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Copyright © 2009, American Society of Plant Biologists Overexpression of the Transcription Factor AP37 in Rice Improves Grain Yield under Drought Conditions1[W][OA] School of Biotechnology and Environmental Engineering, Myongji University, Yongin 449–728, Korea *Corresponding author; e-mail jukon306/at/gmail.com. 2These authors contributed equally to the article. 3Present address: Syngenta Seeds Co., Ltd., 100 Gongpyeong-dong Jongro-gu, Seoul 110–702, Korea. Received February 23, 2009; Accepted May 3, 2009. Abstract Transcription factors with an APETELA2 (AP2) domain have been implicated in various cellular processes involved in plant development and stress responses. Of the 139 AP2 genes predicted in rice (Oryza sativa), we identified 42 genes in our current study that are induced by one or more stress conditions, including drought, high salinity, low temperature, and abscisic acid. Phylogenic analysis of these 42 stress-inducible AP2 genes revealed the presence of six subgroups (I–VI) with distinct signature motifs. Two genes, AP37 and AP59, representing subgroups I and II, respectively, were functionally characterized. Both genes were found to be induced upon 2 h of exposure to drought and high-salinity conditions but to differ in their expression profile upon exposure to low temperature and abscisic acid. The overexpression of AP37 and AP59 in rice under the control of the constitutive promoter OsCc1 increased the tolerance to drought and high salinity at the vegetative stage. Increased tolerance to low temperatures was observed only in OsCc1:AP37 plants. More importantly, the OsCc1:AP37 plants showed significantly enhanced drought tolerance in the field, which increased grain yield by 16% to 57% over controls under severe drought conditions, yet exhibited no significant difference under normal growth conditions. In contrast, grain yield in OsCc1:AP59 plants in the field was reduced by 23% to 43% compared with controls under both normal and drought stress conditions. Microarray experiments identified 10 and 38 genes that are up-regulated by AP37 and AP59, respectively, in addition to 37 genes that are commonly induced by both factors. Our results suggest that the AP37 gene has the potential to improve drought tolerance in rice without causing undesirable growth phenotypes. Drought stress is among the most serious challenges to crop production worldwide. Upon exposure of plants to drought conditions, many stress-related genes are induced, and their products are thought to function as cellular protectants of stress-induced damage (Thomashow, 1999; Shinozaki et al., 2003). The expression of stress-related genes is largely regulated by specific transcription factors. Members of the APETELA2 (AP2), bZIP, zinc finger, and MYB families have been shown to have regulatory roles in stress responses. The rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genomes code for more than 1,300 transcriptional regulators, accounting for about 6% of the estimated total number of genes in both cases. About 45% of these transcription factors were reported to be from plant-specific families (Riechmann et al., 2000; Kikuchi et al., 2003). One example of such a plant-specific family of transcription factors is APETALA2 (AP2), whose members share a highly conserved DNA-binding domain known as AP2 (Weigel, 1995). AP2 factors appear to be widespread in plants, with the genomes of rice and Arabidopsis predicted to contain 139 and 122 AP2 genes, respectively (Nakano et al., 2006). Members of the AP2 family have been implicated in diverse functions in cellular processes involving flower development, spikelet meristem determinacy, plant growth, and stress tolerance (Chuck et al., 1998; Liu et al., 1998; Haake et al., 2002; Dubouzet et al., 2003; Gutterson and Reuber, 2004). Of these diverse functions, the involvement of the AP2 family in stress response has been relatively well characterized. In particular, CBF/DREB genes from Arabidopsis have been shown to play crucial roles in response to low temperature, salt, and drought stresses in transgenic Arabidopsis (Stockinger et al., 1997; Gilmour et al., 1998; Liu et al., 1998; Jaglo et al., 2001). CBF/DREBs are members of the AP2 family and identifiable by the presence of CBF/DREB signature motifs (PKK/RPAGRxKFxETRHP and DSAWR) directly flanking the AP2 domain (Jaglo et al., 2001). Overexpression of CBF/DREBs in transgenic Arabidopsis increases the transcript levels of stress-related genes and enhances tolerance to drought, high-salinity, and freezing stresses (Jaglo-Ottosen et al., 1998; Kasuga et al., 1999; Haake et al., 2002). CBF/DREBs are also heterologously effective in canola (Brassica napus; Jaglo et al., 2001), tomato (Solanum lycopersicum; Hsieh et al., 2002), tobacco (Nicotiana tabacum; Kasuga et al., 2004), and rice (Oh et al., 2005b), enhancing stress tolerance in the corresponding transgenic plants. CBF/DREB orthologs have also been identified in canola, tomato, wheat (Triticum aestivum), rye (Secale cereale), barley (Hordeum vulgare), and rice, and all of them are inducible by low-temperature treatments (Jaglo et al., 2001; Choi et al., 2002; Xue, 2002, 2003; Dubouzet et al., 2003; Skinner et al., 2005; Oh et al., 2007). The AP2 gene family from other plant species, including DBF1 and DBF2 (Kizis and Pages, 2002) from maize (Zea mays), AhDREB1 (Shen et al., 2003) from Atriplex hortensis, OPBP1 (Guo et al., 2004) from tobacco, CaPF1 (Yi et al., 2004) from pepper (Capsicum annuum), HvRAF (Jung et al., 2006) from barley, and SodERF3 (Trujillo et al., 2008) from sugarcane (Saccharum officinarum), have been found to be involved in responses to various abiotic stress conditions. Approximately 20% of rice-growing areas worldwide are prone to drought. Although drought conditions can alter the growth and development of rice at any time during its life cycle, drought stress during reproductive growth directly results in a loss of grain yield. To evaluate improvements in grain yield under drought conditions, it is important to subject the plants to the stress during the transition to the reproductive phase. To date, a number of studies have suggested that overexpression of stress-related genes could improve drought tolerance in rice to some extent (Xu et al., 1996; Garg et al., 2002; Jang et al., 2003; Ito et al., 2006; Hu et al., 2006, 2008; Nakashima et al., 2007). Despite such efforts to develop drought-tolerant rice plants, very few of these have been shown to improve grain yields under field conditions. Examples of positive effects include transgenic rice plants expressing SNAC1 (Hu et al., 2006) and OsLEA3 (Xiao et al., 2007), which was shown to improve grain yield under field drought conditions. In this study, we identified 42 rice genes encoding transcription factors with the AP2 domain that were stress inducible. Two closely related yet distinct genes, AP37 and AP59, were functionally characterized. The overexpression of these genes in transgenic rice improved the plant tolerance to both drought and high salinity at the vegetative stage. However, increased tolerance to low temperature was observed only in plants overexpressing AP37. These AP37 overexpressors showed significantly enhanced drought tolerance in the field, increasing grain yield by 16% to 57% over the controls under severe drought conditions, yet they displayed no significant difference in yield under normal growth conditions. In contrast, grain yield in OsCc1:AP59 plants was reduced by 23% to 43% under both normal and drought stress conditions. We discuss the similarities and differences between the functions of AP37 and AP59 with respect to stress tolerance and grain yield. RESULTS Identification of Stress-Inducible AP2 Transcription Factors in Rice Previously, the rice genome was predicted to contain 139 AP2 domain genes (Nakano et al., 2006). To identify stress-inducible AP2 genes, we performed expression profiling with the Rice 3′-Tiling microarray (GreenGene Biotech) using RNAs from 14-d-old leaves of rice seedlings subjected to drought, high salinity, abscisic acid (ABA), and low temperature. When three replicates were averaged and compared with untreated leaves, a total of 42 genes were found to be up-regulated by 1.6-fold or greater (P < 0.05) by one or more of these stress conditions (Table I). Phylogenic analysis of the amino acid sequences of 42 factors revealed the presence of six subgroups (I–VI), with AP37 assigned to subgroup I, AP59 to subgroup II, OsDREB1A to subgroup V, and OsDREB2A to subgroup VI (Fig. 1
Two genes, AP37 (AK061380) and AP59 (AK073812), representing subgroups I and II, respectively, were functionally characterized in this study. The transcript levels of AP37 and AP59 were measured by RNA gel-blot analysis using total RNAs from leaf tissues of 14-d-old seedlings exposed to high salinity, drought, ABA, and low temperature (Fig. 2
Stress Tolerance of OsCc1:AP37 and OsCc1:AP59 Plants at the Vegetative Stage To enable the overexpression of the AP37 and AP59 genes in rice, their full-length cDNAs were isolated and linked to the OsCc1 promoter for constitutive expression (Jang et al., 2002), generating the constructs OsCc1:AP37 and OsCc1:AP59 (Fig. 3A
To further verify the stress-tolerance phenotype, we measured the Fv/Fm values of the transgenic and NT control plants, all at the vegetative stage (Fig. 4
Identification of Genes Up-Regulated by Overexpressed AP37 and AP59 To identify genes that are up-regulated by the overexpression of AP37 and AP59, we performed expression profiling of OsCc1:AP37 and OsCc1:AP59 plants in comparison with NT controls under normal growth conditions. Expression profiling with the Rice 3′-Tiling microarray was conducted using RNA samples extracted from 14-d-old leaves of these transgenic plants and NT controls, all grown under normal growth conditions. Each data set was obtained from three biological replicates. As listed in Supplemental Table S1, statistical analysis of each data set using one-way ANOVA identified 85 genes that are up-regulated by AP37 and/or AP59 with 3-fold or greater induction in the transgenic plants than in NT plants (P < 0.05). Specifically, a total of 37 genes were found to be commonly activated by AP37 and AP59, whereas 10 and 38 genes are specific to AP37 and AP59, respectively. Based on our microarray data shown in Table I and Supplemental Table S1, we selected eight stress-inducible genes out of 37 common target genes and verified their AP37- and AP59-dependent expression patterns under normal growth conditions by reverse transcription (RT)-PCR (Fig. 5A
Overexpression of AP37 Increases Rice Grain Yield under Drought Conditions A phenotypic evaluation of OsCc1:AP37, OsCc1:AP59, and NT control plants revealed no major differences in the vegetative growth of the entire plants. To then investigate whether the overexpression of AP37 and AP59 improved the rice grain yield of transgenic plants under field conditions, we transplanted three independent T5 homozygous lines of the OsCc1:AP37 and OsCc1:AP59 plants, together with their respective NT controls, to a paddy field and grew them to maturity. A completely randomized design with two replicates was employed. The subsequent evaluation of the yield parameters of these plants revealed that the grain yield of OsCc1:AP37 plants remained similar to that of the NT controls under normal field conditions (Fig. 6
DISCUSSION In this study, expression profiling using RNAs from stress-treated rice plants identified 42 AP2 domain factors that are stress inducible (Table I). Alignment of these stress-inducible factors revealed six subgroups within which the members are more closely related, suggesting a common function during the stress response. The overexpression of AP37 (OsCc1:AP37) and AP59 (OsCc1:AP59), representative members of subgroups I and II, respectively, increased the rice plant tolerance to drought and high salinity at the vegetative stage. Increased tolerance to low temperature was observed only in OsCc1:AP37 plants, suggesting a functional difference between the two closely related AP2 factors in the stress response. Given the different numbers of target genes up-regulated in OsCc1:AP37 and OsCc1:AP59 plants, the difference in their response to low temperature was not unexpected. Our microarray experiments identified 10 and 38 putative target genes that are specific to the AP37 and AP59 proteins, respectively, in addition to a further 37 target genes that appeared to be common to both factors (Supplemental Table S1). Similarly contrasting results were previously obtained for two closely related AP2 factors, HvCBF4 from barley and CBF3/DREB1A from Arabidopsis. The overexpression of HvCBF4 and CBF3/DREB1A in rice confers increased tolerance to drought, high salinity, and low temperature, and in the case of low temperature this effect was more pronounced in plants overexpressing HvCBF4 (Oh et al., 2007). The composition of the target rice genes was also different between the HvCBF4 and CBF3/DREB1A plants. The expression patterns of the AP37 and AP59 genes are similar under drought and high-salinity conditions but different under low-temperature and ABA stress, consistent with the observed differences between these genes in conferring low-temperature tolerance. The AP37 transcript levels were rapidly increased in rice plants within 30 min of exposure to stress conditions, similar to the reported observations for the OsDREB1A transcripts (Dubouzet et al., 2003). The OsDREB1A gene was classified in subgroup V in this analysis, the members of which have a similar expression pattern to the genes in subgroups I and II (Table I). Consistent with our results for OsCc1:AP37 rice plants, the overexpression of OsDREB1A in rice (Ito et al., 2006) and in Arabidopsis (Dubouzet et al., 2003) has been shown previously to confer tolerance to low temperature, in addition to drought and high-salinity resistance. The overexpression of OsDREB1A in rice induced the accumulation of soluble sugars, including raffinose, Suc, Glc, and Fru, which may act as osmoprotectants (Ito et al., 2006). The target genes we identified in OsCc1:AP37 plants included genes that function in carbon metabolism such as phosphogluconate aldolase, phosphoglucomutase, UDP-glucosyl transferase, and isocitrate lyase. These genes may similarly increase the content of soluble sugars. It is also generally accepted that excessive amounts of reactive oxygen species are generated upon exposure of plants to stress stimuli, and these must be removed in order to maintain cellular homeostasis. Overexpression of JERF3, a tomato ortholog of our subgroup IV (Table I), was shown previously to enhance stress tolerance by increasing the expression of genes encoding antioxidant enzymes (Wu et al., 2008). Similarly, the expression levels of several antioxidant genes, such as thioredoxin, peroxidase, and ascorbate oxidoreductase, were found to be increased in our OsCc1:AP37 and OsCc1:AP59 transgenic rice plants, which may indicate the activation of a reactive oxygen species-scavenging system. Loss-of-function mutants on AP37 and AP59 may provide us with further evidence of molecular mechanisms for stress tolerance, although the presence of many homologous AP2 domain genes may not allow knockout phenotypes to be displayed. Grain yield from rice plants is severely affected when they are exposed to drought stress at the reproductive stage. Therefore, it was important to examine the effects of drought stress on grain yield at this stage of growth in our transgenic plants. It was also important to use transgenic lines that were not genetically segregating under field conditions. It is relatively straightforward to identify segregating families of transgenic rice plants up to the T4 generation in the field, even though they are homozygous for a transgene. To evaluate whether any improvements in grain yield had occurred in our transgenic rice under drought conditions, we transplanted T5 homozygous lines of OsCc1:AP37 and OsCc1:AP59 plants to the field in 2008, which had been prescreened in the field for segregation in 2007. The plants were exposed to drought stress at the panicle heading stage from 10 d prior to heading to 20 d after heading in field conditions. The OsCc1:AP37 plants showed significantly enhanced drought tolerance in the field, with a grain yield of 16% to 57% higher than the controls under severe drought conditions yet displayed no significant differences under normal growth conditions (Fig. 6 MATERIALS AND METHODS Plasmid Construction and Transformation of Rice The expression plasmids OsCc1:AP37 and OsCc1:AP59 contained the bar gene under the control of the cauliflower mosaic virus 35S promoter to enable herbicide-based plant selection. The OsCc1 promoter was used to drive constitutive plasmid gene expression (Jang et al., 2002). The coding regions of AP37 and AP59 were amplified from rice (Oryza sativa) total RNA using an RT-PCR system (Promega) according to the manufacturer's instructions. Primer pairs were as follows: AP37 forward (5′-ATGGCGCCCAGAGCAGCTAC-3′) and AP37 reverse (5′-CTAGTTCTCTACCGGCGGCG-3′); and AP59 forward (5′-ATGCTGCTTAATCCGGCGTC-3′) and AP59 reverse (5′-TTAGCTCACCAGCTGCTGGA-3′). Plasmids were introduced into Agrobacterium tumefaciens LBA4404 by triparental mating, and embryogenic calli from mature seeds (cv Nakdong) were transformed as described previously (Jang et al., 1999). Drought Treatments at the Vegetative Stage Transgenic and NT rice seeds were germinated in half-strength Murashige and Skoog (MS) solid medium in a growth chamber in the dark at 28°C for 4 d, transplanted into soil, and then grown in a greenhouse (16-h-light/8-h-dark cycles) at 28°C to 30°C. Eighteen seedlings from each transgenic and NT line were grown in pots (3 × 3 × 5 cm; one plant per pot) for 4 weeks before undertaking the drought stress experiments. To induce drought stress, 4-week-old transgenic and NT seedlings were unwatered for 3 d followed by 7 d of watering. The numbers of plants that survived or continued to grow were then scored. Chlorophyll Fluorescence under Conditions of Drought, High Salinity, and Low Temperature Transgenic and NT rice seeds were germinated and grown in half-strength MS solid medium for 14 d in a growth chamber (16-h-light [150 μmol m−2 s−1]/8-h-dark cycles at 28°C). The green portions of approximately 10 seedlings were cut using a scissors prior to stress treatments in vitro. To induce low-temperature stress, the seedlings were incubated at 4°C in water for up to 6 h under continuous 150 μmol m−2 s−1 light. For high-salinity stress treatments, they were incubated in 400 mm NaCl for 2 h at 28°C under continuous 150 μmol m−2 s−1, and to simulate drought stress, they were air dried for 2 h at 28°C under continuous 150 μmol m−2 s−1 light. The Fv/Fm values were then measured as described previously (Oh et al., 2005b). Rice 3′-Tiling Microarray Analysis Expression profiling was conducted using the Rice 3′-Tiling microarray manufactured by NimbleGen (http://www.nimblegen.com/), which contains 27,448 genes deposited at the International Rice Genome Sequencing Project Rice Annotation Project 1 database (http://rapdb.lab.nig.ac.jp). Further information on this microarray, including statistical analysis, can be found at http://www.ggbio.com (GreenGene Biotech). Among the genes on the microarray, 20,507 are from representative Rice Annotation Project 1 sequences with cDNA/EST supports, and 6,941 genes have been predicted without cDNA/EST supports. Ten 60-nucleotide-long probes were designed from each gene starting at 60 bp ahead of the stop codon and with 10-bp shifts in position, so that 10 probes covered 150 bp within the 3′ region of the gene. In total, 270,000 probes were designed in this way (average size, 60 nucleotides) to have melting temperature values of between 75°C and 85°C. Random GC probes (38,000) were used to monitor the hybridization efficiency, and fiducial markers at the four corners (225) were included to assist with overlaying of the grid on the image. To identify stress-inducible AP2 genes in rice, total RNA (100 μg) was prepared using 14-d-old rice leaves from plants subjected to drought, high-salinity, ABA, and low-temperature stress conditions. For the high-salinity and ABA treatments, the 14-d-old seedlings were transferred to a nutrient solution containing 400 mm NaCl or 100 μm ABA for 2 h in the greenhouse under continuous light of approximately 1,000 μmol m−2 s−1. For drought treatment, 14-d-old seedlings were air dried for 2 h under continuous light of approximately 1,000 μmol m−2 s−1. For low-temperature treatment, 14-d-old seedlings were exposed at 4°C in a cold chamber for 6 h under continuous light of 150 μmol m−2 s−1. For identification of genes up-regulated in OsCc1:AP37 and OsCa1:AP59 plants, total RNA (100 μg) was prepared from leaf tissues of 14-d-old transgenic and NT rice seedlings grown under normal growth conditions. The mRNA was purified using the Qiagen Oligotex column according to the manufacturer's instructions. For normalization, data were processed with cubic alpine normalization using quartiles to adjust signal variation between chips and with robust multi-chip analysis using a median polish algorithm implemented in NimbleScan (Workman et al., 2002; Irizarry et al., 2003). To assess the reproducibility of the microarray analysis, we repeated the experiments three times with independently prepared total RNAs and analyzed each data set statistically using one-way ANOVA. RT-PCR and Quantitative PCR Analysis Total RNA was prepared as reported previously (Oh et al., 2008). For the analysis of target by RT-PCR, a cDNA synthesis system (Invitrogen) was used according to the manufacturer's instructions. PCR products were amplified using primers designed with Primer Designer 4 software (Sci-ed Software). RT-PCR was carried out using the primer pairs listed in Supplemental Table S3 at a final concentration of 10 pm each and 2 μL (equivalent to 5 ng of total RNA) of cDNA as the template. PCR was performed at 95°C for 10 min, followed by 20 to 25 cycles of 94°C for 30 s, 57°C for 30 s, and 68°C for 1 min. Amplified products were resolved on a 2% agarose gel. To validate our RT-PCR results, we repeated each experiment twice with independently prepared total RNA. For quantitative real-time PCR experiments, the SuperScript III Platinum One-Step Quantitative RT-PCR system (Invitrogen) was used. For PCR, a master mix of reaction components was prepared according to the manufacturer's protocol for Platinum SYBR Green qPCR SuperMix-UDG (Invitrogen). Thermocycling and fluorescence detection were performed using the Mx3000p Real-Time PCR machine (Stratagene). PCR was performed at 95°C for 10 min, followed by 20 to 25 cycles of 94°C for 30 s, 57°C for 30 s, and 68°C for 1 min. To validate our quantitative PCR results, we repeated each experiment three times. Drought Treatments in the Field for Reproductive-Stage Rice Plants To evaluate yield components of transgenic plants under normal field conditions, three independent T5 homozygous lines of the OsCc1:AP37 and OsCc1:AP59 plants, together with NT controls, were transplanted to a paddy field at the Rural Development Administration, Suwon, Korea. A completely randomized design was employed with two replicates, each consisting of four plots of 5 m2. At 25 d after sowing, the seedlings were randomly transplanted within a 15- × 30-cm distance. Fertilizer was applied at 70:40:70 (nitrogen:phosphorus:potassium) kg ha−1 after the last paddling and 45 d after transplantation. Yield parameters were scored for 10 plants per plot and 20 plants per line. To evaluate yield components of transgenic plants under drought field conditions, three independent T5 homozygous lines of the OsCc1:AP37 and OsCc1:AP59 plants and NT controls were transplanted to a removable rain-off shelter with a 1-m-deep container filled with natural paddy soil located at Myongji University, Yongin, Korea. Completely randomized design, transplanting distance, and use of fertilizer were employed as described above for normal field conditions. Drought stress was applied at the panicle heading stage (from 10 d before heading to 20 d after heading) by flowing water through a drain at the bottom of the container. To prevent plants from dying, we irrigated twice when the plant leaves rolled during drought stress. After exposure to drought stress conditions, the polyvinyl roofs were removed and plants were irrigated until harvesting. When the plants grown under normal and drought conditions had reached maturity and the grains had ripened, they were harvested and threshed by hand (separation of seeds from the vegetative parts). The unfilled and filled grains were taken apart, independently counted using a Countmate MC1000H (Prince), and weighed. The following agronomic traits were scored: flowering date, panicle length, number of tillers, number of panicles, spikelets per panicle, filling rate (%), total grain weight (g), and 1,000 grain weight (g). The results from three independent lines were separately analyzed by one-way ANOVA and compared with those of the NT controls. The ANOVA was used to reject the null hypothesis of equal means of transgenic lines and NT controls (P < 0.05). SPSS version 16.0 was used to perform statistical analysis. Supplemental Data The following materials are available in the online version of this article.
[Supplemental Data]
Acknowledgments We are grateful to Dr. Soon Jong Kweon at the National Academy of Agricultural Sciences, Rural Development Administration, of Korea for making critical comments on the field experiments and analyses. Notes 1This work was supported by the Ministry of Education, Science, and Technology of Korea through the Crop Functional Genomics Center (grant no. CG2111 to J.-K.K.), by the Biogreen21 Program (grant to J.-K.K.), and by the Korea Science and Engineering Foundation through the Plant Metabolism Research Center at Kyung-Hee University (grant to J.-K.K.) The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Ju-Kon Kim (jukon306/at/gmail.com). [W]The online version of this article contains Web-only data. [OA]Open Access articles can be viewed online without a subscription. References
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Annu Rev Plant Physiol Plant Mol Biol. 1999 Jun; 50():571-599.
[Annu Rev Plant Physiol Plant Mol Biol. 1999]Curr Opin Plant Biol. 2003 Oct; 6(5):410-7.
[Curr Opin Plant Biol. 2003]Science. 2000 Dec 15; 290(5499):2105-10.
[Science. 2000]Science. 2003 Jul 18; 301(5631):376-9.
[Science. 2003]Plant Cell. 1995 Apr; 7(4):388-9.
[Plant Cell. 1995]Plant Physiol. 1996 Jan; 110(1):249-257.
[Plant Physiol. 1996]Proc Natl Acad Sci U S A. 2002 Dec 10; 99(25):15898-903.
[Proc Natl Acad Sci U S A. 2002]Plant Physiol. 2003 Feb; 131(2):516-24.
[Plant Physiol. 2003]Plant Cell Physiol. 2006 Jan; 47(1):141-53.
[Plant Cell Physiol. 2006]Proc Natl Acad Sci U S A. 2006 Aug 29; 103(35):12987-92.
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[Plant Physiol. 2006]Plant J. 2003 Feb; 33(4):751-63.
[Plant J. 2003]Plant Physiol. 2005 May; 138(1):341-51.
[Plant Physiol. 2005]Plant J. 1994 Aug; 6(2):271-82.
[Plant J. 1994]Plant Physiol. 2005 May; 138(1):341-51.
[Plant Physiol. 2005]Plant Biotechnol J. 2007 Sep; 5(5):646-56.
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[Plant Cell Physiol. 2006]Plant Physiol. 2008 Dec; 148(4):1953-63.
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[Plant Physiol. 2005]Genome Biol. 2002 Aug 30; 3(9):research0048.
[Genome Biol. 2002]Nucleic Acids Res. 2003 Feb 15; 31(4):e15.
[Nucleic Acids Res. 2003]