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Natural Variation in the Sequence of SNAC1 and Its Expression Level Polymorphism in Rice Germplasms under Drought Stress
Available online at www.sciencedirect.com
ScienceDirect Journal of Genetics and Genomics 41 (2014) 609e612
JGG LETTER TO THE EDITOR
Natural Variation in the Sequence of SNAC1 and Its Expression Level Polymorphism in Rice Germplasms under Drought Stress Water is a major limiting factor for food production and many countries fail to produce sufficient food for their population due to severe water scarcity (Jury and Vaux, 2005). Rice is the main staple food worldwide. More than 50% of rice in the world is rain-fed and drought causes severe reduction in rice grain yield in rain-fed environments (Venuprasad et al., 2007; Zhang, 2007; Sandhu et al., 2014). Therefore, enhancing drought resistance (DR) of rice is important for food security. However, DR is a complex trait, which is controlled by a large number of loci with small effect and is also affected by different genetic background, genotype-by-environment interaction and other stresses such as heat (Hu and Xiong, 2014). Some quantitative trait loci (QTLs) associated with DR have been mapped in rice (Zhang et al., 2001; Yue et al, 2006). However, few of DR-related QTLs were repeatedly detected in different mapping studies (Fukao and Xiong, 2013). As an alternative approach, reverse genetics has been exploited to identify some DR-related genes in rice. So far, many stress responsive genes have been identified, including OsMAPK5 (Xiong and Yang, 2003), SNAC1 (Hu et al., 2006), OsSKIPa (Hou et al., 2009), DST (Huang et al., 2009), and DWA1 (Zhu and Xiong, 2013). However, understanding of the genetic architecture of quantitative traits is not complete until we can identify which polymorphic sites actually underlie the phenotypic variation (Mackay, 2001). To address this problem, candidate-gene association mapping arises as a very practical approach. It has been reported that over-expression of the SNAC1 gene in Nipponbare enhanced the DR of the transgenic rice in the field under severe drought stress conditions at the reproductive stage, and the transgenic rice showed no phenotypic change or decline in grain yield under normal growth conditions (Hu et al., 2006). Here, we identified several single nucleotide polymorphism sites (SNPs) in the region of SNAC1 underlying its expression level polymorphisms (ELPs) under drought conditions. Several haplotypes defined by these SNPs conferring significantly higher expression levels under drought stress conditions were identified, which will be useful for
breeding drought-resistant rice varieties through markerassisted selection (MAS). Prior to the association analysis of SNAC1 expression, we conducted population structure analysis for the whole population including 202 accessions (Fig. 1A and Table S1) used in this study. As the membership probability threshold was set as 0.75 (Yang et al., 2010, 2011), 68 and 132 accessions were classified into the japonica and the indica subgroups (Table S1). The remaining two accessions, which had membership probability <0.75 in any group, were assigned into admixed group. Leaf samples of 170 rice accessions grown under normal (N) or moderate drought stress (D1) conditions, and those of 122 rice accessions grown under severe drought stress (D2) conditions were successfully collected (Table S2) (see Supplementary Data for details) for expression diversity analysis (Fig. 1B). The expression levels of SNAC1 (see Supplementary Data for the definition of ‘expression level’) in the rice accessions ranged from 1.00 to 82.54 for N, from 2.36 to 203.23 for D1, and from 4.42 to 338.31 for D2. The average expression levels were 12.90, 39.83, and 70.63 for N, D1, and D2, respectively. In the indica subgroup, the expression levels ranged from 1.58 to 82.54 for N, from 4.00 to 203.23 for D1, and from 4.70 to 338.31 for D2, respectively. In the japonica subgroup, the expression level ranges were 1 to 44.88 for N, 2.36 to 76.19 for D1, and 4.42 to 261.90 for D2, respectively. The average expression levels were 12.89, 48.80, and 84.17 for the indica subgroup, and 12.56, 24.66, and 50.63 for the japonica subgroup for N, D1, and D2, respectively (Table S1). In general, the expression level was higher in D1 and even higher in D2 than that in N for most of the accessions, suggesting that SNAC1 is induced by drought stress treatment. Under normal condition, no significant (P ¼ 0.84, t-test) difference of the expression level of SNAC1 was found between the two subgroups. Under drought stress condition, the expression level of the indica subgroup was significantly (P ¼ 1.54E-07 and 4.98E03 for D1 and D2, respectively, t-test) higher than that of the japonica subgroup.
http://dx.doi.org/10.1016/j.jgg.2014.09.001 1673-8527/Copyright Ó 2014, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Limited and Science Press. All rights reserved.
610
Letter to the Editor / Journal of Genetics and Genomics 41 (2014) 609e612
Fig. 1. Expression level polymorphism and haplotype analysis of SNAC1 in rice. A: Population structure of 202 rice accessions. Red and green zones indicate japonica and indica subgroups, respectively. B: Box-whisker plots showing phenotypic variation of SNAC1 gene expression level in the whole population, indica and japonica subgroups under N, D1, and D2 conditions. The top, central, and bottom lines of the box indicate high quartile (Q3), median, and low quartile (Q1) of expression levels of SNAC1. The difference between Q3 and Q1 was the interquartile range. The upper and lower whiskers indicate the maximum and minimum of expression level after excluding outliers. The stars represent extreme outlier with expression level three times higher than the interquartile range, and the circles represent mild outlier with expression level 1.5 times higher than the interquartile range. C: Average squared allele-frequency correlation coefficient r2 ( y-axis) against physical distance (bp) within the SNAC1 locus (x-axis). D: The average expression levels and standard errors of the four haplotype groups (C1eC4) under D1 (blue bar) and D2 (red bar) conditions after log10-transformation to satisfy homogeneity of variance, an important precondition of ANOVA. E: The distribution of indica/japonica accessions among the four haplotype groups. The area of pie chart represents the number of accessions in each haplotype group, and red, blue, and green sections indicate japonica, indica, and admixed accessions, respectively.
Among the 202 accessions, 57 SNPs were detected in the 3448-bp SNAC1 locus (Table S3). The uneven distribution of SNPs in different regions was observed (Table S4). Only 20 SNPs with minor allele-frequency (MAF) >0.05 were
detected in the whole population. Pair-wise linkage disequilibrium (LD) at the SNAC1 locus as measured by the r2 declined to 0.20 at a distance of about 3000 bp (Fig. 1C), and the average of r2 at the SNAC1 locus was 0.40 (Table S5).
Letter to the Editor / Journal of Genetics and Genomics 41 (2014) 609e612
As mentioned, the samples for N and D1 were from 170 accessions and those for D2 were from 122 accessions. Considering missing values, the population structure (Q) and kinship (K) for the 170 accessions (classified into D1 subpopulation) and the 122 accessions (classified into D2 subpopulation) were specially estimated for association analysis. And there existed a total of the same 20 SNPs with MAF >0.05 among these populations. The association analysis between the 20 SNPs and five “traits” including the expression levels of SNAC1 under N, D1, and D2 conditions, and D1/N (the ratio of the expression level of D1 to that of N) and D2/N (the ratio of the expression level of D2 to that of N) were performed using the Q model, K model, and the QþK model (Table S6). Based on mixed linear model (MLM) analysis (QþK model), two SNP sites (S27 and S40) with low LD level (r2 ¼ 0.166) were found to be significantly associated (P < 0.05) with the expression level of SNAC1 under D1 condition. S27 at 637-bp upstream of the TSS (transcription start site) of SNAC1 is located in a cis-element MYBCORE according to the plant cis-acting regulatory DNA elements database, which is a binding site of all animal MYBs (myeloblastosis viral oncogene homologs) and at least two plant MYBs (ATMYB1 and ATMYB2) isolated from Arabidopsis. ATMYB2 is involved in the regulation of genes that are responsive to water stress in Arabidopsis (Urao et al., 1993). S40 was also located in the promoter region, at 98-bp upstream of the TSS of SNAC1. With the QþK model, seven SNPs significantly associated with the expression level of SNAC1 under D2 condition were identified. These sites could be classified into two groups based on the extent of the LD: group I with r2 > 0.80 between any two SNPs (S11, S12, S21, and S49) and group II with r2 > 0.55 between any two SNPs (S15, S50, and S51). The LD between the two groups was much lower (r2 between any two SNPs from different groups <0.09) than that within the groups (Table S7). For the first group, S11, S12 and S21 were located in the promoter region, and S49 was located in exon 1. For the second group, only S15 was located at 1049-bp upstream of
611
the TSS. S50 caused a non-synonymous change with the transition from A to G in exon 1. S51, in complete LD with S50, was located in an intron. Besides, S21 was also associated with the drought-induction fold of expression level in D2 (the ratio of the ‘expression level’ in D2 to the ‘expression level’ in N). Because of the LD, the exact causal sites or SNPs could not be determined, and potential association with the expression of the gene was further evaluated by haplotype association analysis. SNAC1 haplotypes were used to determine the joint effect of significant SNPs on the expression level of SNAC1 under different conditions. Only the haplotype groups containing 5 accessions remained for subsequent haplotype analysis. Due to the LD, only four haplotypes defined by seven significant SNPs in D1 and D2 were detected (Table 1). Finally, we identified the most favorable haplotype (C1) with favorable alleles of the seven significant SNPs in D1 and D2. The average expression level of haplotype C1 was 2.75 times as much as C4 in D1, and 5.13 times as much as C3 in D2 (Table 1 and Fig. 1D). The combination of the integrated seven sites explained 25.25% and 19.06% of the expression variation in D1 and D2, respectively. All of the japonica accessions were assigned into the haplotype C4, while the indica accessions existed in different haplotypes (Fig. 1E). In addition, the haplotype of the japonica accessions had the lowest or relatively lower expression levels compared to the other haplotypes for D1 and D2. These results indicate that indica rice harbors more expression level diversity than japonica rice. The degree of DR for the rice accessions was evaluated by visual estimation with a 1e9 scale (1 ¼ most droughtresistant, 9 ¼ most drought-sensitive, using half scores when needed) based mainly on leaf-drying scores (Yue et al., 2006). The result showed that almost all of the accessions from haplotype C1 were prone to drought resistance (the average score was 2.9 with the range of scores between 1.5 to 4.5), with the exception of one accession which was easily broken by wind (Table S8). The days spanning from the start of
Table 1 Four haplotypes defined by seven significant SNPs Haplotype
Polymorphic sitea S11
S15
Expression leveld S21
S27
S40
S49
S50
D1
D2
Trans (x) C1
A
T
G
A
T
T
A
a
1.74
a
x
Inverse (10 ) 54.75
Trans (x)
Inverse (10x)
a
96.64
a
1.99
C2
A
T
G
A
C
T
A
1.59
38.81
1.79
61.99
C3
T
C
A
A
C
G
G
1.68a
47.64
1.28b
18.85
C4
T
T
A
G
C
G
A
1.30b
19.90
1.54b
34.55
2b
R
25.25%
19.06%
Pc
2.22E-09
5.10E-05
a
Favorable genotypes are in boldface type, and only observed haplotypes are listed; b Values from analysis of variance (ANOVA) showing percentage of phenotypic variation explained; c P value from ANOVA analysis; d Average expression levels of each haplotype group and multiple comparisons of the four haplotype groups under D1 and D2 conditions. ‘Trans’ indicated the log10-transformed values of expression levels to satisfy homogeneity of variance, an important precondition of ANOVA (x); ‘Inverse’ indicated the values after inverse transformation based on ‘Trans’ (10x). The expression level of the groups with letter ‘a’ was significantly higher than that of the groups with letter ‘b’ under D1 and D2 conditions, respectively (P < 0.05).
612
Letter to the Editor / Journal of Genetics and Genomics 41 (2014) 609e612
drought treatment to the time when the leaves just began to roll (stress days in D1), and to the time when the rolled leaves could not re-expand (stress days in D2) were also recorded. The number of stress days in D1 and D2 for most of the accessions belonging to haplotype C1 was larger than the overall mean of the accessions in our panel (Table S8). Based on these data, it can be expected that most, if not all, of the C1 accessions that had a significantly higher expression level of SNAC1 under moderate and severe drought stress conditions should have increased DR capabilities. In summary, the haplotype C1 could be especially useful in DR breeding since SNAC1 in this haplotype was strongly induced regardless of the degree of drought stress. This work suggests that exploring the expression diversity of important DR-related endogenous genes may provide new opportunities for DR breeding through non-transgenic approaches.
ACKNOWLEDGEMENTS This work was supported by grants from the National Program for Basic Research of China (No. 2012CB114305), the National Program on High Technology Development (No. 2012AA10A303), and the Oversea Graduate Program from Ministry of Education to K. Songyikhangsuthor.
SUPPLEMENTARY DATA Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jgg.2014.09.001.
Khamdok Songyikhangsuthor1,2, Zilong Guo1, Nili Wang, Xiaoyi Zhu, Weibo Xie, Tongmin Mou, Lizhong Xiong* National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China *Corresponding author. Tel: þ86 27 8728 1536, fax: þ86 27 8728 7092. E-mail address: [email protected] (L. Xiong) 1 These authors equally contributed to this work. 2 Current address: Northern Agriculture and Forestry Research Center, Luang Prabang P.O. Box 600, Laos. Received 17 May Revised 5 September Accepted 10 September Available online 28 September
2014 2014 2014 2014
REFERENCES Fukao, T., Xiong, L., 2013. Genetic mechanisms conferring adaptation to submergence and drought in rice: simple or complex? Curr. Opin. Plant Biol. 16, 196e204. Hou, X., Xie, K., Yao, J., Qi, Z., Xiong, L., 2009. A homolog of human skiinteracting protein in rice positively regulates cell viability and stress tolerance. Proc. Natl. Acad. Sci. USA 106, 6410e6415. Hu, H., Dai, M., Yao, J., Xiao, B., Li, X., Zhang, Q., Xiong, L., 2006. Overexpressing a NAM, ATAF, and CUC (NAC) transcription factor enhances drought resistance and salt tolerance in rice. Proc. Natl. Acad. Sci. USA 103, 12987e12992. Hu, H., Xiong, L., 2014. Genetic engineering and breeding of drought-resistant crops. Annu. Rev. Plant Biol. 65, 715e741. Huang, X., Chao, D., Gao, J., Zhu, M., Shi, M., Lin, H., 2009. A previously unknown zinc finger protein, DST, regulates drought and salt tolerance in rice via stomatal aperture control. Genes Dev. 23, 1805e1817. Jury, W.A., Vaux, H., 2005. The role of science in solving the world’s emerging water problems. Proc. Natl. Acad. Sci. USA 102, 15715e15720. Mackay, T.F., 2001. The genetic architecture of quantitative traits. Annu. Rev. Genet. 35, 303e339. Sandhu, N., Singh, A., Dixit, S., Sta Cruz, M.T., Maturan, P.C., Jain, R.K., Kumar, A., 2014. Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet. 15, 63. Urao, T., Yamaguchi-Shinozaki, K., Urao, S., Shinozaki, K., 1993. An Arabidopsis myb homolog is induced by dehydration stress and its gene product binds to the conserved MYB recognition sequence. Plant Cell 5, 1529e1539. Venuprasad, R., Lafitte, H.R., Atlin, G.N., 2007. Response to direct selection for grain yield under drought stress in rice. Crop Sci. 47, 285e293. Xiong, L., Yang, Y., 2003. Disease resistance and abiotic stress tolerance in rice are inversely modulated by an abscisic acid-inducible mitogen-activated protein kinase. Plant Cell 15, 745e759. Yang, X., Xu, Y., Shah, T., Li, H., Han, Z., Li, J., Yan, J., 2011. Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetica 139, 1045e1054. Yang, X., Yan, J., Shah, T., Warburton, M.L., Li, Q., Li, L., Gao, Y., Chai, Y., Fu, Z., Zhou, Y., Xu, S., Bai, G., Meng, Y., Zheng, Y., Li, J., 2010. Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection. Theor. Appl. Genet. 121, 417e431. Yue, B., Xue, W., Xiong, L., Yu, X., Luo, L., Cui, K., Jin, D., Xing, Y., Zhang, Q., 2006. Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics 172, 1213e1228. Zhang, J., Zheng, H.G., Aarti, A., Pantuwan, G., Nguyen, T.T., Tripathy, J.N., Sarial, A.K., Robin, S., Babu, R.C., Nguyen, B.D., Sarkarung, S., Blum, A., Nguyen, H.T., 2001. Locating genomic regions associated with components of drought resistance in rice: comparative mapping within and across species. Theor. Appl. Genet. 103, 19e29. Zhang, Q., 2007. Strategies for developing Green Super Rice. Proc. Natl. Acad. Sci. USA 104, 16402e16409. Zhu, X., Xiong, L., 2013. Putative megaenzyme DWA1 plays essential roles in drought resistance by regulating stress-induced wax deposition in rice. Proc. Natl. Acad. Sci. USA 110, 17790e17795.
ScienceDirect Journal of Genetics and Genomics 41 (2014) 609e612
JGG LETTER TO THE EDITOR
Natural Variation in the Sequence of SNAC1 and Its Expression Level Polymorphism in Rice Germplasms under Drought Stress Water is a major limiting factor for food production and many countries fail to produce sufficient food for their population due to severe water scarcity (Jury and Vaux, 2005). Rice is the main staple food worldwide. More than 50% of rice in the world is rain-fed and drought causes severe reduction in rice grain yield in rain-fed environments (Venuprasad et al., 2007; Zhang, 2007; Sandhu et al., 2014). Therefore, enhancing drought resistance (DR) of rice is important for food security. However, DR is a complex trait, which is controlled by a large number of loci with small effect and is also affected by different genetic background, genotype-by-environment interaction and other stresses such as heat (Hu and Xiong, 2014). Some quantitative trait loci (QTLs) associated with DR have been mapped in rice (Zhang et al., 2001; Yue et al, 2006). However, few of DR-related QTLs were repeatedly detected in different mapping studies (Fukao and Xiong, 2013). As an alternative approach, reverse genetics has been exploited to identify some DR-related genes in rice. So far, many stress responsive genes have been identified, including OsMAPK5 (Xiong and Yang, 2003), SNAC1 (Hu et al., 2006), OsSKIPa (Hou et al., 2009), DST (Huang et al., 2009), and DWA1 (Zhu and Xiong, 2013). However, understanding of the genetic architecture of quantitative traits is not complete until we can identify which polymorphic sites actually underlie the phenotypic variation (Mackay, 2001). To address this problem, candidate-gene association mapping arises as a very practical approach. It has been reported that over-expression of the SNAC1 gene in Nipponbare enhanced the DR of the transgenic rice in the field under severe drought stress conditions at the reproductive stage, and the transgenic rice showed no phenotypic change or decline in grain yield under normal growth conditions (Hu et al., 2006). Here, we identified several single nucleotide polymorphism sites (SNPs) in the region of SNAC1 underlying its expression level polymorphisms (ELPs) under drought conditions. Several haplotypes defined by these SNPs conferring significantly higher expression levels under drought stress conditions were identified, which will be useful for
breeding drought-resistant rice varieties through markerassisted selection (MAS). Prior to the association analysis of SNAC1 expression, we conducted population structure analysis for the whole population including 202 accessions (Fig. 1A and Table S1) used in this study. As the membership probability threshold was set as 0.75 (Yang et al., 2010, 2011), 68 and 132 accessions were classified into the japonica and the indica subgroups (Table S1). The remaining two accessions, which had membership probability <0.75 in any group, were assigned into admixed group. Leaf samples of 170 rice accessions grown under normal (N) or moderate drought stress (D1) conditions, and those of 122 rice accessions grown under severe drought stress (D2) conditions were successfully collected (Table S2) (see Supplementary Data for details) for expression diversity analysis (Fig. 1B). The expression levels of SNAC1 (see Supplementary Data for the definition of ‘expression level’) in the rice accessions ranged from 1.00 to 82.54 for N, from 2.36 to 203.23 for D1, and from 4.42 to 338.31 for D2. The average expression levels were 12.90, 39.83, and 70.63 for N, D1, and D2, respectively. In the indica subgroup, the expression levels ranged from 1.58 to 82.54 for N, from 4.00 to 203.23 for D1, and from 4.70 to 338.31 for D2, respectively. In the japonica subgroup, the expression level ranges were 1 to 44.88 for N, 2.36 to 76.19 for D1, and 4.42 to 261.90 for D2, respectively. The average expression levels were 12.89, 48.80, and 84.17 for the indica subgroup, and 12.56, 24.66, and 50.63 for the japonica subgroup for N, D1, and D2, respectively (Table S1). In general, the expression level was higher in D1 and even higher in D2 than that in N for most of the accessions, suggesting that SNAC1 is induced by drought stress treatment. Under normal condition, no significant (P ¼ 0.84, t-test) difference of the expression level of SNAC1 was found between the two subgroups. Under drought stress condition, the expression level of the indica subgroup was significantly (P ¼ 1.54E-07 and 4.98E03 for D1 and D2, respectively, t-test) higher than that of the japonica subgroup.
http://dx.doi.org/10.1016/j.jgg.2014.09.001 1673-8527/Copyright Ó 2014, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Limited and Science Press. All rights reserved.
610
Letter to the Editor / Journal of Genetics and Genomics 41 (2014) 609e612
Fig. 1. Expression level polymorphism and haplotype analysis of SNAC1 in rice. A: Population structure of 202 rice accessions. Red and green zones indicate japonica and indica subgroups, respectively. B: Box-whisker plots showing phenotypic variation of SNAC1 gene expression level in the whole population, indica and japonica subgroups under N, D1, and D2 conditions. The top, central, and bottom lines of the box indicate high quartile (Q3), median, and low quartile (Q1) of expression levels of SNAC1. The difference between Q3 and Q1 was the interquartile range. The upper and lower whiskers indicate the maximum and minimum of expression level after excluding outliers. The stars represent extreme outlier with expression level three times higher than the interquartile range, and the circles represent mild outlier with expression level 1.5 times higher than the interquartile range. C: Average squared allele-frequency correlation coefficient r2 ( y-axis) against physical distance (bp) within the SNAC1 locus (x-axis). D: The average expression levels and standard errors of the four haplotype groups (C1eC4) under D1 (blue bar) and D2 (red bar) conditions after log10-transformation to satisfy homogeneity of variance, an important precondition of ANOVA. E: The distribution of indica/japonica accessions among the four haplotype groups. The area of pie chart represents the number of accessions in each haplotype group, and red, blue, and green sections indicate japonica, indica, and admixed accessions, respectively.
Among the 202 accessions, 57 SNPs were detected in the 3448-bp SNAC1 locus (Table S3). The uneven distribution of SNPs in different regions was observed (Table S4). Only 20 SNPs with minor allele-frequency (MAF) >0.05 were
detected in the whole population. Pair-wise linkage disequilibrium (LD) at the SNAC1 locus as measured by the r2 declined to 0.20 at a distance of about 3000 bp (Fig. 1C), and the average of r2 at the SNAC1 locus was 0.40 (Table S5).
Letter to the Editor / Journal of Genetics and Genomics 41 (2014) 609e612
As mentioned, the samples for N and D1 were from 170 accessions and those for D2 were from 122 accessions. Considering missing values, the population structure (Q) and kinship (K) for the 170 accessions (classified into D1 subpopulation) and the 122 accessions (classified into D2 subpopulation) were specially estimated for association analysis. And there existed a total of the same 20 SNPs with MAF >0.05 among these populations. The association analysis between the 20 SNPs and five “traits” including the expression levels of SNAC1 under N, D1, and D2 conditions, and D1/N (the ratio of the expression level of D1 to that of N) and D2/N (the ratio of the expression level of D2 to that of N) were performed using the Q model, K model, and the QþK model (Table S6). Based on mixed linear model (MLM) analysis (QþK model), two SNP sites (S27 and S40) with low LD level (r2 ¼ 0.166) were found to be significantly associated (P < 0.05) with the expression level of SNAC1 under D1 condition. S27 at 637-bp upstream of the TSS (transcription start site) of SNAC1 is located in a cis-element MYBCORE according to the plant cis-acting regulatory DNA elements database, which is a binding site of all animal MYBs (myeloblastosis viral oncogene homologs) and at least two plant MYBs (ATMYB1 and ATMYB2) isolated from Arabidopsis. ATMYB2 is involved in the regulation of genes that are responsive to water stress in Arabidopsis (Urao et al., 1993). S40 was also located in the promoter region, at 98-bp upstream of the TSS of SNAC1. With the QþK model, seven SNPs significantly associated with the expression level of SNAC1 under D2 condition were identified. These sites could be classified into two groups based on the extent of the LD: group I with r2 > 0.80 between any two SNPs (S11, S12, S21, and S49) and group II with r2 > 0.55 between any two SNPs (S15, S50, and S51). The LD between the two groups was much lower (r2 between any two SNPs from different groups <0.09) than that within the groups (Table S7). For the first group, S11, S12 and S21 were located in the promoter region, and S49 was located in exon 1. For the second group, only S15 was located at 1049-bp upstream of
611
the TSS. S50 caused a non-synonymous change with the transition from A to G in exon 1. S51, in complete LD with S50, was located in an intron. Besides, S21 was also associated with the drought-induction fold of expression level in D2 (the ratio of the ‘expression level’ in D2 to the ‘expression level’ in N). Because of the LD, the exact causal sites or SNPs could not be determined, and potential association with the expression of the gene was further evaluated by haplotype association analysis. SNAC1 haplotypes were used to determine the joint effect of significant SNPs on the expression level of SNAC1 under different conditions. Only the haplotype groups containing 5 accessions remained for subsequent haplotype analysis. Due to the LD, only four haplotypes defined by seven significant SNPs in D1 and D2 were detected (Table 1). Finally, we identified the most favorable haplotype (C1) with favorable alleles of the seven significant SNPs in D1 and D2. The average expression level of haplotype C1 was 2.75 times as much as C4 in D1, and 5.13 times as much as C3 in D2 (Table 1 and Fig. 1D). The combination of the integrated seven sites explained 25.25% and 19.06% of the expression variation in D1 and D2, respectively. All of the japonica accessions were assigned into the haplotype C4, while the indica accessions existed in different haplotypes (Fig. 1E). In addition, the haplotype of the japonica accessions had the lowest or relatively lower expression levels compared to the other haplotypes for D1 and D2. These results indicate that indica rice harbors more expression level diversity than japonica rice. The degree of DR for the rice accessions was evaluated by visual estimation with a 1e9 scale (1 ¼ most droughtresistant, 9 ¼ most drought-sensitive, using half scores when needed) based mainly on leaf-drying scores (Yue et al., 2006). The result showed that almost all of the accessions from haplotype C1 were prone to drought resistance (the average score was 2.9 with the range of scores between 1.5 to 4.5), with the exception of one accession which was easily broken by wind (Table S8). The days spanning from the start of
Table 1 Four haplotypes defined by seven significant SNPs Haplotype
Polymorphic sitea S11
S15
Expression leveld S21
S27
S40
S49
S50
D1
D2
Trans (x) C1
A
T
G
A
T
T
A
a
1.74
a
x
Inverse (10 ) 54.75
Trans (x)
Inverse (10x)
a
96.64
a
1.99
C2
A
T
G
A
C
T
A
1.59
38.81
1.79
61.99
C3
T
C
A
A
C
G
G
1.68a
47.64
1.28b
18.85
C4
T
T
A
G
C
G
A
1.30b
19.90
1.54b
34.55
2b
R
25.25%
19.06%
Pc
2.22E-09
5.10E-05
a
Favorable genotypes are in boldface type, and only observed haplotypes are listed; b Values from analysis of variance (ANOVA) showing percentage of phenotypic variation explained; c P value from ANOVA analysis; d Average expression levels of each haplotype group and multiple comparisons of the four haplotype groups under D1 and D2 conditions. ‘Trans’ indicated the log10-transformed values of expression levels to satisfy homogeneity of variance, an important precondition of ANOVA (x); ‘Inverse’ indicated the values after inverse transformation based on ‘Trans’ (10x). The expression level of the groups with letter ‘a’ was significantly higher than that of the groups with letter ‘b’ under D1 and D2 conditions, respectively (P < 0.05).
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Letter to the Editor / Journal of Genetics and Genomics 41 (2014) 609e612
drought treatment to the time when the leaves just began to roll (stress days in D1), and to the time when the rolled leaves could not re-expand (stress days in D2) were also recorded. The number of stress days in D1 and D2 for most of the accessions belonging to haplotype C1 was larger than the overall mean of the accessions in our panel (Table S8). Based on these data, it can be expected that most, if not all, of the C1 accessions that had a significantly higher expression level of SNAC1 under moderate and severe drought stress conditions should have increased DR capabilities. In summary, the haplotype C1 could be especially useful in DR breeding since SNAC1 in this haplotype was strongly induced regardless of the degree of drought stress. This work suggests that exploring the expression diversity of important DR-related endogenous genes may provide new opportunities for DR breeding through non-transgenic approaches.
ACKNOWLEDGEMENTS This work was supported by grants from the National Program for Basic Research of China (No. 2012CB114305), the National Program on High Technology Development (No. 2012AA10A303), and the Oversea Graduate Program from Ministry of Education to K. Songyikhangsuthor.
SUPPLEMENTARY DATA Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jgg.2014.09.001.
Khamdok Songyikhangsuthor1,2, Zilong Guo1, Nili Wang, Xiaoyi Zhu, Weibo Xie, Tongmin Mou, Lizhong Xiong* National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China *Corresponding author. Tel: þ86 27 8728 1536, fax: þ86 27 8728 7092. E-mail address: [email protected] (L. Xiong) 1 These authors equally contributed to this work. 2 Current address: Northern Agriculture and Forestry Research Center, Luang Prabang P.O. Box 600, Laos. Received 17 May Revised 5 September Accepted 10 September Available online 28 September
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