Arabidopsis R2R3-MYB transcription factor AtMYB60 functions as a transcriptional repressor of anthocyanin biosynthesis in lettuce (Lactuca sativa)

The MYB transcription factors play important roles in the regulation of many secondary metabolites at the transcriptional level. We evaluated the possible roles of the Arabidopsis R2R3-MYB transcription factors in flavonoid biosynthesis because they are induced by UV-B irradiation but their associated phenotypes are largely unexplored. We isolated their genes by RACE-PCR, and performed transgenic approach and metabolite analyses in lettuce (Lactuca sativa). We found that one member of this protein family, AtMYB60, inhibits anthocyanin biosynthesis in the lettuce plant. Wild-type lettuce normally accumulates anthocyanin, predominantly cyanidin and traces of delphinidin, and develops a red pigmentation. However, the production and accumulation of anthocyanin pigments in AtMYB60-overexpressing lettuce was inhibited. Using RT-PCR analysis, we also identified the complete absence or reduction of dihydroflavonol 4-reductase (DFR) transcripts in AtMYB60- overexpressing lettuce (AtMYB60-117 and AtMYB60-112 lines). The correlation between the overexpression of AtMYB60 and the inhibition of anthocyanin accumulation suggests that the transcription factorAtMYB60 controls anthocyanin biosynthesis in the lettuce leaf. Clarification of the roles of the AtMYB60 transcription factor will facilitate further studies and provide genetic tools to better understand the regulation in plants of the genes controlled by the MYB-type transcription factors. Furthermore, the characterization of AtMYB60 has implications for the development of new varieties of lettuce and other commercially important plants with metabolic engineering approaches.
Introduction
Flavonoid metabolism is unique to plants and produces a great number and variety of compounds including flavones, flavonols, anthocyanins, and proanthocyanins (Winkel-Shirley 2001; Park et al. 2004). These molecules play important roles in many fundamental processes in plants, such as the pigmentation of flowers and fruits, UV resistance, pollen fertility, responses to wounding and fungal attacks, and environmental stress responses (Shirley et al. 1995; Weisshaar and Jenkins 1998; Harborne and Williams 2000; Dixon and Piava 1995; Moyano et al. 1996; Pietta 2000; Cominelli et al. 2005). The regulation of the anthocyanin pigments in plants is particularly important in metabolic engineering applications because these compounds act as visual signals that attract the insects and animals required for pollination and seed dispersal (Holton and Cornish 1995).
The metabolic pathways involved in anthocyanin biosynthesis are well established, and the central pathways in this process are highly conserved in plants (Shih et al. 2006). Moreover, studies in model plants, such as Arabidopsis and maize, have facilitated a sound understanding of the genes involved and the assembly of the relevant enzyme complexes (Winkel-Shirley 2001). Two groups of genes are required for anthocyanin biosynthesis: structural genes that encode the enzymes that directly participate in the biosynthetic reactions, and genes for transcription factors that regulate the expression of these structural genes and the accumulation of flavonoid metabolites. Transcription factors can act as activators or repressors of gene expression, and mediate either increases or decreases in transcription through sequence-specific DNA binding and protein-protein interactions (Broun 2004).
MYB proteins have been identified in a large number of eukaryotes, including fungi, plants and vertebrates (Ohi et al. 1994; Martin and Paz-Ares 1997; Slamon et al. 1986), and have roles in a wide array of cellular processes. These include the regulation of secondary metabolism, signal transduction, cell division, and responses to plant diseases and various forms of stress (UV-B light, cold and drought) (Larkin et al. 1994; Borevitz et al. 2000; Stracke et al. 2001; Vailleau et al. 2002; Cominelli et al. 2005). The R2R3-MYB-related proteins activate the transcription of structural genes that function in different branches of phenylpropanoid metabolism (Martin and Paz-Ares 1997). The promoters of these structural genes each contain potential MYB protein recognition motifs and also bind with the basic helix-loop-helix (bHLH) domain and conserved WD40 repeat proteins (Park et al. 2007a, b; Martin and Paz-Ares 1997; Winkel-Shirley 2001).
The classification of MYB transcription factors is based on the strong conservation of imperfect repeats in the MYB DNA-binding domain of the member proteins (Martin and Paz-Ares 1997). In contrast to animals (R1R2R3), the MYB domain transcription factors found in plants are characterized by the R2R3-type MYB domain, comprising a DNA-binding domain and an activation/repression domain. Moreover, the balance between activators and repressors in this transcription factor family in plants may provide extra flexibility in terms of transcriptional control (Jin et al. 2000). There have been 126 R2R3 MYB genes identified in Arabidopsis thaliana, all of which exhibit strong homology within their N-terminal MYB DNA-binding domains and can be divided into 24 subgroups on the basis of their sequences (Stracke et al. 2001; Riechmann and Ratcliffe 2000; Kranz et al. 1998). To determine the biological roles of the MYB-related transcription factors in the production of phenylpropanoids, we isolated six R2R3-MYB proteins from Arabidopsis based on available information indicating that these genes are induced by environmental stress (UV-B irradiation) (Kranz et al. 1998). However, they do not have an informative phenotype, because of the structural and functional redundancy among these factors (Bouche and Bouchez 2006). Furthermore, although there have been other studies of anthocyanin accumulation in lettuce, one of the most popular and commercially important vegetables in the world, this process has not been characterized before at the level of transcriptional regulation. The regulation of anthocyanin accumulation in plants is also a key issue in our understanding of the regulation of leaf color. The identification of the factors that exert this control will provide valuable tools to moderate the extent and distribution of anthocyanin-derived pigmentation in plant tissues.
In this study, we overexpressed the AtMYB60 gene in lettuce plants (“Jinjachuckmyun” cultivar) that are highly pigmented with a red color throughout their leaf tissues. Because the loss of these pigments is easily detected, the activity and repression of the MYB-type transcription factors that affect these pathways can be investigated. In the leaves of lettuce, coordinated transcriptional regulation controls virtually each step of the anthocyanin biosynthetic pathway (Park et al. 2007a). We compared the expression of the major anthocyanin biosynthetic genes and the AtMYB60 gene by RT-PCR. We show that this transcription factor is functionally active in repressing anthocyanin accumulation and can thus generate green lettuce leaves. We also show, by functionally repressing the accumulation of this compound, that AtMYB60 plays a significant role in controlling anthocyanin biosynthesis via the inhibition of a key target gene encoding dihydroflavonol reductase (DFR). Thus, we report here for the first time that anthocyanin biosynthesis and the transcription of the DFR gene are repressed through the overexpression of Arabidopsis R2R3-MYB transcription factors in transgenic lettuce plants. We also discuss the function of transcriptional regulators in the control of the expression of the structural protein that are required for anthocyanin biosynthesis in lettuce.
Materials and methods
Plant materials and growth condition
To investigate the effects of exogenous AtMYB proteins on anthocyanin biosynthesis in lettuce leaves at the molecular level, Jinjachuckmyun plants, supplied by Syngenta (Seoul, Korea), were used. The plants were grown at the National Institute of Agricultural Biotechnology in Suwon, South Korea. Fresh lettuce leaves were plucked from the plants, frozen immediately in liquid nitrogen and stored at −80°C until required.
Isolation of AtMYB cDNAs
To isolate the AtMYB genes, cDNAs were isolated from UV-B-irradiated Arabidopsis plants using the BD SMART RACE cDNA Amplification Kit (Clontech, USA) and then used as templates for PCR cloning. The following gene-specific primers were used for RACE-PCR: AtMYB4 (forward: 5′-GTATGGGAAGGTCACCGTGCTGTGAGAAA-3′; reverse: 5′-TATTATTTCATCTCCAAGCTTCGAAAGCC-3′), AtMYB29 (forward: 5′-AAATGTCAAGAAAGCCATGTTGTGTGGGA-3′; reverse: 5′-GATCATATGAAGTTCTTGTCGTCATAATC-3′), AtMYB30 (forward: 5′-TAATGGTGAGGCCTCCTTGTTGTGACAAA-3′; reverse: 5′-CTTCAGAAGAAATTAGTGTTTTCATCCAA-3′), AtMYB34 (forward: 5′-AGATGGTGAGGACACCATGTTGCAAAGAA-3′; reverse: 5′-CGTCAGACAAAGACTCCAACCATATTGTC-3′), AtMYB51 (forward: 5′-GAATGGTGCGGACACCGTGTTGCAAAGCC-3′; reverse: 5′-ACTCATCCAAAATAGTTATCAATTTCGTC-3′) and AtMYB60 (forward: 5′-AGATGGGTAGGCCTCCATGCTGTGACAAG-3′; reverse: 5′-AATTAAAGCATATTAGAGAGCTCCATCAA-3′). The resulting PCR products were then cloned into the pGEM-T Easy vector (Promega, USA) and sequenced on both strands.
Vector construction
AtMYB coding regions were cloned into an expression vector containing the CaMV 35S promoter and the nopaline synthase 3′ terminator. The resulting constructs were then introduced into the binary vector 22103 (a derivative of pCAMBIA3301; Cambia, Australia). The complete coding sequence of each AtMYB cDNA was amplified with a specific forward primer designed to introduce an MluI restriction site and a reverse primer designed to introduce an XbaI restriction site to allow subcloning, as follows: AtMYB4 (forward: 5′-ATACGCGTATGGGAAGGTCACCGTGCTGTG-3′; reverse: 5′-TATCT AGATTATTTCATCTCCAAGCTTCGA-3′), AtMYB29 (forward: 5′-ATACGCGTATGTCAAGAAAGCCATGTTGTG-3′; reverse: 5′-TATCTAGATCATATGAAGTTCTTGTCGTCA-3′), AtMYB30 (forward: 5′-ATACGCGTATGGTGAGGCCTCCTTGTTGTG-3′; reverse: 5′-TATCTAGATCAGAAGAAATTAGTGTTTTCA-3′), AtMYB34 (forward: 5′- ATACGCGTATGGTGAGGACACCATGTTGCA-3′; reverse: 5′-TATCTAGATCAGACAAAGACTCCAACCATA-3′), AtMYB51 (forward: 5′-ATACGCGTATGGTGCGGACACCGTGTTGCA-3′; reverse:5′-TATCTAGATCATCCAAAATAGTTATCAATT-3′), AtMYB60 (forward: 5′-ATACGCGTATGGGTAGGCCTCCATGCTGTG-3′; reverse: 5′-TATCTAGATTAAAGCATATTAGAGAGCTCC-3′). After PCR and digestion, the MluI/XbaI fragments were cloned into the binary vector 22103 between CaMV 35S promoter and the nopaline synthase poly(A) addition site, creating the binary vector 22103-AtMYB plasmids (Fig. 2). These constructs were then introduced into the Agrobacteriumtumefaciens EHA105 strain using the freeze–thaw method.
Generation of AtMYB transgenic lettuce
Transgenic lettuce plants were generated using the Agrobacterium-mediated transformation of cotyledons. Lettuce seeds of the Jinjachuckmyun cultivar were supplied by Syngenta (Korea) and were surface sterilized in 25% (v/v) bleach for 7 min, washed (three changes) in sterile water, and placed on phytogel-solidified (0.2%, w/v) MS medium at pH 5.8. The seeds were germinated and maintained at 25°C (16 h photoperiod, 200 μmol m−2 s−1, daylight fluorescent tubes). The cotyledons were excised from the 4-day-old lettuce seedling and inoculated with freshly grown A. tumefaciens. The inoculated cotyledons were then co-cultivated on MS salt medium solidified with 0.2% purified phytogel (Sigma, USA), for 2 days at 24°C in the dark. The cotyledon explants were then transferred to shoot initiation medium supplemented with 400 mg l−1 carbenicillin (Duchefa, the Netherlands). After a washing step, these explants were cultured (ten per plate) on selection medium (MS salt 4.43 g l−1, sucrose 30 g l−1, phytogel 2 g l−1, kinetin 0.5 mg l−1, carbenicillin 400 mg l−1, phosphinothricin 1.0 mg l−1 and cysteine 10 mg l−1) and subcultured every 2 weeks on the same medium. Shoots that regenerated from explants on medium containing phosphinothricin were rooted in rooting medium (MS salt 4.43 g l−1, sucrose 30 g l−1, phytogel 2 g l−1 and phosphinothricin 1.0 mg l−1), before transfer to the greenhouse, where they were allowed to self-pollinate and to set seed. The seeds were harvested and stored at 4°C.
Anthocyanin extraction and HPLC analysis
Anthocyanin in the lettuce plants was quantified by HPLC at a detection wavelength of 510 nm, comparing the sample retention times and peaks with those of known standards. The relative quantification of each phenolic compound samples was expressed as the equivalent quantity of purified standards for each treatment.
About 0.2 g of ground and freeze-dried leaves was accurately weighed and extracted with 1 ml of a solution of methanol:water (80:20, v/v) at 80°C for 1 h, then sonicated for 20 min. The extracted solution was filtered into a 50-ml volumetric flask, and the flask and filter were rinsed with a solution of methanol:water (80:20, v/v). The filtrate was then made up to the required volume with the same solvent. Approximately 1 mL of the sample solution was passed through a 0.4 μm filter before analysis by HPLC. A Shimadzu HPLC system with 10AD dual pumps was used, with an RP18 (LiChrospher, 250 mm × 4 mm × 5 μm) column. The HPLC parameters were as follows: column temperature 30°C, solvent A = 0.1% trifluoroacetic acid in water, solvent B = 98% acetonitrile with 0.1% trifluoroacetic acid, solvent gradient, 0 min = 0% B, 3 min = 6% B 12 min = 18% B, 25 min = 25% B, 35 min = 100% B, 40 min = 100% B. The flow rate was 0.35 mL min−1. The peaks were classified as either cyanidin or delphinidin derivatives by UV spectral analysis.
RT-PCR analysis
Total RNA from the transgenic and wild-type lettuce leaves was extracted using RNeasy Plant Mini Kit (Qiagen, USA), according to the manufacturer’s instructions. To estimate the transcript levels of the exogenous AtMYB4 and AtMYB60 genes in lettuce, we used the primers 5′-ATGGGAAGATCGCCTTGTTGTGAA-3′ (F) and 5′-TCATTTCATCTCTAAGCTTCTGTAGTCCAAAA-3′ (R) for AtMYB4 and 5′-ATGGGGAGGCCTCCTTGTTGTG-3′ (F) and 5′-TCAATTATCGAAAAAATTAGGGTTTTCATCA-3′ (R) for AtMYB60. The mRNA expression patterns of the CHS, F3H, DFR and UFGT genes were analyzed by RT-PCR with the All-in-one RT/PCR Premix (SuperBio P7003, Suwon, Korea). The amplification of rRNA was used as the internal control. Total RNA (100 ng) in a volume of 20 μL containing 20 mM Tris–HCl (pH 7.9), 100 mM KCl, 0.1 mM EDTA, 1 mM DTT, M-MLV reverse transcriptase (RNaseH Minus), Super Taq Plus DNA polymerase and 10 pmol of each gene-specific amplification primer was used for RT-PCR, according to the manufacturer’s instructions. Gene-specific primers for CHS (forward: 5′-GGTTTGCTCTGAGATTACAGCGGTTACC-3′, reverse: 5′-TCCTTGAGACCAAGCTTGAGCTCCACCT-3′), F3H (forward: 5′-GAGATCTTATCAGAGGCAATGGGCCTTG-3′, reverse: 5′-ACAACGGCCCGATGGTCTGCGTTCTTAA-3′), DFR (forward: 5′-GGTCTATGACGAGTCTCATTGGAGCGAT-3′, reverse: 5′-CTATCAATTGCTCCTTTGAACATCTCCT-3′), UFGT (forward: 5′-TGGAGAAGCGGGTTAGACAGTTGATGGA-3′, reverse: 5′-TATAGCTACCATGATTCAACCAACTTCG-3′), rRNA (forward: 5′-TACGGCACTGAAGGTGCCAAGCTCGTG-3′, reverse: 5′-CATCCTCTTGGCAGTCTTGGCGTAGGG-3′) and bar (forward 5′-GCCGCAGGAACCGCAGGAGT-3′, reverse: 5′-AGCCCGATGACAGCGACCAC-3′) were used to confirm the expression levels of these genes in the lettuce plants. Reverse transcription of the total RNA was carried out at 50°C for 30 min, and the reaction was inactivated at 96°C for 3 min. The amplification conditions comprised 35 cycles of denaturation at 94°C for 30 s, annealing at 60°C for 30 s, extension at 72°C for 2 min, and a final extension at 72°C for 10 min. PCR was performed using a GeneAmp PCR system 9700 Cycler (Perkin-Elmer, MA, USA). The resultant RT-PCR products were resolved on a 1% agarose gel, stained with ethidium bromide, and photographed. The amplified products of the CHS, F3H, DFR, UFGT, rRNA and bar transcripts were 398, 319, 534, 153, 386 and 267 bp, respectively.
Results
Cloning of MYB-type transcription factor genes from Arabidopsis
To isolate and characterize the MYB-type transcription factors in Arabidopsis (AtMYB) that play a role in phenylpropanoid metabolism, primers were designed to amplify the entire coding regions of the AtMYB genes that are induced by environmental stress (UV-B irradiation) (Meissner et al. 1999). Previous studies of Arabidopsis have suggested that the R2R3-MYB genes of this plant have important functions in the regulation of secondary metabolism, disease resistance, and hormonal responses (Kranz et al. 1998). We isolated six Arabidopsis R2R3-MYB transcription factor genes based on available information concerning the AtMYB genes induced by UV-B irradiation (Kranz et al. 1998). These were AtMYB4 (AF062860, AT4G38620), AtMYB29 (AF062872, At5G07690), AtMYB30 (AF062873, AT3G28910), AtMYB34 (U66462, AT5G60890), AtMYB51 (AF062887, AT1G18570) and AtMYB60 (AF062895, AT1G08810), with apparent full-length cDNAs of 849, 1011, 972, 885, 1059 and 843 bp, respectively, encoding proteins of 283, 337, 324, 295, 353 and 281 amino acids, respectively (Fig. 1). The amino termini of these proteins contain the R2R3 repeats responsible for their binding to target DNA sequences and which are highly conserved among the R2R3-MYB proteins.
Fig. 1Multiple alignment of AtMYB proteins. Amino acids identical in all six proteins are marked in black, amino acids found in four proteins are marked in gray. The line above indicates the R2 and R3 MYB repeats. GenBank accession numbers for the sequences: AtMYB4 (AF062860), AtMYB29 (AF062872), AtMYB30 (AF062873), AtMYB34 (U66462), AtMYB51 (AF062887) and AtMYB60 (AF062895)
Expression of AtMYB genes in lettuce plants
To examine the effects of the six isolated AtMYB genes on leaf color changes and on the anthocyanin composition of lettuce, cotyledons of the cultivar “Jinjachuckmyun” were transformed using the Agrobacterium tumefaciens strain EHA105 with the binary vector 22103-AtMYB (a derivative of pCAMBIA3301; Fig. 2), carrying these genes and a barstar selection marker. Shoots were regenerated from the callus of phosphinothricin-resistant transformatants. To confirm the introduction and expression of the exogenous AtMYB genes in the Jinjachuckmyun cultivars, total RNA was extracted from the leaves of six-week-old wild-type and transformed lettuce plants (T1 generation) and analyzed by RT-PCR.
Fig. 2Schematic diagram of the T-DNA region of the 22103-AtMYB binary vector used in the transformation of lettuce cultivars. LB left border, RB right border, P35S Cauliflower mosaic virus 35S RNA promoter, PolyA Cauliflower mosaic virus 3′UTR terminator, Tnos nopaline synthase terminator
Transgenic plants were confirmed by their resistance to a barstar spray and by subsequent RT-PCR with specific primers for six AtMYB genes. Because the T1 plants of the AtMYB4, AtMYB29, AtMYB30, AtMYB34 and AtMYB51 transformants were similar in leaf color and in the anthocyanin accumulation in their leaves, the AtMYB4 transformant (AtMYB4-101) and the AtMYB60 transformant (AtMYB60-117 and AtMYB60-112) were selected for further RT-PCR analysis to detect the expression of the structural genes that are involved in anthocyanin biosynthesis (Fig. 3). RT-PCR analysis was performed using six-week-old lettuce leaves of wild-type and transgenic lettuce plants. The wild-type lettuce plants had no integrated bar gene whereas each of the transgenic plants expressed bar mRNA. Analysis of the exogenously expressed AtMYB4 and AtMYB60 genes showed that AtMYB4 was specifically expressed in AtMYB4-101 and AtMYB60 in AtMYB60-117 and AtMYB60-112. Ribosomal RNA levels were used as a normalization control.
Fig. 3RT-PCR analysis confirming the transcription of the AtMYB4, AtMYB60 and Bar genes in the indicated transgenic lettuce lines and not in the wild-type (WT) plants. rRNA was used as the control
Plant morphology in AtMYB-transformed lettuce
In our previous study, we demonstrated that the wild-type lettuce leaf accumulates anthocyanin, with cyanidin and delphinidin representing the major and minor components, respectively (Park et al. 2007a). To further characterize the functions of the AtMYB genes in anthocyanin biosynthesis, we compared the morphologies of transgenic lettuce plants overexpressing these genes with that of wild-type lettuce.
More than 20 independent transgenic plants, selected with barstar spray and subsequently confirmed by PCR analysis (data not shown), were obtained with each A. tumefaciens strain. There was a large variation in the leaf color phenotypes of these transgenic lines. The AtMYB4-101 line showed normal anthocyanin accumulation and a red coloration phenotype, similar to that of the wild-type, but the AtMYB60-117 line showed different levels and patterns of anthocyanin accumulation. Under standard growth conditions, transgenic lettuce plants of the T1 generation that overexpressed AtMYB60 under the control of the cauliflower mosaic virus (CaMV) 35S promoter showed an inhibition (AtMYB60-117) of anthocyanin accumulation (green leaves), compared with that of wild-type plants (red leaves) (Fig. 4a, b). Apart from this inhibition of anthocyanin biosynthesis, no other morphological or developmental abnormalities were detected in this line under standard growth conditions. These results confirm that the exogenous AtMYB60 protein was responsible for the repressed anthocyanin phenotype.
Fig. 4Morphological analysis showing the inhibition of anthocyanin biosynthesis in lettuce leaves, indicated by the lack of red pigmentation, following the introduction of the Arabidopsis MYB gene, AtMYB60: a young (20 days after sowing) wild-type (WT) lettuce and the AtMYB60 transgenic lettuce line, AtMYB60-117; b morphologies of both the WT and AtMYB-overexpressing lettuce plants at a later stage of growth. Both the AtMYB4-101 and AtMYB60-117 transgenic plants are shown and reveal that AtMYB4 does not inhibit anthocyanin production
High-performance liquid chromatography (HPLC) analysis of AtMYB60 transgenic lettuce shows that accumulation of anthocyanin compounds is inhibited
To confirm the identity of the anthocyanins that are synthesized in lettuce after transformation with AtMYB genes, lettuce leaf tissues were harvested and analyzed by HPLC for the presence of soluble anthocyanins. The results confirmed the predominance of cyanidin in the wild-type lettuce leaves, with traces of delphinidin also evident, as we previously described (Park et al. 2007a). The cyanidin and delphinidin peaks were detected in the AtMYB4-overexpressing lettuce (AtMYB4-101) and wild-type lettuce. The AtMYB29-, AtMYB30-, AtMYB34- and AtMYB51-overexpressing lettuce plants that showed a similar red-colored phenotype also produced corresponding cyanidin and delphinidin peaks on HPLC analysis (data not shown). However, clear differences were evident in the patterns of anthocyanin accumulation in the AtMYB60-overexpressing lettuce plants (Fig. 5). With HPLC, we found that the cyanidin (red pigment) peak was absent or sharply reduced in AtMYB60-117 and AtMYB60-112. The delphinidin (blue pigment) peak was absent in every AtMYB60-overpxpressing lines. Hence, the anthocyanin derivatives detected in lettuce leaves by HPLC can be correlated with the morphologies of the plants and with the inhibition of anthocyanin biosynthesis by the AtMYB60 protein.
Fig. 5HPLC analysis of methanolic extracts from both wild-type control (WT) and AtMYB transgenic lettuce leaves (AtMYB4-101, AtMYB60-117 and AtMYB60-112). HPLC chromatograms were recorded at 510 nm. Peak C corresponds to cyanidin and D represents delphinidin. Each chromatogram was generated using the same quantity of injected sample
RNA expression profiles of anthocyanin biosynthetic enzymes suggest their coordinated regulation in AtMYB60 transgenic lettuce plants
Based on the correlation between the plant color morphology and our HPLC analysis of the anthocyanins, we undertook to verify the effects of the AtMYB proteins on the expression of the structural genes encoding the enzymes responsible for anthocyanin biosynthesis. As shown in Figs. 4 and 6, a dramatic leaf color change was evident in the AtMYB60-117 (green leaf) and -112 (mosaic phenotype) lines, in which anthocyanin accumulation is inhibited, and these plants showed a strong green leaf color phenotype. The enzymes that act in the anthocyanin biosynthetic pathway have been well characterized in lettuce in our previous study (Park et al. 2007a), in which we also analyzed the expression of the structural genes encoding the relevant enzymes. Following environmental stimuli such as UV-B irradiation, the expression of chalcone synthase (CHS), flavanone 3-hydroxylase (F3H) and dihydroflavonol 4-reductase (DFR) is positively induced, and the accumulation of anthocyanin is increased in lettuce. We also performed RT-PCR analysis using gene-specific primers in that study to analyze the expression of these structural genes. They were identified from young UV-B-irradiated lettuce leaves and designated as CHS (CV700441), F3H (CV700152), DFR (CV700105) and UFGT (CV700246) (Park et al. 2007a). The transcript levels of these four anthocyanin biosynthetic genes were compared between the wild-type, AtMYB4- and AtMYB60-overexpressing lettuce plants in the present study.
Fig. 6RT-PCR analysis of the genes involved in anthocyanin biosynthesis in the leaves of wild-type and AtMYB4- and AtMYB60-overexpressing lettuce plants. The transcripts of four biosynthetic genes involved in general anthocyanin metabolism (CHS, F3H, DFR and UFGT) were analyzed by RT-PCR in the AtMYB4-101 and AtMYB60-117 transgenic lettuce lines and were compared with the wild-type expression patterns. rRNA was used as the quantitative control
Total RNA extracts were prepared from the leaves of six-week-old lettuce plants of wild-type, AtMYB4-101 and AtMYB60-117 lines and were analyzed by RT-PCR for the expression of the previously determined structural genes that are involved in the major biochemical pathways of anthocyanin biosynthesis (Fig. 6b). No reduction in the expression of any of these genes was observed in wild-type or AtMYB4-101 lines, both of which exhibited red coloration in their leaves. Moreover, the mRNA levels of the CHS, F3H and UFGT genes, which catalyze the synthesis of chalcones, flavanones and anthocyanin, respectively, were unchanged in any of the plants. In contrast, the mRNA levels of DFR, the production of which catalyzes the conversion of dihydroquercetin and dihydrokaempferol to leucocyanidin and leucopelargonidin (of the leucoanthocyanidins), respectively, was inhibited in the AtMYB60-117 line, which exhibits green-colored leaves with no red pigmentation.
Discussion
To isolate the MYB-type transcription factor genes involved in phenylpropanoid metabolism, the transgenic expression of six AtMYB genes was analyzed in lettuce. To date, a large number of transcription factors containing DNA-binding domains that are similar to those of the MYB proteins have been identified across the eukaryotic kingdom (Kranz et al. 1998). Several MYB transcription factors are known to regulate phenylpropanoid metabolism, and the quantities of the various end-products of the associated pathways that accumulate in specific cells, tissues and organs are thought to respresent the balanced expression of several interacting MYB proteins (Tamagnone et al. 1998; Espley et al. 2007). Recently, biochemical studies have also suggested that the MYB-type transcription factors are involved in regulating the branches of the phenylpropanoid metabolic networks in higher plants. Multiple MYBs are also thought to regulate the expression of the structural proteins that drive several downstream pathways of flavonoid metabolism (Moyano et al. 1996). A previous study has also shown that MYB proteins can regulate flavonoid biosynthesis via the transcriptional regulation of the gene encoding CHS, an enzyme that catalyzes the first committed step in flavonoid biosynthesis (Feldbrugge et al. 1997).
The lettuce cultivar “Jinjachuckmyun”, which we used in our current transformation experiments, shows anthocyanin pigment accumulation in its leaves during development. We have shown in a recent study that environmental stresses upregulate the expression of the genes that encode anthocyanin biosynthetic enzymes in this same lettuce variety, as demonstrated by their responsiveness to UV-B irradiation (Park et al. 2007a).
The lettuce has merit as a model plant system to further characterize the functions of those gene products in anthocyanin metabolism, because of its leaf color is red under field conditions and green under chamber condition when exposed to UV-B irradiation. The findings described in this study further validate the use of lettuce as a model plant because it offers experimental versatility in terms of its genetic regulation of the inhibition of leaf pigmentation. The function of the MYB proteins as activators (green to red in a growth chamber) or repressors (red to green in the field) of anthocyanin biosynthesis can be characterized directly from a phenotypic analysis of the lettuce leaf color. This system has potential applications as a research tool in a number of areas of plant molecular biology.
We show in our present experiments that the anthocyanin accumulation in leaves is strongly inhibited in AtMYB60 transgenic plants compared with that in wild-type plants (Fig. 4a, b). These results suggest that AtMYB60 functions as a repressor of anthocyanin biosynthesis. Based upon our current findings of the inhibition of anthocyanin biosynthesis and our metabolite analysis of AtMYB60-overexpressing lettuce plants compared with the wild-type, we speculate that the synthesis of anthocyanin is principally controlled by the AtMYB60 transcription factor. The production of anthocyanin is inhibited in the AtMYB60-117 and AtMYB60-112 lines, and this is likely to underlie the green leaf phenotype of these plants. Hence, this characterization of the function of AtMYB60 represents a crucial step in furthering our understanding of the molecular regulation of anthocyanin biosynthesis and the transcription of related genes in lettuce and other plants. It is noteworthy in this context that MYB-related proteins generally act as transcriptional activators (Foos et al. 1994).
The R2R3-MYB-related proteins have so far been shown to activate the transcription of structural proteins that act in different branches of the phenylpropanoid metabolism machinery (Martin and Paz-Ares 1997) or that play roles in the plant response to stress signals (Cominelli et al. 2005). Anthocyanin expression in maize is dependent on the presence of both the MYB and MYC types of regulatory factors. It is of some interest, therefore, that in this study, the expression of AtMYB60 alone, under the control of the CaMV 35S promoter, was sufficient to repress anthocyanin accumulation in a red lettuce variety.
We also investigated the effects of AtMYB60 on the expression of the different structural proteins involved in anthocyanin production, which we have identified previously in lettuce. Our results reveal that this MYB protein represses the expression of the DFR gene in the AtMYB-117 and -112 lines. Significantly, DFR is a crucial structural protein that facilitates anthocyanin pigmentation in lettuce and that no such reduction in the expression of this gene was found in the wild-type or AtMYB4-101 lines that also develop red-colored leaves. The DFR protein represents an important branching point within the anthocyanin biosynthetic pathway and catalyzes the commitment step in this process, which is the formation of leucoanthocyanidins from the substrate dihydroflavonol. This indicates that AtMYB60 specifically inhibits the flux of flavonoid intermediates toward the production of leucoanthocyanins by repressing the transcription of DFR, with a resulting green leaf phenotype (Fig. 7).
Fig. 7Schematic representation of the relationship between AtMYB60 expression and the structural proteins that play roles in anthocyanin biosynthesis
It is interesting to note that the transcriptional response to UV-B irradiation that operates through AtMYB60 focuses on the regulation of DFR expression and anthocyanin accumulation. Recently there has been some debate about the identity of the phenylpropanoids involved in the response to UV-B, which may provide the most effective components of sunscreen formulations. MYB transcription factors that are known to regulate the transcription of genes in the phenylpropanoid biosynthetic pathway have been studied previously (Jin and Martin 1999). The overexpression of PAP1 in Arabidopsis results in the up-regulation of the genes encoding phenylalanine ammonia-lyase (PAL), CHS and DFR (Borevitz et al. 2000; Tohge et al. 2005). Moreover, mutations in the CHS, DFR and LDOX genes reduce the pigmentation of plants (Shirley et al. 1995; Abrahams et al. 2003), suggesting that these proteins are required for the synthesis of both anthocyanidin and proanthocyanidin. Because MYB-binding sites are present in the promoter regions of these flavonoid biosynthetic genes (Nesi et al. 2001; Debeaujon et al. 2003), it is possible that the AtMYB60 transcription factor might also specifically and directly target them. It is also possible that additional target genes that are related to phenylpropanoid metabolism can be negatively regulated by AtMYB60 when it is overexpressed in lettuce.
The effects of AtMYB60 suggest that this MYB protein acts as a direct transcriptional regulator by binding to the MYB motifs common to the promoters of many structural genes in the same metabolic pathway, thus resulting in the downregulation of this pathway. The precise determination of this regulatory system at the molecular level remains to be achieved. Recently, Cominelli et al. (2005) reported that AtMYB60 is involved in the plant response to stress and its expression is negatively modulated during conditions of drought (Cominelli et al. 2005). A null mutation in AtMYB60 results in a constitutive reduction in stomatal openings and in decreased wilting under water-stress conditions. Therefore, we hypothesize that AtMYB60 has multiple functions in the regulation of anthocyanin biosynthesis and in the plant responses to different environmental conditions, including drought and exposure to UV-B light.
AtMYB60 is the first MYB protein identified that functions as a transcriptional repressor of the lettuce DFR gene in anthocyanin biosynthesis, and thus extends the known roles of the R2R3-MYB proteins in secondary metabolism. Evidence from our transgenic lettuce experiments suggests that the direct repression exerted by AtMYB60 protein produces phenotypic effects in transgenic plants. The engineering of the anthocyanin biosynthetic pathway is potentially of great commercial significance. Because the AtMYB60 transcription factor should act effectively in most plant species, it can potentially be used to control anthocyanin biosynthesis in agriculturally and industrially important plants. This method of modifying the anthocyanin content in plant tissues also opens up new avenues to engineering improvements in commercial crops by altering metabolic pathways via the regulation of different structural genes in these pathways.

arabidopsis
r2r3-myb transcription factor
repressor
lactuca sativa


Results and harmonization guidelines from two large-scale international Elispot proficiency panels conducted by the Cancer Vaccine Consortium (CVC/SVI)

The Cancer Vaccine Consortium of the Sabin Vaccine Institute (CVC/SVI) is conducting an ongoing large-scale immune monitoring harmonization program through its members and affiliated associations. This effort was brought to life as an external validation program by conducting an international Elispot proficiency panel with 36 laboratories in 2005, and was followed by a second panel with 29 participating laboratories in 2006 allowing for application of learnings from the first panel. Critical protocol choices, as well as standardization and validation practices among laboratories were assessed through detailed surveys. Although panel participants had to follow general guidelines in order to allow comparison of results, each laboratory was able to use its own protocols, materials and reagents. The second panel recorded an overall significantly improved performance, as measured by the ability to detect all predefined responses correctly. Protocol choices and laboratory practices, which can have a dramatic effect on the overall assay outcome, were identified and lead to the following recommendations: (A) Establish a laboratory SOP for Elispot testing procedures including (A1) a counting method for apoptotic cells for determining adequate cell dilution for plating, and (A2) overnight rest of cells prior to plating and incubation, (B) Use only pre-tested serum optimized for low background: high signal ratio, (C) Establish a laboratory SOP for plate reading including (C1) human auditing during the reading process and (C2) adequate adjustments for technical artifacts, and (D) Only allow trained personnel, which is certified per laboratory SOPs to conduct assays. Recommendations described under (A) were found to make a statistically significant difference in assay performance, while the remaining recommendations are based on practical experiences confirmed by the panel results, which could not be statistically tested. These results provide initial harmonization guidelines to optimize Elispot assay performance to the immunotherapy community. Further optimization is in process with ongoing panels.
Introduction
Elispot is a widely used assay for immune monitoring purposes [6, 17, 25, 39]. Measuring immune responses has been accepted as an important endpoint in early clinical trial settings in order to prioritize further vaccine or other immunotherapy development [11, 22, 24, 26, 38]. Despite the overwhelming use of various immune assays for exactly that purpose, reported results are often met with skepticism, caused mainly by two reasons: (1) high variability among results from the same laboratories and/or among different laboratories, and (2) the lack of demand to report standardization, validation and training strategies as well as assay acceptance criteria by the laboratories conducting immune testing. This is surprising since the reporting of results for other clinical endpoints, e.g., side effects, has to follow strict guidelines and definitions.
The Clinical Laboratory Standard Institute (CLSI) clearly phrased the demand for internal validation of immune assays as well as external validation in proficiency panels [28]. Proficiency panels are a common strategy for clinical laboratories to prove their ability to perform clinical tests at a level that permits patient testing. Defined as a program in which multiple specimens are periodically sent to laboratories for analysis, and in which each laboratory’s results are compared with those of the other laboratories and/or with an assigned value [27], proficiency panels serve various purposes:To provide regulatory and sponsoring agencies with confidence that reported data are generated following necessary standards and rigor that supports product licensure.To provide an external validation tool for individual labs.To provide proof to patients and volunteers that necessary measures have been taken to allow successful study participation [3].Some reports about comparison of Elispot performances among laboratories have been published [5, 32, 33]. A two-step approach to assay harmonization is being described by the C-IMT monitoring panel in this issue [2]. The earliest report, a four-center comparative trial in 2000, showed that most participants were able to correctly detect low frequency responses or absence of response against specific peptides in PBMCs from six donors, following their own protocol [33]. Another four-center comparison was conducted among members of the Elispot standardization group of the ANRS in France [32]. This study demonstrated overall good qualitative and quantitative agreement in Elispot results in the participating labs when testing HIV negative and positive donors for reactivity against a variety of peptides. All labs used their own protocol, but shared a high level of Elispot experience. An important program was launched by the NIAID for 11 international laboratories participating in HIV-1 clinical trials [5]. The panel demonstrated good concordance in qualitative detection of specific immune responses in previously defined and tested donors, but also notable inter-laboratory and intra-sample variability in spot counts, cell recovery and viability. This observation was met with strict standardization strategies across all panel members, and the panel was repeated twice with all laboratories following a standardized protocol. Variability was decreased under these conditions, but not abrogated (Cox, personal communication). Justification for the strict standardization approach among participating HIV vaccine laboratories can be found in the nature of HIV vaccine testing programs. Immune monitoring for these vaccine trials is performed at many sites simultaneously. Further, results from different trials need to be comparable in order to identify most suitable vaccine candidates. Importantly, the experimental Elispot setup is similar or identical in most laboratories, where PBMCs are tested against a variety of peptides [25, 31].
In contrast, immune monitoring approaches in the cancer vaccine field are more heterogeneous, based on the vast variety of vaccine design, type of cancer, and availability of antigen presenting cells. Standardization of the entire Elispot protocol across laboratories is therefore not feasible. We set out to devise a strategy to identify issues and deficiencies in current Elispot practices, and to identify common sources of assay variability within and between laboratories, with the extended goal of standardizing the identified factors in an assay harmonization effort across laboratories.
In 2005, the Cancer CVC/SVI initiated an Elispot proficiency panel program to achieve this goal. In addition to offering an external validation program, the CVC addressed the need for such strategy by comparing assay performance across the field, identifying critical protocol choices and gaining an overview of training and validation practices among participating laboratories.
For this program, predefined PBMCs from four donors with different ranges of reactivity against two peptide pools were sent to participants for Elispot testing. Laboratories had to further provide cell recovery and viability data, as well as respond to surveys describing their protocol choices and training and validation status.
In response to the survey results, the CVC/SVI established requirements for laboratories to participate in future proficiency panels, which included the existence of a Standard Operating Procedure (SOP) prior to joining the program. Further, individualized assay performance assessment was offered to all laboratories, together with suggestions for implementation of protocol optimization steps.
The results of the second Elispot proficiency panel conducted a year later demonstrated a significant improvement. The results and survey data allowed the identification of critical protocol choices for a successful assay performance (Fig. 1).
Fig. 1Initial guidelines for harmonization of the Elispot assay to optimize assay performance and reproducibility derived from two international proficiency panels, based on their findings and trends observed
Materials and methods
Participants and organizational setup
All participants were members of the Cancer Vaccine Consortium or its affiliated institutions, the Ludwig Institute for Cancer Research (LICR) and the Association for Immunotherapy of Cancer (C-IMT). Laboratories were located in ten countries (Australia, Belgium, Canada, Germany, Italy, Japan, France, Switzerland, UK, and USA). Each laboratory received an individual lab ID number. Panel leadership was provided by a scientific leader experienced in Elispot, in collaboration with the CVC Executive office. SeraCare BioServices, Gaithersburg, MD, served as central laboratory, providing cells, pretesting and shipping services, as well as logistical services like blinding of panelists. IDs were not revealed to panel leader, CVC or statistician during the panel.
Thirty-six laboratories including the central lab participated in panel 1, 29 including the central lab in panel 2. Twenty-three laboratories participated in both panels. Six new panelists were added to the second testing round. Thirteen dropped out after the first panel. Main reasons for drop out were switch of assay priorities and not meeting criteria for panel participation. Various groups stated that one time participation fulfilled their need for external validation.
PBMCs and peptides
PBMCs from healthy donors were obtained from a commercial donor bank, manufactured and processed under GMP conditions using established Standard Operating Procedures at SeraCare. PBMCs were frozen using a rate controlled freezer and transferred to the vapor phase of liquid nitrogen. A three lot validation study was performed [20] in which the following was validated: cell separation procedure, freezing media, dispensing effect on function, freezing procedure, and shipping on dry ice. It was demonstrated that functionality and viability were maintained throughout the procedure. In addition, Elispot values from fresh and frozen PBMCs, shipped on dry ice, were nearly equivalent.
Each vial of PBMCs contained enough cells to ensure a recovery of 10 million cells or more under Seracare’s SOP.
PBMCs were pretested at the central laboratory for reactivity against the CEF [7] and CMV pp65 peptide pools [23]. The CEF peptide pool was obtained through the NIH AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH. It contains 32 8-11mers known to elicit CD8-restricted responses, and serves as a widely used control in IFNγ Elispot assays [7]. The CMV pp65 peptide pool was a generous gift of the NIAID and Becton Dickenson. This pool consists of 135 15mers overlapping by 11 amino acids, and has been shown to elicit CD8- and CD4-restricted responses [23]. PBMCs were selected for no, low, medium and strong responses against both peptide pools, and repeatedly Elispot-tested at Seracare in order to confirm responder status. Response definition was set arbitrarily for the spot number/well (200,000 PBMC): no responder: average median/panel 1 + 2 = 1 (CMV and CEF); low responder: average median/panel 1 + 2 = 18 (CMV) and 40 (CEF); medium responder: average median/panel 1 + 2 = 98 (CMV) and 127 (CEF); and high responder: average median/panel 1 + 2 = 396 (CMV) and 398 (CEF).
Peptide pools were resuspended in DMSO and further diluted with PBS to a final concentration of 20 μg/ml. Aliquots of 150 μl of peptide pool were prepared for final shipment to participants. Corresponding PBS/DMSO aliquots for medium controls were also prepared. Participants were blinded to the content of these vials, which were labeled as “Reagent 1, 2 or 3”.
All cells and reagents sent to participants in both panels were obtained from the same batches.
Cells and reagent vials were shipped to all participants for overnight delivery on sufficient dry ice for 48 h. Shipping was performed by Seracare under their existing SOPs.
Elispot
Participants received a plate layout template and instructions for a direct IFNγ Elispot assay which had to be performed in one Elispot plate. Each donor was tested in six replicates against three reagents (medium, CEF and CMV peptide pool). Further, 24 wells were tested for the occurrence of false positive spots by the addition of T cell medium only. About 200,000 PBMC/well were tested against 1 μg/ml peptide pool or the equivalent amount of PBS/DMSO. All other protocol choices were left to the participants, including choices about: Elispot plate, antibodies, spot development, use of DNAse, resting of cells, T cell serum, cell counting and spot counting method. All plates were reevaluated at ZellNet Consulting (Fort Lee, NJ) with a KS Elispot system (Carl Zeiss, Thornwood, NY), software KS Elispot 4.7 (panel 1) and KS Elispot 4.9 (panel 2) in blinded fashion. Each well in each plate was audited.
Since the focus of this study was on assay performance and protocol evaluation and not on the definition of a positive response, we prospectively defined the ability to detect a response (independent of magnitude) by using the following “empirical” method: the antigen-specific spot counts per 2 × 105 PBMCs had to be >10, and at least 3× as high as the background reactivity. Similar approaches have been described elsewhere [5, 8].
Statistical analysis
The following parameters were calculated for the overall panel and the individual participant’s performance, using either lab-specific counts or central recounts: the mean, standard deviation, and coefficient of variation (CV), the median, minimum, and maximum spot counts for each donor and reagent and the media only wells. Box plots were used to illustrate the distribution of spot counts across the panel per given test condition. Further, individual results were represented as box plots comparing lab counts with recounts, central lab results and overall panel results. For panel 2, results from repeating laboratories were also compared in box plot format for each donor and condition. The Fisher’s Exact test was used to compare the proportions of laboratories which missed to detect responses in each panel. For the comparison of recovery and viability, the Student’s t test was applied.
Results
Feasibility
In the first proficiency panel, shipping and Elispot testing among 36 laboratories from 9 countries were conducted without delays. The success of this panel demonstrates the feasibility of such large international studies, the biggest of such format as of today, under the described organizational setup. The second panel with 29 participating laboratories from 6 countries followed the approach of panel 1. However, customs delays of dry ice shipments to some international sites required repeated shipments of cells and antigens to these destinations. Based on this experience, the use of dryshippers with liquid nitrogen is being implemented for international destinations in the third CVC panel round in 2007.
Recovery and viability of PBMCs in panel 1 and 2
Participants were asked to record recovery and viability of cells immediately after thawing (Table 1). The mean and median for both parameters were almost identical in both panels, and for all four donors (mean cell recovery between 11.5 and 13.3 million cells and mean viability between 85 and 91%). Only a small percentage of laboratories recovered less than 8 million cells (13% in panel 1 and 14% in panel 2). The percentage of groups reporting less than 70% viability was 7% for both panels.
Table 1Cell recovery and viability in both proficiency panelsDonoraMean P1/P2bMedian P1/P2Minimum P1/P2Maximum P1/P2Cell recovery in 106 cells112.5/11.512.0/12.06.8/1.026.4/18.8213.3/12.313.2/12.65.5/0.328.4/22.4313.1/12.813.6/12.86.7/1.725.1/24.3412.8/12.411.9/12.95.6/6.234.4/22.7Viability (%)188/8589/8948/58100/98287/8889/9057/67100/98391/8793/9054/69100/100486/8990/9243/75100/98aPBMCs from same donors and batches were used in both panelsbP1/P2 refer to data from panel 1 (P1) and panel 2 (P2)
Interestingly, only 4/10 laboratories in panel 1, and 4/7 laboratories in panel 2 with recoveries below 8 million cells were from international locations. Similar, only 1/5 laboratories in panel 1 and 2/5 in panel 2 reporting viabilities less than 70% belonged to international sites. This clearly demonstrates that location for dry ice shipment had no effect on overall cell recovery and viability.
We also investigated whether low (<8 million) or very high (>20 million) cell recovery had an influence on spot counts, assuming that these were potential erroneous cell counts, leading to too low or too high cell dilutions, respectively, what in turn would lead to too high (in case of underestimating cell number) or too low (in case of overestimating cell number) spots counts. However, except for a few sporadic incidents, there was no correlation between cell counts and spot counts (data not shown) in either direction. Only one laboratory with low recovery and low viability was found to have peptide pool-specific spot counts for all donors much below the panel median.
The use of an automated cell counter (Guava) did not reveal any trend in recovery for this group (recovery ranged from 6.7 to 25.1 million cells), nor a difference compared to the overall panel recovery data. In contrast, the mean cell viability per donor measured by Guava counter users was significantly lower than the overall cell viability in users of a hemocytometer (P < 0.01, see table in Fig. 5).
Elispot results in panel 1
All 36 laboratories completed testing of all 4 donors against medium, CEF and CMV peptide pool. Spot appearance and size as well as occurrence of artifacts differed dramatically among laboratories (not shown). Four outlier laboratories were identified, which detected less than half of the responses correctly. In all four cases, detected responses were well below the panel median, and often, there was high background reactivity (up to 270 spots/well) in medium controls. No obvious protocol choices could be identified which could have been responsible for the suboptimal performance. One out of the four laboratories had little experience at the date of the panel. Another group reported a less experienced scientist performing the assay. A third outlier repeated the assay, and was able to perform adequately. No feed back was available from the fourth group.
Thirty-two out of 36 labs were able to detect medium and strong responders as well as the non-responder. However, almost 50% of participants were challenged with the detection of the low responder. The responses measured for that particular donor (mean spot counts/well for CEF = 42 and for CMV = 22) has been chosen for illustration purposes. Figure 2 demonstrates the distribution of responses measured including intra-laboratory variability for this donor against all three reagents and across both panels.
Fig. 2Laboratory spot counts and variability for low responder across panel participants. Panel 1 left column, panel 2 right column. The reagent tested is indicated. Lab-specific spot counts per 200,000 cells are depicted as box plots with the box presenting the interquartile range, the triangle the mean and the horizontal line the median. Maximum and minimum spot counts are illustrated through the upper and lower mark. The horizontal line across a graph demonstrates the overall panel median. The central laboratory performed the assay under two different conditions in panel 1. Results from both experiments are presented; therefore 37 laboratories for panel 1. Laboratory ID numbers do not correlate in both panels. In panel 1, laboratory #18 reported spot counts for the medium control as high as 270 per well (mean 81, median 34.5). For proper illustration of all other panel data, these data were omitted from the graph. Intra-laboratory variability and variability among participants as well as reactivity against medium are representative for all responder PBMCs tested
In panel 1, 17 labs did not detect the response against the CMV peptide pool. In one case, the response was missed due to high reactivity against medium. Three other labs missed to detect the response due to inaccurate spot counting, specifically in the medium control wells. Artifacts were erroneously included in counts (Fig. 3). Reevaluation of those plates revealed that those labs had spot counts indicating a response against CMV.
Fig. 3Elispot assay results can be confounded by plate evaluation accuracy. The table demonstrates spot counts for PBMC/medium control wells with many artifacts from three different laboratories (Lab X, Y, Z). Respective well images are shown below each column for that specific group. Differences in lab-specific spot counts (“own”) and counts from reevaluation in an independent laboratory (“central”) including resulting variability measures are presented in the table
A similar scenario was found for response detection against the CEF peptide pool. Thirteen laboratories missed to detect the response, one of which due to high reactivity against medium, and one due to inaccurate spot counting.
An interesting observation was that 23 groups reported false positive spots in a range of 1–26 spots/well. Reevaluation revealed that the actual number of groups with false positive spots was lower (12), and the false positive spot number range per well fell between 0 and 8.
Survey results about protocol
During the first panel, participants had to provide information about their protocol choices: plates, potential prewetting of PVDF, antibodies, enzyme, substrate, use of DNAse during PBMC thawing, resting of cells, serum used, cell counting, and plate reader. There was a wide range of protocol choices across the panel participants. The most common choices for the parameters listed above were as follows: use of PVDF plates (64%) prewetted with Ethanol (52%), coated with Mabtech antibodies (67%) at 0.5–0.75 μg/well (33%); spot development with HRP (53%) from Vector Laboratories (33%) using AEC (44%) from Sigma (42%); no use of DNAse when thawing cells (83%), and no resting period for cells prior to the assay (53%); use of human serum (64%); cell counting with trypan blue exclusion using a hemocytometer (78%); and plate evaluation with a Zeiss reader (36%).
There were some clear trends for international sites with preferred use of the AP/BCIP/NBT development system (83 via 29% in the US), the use of nitrocellulose plates (75 via 21% in the US), and the use of Mabtech antibodies (83 via 58% in the US). On the other hand, 7/8 laboratories using an automated cell counter were located in the US.
We stratified our panel participants into groups consisting of: (1) outliers (failed to detect more than 50% of responses correctly), (2) labs which missed weak response detection only, and (3) labs with correct qualitative response detection in all four donors. We then checked for specific parameters that could be potentially responsible for the groups’ panel performance. We were not able to detect any parameter that seemed to be responsible for missed response detection. In contrast, laboratories with the same overall protocol choices (except serum) could have very different assay performance, whereas laboratories with different protocol choices could have almost identical spot counts (Fig. 4). Reasons for the failure to detect protocol choices which significantly proved the advantage or disadvantage of their use can be found in (1) the study design with open protocol choices allowing participants to follow their own SOP, (2) the overall wide range in choices of reagents and materials (e.g., 7 different sources for the coating antibody with 10 different total antibody amounts reportedly used, 10 different enzyme sources, 7 different Elispot reader systems, cell resting times between 0.5 and 20 h, serum choices unique for each laboratory etc.), and (3) the small sample sizes resulting from this dispersion of protocol choices. These widely spread individual protocol choices among participants did not allow the aggregation of data for an adequately powered statistical analysis.
Fig. 4Demonstration of examples of concordance of spot counts among three laboratories (Lab I–III) using different Elispot protocols (a, high responder), or of disagreement of spot counts for two laboratories (Lab IV–V) using almost identical protocol choices (b, medium responder). Lab-specific spot counts per 200,000 cells are depicted as box plots with the box presenting the interquartile range, the triangle the mean and the horizontal line the median. Maximum and minimum spot counts are illustrated through the upper and lower mark. The triangle refers to the overall panel mean for that specific condition. Results of Lab I–III are shown in a, and results of IV–V in b. The table contains reference to the figure above, and information about specific protocol choices
One trend observed was that the users of an automated cell counter (7 labs used a Guava cell counter) had an overall better assay performance (Fig. 5), with no outliers in this group and only two laboratories who missed to detect the weak responders (29 vs. 52% in non-Guava users).
Fig. 5Distribution of mean spot counts (upper two graphs) and cell viability (table) among the users of a Guava automated cell counter (a) and users of a hemocytometer (b). Spot counts per well (200,000 cells) are represented on a logarithmic scale. D1–4 refer to the donor tested with D1 being the strong, D2 the low, D3 the medium and D4 the non-responder. The tested reagent is indicated (med medium). The mean viability per donor reported by Guava users and users of other cell counting methods (one lab used an automated cell counter from Beckman Coulter, all others used a hemocytometer with trypan blue exclusion) is presented in the table below the figure
Almost half of the participants (47%) introduced a resting period for cells before adding them to the Elispot plate. The time frame varied from 0.5 to 20 h. A valuable side-by-side comparison was performed by the central laboratory, in which two tests were run in parallel using the lab’s SOP with the exception that their standard resting period of 20 h was replaced by a shorter resting period of 2 h in the second test. The results are summarized in Fig. 6, demonstrating a significant increase in spot numbers for all peptide pool-specific responders when cells were rested for 20 h, without an increase in background reactivity (P < 0.05 for the weak responder against CMV; for all other antigen-specific responses in all donors P < 0.01).
Fig. 6Comparison of the effect of 2 h (checkered bars) and 20 h (solid bars) resting periods for cells after thawing, before adding to the assay. D1–4 CEF/CMV refer to the specific donor and peptide pool tested. Background reactivity for all donors and testing conditions was between 0 and 5 spots (not shown). The standard error is shown. * Indicates a statistical significant difference of P < 0.01 in spot counts between 2 and 20 h resting periods for a given donor and reagent; # indicates in statistical difference with P < 0.05 (Student’s t test)
Survey results about validation and training practices
During the lively discussion of the results of panel 1 and its protocol survey at the Annual CVC meeting in Alexandria in November 2005, it was suggested that the level of experience, standardization and validation of participating laboratories might have been the cause for the variability and performances observed. In response, we conducted a survey among panelists, in which 30 laboratories participated. As expected, the experience and Elispot usage varied significantly. Some laboratories had the Elispot assay established less than one year before panel testing, whereas others used the assay for more than 10 years. The experience of the actual performer of the panel assay also varied widely.
Interestingly, even though 2/3 of participants reported to have specific training guidelines for new Elispot performers in place, more than 50% never or rarely checked on the scientist’s performance after the initial training.
Almost all laboratories indicated that they use an SOP that had been at least partially qualified and/or validated. Validation tools and strategies varied widely. Only 12 groups monitored variability, whereas 23 reported the use of external controls of some kind (e.g., T cell lines, predefined PBMC, parallel tetramer testing).
Thirteen groups were found to have some kind of criteria implemented for assay acceptance. Among the 20 different criteria reported, not one was described by more than one lab.
Mirroring these survey results, 20 laboratories believed that they need to implement more validation steps. All except one group expressed their strong interest in published guidelines for validation and training strategies for Elispot.
Elispot results in panel 2
Based on the experience from the first panel, acceptance criteria for participation in the program were redefined. Only laboratories with established SOPs were accepted. The second panel was repeated with identical experimental setup as the first panel, and with the same batches of PBMCs, peptides and control reagents. Twenty-nine laboratories participated including the central lab. This time, there were no outlier performers identified. The number of groups that did not detect the weak responder dropped dramatically (P < 0.01) from 47% (17/36 labs) in panel 1 to 14% (4/29) in panel 2 (Fig. 2). The overall panel median for the CMV-response in the weak responder increased from 14 spots in panel 1 to 21 spots in panel 2, and for the CEF response from 30 to 51, respectively.
Of the 23 labs repeating the panel, 8 had changed their protocol before panel 2, 3 of which as an immediate response to results from the first panel. One outlier lab from panel 1 participated in panel 2, and improved its performance by detecting all responses correctly as per reevaluation counts. Only their lab-specific evaluation did not detect the low CMV responder. Overall, 4/23 panel-repeating labs (17%) did not detect the low CMV-responder, 3 of which also did not detect this response in panel 1. About 10/23 groups missed the low CMV responder in panel 1, but 7 of these laboratories were able to detect it in panel 2. Only one repeater detected the low CMV response in panel 1, but not panel 2. This is a clear performance improvement for that group (47% missed this response in panel 1), and highlights the usefulness of multiple participation in panel testing as an external training program.
We ran an in-depth analysis of the results and previous survey responses, where available, from participants who missed the weak responder, as well as from laboratories with marginal detection of response, including personal communication. We were able to narrow down the possible sources for these performances. Two laboratories missed responses due to inaccurate evaluation, during which they either included artifacts into spot counts or simply did not count the majority of true spots, as central reevaluation revealed. One laboratory did not follow the assay guidelines. The majority of laboratories, however, followed common protocol choices, but had either very low response detection across all donors and antigens, or detected very high background reactivity in some or all donors. This pattern pointed to serum as the possible cause for suppressed reactivity or non-specific stimulation. Three of these laboratories shared with the CVC that retesting their serum choice indicated that they had worked with a suboptimal serum during the panel; and that they now successfully introduced a different serum/medium to their protocol with improved spot counts. Serum choices included human AB serum, FCS, FBS, and various serum-free media. There was no difference in assay performance detectable between these groups.
Discussion
The CVC conducted two large international Elispot proficiency panels in which participants tested four batches of predefined donor PBMC for CEF and CMV peptide pool reactivity. Common guidelines like number of cells per well and amount of antigen had to be followed, in order to allow result comparison and reduce variability due to the known influence of cell numbers plated [2, 14]. A surprising finding from the first panel was that almost half of the participants were not able to detect the weak responder. The initial protocol survey did not allow the detection of common sources for this sub-optimal performance. In some cases, laboratories with identical protocol choices performed very differently, whereas others with distinct protocols had almost identical spot counts (Fig. 4). This observation supports the premise that many common Elispot materials and reagents (e.g., plates, antibodies, spot development reagents) perform equally or similarly well; and that there are other factors which influence the outcome of the Elispot assay.
Various laboratories missed the response detection by inaccurately evaluating their Elispot plate, independent of the reader used, as reevaluation revealed (Fig. 3). This observation is in contrast to results from the first NIAID proficiency panel, which describes good agreement in spot counts from participants and experienced, independent centers [5]. Operator-dependent variability in Elispot evaluation results is a known phenomenon [15]. Despite the availability of high resolution readers and software features for automated spot gating and other potentially helpful options, it is essential to employ well trained operators for spot counting, to audit all plates, and to implement changes of reading parameter in cases when well and spot appearances differ from the overall assay, typically for technical reasons. For that, SOPs used for plate evaluation might require revision. The use of available certification and training services can be helpful.
Cell counting is a protocol step known for introducing variability. Because of the wide range in PBMC recovery, we investigated whether cell counts could have been potentially erroneous, which would lead to wrong final cell dilutions, and can therefore lead to too high or low spot counts. Despite large differences in cell recovery (from 5.5 to 34.4 million cells per vial in panel 1), we did not find a correlation between cell recovery and spot counts. Very low (<8 million) and very high (>20 million) cell counts were consistently found for all donors among the same few laboratories. Both, hemocytometers and automated counters were used in those laboratories. Various automated cell counters have been introduced to the market, which do not only offer automated spot counting features, but also the discrimination of apoptotic, viable and dead cells. The use of such systems can potentially decrease variability in cell counts, and most importantly increase the accuracy of viable cell counts [4, 21]. Seven laboratories in panel 1 used the Guava counter, which allows the discrimination of apoptotic cells. Even though our panel did not reveal a difference in cell counts between Guava and hemocytometer users, it could be demonstrated that the overall cell viability reported by Guava users was significantly lower. This could likely be attributed to the ability to discriminate apoptotic cells with this method. Smith et al. [35] recently reported the usefulness of apoptosis acceptance criteria that allowed the separation of PBMC samples by their ability to respond to an antigenic stimulus or not.
The introduction of a resting period for thawed cells is known to be advantageous since apoptotic cells die during the resting period, and final dilutions for the assay are based on a more homogenous population of viable cells [16, 18]. In contrast, the addition of a mixture of viable and apoptotic cells, which are prone to die during assay incubation time, directly after thawing leads to lower spot counts. During panel 1, the central laboratory performed a side-by-side comparison of the influence of a 2 and 20 h resting period on final spot counts, and demonstrated that a 20 h resting period yielded significantly higher counts (Fig. 6). Proficiency testing results from the C-IMT also support the introduction of a cell resting period for Elispot assays [2]. Even though almost half of participants in panel 1 let cells rest before addition to the plate, there was no clear correlation to the magnitude of peptide-specific spot counts. This might have been due to the variation of resting times between 0.5 and 20 h, and other protocol variables, which included the actual resting protocol. Factors like serum and serum concentration, cell concentration, and actual storage condition (e.g., tissue culture flasks or plates can lead to cell adherence and therefore loss of professional antigen-presenting cells) are known to influence the success of cell resting.
There are no scientific studies published about the effect of serum choices on immune assay results, one of the best known “secrets” in immunology [16]. It is critical to choose serum that supports low background reactivity, but strong signals. The leading choice in this panel to use human AB serum reflects the historic preference for human immune assays. Each serum batch, however, is unique in its ability to support optimal assay resolution, and may potentially contain mitogenic or immune suppressive factors. There was some anecdotal evidence that the serum choice among our panelists was responsible for suboptimal performance. Interestingly, six laboratories preferred to work with serum-free medium. None of these groups observed high background reactivity, but two failed to detect the weak responder.
A survey conducted among participating laboratories shed light on validation and training practices. The wide experience range of participating laboratories, in combination with various levels and approaches to validation and training, correlated with the overall high variability in panel results. In response, new criteria for panel participation were introduced for panel 2. The outlier lab number decreased from 4 to 0 in the new panel, however 3/4 outliers from panel 1 did not participate in panel 2. The percentage of laboratories that did not detect the weak responder decreased significantly from 47 to 14% (P < 0.01). This improvement might have been partially due to the stricter participant selection in panel 2. However, 7/10 labs repeating the panel improved their performance by correctly detecting the low CMV responder in panel 2, while missed in panel 1. A striking finding was that 2/3 of all laboratories stated that they believed they needed to implement more validation. And all but one group expressed their wish for published guidelines for Elispot assay validation and training.
The outcomes from these two proficiency panels first provide Initial Elispot Harmonization Guidelines for Optimizing Assay Performance (Fig. 1) that can fulfill this need and may provide—if implemented widely—the grounds for substantial improvement of assay utility for research applications and development of immune therapies. This can be implemented in accordance with assay recommendations made for cancer immunotherapy clinical trials by the Cancer Vaccine Clinical Trial Working Group [12]. Further optimization is aimed for through ongoing proficiency panel work conducted by the CVC.
Validation of assays is now a requirement for all endpoint parameters in clinical trials [30]. There are an increasing number of publications available describing validated Elispot assays [1, 19, 25, 34]. These papers contain valuable scientific information, but only limited referral to FDA regulations. The FDA guidelines for validation of analytical procedures [9] describe validation as the process of determining the suitability of a given methodology for providing useful analytical data, which consists of analyzing or verifying the eight or nine assay parameters as described in the US pharmacopeia or the ICH guidelines [13, 37]. Only few publications address validation of bioassays and Elispot in FDA terms [10, 16, 29, 36]. And even these few publications give only limited advice on how to validate the Elispot assay in a given laboratory setting, not to mention specific training guidelines.
Furthermore, acceptance criteria for assay performance were only used by a limited number of laboratories, and each criterion was unique for the laboratory that used it.
These observations should be a wake-up call for the immune monitoring community, which does not only include the cancer vaccine field, but also the infectious disease and autoimmunity field and others. General assay practices for the detection of antigen-specific T cells are comparable across all fields. The CVC as part of the Sabin Vaccine Institute is intending to develop and tighten collaborations with groups from other research and vaccine development areas. Published documents with specific criteria for Elispot assay validation, assay acceptance criteria and training guidelines will be most valuable for the immune monitoring field, and are now being established as CVC guidelines as a result of the described studies. Continuous external validation programs need to be a part of these efforts in order to check upon the success of inter-laboratory harmonization including assay optimization, standardization and validation as well as of laboratory-specific implementation of guidelines and protocol recommendations. These efforts are essential to establish the Elispot assay and other immune assays as standard monitoring tools for clinical trials.

harmonization
elispot
proficiency panel
immune monitoring
validation