CSIRO PUBLISHING Functional Plant Biology,2012,39,342-350 http://dx.doi.org/10.1071/FP11246 Validation of reference genes for real-time quantitative PCR normalisation in non-heading Chinese cabbage Dong XiaoB,Ning-Wen ZhangB,Jian-Jun ZhaoB.C,Guusje BonnemaB.D and Xi-Lin HouD AState Key Laboratory of Crop Genetics and Germplasm Enhancement;Horticultural College, Nanjing Agricultural University,Nanjing,Jiangsu 210095,China. BLaboratory of Plant Breeding,Wageningen University,The Netherlands. CHorticultural College,Hebei Agricultural University,Baoding,Hebei,China. PCorresponding author.Emails:guusje.bonnema@wur.nl;hxl@njau.edu.cn Abstract.Non-heading Chinese cabbage is an important vegetable crop that includes pak choi,caixin and several Japanese vegetables like mizuna,mibuna and komatsuna.Gene expression studies are frequently used to unravel the genetics of complex traits and in such studies the proper selection of reference genes for normalisation is crucial.We assessed the expression of 13 candidate reference genes including ACTIN,ACTIN-1,ACTIN-2,GAPDH,Tub_a,CyP,EFI-a,18SrRNA, UBO,UBC30,PPR,PP24 and MDH.Their expression stabilities were analysed using two programs,geNorm and NormFinder,in 20 different samples that represent four strategic groups.Results showed that no single gene was uniformly expressed in all tested samples.ACTINand CyP are proposed as good reference genes when studying developmental stages. CyP,Tub_a and UBC30 are good reference genes when studying different tissues(from flowering to seed set).CyP and Tub_a are the most stable reference genes under biotic stress treatments using the fungi Peronospora parasitica and Alternaria brassicicola.UBC30,EF/-a and ACTIN are recommended for normalisation in abiotic stress studies,including hormone,salt,drought,cold and heath treatments.Moreover,at least five reference genes(ACTIN,CyP,UBC30,EFI-a and UBO)are required for accurate qRT-PCR data normalisation when studying gene expression across all tested samples. Additional keywords:Brassica rapa ssp.chinensis,gene expression,qRT-PCR,reference genes. Received 29 October 2011,accepted 7 March 2012,published online 24 April 2012 Introduction expression level under all the experimental situations tested The quantification of mRNA (mRNA)transcript levels has (Kim et al.2003;Ding et al.2004;Argyropoulos et al.2006). become an important research tool in recent years.Changes in Use of inappropriate reference genes in relative quantification mRNA transcript levels are crucial during plant developmental of gene expression profiles may lead to erroneous normalisation processes,between different tissues and under changing and consequently,misinterpretation of the results.Therefore,it environmental conditions.Real-time quantitative PCR (qRT- is essential to validate the expression stability of reference genes PCR)has become the most popular method to quantify mRNA in each experimental system transcription levels and to validate whole-genome microarray In plant research, glyceraldehyde-3-phosphate data because of its outstanding accuracy,broad dynamic range dehydrogenase (GAPDH),B-ACTIN (ACTIN),tubulin a and high sensitivity not only in the fields of molecular medicine, (Tub_a)and 18S rRNA were considered to have a constant biotechnology,microbiology and molecular diagnostics but also expression level and as a consequence have been widely used in plant research (Vandesompele et al.2002;Jian et al.2008; as reference genes for normalisation of gRT-PCR data in Paolacci et al.2009).Estimating the expression levels of target various experimental conditions (Kim et al.2003;Ding et al. genes of interest by qRT-PCR depends on endogenous control 2004;Jian et al.2008;Lovdal and Lillo 2009).However,it has genes to normalise qRT-PCR;control genes are also called also been reported that the transcript levels of these genes can reference genes or housekeeping genes (HKGs)(Wierschke change significantly under different experimental conditions et al.2010;Martinez-Beamonte et al.2011).HKGs play a (Czechowski et al.2005;Terrier and Glissant et al.2005; general role in basic cellular processes,such as cell structure Basa et al.2009;Chen et al.2010).Recently,many novel maintenance and primary cellular metabolism and thus,their reference genes have been identified from Affymetrix expression is usually unaffected by external factors.An 'ideal' GeneChip data and Microarray datasets in Arabidopsis.One of reference gene for gRT-PCR has a constant and consistent the findings was that among them F-box protein(F-box),SAND expression level over all samples across different experimental family protein and mitosis protein YLS8 were more stably conditions and different tissues.However,several reports expressed than the commonly used reference genes ACTIN-2, demonstrated that there was no single gene with a constant elongation-factor-1-a (EF/-)and ubiquitin-conjugating Journal compilation CSIRO 2012 www.publish.csiro.au/joumals/fpb
Validation of reference genes for real-time quantitative PCR normalisation in non-heading Chinese cabbage Dong XiaoA,B , Ning-Wen Zhang B , Jian-Jun Zhao B,C , Guusje Bonnema B,D and Xi-Lin HouA,D AState Key Laboratory of Crop Genetics and Germplasm Enhancement; Horticultural College, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China. B Laboratory of Plant Breeding, Wageningen University, The Netherlands. CHorticultural College, Hebei Agricultural University, Baoding, Hebei, China. DCorresponding author. Emails: guusje.bonnema@wur.nl; hxl@njau.edu.cn Abstract. Non-heading Chinese cabbage is an important vegetable cropthatincludes pak choi, caixin and several Japanese vegetables like mizuna, mibuna and komatsuna. Gene expression studies are frequently used to unravel the genetics of complex traits and in such studies the proper selection of reference genes for normalisation is crucial. We assessed the expression of 13 candidate reference genes includingACTIN,ACTIN-1,ACTIN-2,GAPDH, Tub_a,CyP,EF1-a, 18S rRNA, UBQ, UBC30, PPR, PP2A and MDH. Their expression stabilities were analysed using two programs, geNorm and NormFinder, in 20 different samples that represent four strategic groups. Results showed that no single gene was uniformly expressed in all tested samples. ACTIN and CyP are proposed as good reference genes when studying developmental stages. CyP, Tub_a and UBC30 are good reference genes when studying different tissues (from flowering to seed set). CyP and Tub_a are the most stable reference genes under biotic stress treatments using the fungi Peronospora parasitica and Alternaria brassicicola. UBC30, EF1-a and ACTIN are recommended for normalisation in abiotic stress studies, including hormone, salt, drought, cold and heath treatments. Moreover, at least five reference genes (ACTIN,CyP, UBC30, EF1-a and UBQ) are required for accurate qRT–PCR data normalisation when studying gene expression across all tested samples. Additional keywords: Brassica rapa ssp. chinensis, gene expression, qRT-PCR, reference genes. Received 29 October 2011, accepted 7 March 2012, published online 24 April 2012 Introduction The quantification of mRNA (mRNA) transcript levels has become an important research tool in recent years. Changes in mRNA transcript levels are crucial during plant developmental processes, between different tissues and under changing environmental conditions. Real-time quantitative PCR (qRT– PCR) has become the most popular method to quantify mRNA transcription levels and to validate whole-genome microarray data because of its outstanding accuracy, broad dynamic range and high sensitivity not only in the fields of molecular medicine, biotechnology, microbiology and molecular diagnostics but also in plant research (Vandesompele et al. 2002; Jian et al. 2008; Paolacci et al. 2009). Estimating the expression levels of target genes of interest by qRT–PCR depends on endogenous control genes to normalise qRT–PCR; control genes are also called reference genes or housekeeping genes (HKGs) (Wierschke et al. 2010; Martínez-Beamonte et al. 2011). HKGs play a general role in basic cellular processes, such as cell structure maintenance and primary cellular metabolism and thus, their expression is usually unaffected by external factors. An ‘ideal’ reference gene for qRT–PCR has a constant and consistent expression level over all samples across different experimental conditions and different tissues. However, several reports demonstrated that there was no single gene with a constant expression level under all the experimental situations tested (Kim et al. 2003; Ding et al. 2004; Argyropoulos et al. 2006). Use of inappropriate reference genes in relative quantification of gene expression profiles may lead to erroneous normalisation and consequently, misinterpretation of the results. Therefore, it is essential to validate the expression stability of reference genes in each experimental system. In plant research, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), b-ACTIN (ACTIN), tubulin a (Tub_a) and 18S rRNA were considered to have a constant expression level and as a consequence have been widely used as reference genes for normalisation of qRT–PCR data in various experimental conditions (Kim et al. 2003; Ding et al. 2004; Jian et al. 2008; Løvdal and Lillo 2009). However, it has also been reported that the transcript levels of these genes can change significantly under different experimental conditions (Czechowski et al. 2005; Terrier and Glissant et al. 2005; Basa et al. 2009; Chen et al. 2010). Recently, many novel reference genes have been identified from Affymetrix GeneChip data and Microarray datasets in Arabidopsis. One of the findings was that among them F-box protein (F-box), SAND family protein and mitosis protein YLS8 were more stably expressed than the commonly used reference genes ACTIN-2, elongation-factor-1-a (EF1-a) and ubiquitin-conjugating CSIRO PUBLISHING Functional Plant Biology, 2012, 39, 342–350 http://dx.doi.org/10.1071/FP11246 Journal compilation CSIRO 2012 www.publish.csiro.au/journals/fpb
Validation of reference genes in a wide of samples Functional Plant Biology 343 enzyme 10 (UBC10)(Remans et al.2008).In a recent paper a germinated and grown under controlled conditions in pots in a Brassica napus L.microarray database was analysed,which climate room:25C day/20C night temperature,12h light/12h showed that EF/-a.and a new unknown protein I (UP1)were dark cycles.The plants were used to collect tissues under normal the most suitable reference genes among the given set of tissues growth conditions and after biotic and abiotic stress treatments. (Chen et al.2010).Furthermore,two commonly used reference All samples were snapped frozen in liquid nitrogen and kept genes ACTIN-7 and UBC21,plus two new genes,TIP41-like at-80°C until use. protein (TIP41)and PP2A that were selected from a microarray dataset,were identified as the most stable reference gene set for Developmental stages(Ds) normalisation during B.napus embryo maturation (Chen et al. Three young leaves per plant were harvested and leaves of three 2010).A study in Chinese cabbage showed that EF/-o and plants were pooled for each developmental stage:(i)early stage adenine phosphoribosyl-transferase (Apr)were the most stably (third leave present)(Ds1);(ii)before bolting (8 weeks after expressed genes among different tissues (root,stem,heading sowing)(Ds2);and (iii)after bolting (10 weeks after sowing) leaves and lateral sprout)(Qi et al.2010) Ds3). The morphological variation present within Brassica rapa (L.)Hanelt is enormous.This includes the leaves in crops like Different tissues (Dt) heading Chinese cabbages and the leafy types that do not form Eight different tissues including root(Dtl)and stem(Dt2)at the heads (pak choi,caixin and several Japanese vegetables like third leaf stage,leaves after bolting (Dt3,same sample as Ds3) mizuna,mibuna and komatsuna),the enlarged roots of turips, flower buds(Dt4),petiole s(Dt5),stamens(Dt6),pistils(Dt7)and the inflorescences and stems of broccoletto and the seeds of seed pods (Dt8),were collected from three plants and pooled. the oil types.When studying the genetic relationship among accessions using AFLP and SSR marker profiling,clusters or Biotic stress treatments (Bs) groups of accessions were identified that were represented by different crop types,but it was also clear that genetic Two fungi,Peronospora parasitica (P.p)and Alternaria distance was more defined by geographical origin than by crop brassicicola (A.b),were isolated from the leaves of different type (Zhao et al.2007,2010).There is no information about susceptible B.rapa cultivars in the farm of Nanjing selection of reference genes for nommalisation of gRT-PCR Agricultural University,China.Conidial suspensions were results for gene expression studies in Chinese cabbage.With adjusted to Ix 10%sporesmL and Tween-20 was added as a the recently released B.rapa genome sequence (The Brassica surfactant to a final concentration of 0.1%.For each treatment, rapa Genome Sequencing Project Consortium 2011)and 53-week-old seedlings were sprayed either with 50 mL pathogen development of gene expression platforms for B.rapa, suspension,or demi water (as control).Treated seedlings genome-wide large-scale gene expression studies will become were placed in a climate chamber (25C,85%+5%RH, available and will be mined to select reference genes for real 12 hour light/12 hour dark)and the second leaves from three time PCR studies. plants per treatment were harvested and pooled at 48h after In the present study,we selected and validated 13 reference inoculation.The two treatments are referred to as Bsl and Bs2 genes particularly for accurate normalisation ofgRT-PCR results respectively in non-heading Chinese cabbage.These reference genes include seven widely used reference genes in plant research (ACTIN, Abiotic stress treatments (As) ACTIN-2,GAPDH,Tub_a,CyP,EFI-a and 18S rRNA)and six Fifty seedlings (3 weeks old)were sprayed respectively with potential reference genes (ACTIN-1,UBO,UBC30,PPR,PP2A 50 mL solutions,water(as control),SA(2 mmolL,PH=6.5), and MDA)that were identified based on their stability in ABA(50 umol),NaCl(200 mM),H2O2(100 uM)and Mannitol expression studies comparing plant developmental stages, (400 mM).For salt (Asl)and drought (As2)stress treatments, different tissues or various environmental stimuli including leaves were harvested at 12 h(for NaCl and Mannitol)after stress biotic and abiotic stress (Brunner et al.2004;Czechowski treatments.For hormone treatments.leaves were harvested at et al.2005;Reid et al.2006).The 13 genes were tested in 20 6h after SA treatment(As3),24h after ABA treatment(As4) different samples,including three developmental stages,eight and 24 h after H2O2 treatment(As5).In addition,leaves from different tissues harvested at the mature plant developmental 50 seedlings(3 weeks old)that were exposed to cold(4C)and stage (between flowering and seed set)and from seedlings heat shock (40C)were harvested 2h after temperature stress exposed to two biotic stresses and seven abiotic stress treatments (As6 and As7). treatments,ranging from hormone-,salt-,drought-,till temperature stress treatments.We used the statistical algorithms RNA isolation,quality control and cDNA synthesis geNorm (Vandesompele et al.2002)and NormFinder (Andersen Total RNA was isolated by RNA simple Total RNA Kit et al.2004),which have been widely employed to select the extractions (Bio Teke.Beijing.China).Genomic DNA best suitable reference genes from given biological samples contaminations were effectively removed using RNase-free (Silver et al.2006;Wierschke et al.2010). DNase I treatment(Invitrogen,Carlsbad,CA,USA)according to manufacturer's instructions,as melting curve gave single peak and genomic amplification was larger than RNA/cDNA Materials and methods amplification(see Fig.S1,available as Supplementary Material Seeds of one pakchoi inbred line (Brassica rapa ssp.chinensis to this paper).RNA integrity was electrophoretically verified (L.)Hanelt;Suzhou Qing,a non-heading Chinese cabbage)were by agarose gel and by 260/280nm absorption ratio 1.9~2.1
enzyme 10 (UBC10) (Remans et al. 2008). In a recent paper a Brassica napus L. microarray database was analysed, which showed that EF1-a and a new unknown protein 1 (UP1) were the most suitable reference genes among the given set of tissues (Chen et al. 2010). Furthermore, two commonly used reference genes ACTIN-7 and UBC21, plus two new genes, TIP41-like protein (TIP41) and PP2A that were selected from a microarray dataset, were identified as the most stable reference gene set for normalisation during B. napus embryo maturation (Chen et al. 2010). A study in Chinese cabbage showed that EF1-a and adenine phosphoribosyl-transferase (Apr) were the most stably expressed genes among different tissues (root, stem, heading leaves and lateral sprout) (Qi et al. 2010). The morphological variation present within Brassica rapa (L.) Hanelt is enormous. This includes the leaves in crops like heading Chinese cabbages and the leafy types that do not form heads (pak choi, caixin and several Japanese vegetables like mizuna, mibuna and komatsuna), the enlarged roots of turnips, the inflorescences and stems of broccoletto and the seeds of the oil types. When studying the genetic relationship among accessions using AFLP and SSR marker profiling, clusters or groups of accessions were identified that were represented by different crop types, but it was also clear that genetic distance was more defined by geographical origin than by crop type (Zhao et al. 2007, 2010). There is no information about selection of reference genes for normalisation of qRT–PCR results for gene expression studies in Chinese cabbage. With the recently released B. rapa genome sequence (The Brassica rapa Genome Sequencing Project Consortium 2011) and development of gene expression platforms for B. rapa, genome-wide large-scale gene expression studies will become available and will be mined to select reference genes for real time PCR studies. In the present study, we selected and validated 13 reference genes particularly for accurate normalisation of qRT–PCR results in non-heading Chinese cabbage. These reference genes include seven widely used reference genes in plant research (ACTIN, ACTIN-2, GAPDH, Tub_a, CyP, EF1-a and 18S rRNA) and six potential reference genes (ACTIN-1, UBQ, UBC30, PPR, PP2A and MDH) that were identified based on their stability in expression studies comparing plant developmental stages, different tissues or various environmental stimuli including biotic and abiotic stress (Brunner et al. 2004; Czechowski et al. 2005; Reid et al. 2006). The 13 genes were tested in 20 different samples, including three developmental stages, eight different tissues harvested at the mature plant developmental stage (between flowering and seed set) and from seedlings exposed to two biotic stresses and seven abiotic stress treatments, ranging from hormone-, salt-, drought-, till temperature stress treatments. We used the statistical algorithms geNorm (Vandesompele et al. 2002) and NormFinder (Andersen et al. 2004), which have been widely employed to select the best suitable reference genes from given biological samples (Silver et al. 2006; Wierschke et al. 2010). Materials and methods Seeds of one pakchoi inbred line (Brassica rapa ssp. chinensis (L.) Hanelt; Suzhou Qing, a non-heading Chinese cabbage) were germinated and grown under controlled conditions in pots in a climate room: 25 C day/20 C night temperature, 12 h light/12 h dark cycles. The plants were used to collect tissues under normal growth conditions and after biotic and abiotic stress treatments. All samples were snapped frozen in liquid nitrogen and kept at 80 C until use. Developmental stages (Ds) Three young leaves per plant were harvested and leaves of three plants were pooled for each developmental stage: (i) early stage (third leave present) (Ds1); (ii) before bolting (8 weeks after sowing) (Ds2); and (iii) after bolting (10 weeks after sowing) (Ds3). Different tissues (Dt) Eight different tissues including root (Dt1) and stem (Dt2) at the third leaf stage, leaves after bolting (Dt3, same sample as Ds3), flower buds (Dt4), petiole s(Dt5), stamens (Dt6), pistils (Dt7) and seed pods (Dt8), were collected from three plants and pooled. Biotic stress treatments (Bs) Two fungi, Peronospora parasitica (P.p) and Alternaria brassicicola (A.b), were isolated from the leaves of different susceptible B. rapa cultivars in the farm of Nanjing Agricultural University, China. Conidial suspensions were adjusted to 1 105 spores mL–1 and Tween-20 was added as a surfactant to a final concentration of 0.1%. For each treatment, 53-week-old seedlings were sprayed either with 50 mL pathogen suspension, or demi water (as control). Treated seedlings were placed in a climate chamber (25 C, 85% 5% RH, 12 hour light/12 hour dark) and the second leaves from three plants per treatment were harvested and pooled at 48 h after inoculation. The two treatments are referred to as Bs1 and Bs2 respectively. Abiotic stress treatments (As) Fifty seedlings (3 weeks old) were sprayed respectively with 50 mL solutions, water (as control), SA (2 mmol L1 , PH = 6.5), ABA (50 mmol), NaCl (200 mM), H2O2 (100 mM) and Mannitol (400 mM). For salt (As1) and drought (As2) stress treatments, leaves were harvested at 12 h (for NaCl and Mannitol) after stress treatments. For hormone treatments, leaves were harvested at 6 h after SA treatment (As3), 24 h after ABA treatment (As4) and 24 h after H2O2 treatment (As5). In addition, leaves from 50 seedlings (3 weeks old) that were exposed to cold (4 C) and heat shock (40 C) were harvested 2 h after temperature stress treatments (As6 and As7). RNA isolation, quality control and cDNA synthesis Total RNA was isolated by RNA simple Total RNA Kit extractions (Bio Teke, Beijing, China). Genomic DNA contaminations were effectively removed using RNase-free DNase I treatment (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions, as melting curve gave single peak and genomic amplification was larger than RNA/cDNA amplification (see Fig. S1, available as Supplementary Material to this paper). RNA integrity was electrophoretically verified by agarose gel and by 260/280 nm absorption ratio 1.9~2.1 Validation of reference genes in a wide of samples Functional Plant Biology 343
344 Functional Plant Biology D.Xiao et al. (see Fig.S2).The first-strand ofcDNA was synthesised by reverse stress treatments)and were also treated as one group for transcribing I ug of total RNA in a final reaction volume of analysis of candidate reference gene stabilities.Data or mean 20 uL using the M-MLV reverse transcriptase (Takara,Dalian, Ct values obtained from gRT-PCR were transformed to China)according to the manufacturer's protocol and diluted quantities with PCR efficiency derived straight from 1:10 before use in gRT-PCR assays.The concentration and amplification plots using LinReg (ver.7.0;Amsterdam,The quality ofeach RNA and cDNA sample was also measured using Netherlands)software (Ramakers et al.2003).The normalised the nucleic acid analytic apparatus K6000 (Bio Photometer, data were imported and analysed by two stability analysis Eppendorf,Germany). programs for reference genes,geNorm ver.3.4 (Vandesompele et al.2002)and NormFinder (Andersen et al.2004)for ranking Primer design and gRT-PCR the reference genes Specific primer pairs were designed for 10 single genes(GAPDH, geNorm determines the gene stability measure (M)value Tub_d,Cyclophilin (CyP),EFI-a,18S rRNA,poly ubiquitin for each gene,based on the average pair-wise variation for a enzyme(UBO),UBC30,Pentatricopeptide repeat(PPR),PP2A particular gene with all the other tested genes.Thus,genes can be and Malate dehydrogenase(MDH))and three orthologous genes ranked according to their expression stability through the (ACTIN,ACTIN-1 and ACTIN-2)of the ACTIN gene family.The stepwise exclusion of the least stable gene.The genes with an sequences of the genes were retrieved from Arabidopsis thaliana M value were arbitrarily suggested to be lower than 1.5;genes (L.)Heynh.and blasted against EST(expressed sequence tag) with the lowest M values have the most stable expression.A pair- libraries (Brassica)via nucleotide blast.The Arabidopsis wise stability measure aims at determining the benefit of adding consensus sequences were used for comparison with B.rapa extra reference genes for the normalisation process.For this,an EST sequences,if available,to reveal the exon-intron structure. arbitrary cut off value of0.15 for pair-wise variation (Vn/Vn+1) When no B.rapa L.ssp.chinensis (L.)Hanelt EST sequence of normalisation factor (NF)(NFn and NFn+1)is calculated, was available the gene structure was obtained by comparison of reflecting the effect of including additional(n+1)genes. the A.thaliana sequence with Brassica napus L.and Brassica The second different statistical algorithm software, oleracea L.EST sequences assuming that the exon-exon NormFinder,generates a stability value for each gene,which boundaries are conserved between A.thaliana and B.napus/ is a direct measure for its estimated expression variation.It ranks B.rapa/B.oleracea (see Fig.S3).Sequence comparisons were the stability level of each candidate gene and highlights the most done by DNASTAR,Lasergene 9.1 (Lasergene,Madison,WI stably expressed gene with the lowest stability value by using a USA)and primers were designed using Primer Express 2.0 model-based variance estimation approach. software (PE Applied Biosystems,Foster,CA,USA)under default parameters.Primer sequences and exon-exon junctions Results of the reference genes are listed in Table 1.Primer specificity and Expression profiling of the candidate genes DNA contamination were visualised by separating PCR products from cDNA and DNA on agarose gel (Fig.S1A)and when only a Melting curve analysis of the amplification products confirmed single band was observed,the band was purified to be template for that the primers amplified a single PCR product(Fig.S3).The preparing the standard curves(which takes into account primer standard curves for each of the candidate reference genes were efficiency)using gRT-PCR (Ramakers et al.2003).The PCR found to have R2>0.995 (Table 1),indicating a strong reaction efficiencies (E)were calculated using the equation linear relationship between the detected Ct values and the E=10(-1/slope)(Lekanne Deprez et al.2002).This calculation corresponding relative amount of cDNA in all the PCR method results in efficiencies ranging from 95 to 108%.The reactions.Based on the slopes of the standard curves,the 13 standard curve was generated using a dilution series of the Dsl gene assays were found to have PCR efficiencies >90.7% (leaf developmental stage 1)sample over at least five dilution (Table 1).It was apparent that each candidate reference gene points (Fig.S4).gRT-PCR was performed in triplicate on a had variable Ct values in the wide variety of samples tested,as the 32-position carousel (Light Cycler)with the Light Cycler-RNA Ct values ranged widely from 14.56(18S rRNA)to 39.53(PP24) amplification kit SYBR Green I(Roche,Mannheim,Germany) across all samples (Table 2).None of the 13 candidate reference and conducted in 25 uL reaction volumes containing 30ng uL genes had a uniform expression over all samples tested.As a cDNA sample,along with an RNA template control in parallel consequence,for accurate normalisation of gene expression in for each gene.The thermal cycling consisted of 95C for 2 min different experimental conditions,specific sets ofreference genes and 40 cycles of95C for 20s,55C for 20s and 72C for 20s.After are needed. the PCR a melting curve was generated to check the specificity of the amplified fragment.Data analysis was performed with geNorm analysis the Rotor-gene 6 ver.6.1 software(Applied Biosystems).All the The geNorm program conducts sequential elimination ofthe least cycle threshold(Ct)values from one gene were determined at stable gene in any given experimental group,resulting in the the same threshold fluorescence value of 0.2.The single-peak exclusion of all but the two most stable genes in each strategic melting curves obtained using the 13 primer pairs to amplify the group(Table 3).For all 20 samples tested,ACTIN and CyP were candidate reference genes are displayed in Fig.S1B. the most stable genes,followed by UBC30,EFI-a and UBO. In contrast,PP2 A was the least stable gene tested.The two most Data analyses stably expressed genes when comparing samples of different The 20 samples were divided into four strategic groups developmental stages were ACTIN and CyP that were also most (developmental stages,different tissues,biotic and abiotic stable when all 20 samples were analysed together,followed by
(see Fig. S2). Thefirst-strand of cDNA was synthesised by reverse transcribing 1 mg of total RNA in a final reaction volume of 20 mL using the M-MLV reverse transcriptase (Takara, Dalian, China) according to the manufacturer’s protocol and diluted 1 : 10 before use in qRT–PCR assays. The concentration and quality of each RNA and cDNA sample was also measured using the nucleic acid analytic apparatus K6000 (Bio Photometer, Eppendorf, Germany). Primer design and qRT–PCR Specific primer pairs were designed for 10 single genes (GAPDH, Tub_a, Cyclophilin (CyP), EF1-a, 18S rRNA, poly ubiquitin enzyme (UBQ), UBC30, Pentatricopeptide repeat (PPR), PP2A and Malate dehydrogenase (MDH)) and three orthologous genes (ACTIN, ACTIN-1 and ACTIN-2) of the ACTIN gene family. The sequences of the genes were retrieved from Arabidopsis thaliana (L.) Heynh. and blasted against EST (expressed sequence tag) libraries (Brassica) via nucleotide blast. The Arabidopsis consensus sequences were used for comparison with B. rapa EST sequences, if available, to reveal the exon-intron structure. When no B. rapa L. ssp. chinensis (L.) Hanelt EST sequence was available the gene structure was obtained by comparison of the A. thaliana sequence with Brassica napus L. and Brassica oleracea L. EST sequences assuming that the exon-exon boundaries are conserved between A. thaliana and B. napus/ B. rapa/B. oleracea (see Fig. S3). Sequence comparisons were done by DNASTAR, Lasergene 9.1 (Lasergene, Madison, WI, USA) and primers were designed using Primer Express 2.0 software (PE Applied Biosystems, Foster, CA, USA) under default parameters. Primer sequences and exon-exon junctions of the reference genes are listed in Table 1. Primer specificity and DNA contamination were visualised by separating PCR products from cDNA and DNA on agarose gel (Fig. S1A) and when only a single band was observed, the band was purified to be template for preparing the standard curves (which takes into account primer efficiency) using qRT–PCR (Ramakers et al. 2003). The PCR reaction efficiencies (E) were calculated using the equation E = 10(–1/slope) (Lekanne Deprez et al. 2002). This calculation method results in efficiencies ranging from 95 to 108%. The standard curve was generated using a dilution series of the Ds1 (leaf developmental stage 1) sample over at least five dilution points (Fig. S4). qRT–PCR was performed in triplicate on a 32-position carousel (Light Cycler) with the Light Cycler-RNA amplification kit SYBR Green I (Roche, Mannheim, Germany) and conducted in 25 mL reaction volumes containing 30ng mL–1 cDNA sample, along with an RNA template control in parallel for each gene. The thermal cycling consisted of 95 C for 2 min and 40 cycles of 95 C for 20s, 55 C for 20s and 72 C for 20s. After the PCR a melting curve was generated to check the specificity of the amplified fragment. Data analysis was performed with the Rotor-gene 6 ver. 6.1 software (Applied Biosystems). All the cycle threshold (Ct) values from one gene were determined at the same threshold fluorescence value of 0.2. The single-peak melting curves obtained using the 13 primer pairs to amplify the candidate reference genes are displayed in Fig. S1B. Data analyses The 20 samples were divided into four strategic groups (developmental stages, different tissues, biotic and abiotic stress treatments) and were also treated as one group for analysis of candidate reference gene stabilities. Data or mean Ct values obtained from qRT–PCR were transformed to quantities with PCR efficiency derived straight from amplification plots using LinReg (ver. 7.0; Amsterdam, The Netherlands) software (Ramakers et al. 2003). The normalised data were imported and analysed by two stability analysis programs for reference genes, geNorm ver. 3.4 (Vandesompele et al. 2002) and NormFinder (Andersen et al. 2004) for ranking the reference genes. geNorm determines the gene stability measure (M) value for each gene, based on the average pair-wise variation for a particular gene with all the other tested genes. Thus, genes can be ranked according to their expression stability through the stepwise exclusion of the least stable gene. The genes with an M value were arbitrarily suggested to be lower than 1.5; genes with the lowest M values have the most stable expression. A pairwise stability measure aims at determining the benefit of adding extra reference genes for the normalisation process. For this, an arbitrary cut off value of 0.15 for pair-wise variation (Vn/Vn + 1) of normalisation factor (NF) (NFn and NFn + 1) is calculated, reflecting the effect of including additional (n + 1) genes. The second different statistical algorithm software, NormFinder, generates a stability value for each gene, which is a direct measure for its estimated expression variation. It ranks the stability level of each candidate gene and highlights the most stably expressed gene with the lowest stability value by using a model-based variance estimation approach. Results Expression profiling of the candidate genes Melting curve analysis of the amplification products confirmed that the primers amplified a single PCR product (Fig. S3). The standard curves for each of the candidate reference genes were found to have R2 0.995 (Table 1), indicating a strong linear relationship between the detected Ct values and the corresponding relative amount of cDNA in all the PCR reactions. Based on the slopes of the standard curves, the 13 gene assays were found to have PCR efficiencies 90.7% (Table 1). It was apparent that each candidate reference gene had variable Ct values in the wide variety of samples tested, as the Ct values ranged widely from 14.56 (18S rRNA) to 39.53 (PP2A) across all samples (Table 2). None of the 13 candidate reference genes had a uniform expression over all samples tested. As a consequence, for accurate normalisation of gene expression in different experimental conditions, specific sets of reference genes are needed. geNorm analysis The geNorm program conducts sequential elimination of the least stable gene in any given experimental group, resulting in the exclusion of all but the two most stable genes in each strategic group (Table 3). For all 20 samples tested, ACTIN and CyP were the most stable genes, followed by UBC30, EF1-a and UBQ. In contrast, PP2A was the least stable gene tested. The two most stably expressed genes when comparing samples of different developmental stages were ACTIN and CyP that were also most stable when all 20 samples were analysed together, followed by 344 Functional Plant Biology D. Xiao et al
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Table 1. Primers and PCR efficiency for the 13 selected candidate reference genes SymbolA Arabidopsis orthologue locus Accession number Primer sequence 50–30 (forward and reverse) Tm (C) Junction BLASTnB Amplicon size (bp) R2 Slope PCR efficiency (%) ACTIN AT5G09810 AF111812 GGAGCTGAGAGATTCCGTTG 60 Exon4 0.0 158 0.999 –3.410 96.50 GAACCACCACTGAGGACGAT 60 ACTIN-1 AT2G37620 AF044573.1 CCAACAGAGAGAAGATGACCC 59 Exon3 e-149 95 0.998 –3.150 107.70 ACTGGCGTAAAGGGAGAGG 59 ACTIN-2 AT3G18780 BG732274.1 ATCGAGCATGGTGTTGTGAG 60 Exon2 e-140 132 0.997 –3.219 104.00 GGCCTTTGGGTTAAGAGGAG 60 GAPDH AT1G12900 AB331373 TCCACCATTGATTCTTCTCTG 58 Exon5 0.0 108 0.999 –3.293 101.20 TCAGCCAAATCAACAACTCTC 58 CyP AT2G16600 M55018.1 AGGAGGAGATTTCACCGC 58 Exon1 0.0 232 0.999 –3.312 100.40 TCTCTAACGACATCCATCCC 58 EF1-a AT5G60390 AF398148.1 TCTGGAAAAGAGATTGAGAAGG 59 Exon3 e-107 129 0.999 –3.465 94.40 AACAGCGAAACGACCCAAT 61 Tub _ a AT5G19780 AC189186.2 TTTGGGTTCTCTCTTGCTAG 59 Exon3 Exon 4 0.0 143 0.999 –3.267 102.30 CGAGTAGAGAATGAGTTGAG 59 18S rRNA AT3G41768 AF513990.1 ATTGACGGAAGGGCACCAC 60 Exon1 0.0 158 0.999 –3.568 90.70 TCGCTCCACCAACTAAGAAC 59 UBC30 AT5G56150 U17250.1 TGAAAGAGCAGTGGAGCC 58 Exon4 Exon5 e-117 122 0.999 –3.480 93.80 GGTCTGTCTTGTAGGTGTGAGC 59 UBQ AT2G36170 L21898.1 CAGCCAAGGTACGACCATCT 60 Exon4 Exon5 e-149 165 0.999 –3.380 97.60 TATTCGTGAAGACGCTGACG 60 PPR AT1G62860 FJ455099.1 AAGAGGGTAGATGATGGAATG 56 Exon2 e-151 180 0.999 –3.333 99.50 TTACAAGTGACGACATTAGGG 55 PP2A AT1G69960 AC240932.1 AGGCTACACGTTCGGACAAG 60 Exon5 2e-152 142 0.997 –3.431 95.60 TGGGGCACTAAACACAGTCA 60 MDH AT1G62480 CB331882 CGAGATGACACCACCAAAGAC 61 Exon2 4e-10 157 0.995 –3.278 101.90 GGTTTCATCTGCTTCTTCGG 60 AAll the selected reference genes were named according to the most similar orthologue locus from A. thaliana. BE-value was obtained from the A. thaliana nucleotide sequence using BLASTn. Validation of reference genes in a wide of samples Functional Plant Biology 345
346 Functional Plant Biology D.Xiao et al. 00FS981 CH016191 00FS981 610+6091 PN019491 17.0116.61 770165.61 6104252 6r019652 6S0769#2 22012102 S#:0HS812 11:0168:62 -0115:52 KE.012592 8#.011592 ￥。：049512 20-.92 H.018212 8:018192 90.0161 0.1112 80:01299t 110+3LL5 5Z.0+8953 喜 6001110.82 22:018982 210422 2101.0M 6E'OF LE'IE #4:010000 00192.0 D0升260 800F E8'8 204101 00t 000190 8P0T13.2E 800 19 20102 #80122.23 52042928 #0US.9 #2:0TG2*2 90013522 t104S.52 1-01622 90:01692 0014902 04:0132 Z:022252 5:0165.22 90.04122 0.0129 0:0125:12 33.0T1522 #8:018202 8001.55.92 60429 650199 65:0139 2019 11018 250t 890T16.t2 150100 SI0 00T13 ST-3+99.53 91'0F9S'I 08.0+59.61 Z.:01.61 08.0199.61 60.012651 .:011.31 01:01561 10.0116.51 6E.031 :012602 H01201 66:012661 61:01*402 .011102 2.0-9202 IS OFtLOC 210121-12 LE'I S8'S 200151-08 250106.52 2025.9 900121-08 860100.2 03 8'OF SI'S 10: 92.01.100.62 20562 H1:010002 101612 29-014822 -0+1612 00.0401.61 91'1FES'I 21-010022 00100.31 2.02961 2205 69.0a5112 101#9212 51-019.22 27092 910+C122 8Z.0512 P'SFLO SP01161 I00180.81 K10110.01 080194 20138 H1OT19.81 C001259i 220T1. 2015.51 HH18.81 101012 620T18.81 01-0125.:02 2P0158.61 #:01191.51 60.015-02 51:012E81 02:016502 10.019000 1汉：016502 H:0195.81 22.0122 002212 6001222 E.0T001 8'0F8I'S 6201602 ?+寸x个 66.101261 X-0160.92 #Z0t5852 YE04168Z 20010 22096212 830416.52 20102 01010 5-018.2 200T02 0115 C10+1192 62042562 820125:62 270H3252 S10H1962 S0011100 S004T900 0016252 CC01182 00.23 1t0452c0 00T11102 SEUT博CTE 18-010662 P'SFLO 10014102 IE0119.61 61-0102:2 61011261 I0F961 0210105.01 ZI'OFSE81 0066sl 8TOFS181 800F8081 70116.2 1E0189212 EEOFEEEZ 91010.02 21016602 CH018612 ZN010002 duns sdnora oons
Table 2. Ct values ( s.d.) of the 20 analysed samples ordered according to their respective strategic groups Strategic groups Sample ACTIN ACTIN-1 ACTIN-2 GAPDH Cyp EF1-a Tub_a 18S rRNA UBC30 UBQ PPR PP2A MDH codes CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. CT ± s.d. Developmental Ds1 20.17 ± 0.01 32.58 ± 0.25 28.09 ± 0.13 20.15 ± 0.09 19.1 ± 0.36 21.45 ± 0.14 27.12 ± 0.10 25.58 ± 0.16 26.77 ± 0.08 24.29 ± 0.24 28.64 ± 0.07 27.87 ± 0.13 22.64 ± 0.14 stages (Ds) Ds2 18.64 ± 0.17 29.41 ± 0.15 25.35 ± 0.24 18.32 ± 0.19 18.08 ± 0.01 20.00 ± 0.14 25.85 ± 1.37 14.56 ± 0.16 25.66 ± 0.39 22.53 ± 0.06 28.68 ± 0.22 25.74 ± 0.1 19.08 ± 0.05 Ds3 19.64 ± 0.31 30.81 ± 0.05 25.91 ± 0.38 20.49 ± 0.20 18.81 ± 0.14 21.91 ± 0.1 30.13 ± 0.06 19.68 ± 0.80 25.68 ± 0.79 25.5 ± 0.13 31.37 ± 0.39 26.38 ± 0.37 18.65 ± 0.04 Different tissues Dt1 23.20 ± 0.19 33.08 ± 0.13 28.59 ± 0.02 31.88 ± 0.41 20.26 ± 0.40 23.25 ± 0.34 27.40 ± 0.57 20.55 ± 0.04 26.46 ± 0.5 24.49 ± 0.11 32.25 ± 0.12 39.53 ± 0.53 24.96 ± 0.13 (between flowering Dt2 19.27 ± 0.19 29.87 ± 0.74 24.96 ± 0.22 20.06 ± 0.01 18.79 ± 0.32 22.55 ± 0.42 26.52 ± 0.24 19.34 ± 0.23 24.38 ± 0.13 24.69 ± 0.26 30.37 ± 0.16 26.34 ± 0.48 16.14 ± 0.16 and seed set) (Dt) Dt3 19.64 ± 0.31 30.81 ± 0.05 25.91 ± 0.38 20.49 ± 0.20 18.81 ± 0.14 21.91 ± 0.1 30.13 ± 0.06 19.68 ± 0.80 25.68 ± 0.79 25.5 ± 0.13 31.37 ± 0.39 26.38 ± 0.37 18.65 ± 0.04 Dt4 17.53 ± 0.10 27.29 ± 0.40 23.08 ± 0.39 18.76 ± 0.34 16.52 ± 0.03 19.10 ± 0.00 24.10 ± 0.48 16.92 ± 0.09 22.65 ± 1.09 20.64 ± 0.07 30.00 ± 0.44 23.56 ± 0.44 14.85 ± 0.03 Dt5 18.35 ± 0.12 26.57 ± 0.16 25.20 ± 0.25 19.83 ± 0.05 17.14 ± 0.26 21.53 ± 1.16 25.12 ± 0.77 18.31 ± 0.40 23.13 ± 0.25 23.91 ± 0.26 30.26 ± 0.01 26.1 ± 0.21 16.79 ± 0.19 Dt6 18.99 ± 0.09 28.44 ± 0.22 22.50 ± 0.10 24.42 ± 0.23 18.09 ± 0.78 22.00 ± 0.12 24.06 ± 0.41 19.47 ± 0.10 22.16 ± 0.42 24.28 ± 0.24 30.92 ± 0.39 25.38 ± 0.49 16.56 ± 0.34 Dt7 18.15 ± 0.18 29.68 ± 0.13 23.54 ± 0.17 20.47 ± 0.03 17.97 ± 0.22 18.80 ± 0.07 24.06 ± 0.56 17.94 ± 0.07 22.88 ± 0.11 20.94 ± 0.22 28.83 ± 0.08 24.28 ± 0.34 17.74 ± 0.23 Dt8 18.08 ± 0.08 28.05 ± 0.20 24.36 ± 0.02 21.26 ± 0.09 17.78 ± 0.23 19.62 ± 0.25 25.15 ± 0.28 18.44 ± 0.39 23.8 ± 0.84 21.59 ± 0.38 31.08 ± 0.26 26.18 ± 0.60 14.79 ± 0.22 Biotic stress (Bs) Bs1 23.91 ± 0.27 32.59 ± 0.01 29.25 ± 0.79 23.73 ± 0.29 21.45 ± 0.28 23.34 ± 0.22 27.27 ± 0.26 20.92 ± 0.56 27.58 ± 0.41 24.54 ± 0.47 30.13 ± 0.01 37.19 ± 0.06 27.2 ± 0.19 Bs2 21.65 ± 0.55 32.79 ± 0.43 26.74 ± 0.47 23.22 ± 0.09 19.88 ± 0.06 22.58 ± 0.55 25.51 ± 0.77 18.43 ± 0.14 25.15 ± 0.57 23.17 ± 0.09 28.76 ± 0.30 34.47 ± 0.11 22.9 ± 0.16 Abiotic stress (As) As1 21.63 ± 0.31 28.13 ± 0.04 26.13 ± 0.27 21.40 ± 0.31 18.42 ± 0.52 21.34 ± 0.31 25.04 ± 0.02 19.75 ± 0.11 24.43 ± 0.34 22.49 ± 0.73 32.71 ± 0.48 34.21 ± 1.75 23.57 ± 0.34 As2 20.85 ± 0.31 28.69 ± 0.36 26.11 ± 0.16 25.18 ± 0.28 18.81 ± 0.44 21.17 ± 0.39 25.77 ± 0.84 19.92 ± 0.59 23.91 ± 0.68 23.21 ± 0.06 32.61 ± 0.08 34.38 ± 0.23 25.96 ± 0.49 As3 23.33 ± 0.33 32.25 ± 0.31 29.72 ± 0.29 26.39 ± 0.29 21.44 ± 0.13 24.64 ± 0.13 25.43 ± 0.36 20.34 ± 0.19 27.67 ± 0.64 24.54 ± 0.34 29.39 ± 0.42 36.62 ± 0.03 24.69 ± 0.59 As4 20.38 ± 0.16 28.11 ± 0.05 25.88 ± 0.73 23.18 ± 0.10 18.81 ± 0.29 22.65 ± 0.19 25.56 ± 0.31 20.11 ± 0.34 24.71 ± 0.33 23.62 ± 0.83 32.18 ± 0.34 34.38 ± 0.46 23.32 ± 0.65 As5 20.49 ± 0.42 28.33 ± 0.02 26.59 ± 0.26 23.54 ± 0.22 20.53 ± 0.10 22.79 ± 0.22 26.07 ± 0.65 20.26 ± 0.32 25.05 ± 0.50 24.73 ± 0.70 32.22 ± 0.54 34.35 ± 1.29 22.56 ± 0.5 As6 21.98 ± 0.43 31.34 ± 0.35 27.73 ± 0.28 22.84 ± 0.29 19.83 ± 0.32 22.12 ± 0.16 27.00 ± 0.26 20.73 ± 0.51 25.34 ± 0.15 22.51 ± 0.23 32.62 ± 0.37 37.75 ± 0.04 23.12 ± 0.22 As7 20.00 ± 0.42 27.90 ± 0.31 25.23 ± 0.22 19.32 ± 0.39 17.68 ± 0.34 21.75 ± 0.28 23.95 ± 0.23 19.47 ± 0.38 24.81 ± 0.09 23.28 ± 0.84 30.45 ± 0.04 35.68 ± 0.25 24.85 ± 0.49 346 Functional Plant Biology D. Xiao et al