. slf14(Yang et al., 2014) has been genome sequenced (GenBank: KM434884.1). The homology between Shiraia sp. slf14 and SUPER-H168 was 99%. We downloaded the genome sequence of Shiraia sp. slf14. Then the genes in this study (Table 1) were blasted with individual conserved domain in biolinux 7.0, and confirmed the pathway of hypocrellin by Augustus and NCBI blast. Primers for nine candidate RGs and four target genes were designed using Beacon Designer 7.0 (Table 1). The primers have a single melting peak, 75–250 bp length, a GC content of 50% to 60%, and avoid secondary structures and selfand cross-annealing. The primer's homology were detected using Blast on the whole genomic sequence of Shiraia sp. slf14. RT-qPCR reactions were carried out in 96-well blocks with a CFX96 Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). The thermal profiles were performed using the following conditions: 95 °C 10 min, 30 cycles of 95 °C for 3 s, 57 °C for 30 s, 72 °C for 30 s. After cycles, a melting curve analysis was carried out to confirm the specificity of amplicons, ranging from 65 °C to 95 °C. No-template controls (NTCs) were used to assess contamination and primer dimers. A standard curve was constructed to test the amplification efficiency using an undiluted pool of all cDNA samples and four five-fold serial dilutions. Every amplification was performed in triplicate. 2.4. Determination of gene expression stability To select appropriate RGs, we analyzed the stability of them with GeNorm (Vandesompele et al., 2002) and NormFinder (Andersen et al., 2004). The relative level of expression (Q) was determined as Q = e(Ctmin - Ctsample), where Ctmin was relative to the lowest threshold cycle (Ct) value in all samples, and Ctsamples corresponded to each sample by Ct value. After importing Q for RGs into GeNorm, we obtained two parameters, an average expression stability (M) value and an average pairwise variation of template normalization factor (Vn/n + 1 value). The M value reversely corresponded to the expression of stability. The higher the M value, the less stable was the candidate reference gene. We used the cut-off value V = 0.15 as a default setting. NormFinder was also used to analyze the stability of nine candidate RGs(Andersen et al., 2004).

Table 1 Primers and relative information of reference and target genes. Gene symbol

Gene name

Primers

Amplification size (bp)

R2

RT-qPCR Efficiency

Act

Actin

127

0.996

1.959

CsA

Citrate synthase A

154

0.973

1.999

CsB

Citrate synthase B

133

0.989

1.902

CyO

Cytochrome oxidase

130

0.996

1.972

PhoK

Phosphoglycerate kinase

129

0.989

1.969

PyrD

Pyruvate decarboxylase

115

0.999

1.945

Tub

Tubulin

169

0.999

2.069

18S rRNA

18S ribosomal RNA

64

1.000

2.051

GAPDH

glyceraldehyde-3-phosphate dehydrogenase

F: ATGGGGACAACGTGAGTGAC R: TCTTCGAGACCTTCAACGCC F: ATCGGCGAAGTCCATGAGTG R: TATGCAGAAGTCCGTCGGTG F: AGCAAGACCGTTCAGACCAG R: GCTGATGCGTCTGTACCTGA F: ACCTGTTTTAGCCGGTGGA R: CTCAGGGTGACCGAAGAATCA F: AGAATGGTCTTGGCCTCAGC R: CGACCAAGACGCAAACACTG F: TCACAGGTTACGCAGCAGAC R: CCGAATATGCACCAGCCAA F: CAGTTACGCACCCGTCATCT R: GTGGGTCGCAAACAACCATC F: GAAAGTTAGGGGATCGAAGA R: TAGTCGGCATAGTTTACGGT F: TTGACCTGACTGTCCGCATC R: CGAGACGAGCTTGACGAAGT

209

0.999

2.058

H. Deng et al. / Gene 580 (2016) 67–72

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To confirm the candidate RGs, PKS from Shiraia sp. SUPER-H168 was selected because of its essential function in the original biosynthesis step of hypocrellin (Choquer et al., 2005). The geometric mean from the combination of the most stable genes were used to normalize relative expression of PKS to validate the usefulness of candidate genes. The least stable gene (Tub) and middle stable gene (PyrD) were used as the control. Relative expression levels of other genes involved in hypocrellin biosynthesis, including methyltransferase (Omef), FAD/ FMN-dependent oxidoreductase (FAD), and monooxygenase (Mono), were also assessed by geometric mean.

the amplification efficiency ranged from 94.5% to 106.9%, and coefficient of determination (R2) varied from 0.973 to 1.000 (Table 1). Fig. 2 shows that the distribution of all quantitation cycle (Ct) values ranged from 12.30 to 28.32. Among all the candidate genes, 18S rRNA had the lowest Cq values (highest expression abundance) with a mean of 12.73, followed by GAPDH (17.41), CyO (18.31), CsB (21.23), PyrD (23.77), Act (22.44), PhoK (23.13), Tub (23.13). CsA showed the least transcript abundance with a mean of 23.91 (Fig. 2). In addition, CyO, 18S rRNA, and GAPDH showed lower gene expression variation, while expression level of several RGs, such as Tub and PhoK, displayed a more variable expression above four cycles in media i and ii from day 1 to 3 days. Therefore, expression stability of RGs needed to be analyzed.

3. Results

3.2. Expression profiling of candidate reference genes

3.1. Primer validation

3.2.1. GeNorm analysis GeNorm 3.5 was used to validate expression stability of the candidate RGs. An M value of less than 1.5 was suggested to validate stability according to Vandesompele (Vandesompele et al., 2002). The higher the M value, the less stable were the candidate RGs. To select suitable RG for all conditions, genes from all samples were ranked from least stable to most stable (Fig. 3). With the lowest M values, CyO and GAPDH were the most stable genes. However, the expression of Tub and Act remained unstable compared with other RGs. To select a suitable combination for analyzing relative expression of target genes, the pairwise variations (Vn/Vn+ 1) were evaluated by GeNorm (Fig. 4). The threshold value was suggested as 0.15. To further normalize of genes expressions, the most stable genes were determined by stepwise removing unstable genes with GeNorm. As Fig. 4 depicted,

2.5. Validation candidate genes and analysis of target genes

RNA samples isolated from various culture conditions were assessed for integrity and quality. All absorbance ratios at 260/280 nm of RNA samples ranged from 1.8 to 2.0. Agarose gel electrophoresis revealed no degradation. RT-qPCR include nine candidate RGs: actin (Act), citrate synthase A (CsA), citrate synthase B (CsB), cytochrome oxidase (CyO), phosphoglycerate kinase (PhoK), pyruvate decarboxylase (PyrD), tubulin (Tub), 18S ribosomal RNA18S rRNA (18S rRNA), and glyceraldehyde3-phosphate dehydrogenase (GAPDH). The specificity of all candidate primer pairs of RGs were confirmed by two factors. These two factors included the single peak of the individual melting curves (Fig. 1) and a single distinct band on 2% agarose gel electrophoresis after confirmatory PCR (data not shown). For the designed primer pairs of candidate genes,

Fig. 1. Melting curves of nine candidate reference genes and four target genes tested in the presence of hypocrellin biosynthesis.

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Fig. 2. Range of Cq values of the candidate reference genes for all experimental conditions. Each box indicates the 25% and 75% percentiles corresponding to each candidate reference gene. Actin (Act), citrate synthase A (CsA), citrate synthase B (CsB), cytochrome oxidase (CyO), phosphoglycerate kinase (PhoK), pyruvate decarboxylase (PyrD), tubulin (Tub), 18S ribosomal RNA (18S rRNA), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH).

V2/3 below 0.15 indicated that two RGs (GAPDH and CyO) can accurately normalize all data. 3.2.2. NormFinder analysis NormFinder software was used to alternatively analyze the nine candidate RGs. Similar to the results from GeNorm software, Table 2 shows that CyO and GAPDH had the more stable expression among all the RGs, followed by CsA. Meanwhile, 18S rRNA exhibited the most stable expression in variation compared with other genes. In addition, Act and Tub were unstable, thus the results were similar to those from GeNorm. 3.2.3. Reference gene validation To confirm the selected stability of RGs, we analyzed relative expression of PKS as described by Chung (Chen et al., 2007) in cercosporin biosynthesis, in the presence of hypocrellin biosynthesis by comparing normalization data with most and least stable RG. The geometric mean of GAPDH and CyO was used to normalize data, the least stable gene (Tub) and middle stable gene (PyrD) were used as the control. Fig. 5 depicts that when normalized to the geometric

Fig. 4. Determination of the optimal number of reference genes for normalization by pairwise variation using GeNorm.

mean of GAPDH and CyO, the highest expression was achieved at 48 h, the expression levels kept relative lower at 24 and 72 h incubation. Whereas PKS expression was normalized to Tub, the least stable gene, relative expression of PKS at 48 h showed a dramatic up-regulation, and up to fivefold changes compared with normalization against geometric mean of GAPDH and CyO. When PKS expression was normalized to PyrD, a middle stable gene, PKS transcription level was slightly upregulated before 48 h incubation compared with geometric mean of GAPDH and CyO, whrease the level slightly decreased at 72 h incubation. Using geometric mean of GAPDH and CyO to normalize data, we further exploited expressions of another three genes including methyltransferase (Omef), FAD/FMN-dependent oxidoreductase (FAD), and monooxygenase (Mono), which are relative to cercosporin biosynthesis(Daub et al., 2005) in present and absent of hypocrellin biosynthesis (media i and ii) at 24, 48, 72, 96 h incubation. Fig. 6 shows that transcription of Omef remained low level all the time, and expression of FAD kept steady level. The transcription of Mono increased from 24 h incubation, reached the maximum level at 48 h. The expression level of Mono at 48 h increased by 2.75 times compared with 24 h incubation, while the expression of Mono reduced by 2.19 times at 72 h than 48 h incubation. 4. Discussion Hypocrellin is a significant perylenequinoid through its widespread use in pharmaceutical applications. Studies on expression levels of essential enzymes for hypocrellin biosynthesis from S.bambusicola have received no attention, although reports on hypocrellin characteristics, fermentation optimization and applications have been introduced. Through improvements in high-output sequencing techniques, the genome of Shiraia sp. slf14 has been sequenced and the hypocrellin

Table 2 Ranking of candidate reference genes in order of their expression stability calculated by NormFinder.

Fig. 3. M value analysis of the expression stability of nine reference genes. The nine genes are ranked according to the M value, calculated by geNorm software.

Ranking order

gene

M value

1 2 3 4 5 6 7 8 9

18S rRNA CyO GAPDH CsA PyrD PhoK CsB Act Tub

0.125 0.181 0.259 0.455 0.575 0.675 0.708 1.078 1.195

H. Deng et al. / Gene 580 (2016) 67–72

Fig. 5. Effect of normalization on polyketide synthase (PKS) gene expression in the presence of hypocrellin biosynthesis. The expression of PKS was normalized the geometric mean of GAPDH and CyO. The least stable gene (Tub) and middle stable gene (PyrD) were used as the control.

biosynthesis pathway has been annotated (GenBank: KM434884.1), while experimental confirmation on expression analysis of the hypocrellin pathway has been not introduced. Based on reliable RGs, RT-qPCR is a frequently accurate and sensitive method for the quantification of gene expression (Thellin et al., 1999). To achieve these goals, many strategies containing sample size, total RNA and the popular ractice of measuring an internal reference or housekeeping gene, are carefully considered (Huggett et al., 2005). As expression levels for RGs were not constant among various experimental conditions, a single reference gene to normalize data could cause fluctuant errors. Accurate normalization methods of expression data with suitable RGs are necessary to normalize data against technically confounding variations (Vandesompele et al., 2002; Rieu and Powers, 2009; Bustin, 2010; Derveaux et al., 2010). Hence, validation is essential for expression analysis in S. bambusicola to eliminate unnecessary or technical errors. Nine RGs (Table 1) have been evaluated in the absence and presence of hypocrellin biosynthesis among different growth stages in Shiraia sp. SUPER-H168 using two algorithms including GeNorm and NormFinder. GAPDH, CyO and 18S rRNA, were identified as the more stable RGs in two

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algorithms, similar to that performed with H. perforatum, that produces perylenequinone hypericin (Velada et al., 2014). Act and Tub were widely used to normalize data because of their low variance and stability among all samples (Tang et al., 2007; Mori et al., 2008), while these two RGs were not appropriate in this study. Due to different demands from individual growth stages, RGs from the glycolytic pathway, such as PhoK and PyrD revealed a slight variance in our experiment. In this study, the V2/3 value was below 0.15, thus indicated that two RGs, CyO and GAPDH, were enough to further quantify relative expression (Vandesompele et al., 2002). As a kind of polyketide, perylenequinoids originated from polymerization carbon backbones, such as acetate and propionate, using PKS by repetitive decarboxylative claisen condensation reaction (Chooi and Tang, 2012). To study the relationship between PKS for hypocrellin biosynthesis from SUPER-H168 and PKS from other fungi, we constructed a phylogenetic tree. Fig. 7 depicts that PKS from SUPER-H168 were closely related to PKS from Shiraia sp. slf14 (97%) and Cercospora nicotianae (54%). While the PKS from SUPER-H168 was lower homology to PKS from Elsinoe fawcettii (36%). PKS was up-regulated when Shiraia sp. SUPER-H168 was cultured at 48 h (logarithmic phase) with relative quantification by geometric mean GAPDH and CyO. PKS expression levels showed a distinct increase at 48 h of growth, then the level slightly decreased at 72 h (stable phase). According to previous work, hypocrellin production increased with strain growth during the logarithmic phase, and the maximum production was not achieved until the stable phase (Cai et al., 2010). Based on other gene's synergy effects on PKS transcription, the highest PKS expression levels were obtained prior to hypocrellin production, and this result is similar to the ACpks expression pattern in Aspergillus carbonarius (Gallo et al., 2009). Daub et al. (2005) has confirmed that these proteins, adjacent to PKS, including Omef, Mono, and FAD, stimulated expression of PKS, and were essential for cercosporin biosynthesis. To exploit the gene synergy effects on PKS expression, we analyzed expression levels of essential genes in hypocrellin biosynthesis gene clusters, including Omef, Mono, and FAD, by geometric mean of GAPDH and CyO. CTB2, a O-methyltransferase from the gene cluster of cercosporin biosynthesis, is essential for cercosporin production, and disruption of this gene caused a loss of cercosporin and inhibits other CTB cluster genes (Staerkel et al., 2013), whereas the transcript of Omef from SUPER-H168 was different from CTB2 of Cercospora and kept low level during the whole developmental stage. This suggested that the Omef from SUPER-H168 was not relevant to hypocrellin biosynthesis. Similar to the transcription enhancer AFLJ (accession NO. AAS90004), FAD/ FMN-dependent oxidoreductase (FAD) from Cercospora nicotianae was a putative co-regulator for aflatoxin biosynthesis (Dekkers et al., 2007), and simulated cercosporin expression, However, the expression of FAD from SUPER-H168 was stable during hypocrellin expression, and may steady promoted hypocrellin biosynthesis. CTB3, a monooxygenase that was adjacent to polyketide synthase, was also involved in cercosporin biosynthesis from C. nicotianae (Dekkers et al., 2007). The expression of Mono from SUPER-168 increased maximum level at 48 h incubation and rapidly decreased at 72 h. The Mono retained an expression pattern relative and similar to the PKS gene, and interacted with PKS for hypocrellin biosynthesis. 5. Conclusion

Fig. 6. Relative expression of three target genes in the presence of hypocrellin biosynthesis by geometric mean of GAPDH and CyO. Methyltransferase (Omef), FAD/FMN-dependent oxidoreductase (FAD), monooxygenase (Mono).

In this study, GeNorm and NormFinder were used to analyze nine RGs. The two methods gave similar results. Tub and Act were the least stable RGs, as a result they were not appropriate for normalization. CyO and GAPDH proved to be optimal reference genes by GeNorm, while 18S rRNA was the most stable RG with NormFinder. Accurate normalization is necessary for reliable interpretation of relative target gene expression. Mono was a co-regulator of PKS, and these two genes expression levels were obtained maximal expression level prior to

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Fig. 7. Phylogenetic tree of PKS from SUPER-H168 compared with other strains productive of perylenequinoid.

highest perylenequinone production, while expression pattern of FAD remained constant during all stages, and expression of Omef remained low. In summary, these results enable the molecular deconstruction of the hypocrellin biosynthesis pathway. Acknowledgments This work was financially supported by the National High Technology and special funds for science and technology innovation from the Science and Technology Department of Jiangsu province (grant No. BY2010117), the National Natural Science Foundation of China (grant No. 21275066), and the fundamental research of a doctor of philosophy in 2014 (grant No. 2050205). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.gene.2016.01.019. References Andersen, C.L., Jensen, J.L., Ørntoft, T.F., 2004. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 64, 5245–5250. Bustin, S.A., 2010. Why the need for qPCR publication guidelines?—the case for MIQE. Methods 50, 217–226. Bustin, S.A., Benes, V., Garson, J.A., Hellemans, J., Huggett, J., Kubista, M., Mueller, R., Nolan, T., Pfaffl, M.W., Shipley, G.L., 2009. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622. Cai, Y.J., Liang, X.H., Liao, X.R., Ding, Y.R., Sun, J., Li, X.H., 2010. High-yield hypocrellin a production in solid-state fermentation by Shiraia sp SUPER-H168. Appl. Biochem. Biotechnol. 160, 2275–2286. Cai, Y., Liao, X., Liang, X., Ding, Y., Sun, J., Zhang, D., 2011. Induction of hypocrellin production by Triton X-100 under submerged fermentation with Shiraia sp. SUPER-H168. New Biotechnol. 28, 588–592. Chen, W., Chen, Y., Wan, X., Friedrichs, E., Puff, H., Breitmaier, E., 1981. The structure of hypocrellin and its photo-oxidation product peroxyhyprocrellin. Liebigs Ann. Chem. 1880–1885. Chen, H., Lee, M.H., Daub, M.E., Chung, K.R., 2007. Molecular analysis of the cercosporin biosynthetic gene cluster in Cercospora nicotianae. Mol. Microbiol. 64, 755–770. Chooi, Y.-H., Tang, Y., 2012. Navigating the fungal polyketide chemical space: from genes to molecules. J. Organomet. Chem. 77, 9933–9953. Choquer, M., Dekkers, K.L., Chen, H.-Q., Cao, L., Ueng, P.P., Daub, M.E., Chung, K.-R., 2005. The CTB1 gene encoding a fungal polyketide synthase is required for cercosporin biosynthesis and fungal virulence of Cercospora nicotianae. Mol Plant Microbe Interact. 18, 468–476. Daub, M.E., Herrero, S., Chung, K.-R., 2005. Photoactivated perylenequinone toxins in fungal pathogenesis of plants. FEMS Microbiol. Lett. 252, 197–206. Dekkers, K.L., You, B.J., Gowda, V.S., Liao, H.L., Lee, M.H., Bau, H.H., Ueng, P.P., Chung, K.R., 2007. The Cercospora nicotianae gene encoding dual O-methyltransferase and FAD-

dependent monooxygenase domains mediates cercosporin toxin biosynthesis. Fungal Genet. Biol. 44, 444–454. Derveaux, S., Vandesompele, J., Hellemans, J., 2010. How to do successful gene expression analysis using real-time PCR. Methods 50, 227–230. Gallo, A., Perrone, G., Solfrizzo, M., Epifani, F., Abbas, A., Dobson, A.D.W., Mule, G., 2009. Characterisation of a pks gene which is expressed during ochratoxin A production by Aspergillus carbonarius. Int. J. Food Microbiol. 129, 8–15. Huggett, J., Dheda, K., Bustin, S., Zumla, A., 2005. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 6, 279–284. Montis, V., Pasquali, M., Visentin, I., Karlovsky, P., Cardinale, F., 2013. Identification of a cisacting factor modulating the transcription of FUM1, a key fumonisin-biosynthetic gene in the fungal maize pathogen Fusarium verticillioides. Fungal Genet. Biol. 51, 42–49. Mori, R., Wang, Q., Danenberg, K.D., Pinski, J.K., Danenberg, P.V., 2008. Both β-actin and GAPDH are useful reference genes for normalization of quantitative RT-PCR in human FFPE tissue samples of prostate cancer. Prostate 68, 1555–1560. Pfaffl, M.W., Tichopad, A., Prgomet, C., Neuvians, T.P., 2004. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–excel-based tool using pair-wise correlations. Biotechnol. Lett. 26, 509–515. Rieu, I., Powers, S.J., 2009. Real-time quantitative RT-PCR: design, calculations, and statistics. The Plant Cell Online 21, pp. 1031–1033. Soyer, J.L., El Ghalid, M., Glaser, N., Ollivier, B., Linglin, J., Grandaubert, J., Balesdent, M.-H., Connolly, L.R., Freitag, M., Rouxel, T., 2014. Epigenetic control of effector gene expression in the plant pathogenic fungus Leptosphaeria maculans. PLoS Genet. 10, e1004227. Staerkel, C., Boenisch, M.J., Kröger, C., Bormann, J., Schäfer, W., Stahl, D., 2013. CbCTB2, an O-methyltransferase is essential for biosynthesis of the phytotoxin cercosporin and infection of sugar beet by Cercospora beticola. BMC Plant Biol. 13, 50. Tang, R.Y., Dodd, A., Lai, D., McNabb, W.C., Love, D.R., 2007. Validation of zebrafish (Danio rerio) reference genes for quantitative real-time RT-PCR normalization. Acta Biochim. Biophys. Sin. 39, 384–390. Taylor, S., Wakem, M., Dijkman, G., Alsarraj, M., Nguyen, M., 2010. A practical approach to RT-qPCR—publishing data that conform to the MIQE guidelines. Methods 50, S1–S5. Thellin, O., Zorzi, W., Lakaye, B., De Borman, B., Coumans, B., Hennen, G., Grisar, T., Igout, A., Heinen, E., 1999. Housekeeping genes as internal standards: use and limits. J. Biotechnol. 75, 291–295. Upchurch, R.G., Ramirez, M.E., 2010. Defense-related gene expression in soybean leaves and seeds inoculated with Cercospora kikuchii and Diaporthe phaseolorum var. meridionalis. Physiol. Mol. Plant Pathol. 75, 64–70. Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., Speleman, F., 2002. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3. Velada, I., Ragonezi, C., Arnholdt-Schmitt, B., Cardoso, H., 2014. Reference genes selection and normalization of oxidative stress responsive genes upon different temperature stress conditions in Hypericum perforatum L. PLoS One 9, e115206. Yang, H., Wang, Y., Zhang, Z., Yan, R., Zhu, D., 2014. Whole-Genome Shotgun Assembly And Analysis Of The Genome of Shiraia sp. Strain Slf14, A Novel Endophytic Fungus Producing Huperzine A And Hypocrellin A. Genome announcements 2 pp. e00011–e00014. Zhou, Y.-H., Zhang, Y.-J., Luo, Z.-B., Fan, Y.-H., Tang, G.-R., Liu, L.-J., Pei, Y., 2012. Selection of optimal reference genes for expression analysis in the entomopathogenic fungus Beauveria bassiana during development, under changing nutrient conditions, and after exposure to abiotic stresses. Appl. Microbiol. Biotechnol. 93, 679–685.