.PMFDVMBS1MBOU t 7PMVNF t /VNCFS t 1BHFTo t +VMZ
RESEARCH ARTICLE
Identification of Quantitative Trait Loci for Lipid Metabolism in Rice Seeds Jie-Zheng Yinga,b, Jun-Xiang Shana, Ji-Ping Gaoa, Mei-Zhen Zhua, Min Shia and Hong-Xuan Lina,1 a National Key Laboratory of Plant Molecular Genetics and National Center for Plant Gene Research (Shanghai), Shanghai Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 300 Fenglin Road, Shanghai 200032, China b National Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
ABSTRACT Plant seed oil is important for human dietary consumption and industrial application. The oil trait is controlled by quantitative trait loci (QTLs), but no QTLs for fatty acid composition are known in rice, the monocot model plant. QTL analysis was performed using F2 and F2:3 progeny from a cross of an indica variety and a japonica variety. Gas chromatography–mass spectrometry (GC–MS) analysis revealed significant differences between parental lines in fatty acid composition of brown rice oil, and 29 associated QTLs in F2 and/or F2:3 populations were identified throughout the rice genome, except chromosomes 9 and 10. Eight QTLs were repeatedly identified in both populations across different environments. Five loci pleiotropically controlled different traits, contributing to complex interactions of oil with fatty acids and between fatty acids. Nine rice orthologs of Arabidopsis genes encoding key enzymes in lipid metabolism co-localized with 11 mapped QTLs. A strong QTL for oleic (18:1) and linoleic (18:2) acid were associated with a rice ortholog of a gene encoding acyl–CoA:diacylglycerol acyltransferase (DGAT), and another for palmitic acid (16:0) mapped similarly to the acyl– ACP thioesterase (FatB) gene ortholog. Our approach rapidly and efficiently identified candidate genes for mapped QTLs controlling fatty acid composition and oil concentration, providing information for improving rice grain quality by marker assisted selection. Key words:
Rice; fatty acid; seed oil; lipid metabolism; QTL.
INTRODUCTION Plant seeds store lipids in spherical organelles termed oil bodies for germination and post-germinative seedling growth. In almost all plant species, the storage lipids are mainly triacylglycerol (TAG), and the oil bodies contain a matrix of TAGs surrounded by a monolayer phospholipid membrane embedded with oleosins. During seed development, sugar provided by the maternal plant is converted to acetyl–CoA, the precursor of de novo fatty acid biosynthesis pathway. In the plastid, fatty acids are synthesized from acetyl–CoA and transported to the endoplasmic reticulum (ER) in the form of acyl–CoA. Consequently, fatty acids are transferred from CoA to the glycerol backbone to form TAGs. TAGs are accumulated in the ER and subsequently stored in discrete oil bodies. Unlike membrane glycerolipids with a polar headgroup attached to the sn-3 position of glycerol backbone, TAG is esterified by fatty acids at three positions. Therefore, TAG is not suitable for membrane bilayers but instead serves as an energy storage form. Accumulation of storage lipids is an important part of seed maturation. Expression of genes related to fatty acid synthesis occurs under the direct regulation of WRINKLED1 (WRI1), a transcription
factor of the large APETALA2/ethylene-responsive element binding protein (AP2/EREBP) family, which is controlled by LEAFY COTYLEDON2 (LEC2), a master regulator of seed maturation process (Focks and Benning, 1998; Ruuska et al., 2002; Cernac and Benning, 2004). Many proteins involved in plant lipid synthesis have been purified since homogeneous acyl carrier protein (ACP) was isolated from avocado mesocarp and spinach leaf (Simoni et al., 1967). Several groups of enzymes in the lipid biosynthesis pathway interact to determine the TAG composition, and genes encoding these enzymes have been studied in detail by mutation analysis. Due to the completion of the genome sequences of many organisms, a large number of genes involved in plant lipid metabolism have been
1 To whom correspondence should be addressed. E-mail
[email protected], tel. 86-21-54924129, fax 86-21-54924015.
ª The Author 2011. Published by the Molecular Plant Shanghai Editorial Office in association with Oxford University Press on behalf of CSPB and IPPE, SIBS, CAS. doi: 10.1093/mp/ssr100 Received 23 August 2011; accepted 8 November 2011
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identified, and two plant lipid gene databases were constructed (Beisson et al., 2003; Joyard et al., 2010). However, the variations in lipid composition and content are still poorly understood (Hobbs et al., 2004; Keurentjes et al., 2006). Seed lipid content and fatty acid composition are typical quantitative traits and vary widely among different species and within species (Mandal et al., 2002; O’Neill et al., 2003). These variations are usually controlled by polygenes and can be explained by QTL. A number of QTL analyses for fatty acid composition and oil concentration have been carried out in the major oilseed crops, including oilseed rape (Ecke et al., 1995; Delourme et al., 2006; Li et al., 2006a; Barker et al., 2007; Zhao et al., 2008), soybean (Csanadi et al., 2001; Spencer et al., 2003; Hyten et al., 2004; Panthee et al., 2006), peanut (Sarvamangala et al., 2011), and sunflower (Mokrani et al., 2002; Ebrahimi et al., 2008; Haddadi et al., 2010). These studies have mined alleles involved in seed lipid content and fatty acid composition for the genetic improvement of commercial varieties by means of marker assisted selection (MAS). A number of studies have validated that QTL analysis can be used not only to map metabolic variation, but also to uncover metabolic pathways through mapping a specific group of correlated metabolites, which would greatly strengthen our understanding of metabolic pathways and biology functions of metabolites (Keurentjes et al., 2006; Schauer et al., 2008). For example, genes encoding the key enzymes in fatty acid desaturation and elongation pathway (e.g. FAB2 responsible for the conversion of stearic acid (18:0) to 18:1 (Lightner et al., 1994; Kachroo et al., 2007), FAD2 for 18:1 to 18:2 (Okuley et al., 1994), FAD3 for 18:2 to linolenic acid (18:3) (Arondel et al., 1992), and FAE1 for 18:1 to eicosenoic acid (20:1), 20:1 to erucic acid (22:1) and 18:0 to arachidic acid (20:0) (Kunst et al., 1992)) were often found to be co-localized with the QTLs involved in oil content and fatty acid composition in Arabidopsis (O’Neill et al., 2003; Hobbs et al., 2004), maize (Mikkilineni and Rocheford, 2003; Yang et al., 2010), sunflower (Perez-Vich et al., 2002), and oilseed rape (Jourdren et al., 1996; Barker et al., 2007; Smooker et al., 2011). In rice, seed lipids are stored as TAG in the oil bodies and are mainly present in embryos and aleurone layers (Tanaka et al., 1977; Ogawa et al., 2002). Rice bran, a by-product of milling, is rich in oil and a potential source of edible oil. Oil content varies
greatly, ranging from 17.3–27.4% in rice bran and 2.75–4.49% in brown rice according to dry weight (Goffman et al., 2003; Khatoon and Gopalakrishna, 2004). There are five major fatty acids in brown rice (16:0, 18:0, 18:1, 18:2, and 18:3) and some minor fatty acids, such as myristic (14:0) and 20:0. Substantial variations have been found in fatty acid composition among rice cultivars (Taira and Itani, 1988; Goffman et al., 2003; Kitta et al., 2005). Although quantitative genetic approaches have been applied in the identification of QTLs controlling oil content in brown rice (Wang et al., 2008; Liu et al., 2009; Qin et al., 2010), few or no QTLs analysis has been carried out for fatty acid composition in the monocot model plant to our knowledge. Our objectives in this study were to map QTLs for oil content and fatty acid composition and search for candidate genes for the QTLs, in order to further understand the genetic basis of oil traits in rice.
RESULTS Phenotypic Variation Seven fatty acids were identified using GC–MS and their sum was calculated as the total oil concentration. The concentration means and standard deviations of each fatty acid and oil are shown in Table 1. Although no statistically significant difference was detected in oil content between the parental lines planted in Shanghai in 2008, t-tests showed that all the measured fatty acids except 18:0 showed substantial variations, with 14:0, 16:0, 18:1, and 18:2 contents at the 0.001 level and with 18:3 and 20:0 contents at the 0.05 level. Relative to the parental lines, F2 and F2:3 populations had intermediate levels of all fatty acids, except 18:0 in the F2 population. The distributions of seven fatty acids and oil content are shown in Figure 1. All traits in the F2 and F2:3 populations showed continuous segregation and no significant transgressive segregation, indicating that they are controlled by polygenic gene loci and can be used to map QTLs.
Correlations among Oil and Fatty Acids Correlation coefficients among oil content and fatty acid content are presented in Table 2. The oil concentration positively correlated with 18:1 content and negatively correlated with 16:0, 18:2, and 18:3 contents in the F2 and F2:3 populations.
Table 1. Means and Ranges for Fatty Acid Composition and Oil Concentration of Plants of F2 Population, Lines of F2:3 Population, and Parental Lines. Year
Traita
14:0
16:0
18:0
18:1
18:2
18:3
20:0
Oil
2008
FAZ1
1.78 6 0.06
32.53 6 1.19
2.94 6 0.30
32.92 6 0.55
28.36 6 0.63
1.00 6 0.07
0.49 6 0.05
3.29 6 0.08
2009
JZ1560
2.46 6 0.05
29.28 6 0.67
3.02 6 0.05
29.49 6 0.13
34.09 6 0.66
1.13 6 0.08
0.54 6 0.05
3.25 6 0.17
F2
1.91 6 0.38
32.31 6 1.22
3.17 6 0.51
31.04 6 2.13
30.12 6 2.01
0.96 6 0.10
0.50 6 0.08
3.46 6 0.31
FAZ1
0.92 6 0.08
30.43 6 1.08
2.93 6 0.27
36.26 6 0.64
27.95 6 0.46
0.96 6 0.08
0.54 6 0.07
3.43 6 0.19
JZ1560
1.37 6 0.06
27.83 6 0.86
2.31 6 0.16
32.12 6 0.32
34.84 6 0.73
1.08 6 0.03
0.45 6 0.06
3.76 6 0.26
F2:3
1.21 6 0.23
29.81 6 1.17
2.65 6 0.26
33.73 6 2.18
31.06 6 2.15
1.05 6 0.10
0.51 6 0.06
3.61 6 0.24
a 14:0, myristic acid; 16:0, palmitic acid; 18:0, stearic acid; 18:1, oleic acid; 18:2, linoleic acid; 18:3, linolenic acid; 20:0, arachidic acid.
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867
50
F2
No. of plants//lines
No. of plants//lines
50 40 30 20 10
F2:3
40 30 20 10 0
0 0.90
1.40
1.90
2.40
2.90
27.8
3.40
29.4
31.0
32.6
34.2
35.8
16:0
14:0 40
60
No. of plantts/lines
No. of plantts/lines
50 40 30 20
30
20
10
10
0
0 2.15
2.65
3.15
3.65
4.15
4.65
27.6
5.15
30.0
No. of plaants/lines
No. of plaants/lines
34.8
37.2
39.6
1.12
1.24
1.36
40
30
20
10
0
30
20
10
0
26.0
28.0
30.0
32.0
34.0
36.0
0.76
0.88
1.00
18:3
18:2 40
50 40
No. of p plants/lines
No. of p plants/lines
32.4
18:1
18:0
30 20 10
30
20
10
0
0 0.35
0.45
0.55
0.65
0.75
20:0
2.79
3.07
3.35
3.63
3.91
4.19
oil
Figure 1. Frequency Distribution of the Proportions of the Fatty Acids and Oil Concentrations in Seeds of FAZ1/JZ1560 F2 and F2:3 Populations.
The oil concentration only showed a significant positive correlation with 18:0 content in the F2:3 population and a significant negative correlation with 14:0 content in the F2 population. For the relationships among fatty acid contents, significant positive correlations between 14:0 and 18:2, 18:0 and 20:0, and significant negative correlations between 14:0 and 18:1,
16:0 and 18:0, 16:0 and 18:2, 16:0 and 20:0, 18:1 and 18:2, and 18:1 and 18:3 were observed in both populations. For C18 fatty acids, 18:1 was highly negatively correlated with 18:2. A negative correlation between 16:0 and 18:1 was only detected in the F2 population. A negative correlation between 14:0 and 18:0, and positive correlations between
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Table 2. Correlation Coefficients for Fatty Acid Composition and Oil Content in the F2 Population (Lower) and F2:3 Population (Upper). Traits
14:0
14:0
16:0 0.03
18:0
18:1
18:2
18:3
20:0
Oil
0.24**
0.44**
0.34**
0.14
0.08
0.08
0.25**
0.20
0.30**
0.01
0.36**
0.26**
0.17
0.15
0.09
0.59**
0.25**
0.85**
0.30**
0.10
0.36**
0.26**
0.22**
0.23**
0.07
0.24**
16:0
0.07
18:0
0.06
0.35**
18:1
0.45**
0.24**
0.11
18:2
0.31**
0.23**
0.20
0.83**
18:3
0.12
0.05
0.12
0.24**
0.18
20:0
0.19
0.43**
0.76**
0.18
0.17
0.14
Oil
0.26**
0.29**
0.20
0.56**
0.41**
0.30**
0.22 0.04
** Significant difference at P = 0.01 level.
18:2 and 18:3, 18:2 and 20:0 were only observed in the F2:3 population.
Construction of the Linkage Map A linkage map consisting of 143 DNA markers was constructed by genotyping the F2 population (Figure 2). The linkage map covered 12 chromosomes and spanned 1451.70 centimorgans (cM). Genetic distances between adjacent markers ranged from 0 to 28.70 cM, with an average interval of 11.08 cM.
QTL Mapping Myristic Acid (14:0) For 14:0, five QTLs were detected on chromosomes 2, 6, 11, and 12 (Table 3 and Figure 2). Their effects explained 44.61 and 43.83% of the phenotypic variation in the F2 and F2:3 populations, respectively. Three QTLs (myr2-1, myr6, and myr12) were repeatedly identified in both populations, and the remaining two QTLs were only found in the F2 population. The QTL, myr2-1, with the largest effect (14.26% of the variance in the F2 population and 24.96% in the F2:3 population) was localized on chromosome 2. The myr2-1 allele from JZ1560 had a positive additive effect for the increase in 14:0 concentration.
Palmitic Acid (16:0) The saturated fatty acid with the highest content in rice seed was palmitic acid (Table 1). For 16:0, five QTLs were mapped on chromosomes 1, 3, 4, and 6 (Table 3 and Figure 2). Together, these QTLs accounted for 45.86 and 39.95% of the total variance in the F2 and F2:3 populations, respectively. A major QTL, pal6, with the largest genetic effect was repeatedly identified at the top of chromosome 6, accounting for 29.60 and 22.30% of total phenotypic variance in the F2 and F2:3 populations, respectively, and an additive effect for increasing 16:0 content was contributed by the FAZ1 allele. Other QTLs were only detected in one population.
Stearic Acid (18:0) Stearic acid, a typical C18 saturated fatty acid, usually accounts for about 2% of total fatty acid content in brown rice. Here, four QTLs for 18:0 were detected on chromosomes 1, 4, and 8 (Table 3
and Figure 2). The largest QTL, ste4, was identified and explained 14.67% of the phenotypic variance in the F2 populations. One QTL ste8-2 was detected repeatedly and the JZ1560 allele for ste8-2 had a positive effect on 18:0 content. The remaining QTLs, ste1 and ste8-1, were only detected in the F2 population.
Oleic Acid (18:1) Oleic acid is a predominant monounsaturated fatty acid of rice seed oil. Three QTLs affecting 18:1 concentration were mapped on chromosomes 1, 6, and 8, two of which (ole1 and ole8) were detected in the F2:3 and F2 populations, respectively (Table 3 and Figure 2). One major QTL with a significantly large effect, ole6, was identified repeatedly in the F2 and F2:3 populations, accounting for 37.93 and 36.73% of the phenotypic variance, respectively. The ole6 allele from FAZ1 contributed to the increased level of 18:1. All the QTLs for 18:1 together explained 46.07 and 42.01% of the total phenotypic variance in the F2 and F2:3 populations, respectively.
Linoleic Acid (18:2) Two QTLs with positive effects associated with 18:2 were detected and mapped to different marker intervals of chromosome 6 (Table 3 and Figure 2). The major QTL, lin6-2, identified in the F2 population with additive effect of 1.68, was further detected in the coincident positions in the F2:3 population with the effect of 2.13, respectively. The JZ1560 alleles for both QTLs increased the 18:2 concentration across the two populations. Percentages of phenotypic variance explained by these QTLs were 39.65 and 43.47% in the F2 and F2:3 populations, respectively.
Linolenic Acid (18:3) Linolenic acid is a polyunsaturated C18 fatty acid with a concentration of about 1% of the total fatty acid content in brown rice. Two QTLs related to the 18:3 concentration were identified and located on chromosomes 2 and 4 (Table 3 and Figure 2). Linn2 was only identified in the F2:3 population and linn4 was only detected in the F2 population. The two QTLs (linn2 and linn4) contributed 15.53 and 11.83% of the variance, respectively.
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Figure 2. Genetic Linkage Map Showing the Most Likely Positions of QTLs for the Proportions of Seven Fatty Acids and Oil Concentrations in the FAZ1/JZ1560 F2 and F2:3 Populations.
Arachidic Acid (20:0) Arachidic acid is a C20 very-long-chain saturated fatty acid with a trace amount in brown rice. Three QTLs controlling 20:0 content were detected on chromosomes 5 and 8 in the F2 population, which collectively explained 33.87% of the total phenotypic variation (Table 3 and Figure 2).
and 12.49% of the phenotypic variance, respectively. Three QTLs (oil3, oil7, and oil8) were detected in the F2 population and explained 11.86, 16.32, and 7.67% of the variance, respectively.
DISCUSSION Oil
Comparison of QTLs between F2 and F2:3
Five QTLs for oil concentration were mapped to chromosomes 1, 3, 7, 8, and 11 (Table 3 and Figure 2). No common QTL was identified in both populations. Two QTLs (oil1 and oil11) were detected in the F2:3 population and explained 13.88
In this study, the F2 and F2:3 progeny from a cross of an indica variety (FAZ1) and a japonica cultivar (JZ1560) were planted in different geographic locations in China (Shanghai and Sanya, respectively). A total of 29 QTLs conditioning fatty acid
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Table 3. QTLs for Fatty Acid Composition and Oil Concentration in F2 and F2:3 Populations. Traits
QTLa
Population
14:0
myr2-1
F2
2
RM12987–RM6374
7.01
6.76
0.24
14.26
F2:3
2
RM12987–RM6374
5.51
9.70
0.19
24.96
myr2-2
F2
2
RM1367–RM6
7.51
3.34
0.08
8.23
myr6
F2
6
RM3–RM20236
0.51
3.46
0.15
7.70
F2:3
16:0
18:0
18:2
20:0
oil
Positionb (cM)
LOD
Ac
R2 (%)d
RM3–RM20236
3.01
5.18
0.11
12.11
F2
11
RM27027–RM5997
0.01
4.01
0.09
8.03
myr12
F2
12
RM1261–RM1246
0.01
3.27
0.09
6.38
F2:3
12
RM1261–RM1246
0.01
3.46
0.03
6.76 10.24
pal1-1
F2
1
RM8063–RM3412
2.51
4.73
0.33
pal1-2
F2:3
1
RM3412–RM7075
2.01
3.47
0.25
7.16
pal3
F2:3
3
RM2334–RM3525
14.01
3.78
0.52
10.49
pal4
F2
4
RM3839–RM1388
3.01
3.03
0.36
6.02
pal6
F2
6
RM3805–RM8200
1.01
12.97
0.96
29.60
F2:3
6
RM3805–RM8220
0.01
9.72
0.84
22.30
ste1
F2
1
RM1282–RM1247
6.01
4.73
0.32
11.92
ste4
F2
4
RM1388–RM2636
3.01
5.89
0.24
14.67
ste8-1
F2
8
RM7080–RM3481
0.01
3.90
0.17
8.93 10.60
F2
8
RM8264–RM6976
14.01
3.11
0.24
F2:3
8
RM8264–RM6976
11.01
3.07
0.11
8.97
ole1
F2:3
1
RM1003–RM3403
5.01
3.11
0.73
5.28
ole6
F2
6
RM20236–RM7434
8.51
13.64
1.75
37.93
F2:3
6
RM20236–RM7434
8.71
13.14
2.01
36.73
ole8
F2
8
RM1148–RM22477
0.71
3.97
0.70
8.14
lin6-1
F2
6
RM2126–RM3330
13.21
3.17
0.71
8.52
F2:3
6
RM2126–RM3330
9.01
3.14
1.29
8.44
F2
6
RM20236–RM7434
5.01
12.57
1.68
31.13 35.03
lin6-2 18:3
6
Marker interval
myr11
ste8-2 18:1
Chr
F2:3
6
RM20236–RM7434
6.51
14.57
2.13
linn2
F2:3
2
RM2634–RM1367
6.51
4.60
0.06
15.53
linn4
F2
4
RM16741–RM471
9.51
4.01
0.05
11.83
ara5
F2
5
RM3870–RM480
22.01
5.21
0.04
14.29
ara8-1
F2
8
RM22477–RM5556
1.01
3.47
0.02
8.34
ara8-2
F2
8
RM8264–RM6976
15.01
3.19
0.04
11.24
oil1
F2:3
1
RM3403–RM8232
0.01
4.95
1.26
13.88
oil3
F2
3
RM3131–RM3716
3.01
4.43
0.69
11.86
oil7
F2
7
RM3826–RM2752
8.51
6.09
2.09
16.32
oil8
F2
oil11
F2:3
8
RM1148–RM22477
0.01
3.06
1.17
7.67
11
RM5731–RM26622
0.01
4.83
1.05
12.49
a QTLs are named by abbreviations plus chromosome; numbers following dash represent different QTLs for this trait located on the same chromosome. b Genetic distances (in centimorgans) of the putative QTL from the left marker on the marker interval. c Additive effect on the JZ1560 allele. d Percentage of total phenotypic variance explained by the mapped QTL.
composition and oil concentration were identified (Table 3 and Figure 2), eight of which were repeatedly identified in both F2 and F2:3 populations, indicating stability of these QTLs. The loci were distributed throughout all chromosomes except chromosomes 9 and 10. The genetic effect of a single QTL explained 5.28–37.93% of the total phenotypic variance corresponding to a target trait. Fewer QTLs were detected in the F2:3
population, while 8 of 14 (57.14%) loci were identified repeatedly in the F2 population (three loci for 14:0, one for 16:0, one for 18:0, one for 18:1, two for 18:2). Four major QTLs for each trait explaining from 16.32–37.93% of the total phenotypic variance were identified in the F2 population. Five major QTLs for each trait accounting for 15.53–36.73% of the total variance were mapped in the F2:3 population. The majority of
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the major QTLs could be identified repeatedly in both populations.
Association between Trait Correlations and Mapped QTLs QTLs controlling different traits are often located in similar positions, which may indicate a pleiotropic genetic effect of a single QTL. In this study, five pairs of QTLs controlling different traits were mapped closely at a distance of less than 5 cM (Table 3 and Figure 2). For oil and 18:1, a pair of QTLs (ole1 and oil1) were mapped to positions near each other on chromosome 1, and their effects showed the same direction, which was consistent with the positive correlation between the two traits in the F2:3 population. Likewise, a QTL for 18:1, ole8, and a QTL for oil concentration, oil8, were found at the same region on chromosome 8 with positive genetic effects. This may explain the positive correlation between 18:1 and oil concentration in the F2 population. Positive correlations between 18:1 and oil concentration have been observed in Arabidopsis (Hobbs et al., 2004), rapeseed (Zhao et al., 2008), and maize (Yang et al., 2010). One major QTL for 18:1, ole6, with a negative additive effect and another major QTL for 18:2, lin6-2, with a positive effect were detected at the same region on chromosome 6. This might result in the negative correlation of 18:1 with 18:2 in both populations. 16:0, 18:1, and 18:2 were predominant, accounting for more than 90% of the total fatty acids in rice seed. In the fatty acid biosynthesis pathway, 18:1 is the substrate for 18:2 synthesis, and a highly negative correlation between 18:1 and 18:2 has been observed not only in rice (Kitta et al., 2005), but also in maize (Yang et al., 2010). For the two saturated fatty acids, 18:0 and 20:0, two pairs of QTLs (ste8-1/ara8-1 and ste8-2/ara 8–2) were identified in different regions on chromosome 8. First, both ste8-1 and ara8-1, which were mapped at close positions, showed negative effects. This may contribute to the positive correlation between 18:0 and 20:0 in the F2 population. Another pair of QTLs (ste8-2 and ara8-2) were located at similar positions and their effects showed the same trend, which may again explain the positive correlation between 18:0 and 20:0 in the F2 population. The above results indicated that genetic effects of each of the five pairs of QTLs were consistent with correlations between the traits. Each pair of QTLs may in fact be a single QTL with a pleiotropic effect, contributing to the complex interactions of oil with fatty acids and between fatty acids. Some differences in correlation and frequency distributions for fatty acid composition and oil content were found between F2 Population and F2:3 Population, which might be attributed to two reasons. First, genetic background is different between two populations, since they belong to different generations. Second, the two populations were separately planted in different seasons and locations. The light and temperature conditions are different during the two populations’ growing process. Oil accumulation in seed is influenced by light and temperature conditions (Dybing and Zimmerman, 1966; Li et al., 2006b).
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Candidate Genes for Mapped QTLs Due to previous research advances in Arabidopsis, many key genes involved in lipid metabolism have been cloned and detailed function analysis made by mutation analysis. As a result, two comprehensive Arabidopsis lipid gene databases with improved annotation were constructed (Beisson et al., 2003; Joyard et al., 2010). By comparing with known orthologs of lipid metabolic enzymes, QTLs conditioning fatty acid compositions and oil content can be assigned with candidate genes coding putative enzymes. A group of fatty acids including 14:0, 16:0, 18:0, 18:1, 18:2, 18:3, and 20:0, which are metabolites in the fatty acid synthesis pathway, were used for QTL mapping in this study. This approach enables the study of metabolic pathways and identification of key enzymes involved in lipid metabolism. In the present study, nine rice orthologs of Arabidopsis genes encoding key enzymes involved in lipid metabolism were localized to similar positions in the same marker intervals of 11 mapped QTLs (Table 4). The functions of candidate genes were consistent with the variations controlled by the target QTLs. One rice gene ortholog of ketoacyl–ACP synthase I (KAS I) was localized at a similar position to that of the QTL pal4 conditioning 16:0 content. KAS I is responsible for the elongation of the carbon chain from 4:0-acyl carrier protein (ACP) up to 16:0–ACP (Ohlrogge and Browse, 1995; Wu and Xue, 2010), which is consistent with the additive effect of pal4 on 16:0 content (Table 3). Ketoacyl–CoA synthases are required for formation of very-long-chain fatty acids and are encoded by several genes. FAE1 is a ketoacyl–CoA synthase responsible for the chain elongation of fatty acid from C18 to C20 and C22 (James et al., 1995). One rice ortholog of FAE1 was found located at a similar position to that of QTL ara5 conditioning 20:0 content. Another ortholog of FAE1 fell within the region of QTL oil3 and could be considered as a candidate gene conditioning oil concentration. A gene ortholog of acyl–ACP thioesterase (FatB) was found at a similar position to that of QTL pal6 at the top of chromosome 6. FatB hydrolyzes acyl–ACPs into free fatty acids and ACP and determines the chain-length of 16:0 with highly specific activity (Salas and Ohlrogge, 2002). This could explain the positive additive effect of the pal6 allele from FAZ1 on 16:0 concentration (Table 3). Acyl–CoA thioesterase can hydrolyze Acyl–CoA into free fatty acids and CoA to regulate lipid metabolism. A gene ortholog of Acyl–CoA thioesterase was found in the region of QTL ste4 and could be considered as a candidate gene for the QTL to control 18:0 content. Acyltransferases catalyze the transfer of acyl-chains from CoA ester to the glycerol backbone and are indispensable for the formation of storage lipids. In the present study, several orthologs encoding acyltransferases fell within the regions of QTLs controlling fatty acid compositions and oil concentration. One candidate encoding ER 2-lysophosphatidate acyltransferase (LPAAT) was found in the same region of ole1 and oil1. LPAAT involved in TAG biosynthesis catalyzes the transfer of
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acyl-chain from CoA ester to sn-2 of the glycerol backbone with the pronounced acyl–CoA specificity and selectivity for unsaturated 18-carbon acyl groups (Frentzen, 1998). This well explained the obvious association of LPAAT with the monounsaturated fatty acid 18:1 and oil content. Diacylglycerol acyltransferase (DGAT) catalyzes the acylation of the sn-3 position of sn-1, 2-diacylglycerol (DAG) to form TAG. In the Arabidopsis Lipid Gene Database, acyl–CoA:DGAT are encoded by At3g51520 and At2g19450 (Beisson et al., 2003). At2g19450 was cloned through an EMS-induced mutation (AS11) and designated as TAG1. Reducing TAG1 activity would decrease the 18:1 level and increase the proportion of 18:3 (Katavic et al., 1995; Routaboul et al., 1999; Zou et al., 1999). One rice ortholog of At2g19450 was found in the same region as QTL ole6. A similar tendency was observed between the parental lines in that the proportion of 18:1 in FAZ1 was markedly higher than in JZ1560. In contrast, the content of 18:3 in FAZ1 was obviously lower than in JZ1560, which implied that the activity of DGAT in JZ1560 may be reduced. Further analysis showed that QTL ole6 had a negative additive effect on 18:1 content, indicating that the ole6 allele from JZ1560 decreased the 18:1 concentration (Table 3). All these results indicated that DGAT is a possible candidate gene for ole6. In the present study, lin6-2 was identified in the same region as ole6 with a positive additive effect for 18:2 content. The candidate gene for lin6-2 may also be DGAT. In maize, one gene encoding an acyl– CoA:DGAT was cloned and designated as DGAT1-2. Nearisogenic lines containing DGAT1-2 showed a 61.3% increase in 18:1 content and a 24.1% decrease in 18:2 content (Zheng et al., 2008). Reduction of DGAT activity results in the accumulation of DAG, which can allow an enrichment in conversion from 18:1 to 18:2, as speculated in a previous study (Zou et al., 1999). Considering the ole6 and lin6-2 alleles from JZ1560 decreased the 18:1 level and increased 18:2 content (Table 3), a reasonable candidate may be oleate desaturase gene (fad2), since fad2 is responsible for the conversion of 18:1 to 18:2. However, fad2 is located on chromosome 2 and not
a likely candidate for ole6 and lin6-2. Collectively, the evidence showed that DGAT was the most suitable candidate gene for ole6 and lin6-2. Likewise, two other rice orthologs of At3g51520 were localized in the regions of myr2-2 and lin61, and regarded as candidates for controlling 14:0 and 18:2 content. In our study, nine rice orthologs of seven Arabidopsis genes coding key enzymes involved in lipid metabolism were found at similar positions of 11 mapped QTLs. Functions of these orthologs were highly consistent with genetic effects of the corresponding QTLs. Candidate genes were found not only for major QTLs but also for minor ones. All these results confirmed this approach as a rapid and highly efficient method for identifying candidate genes for mapped QTLs based on previous studies. Of course, all these conclusions will require further biochemical and genetic validation.
Major QTL and MAS Storage lipids are rich in the embryo and aleurone layer of brown rice and closely correlated with rice appearance and eating quality. A number of QTLs involved in fatty acid composition and oil concentration were detected in this study. Some of these QTLs were stable and identified repeatedly across generations and environments. These QTLs and linked markers will offer the possibility of creating a pyramid of favorable alleles for improving rice grain quality through MAS. Notably, some QTLs were shown to have pleiotropic effects contributing to the complex interactions of oil with fatty acids and between fatty acids, which should be considered in rice breeding.
METHODS Plant Materials An F2 population consisting of 145 plants was developed from a cross between an indica variety, Fengaizhan-1 (FAZ1), and
Table 4. Candidate Genes for QTL Conditioning Fatty Acid Composition and Oil Concentration. Enzyme (abbreviation)a
Accessionb
Rice ortholog
QTL
Ketoacyl–ACP Synthase I(KAS I)
At5g46290
LOC_Os04g36800
pal4
Ketoacyl–CoA Synthase (FAE1)
At4g34520
LOC_Os05g49290
ara5
LOC_Os03g12030
oil3
Acyl–ACP Thioesterase (FatB)
At1g08510
LOC_Os06g05130
pal6
Acyl–CoA Thioesterase
At1g01710
LOC_Os04g47120
ste4
ER 2-Lysophosphatidate Acyltransferase (LPAAT)
At1g75020
LOC_Os01g57360
ole1
Acyl–CoA:Diacylglycerol Acyltransferase (DGAT)
At3g51520
LOC_Os02g48350
myr2-2
LOC_Os06g22080
lin6-1
LOC_Os06g36800
ole6
oil1
At2g19450
lin6-2
a Annotation from the Arabidopsis Lipid Gene Database by Beisson et al. (2003). b Locus-based name of Arabidopsis for the enzyme.
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a japonica cultivar, Jizi-1560 (JZ1560). Due to seed availability of a few lines, 133 F3 lines were obtained with each line originating from an individual F2 plant, and 18 plants for each line were planted to generate the F2:3 population.
Field Trials Field trials were carried out in paddy fields in two locations. F2 population together with their parents were planted in Shanghai (31N, 121E), East China. F3 lines and the parents, with each line consisting of 18 plants, were planted in Sanya (18N, 109E), South China. Seeds were harvested at maturity from each F2 plant, F3 line and their parents, dried naturally and subsequently stored at 15C.
Measurements of Fatty Acid and Oil Content Identification and robust quantification of fatty acids in brown rice can be facilitated by GC–MS by assaying fatty acid methyl esters (FAMEs). Mature grains of bulk samples collected from each F2 plant, F2:3 line, and their parents were de-hulled into brown rice and ground into powder. Each sample (50 mg powder) was weighed to determine the fatty acid content. FAMEs were prepared as previously described (Browse et al., 1986) with a few modifications. Fifteen microliters of nonadecanoic acid methyl ester as an internal quantitative standard with 2 mg ml 1 in hexane was added in every sample. FAMEs were derived by adding 20 ll pure pyridine and 15 ll MSTFA reagent (N-methyl-N-trimethylsilyl-trifluoroacetamide) and shaking for 30 min at 37C. Subsequently, the sample was diluted with hexane in preparation for GC–MS analysis. The Agilent 5975 inert GC/MS system and HP-INNOWax column (Agilent) were used in the GC–MS analysis. The concentrations of individual fatty acids were expressed as percentages of oil concentration. The oil content was calculated as the sum of all identified fatty acid contents. Every sample from F2 plants and F2:3 lines was assayed twice at the same time and the mean values were used for further analysis. Four replicates of seed samples of the parents from each location were analyzed.
Construction of the Linkage Map The genetic map was constructed from a total of 143 markers, including simple sequence repeat (SSR) markers and insert/deletion (indel) markers. SSR markers were selected from a public database (www.gramene.org) and a previous report (McCouch et al., 2002). The indel markers developed by our laboratory (W004, W020, and W236) have been used in a previous study (Song et al., 2007) and two newly developed markers were L394–1 (forward primer 5’-CAGTGGCTGATTGAAGGT-3’, reverse primer 5’-TTCCGCATAGTCAAACCC-3’) and L3990 (forward primer 5’-TTCTCATTTCGTCACCCG-3’, reverse primer 5’CAACCACCATCGCCTTCC-3’). DNA extraction for each individual and molecular marker analysis was performed according to a previous report (Shan et al., 2009). The computer program MAP-MAKER/EXP 3.0 (Lander et al., 1987) was used for the establishment of linkage groups and calculation of genetic distances. Kosambi function was used to convert recombination
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frequencies to genetic distances in centimorgans (cM). The linkage groups and order of markers were determined with a LOD score of 3.0.
Statistical Analysis Correlation coefficients were calculated to determine correlations between traits in both populations using PROC CORR in the SAS program (1999, SAS Institute, SAS Software, Cary, NC).
QTL Analysis QTLs for fatty acid composition and oil concentration were mapped with the information from Mapmaker by composite interval mapping (CIM) using Windows QTL Cartographer 2.5 (Wang et al., 2006). CIM was used to scan the genome for QTLs affecting all traits. QTL analysis was performed with 1000 permutations at the 0.05 probability level. The threshold for identification of a QTL was fixed at a LOD score of 3.0 following 500 permutation tests for each trait. The percentages of total phenotypic variation by each QTL and additive effect were calculated.
Candidate Genes for Mapped QTLs In order to find candidate genes for mapped QTLs, 23 key enzymes encoded by a single gene or several genes were selected according to the Arabidopsis Lipid Gene Database (Beisson et al., 2003). Rice gene orthologs of these enzymes were searched through the Rice Genome Annotation Project (http://rice.plantbiology.msu.edu/) and NCBI database (www.ncbi.nlm.nih.gov/BLAST).
FUNDING This work was supported by grants from the Ministry of Agriculture of China (2009ZX08009–067B, 2009ZX08009–102B, 2009ZX08001– 022B), the National Natural Science Foundation of China (30730058), and the Shanghai Science and Technology Development (09DJ1400503).
ACKNOWLEDGMENTS We thank Mr Wenli Hu for performing GC–MS analysis. No conflict of interest declared.
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