1. No category

Molecular Plant
Research Article
Spatiotemporal Distribution of Phenolamides and
the Genetics of Natural Variation of
Hydroxycinnamoyl Spermidine in Rice
Xuekui Dong1, Yanqiang Gao1, Wei Chen1, Wensheng Wang1, Liang Gong1, Xianqing Liu2
and Jie Luo1,*
1
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070,
China
2
College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
*Correspondence: Jie Luo ([email protected])
http://dx.doi.org/10.1016/j.molp.2014.11.003
ABSTRACT
Phenolamides constitute a diverse class of secondary metabolites that are found ubiquitously in plants
and have been implicated to play an important role in a wide range of biological processes, such as plant
development and defense. However, spatiotemporal accumulation patterns of phenolamides in rice, one of
the most important crops, are not available, and no gene responsible for phenolamide biosynthesis has
been identified in this species. In this study, we report the comprehensive metabolic profiling and natural
variation analysis of phenolamides in a collection of rice germplasm using a liquid chromatography–mass
spectrometry-based targeted metabolomics method. Spatiotemporal controlled accumulations were
observed for most phenolamides, together with their differential accumulations between the two major
subspecies of rice. Further metabolic genome-wide association study (mGWAS) in rice leaf and in vivo
metabolic analysis of the transgenic plants identified Os12g27220 and Os12g27254 as two spermidine
hydroxycinnamoyl transferases that might underlie the natural variation of levels of spermidine conjugates
in rice. Our work demonstrates that gene-to-metabolite analysis by mGWAS provides a useful tool for
functional gene identification and omics-based crop genetic improvement.
Key words: Oryza sativa, phenolamides, metabolic profiling, natural variation, spermidine acyltransferase
Dong X., Gao Y., Chen W., Wang W., Gong L., Liu X., and Luo J. (2015). Spatiotemporal Distribution of
Phenolamides and the Genetics of Natural Variation of Hydroxycinnamoyl Spermidine in Rice. Mol. Plant. 8, 111–121.
INTRODUCTION
Phenolamides, frequently referred to as hydroxycinnamic acid
amides or phenylamides, constitute a diverse class of secondary metabolites that are found ubiquitously in plants but not
in animals (Martin-Tanguy et al., 1978; Martin-Tanguy, 1985;
Bassard et al., 2010). A large proportion of phenolamides
occur as mono-, di-, or triphenolic acid (coumaric, caffeic,
or ferulic acid)-substituted polyamines, such as putrescine,
spermidine, and spermine. These phenolic acids can also be
conjugated with arylmonoamines such as tyramine, octopamine, or anthranilate, and the phenolaimdes differ in the chain
length of amine and the type and degree of phenolic acid substitution (Grienenberger et al., 2009; Bassard et al., 2010). The
diversity, abundance, and distribution of phenolamides have
been investigated in a number of plant species (Bienz et al.,
2005; Handrick et al., 2010; Moheb et al., 2011). Basic and
neutral phenolamides such as diferuloyl putrescine and
diferuloyl spermidine have been detected in large amounts
upon seed development and maturation in both dicots (Luo
et al., 2009; Handrick et al., 2010) and monocots (MartinTanguy, 1985; Bonneau et al., 1994). More recently, disinapoyl
spermidine and its glucosyl derivative were found to be the
major phenolamides in Arabidopsis seeds (Luo et al., 2009). In
addition, abundant phenolamides were found to accumulate in
the roots of tobacco, rice, and some tropical plants (Zamble
et al., 2006).
Phenolamides, together with polyamines, have long been implicated to play important roles in a wide range of biological
processes including plant development and defense (Edreva
et al., 2007; Kang et al., 2009; Park et al., 2009a). A high
concentration of phenolamides is often associated with floral
induction and development, whereas male sterility was
Published by the Molecular Plant Shanghai Editorial Office in association with
Cell Press, an imprint of Elsevier Inc., on behalf of CSPB and IPPE, SIBS, CAS.
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
111
Molecular Plant
reported to be accompanied with reduced content in polyamines
and their conjugates, in particular the insoluble phenolamides
(Guo et al., 2003). Some phenolamides were shown to act as
phytoanticipins or phytoalexins in pathogen resistance
(Buanafina, 2009; Park et al., 2009b). Recent work by Kaur
et al. (2010) also revealed a positive role for phenolamides in
insect deterrence by demonstrating that transgenic plants
lacking caffeoyl putrescine and dicaffeoyl spermidine showed
decreased insect resistance in comparison with the wild-type
plants. Moreover, involvement of phenolamides in plant defense
against abiotic stresses, such as mineral deficiency, dehydration,
salt stress, and more recently, UV irradiation has also been proposed (Demkura et al., 2010; Onkokesung et al., 2012).
Although acyltransferases involved in the condensation of polyamines and hydroxycinnamic acids for phenolamide formation
were detected more than two decades ago, genes encoding
those enzymes have been cloned only recently (Grienenberger
et al., 2009; Muroi et al., 2009; Onkokesung et al., 2012), and all
of them encode members of the acyl-coenzyme A (CoA)-dependent BAHD acyltransferases, which was named according to the
first letter of each of the first four biochemically characterized
enzymes of this family BEAT (benzylalcohol O-acetyltransferase),
AHCT (anthocyanin O-hydroxycinnamoyltransferase), HCBT
(anthranilate N-hydroxycinnamoyl/benzoyltransferase), and DAT
(deacetylvindoline 4-O-acetyltransferase) (Grienenberger et al.,
2009; Luo et al., 2009; Onkokesung et al., 2012; Wen et al., 2014).
We have previously identified two spermidine acyltransferases
in Arabidopsis (Luo et al., 2009) and more recently, a putrescine
acyltransferase in maize by genome-wide association study
(GWAS) (Wen et al., 2014). Notably, none of their encoding genes
has been identified by conventional sequence homology, partially
because many BAHD enzymes with similar substrate specificities
have evolved independently through a process of convergent
evolution (Luo et al., 2007; Pichersky and Lewinsohn, 2011),
which results in the lack of sequence similarity among them.
As one of the most important staple crops, which supports more
than half of the world’s population, rice (Oryza sativa) has been regarded as the model plant of monocots in functional genomics
and crop improvement (Xue et al., 2008; Huang et al., 2010;
Jiang et al., 2013; Zhou et al., 2013). However, this success has
not been mirrored to the same extent in dissecting secondary
metabolic pathways and elucidating the biological functions of
these metabolites, because of the relative lack of knowledge
of the secondary metabolism in this species (Fernie and
Schauer, 2009; Gong et al., 2013; Saito, 2013). Spatiotemporal
accumulation patterns of phenolamides in rice are not available
and no genes responsible for the phenolamides biosynthesis
have been identified in this species.
In this study, comprehensive metabolic profiling and natural
variation analysis of phenolamides were carried out in rice using
a liquid chromatography(LC)–mass spectrometry (MS)-based targeted metabolomics method (Chen et al., 2013). Spatiotemporal
controlled accumulations were observed for most phenolamides,
together with their differential accumulations between the two
major subspecies of rice. Further GWASs on the leaves of
rice identified Os12g27220 and Os12g27254 as spermidine
hydroxycinnamoyl transferases that might underlie the natural
variation of levels of spermidine conjugates in rice.
112
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
Rice Phenolamide Profiling and Natural Variation
RESULTS
Tissue-Specific Accumulation of Phenolamides in Rice
It has been suggested that plants produce various types of metabolites in a tissue-specific manner. However, the differences in the
metabolic profile of phenolamides among rice tissues have not
been thoroughly investigated. To understand the tissue-specific
accumulation pattern of individual phenolamides in rice, the metabolic profiles of phenolamides were compared in samples collected
from different tissues, including flag leaf, culm, panicle at the grainfilling stage, mature grain, and root at the vegetative stage.
Using LC–electrospray ionization (ESI)-MS/MS, a highthroughput targeted metabolomics method based on multiple reaction monitoring (MRM) (Chen et al., 2013) was applied in the
comprehensive profiling of phenolamides in rice. To produce
the maximal signal, collision energy and de-clustering potential
were optimized for each precursor–product ion (Q1–Q3) transition, and a total of 16 phenolamides with three different amine
moieties (agmatine, putrescine, and spermidine) were detected
in our targeted metabolite analysis under positive mode (Table 1
and Supplemental Tables 1–3).
Accumulation of phenolamides displayed substantial variation in
their abundance in different tissues, as indicated by hierarchical
cluster analysis (HCA; Figure 1A). Root contained the highest
levels of most phenolamides, followed by flag leaf and panicle.
Grain and culm, however, showed the lowest accumulation
of most phenolamides. In addition to the higher amount of
phenolamide accumulations, comparison of the phenolamide
profiles demonstrated that root also yielded the most complex
profile of the phenolamides in different rice tissues.
Phenolamides with different hydroxycinnamoyl moieties showed
substantially different levels of accumulation. Feruloyated and/or
coumaroylated agmatine and putrescine were detected in large
amounts in rice, while their caffeoylated and sinapoylated counterparts were barely detectable in most of the tissues tested
(Figure 1A). Similar results were also obtained for spermidine
conjugates, except that a modest amount of N0 ,N00 -disinapoyl
or N0 ,N00 ,N%-diferuloyl sinapoyl spermidine were found in some
tissues such as flag leaf, panicle, and root (Figure 1A).
When levels of phenolamides with different amine moieties were
compared, distinct tissue-specific accumulation patterns were
observed. Putrescine conjugates, mainly existing as N-p-coumaroyl and N-feruloyl putrescine, were preferentially accumulated in root with levels of more than 400 mg/g dry weight (DW),
and then in panicle at a level of N-feruloyl putrescine of about
80 mg/g DW (Figure 1B). A root-specific accumulation pattern
also applied to agmatine conjugates (N-p-coumaroyl and
N-feruloyl agmatine), although they were detected at much lower
concentrations. In contrast, spermidine conjugates are highly
abundant as di- or tri-substituted forms and showed different
accumulation patterns. N0 ,N00 -p-Coumaroyl feruloyl spermidine
was specifically accumulated in flag leaf (10 mg/g DW) and
N0 ,N00 -diferuloyl spermidine was mainly accumulated in panicle,
flag leaf, and culm at relatively lower levels. N0 ,N00 -Disinapoyl spermidine accumulated at over 25 mg/g DW in both flag leaf and
panicle, and N0 ,N00 ,N%-diferuloyl sinapoyl spermidine exhibited
root-specific accumulation of more than 60 mg/g DW (Figure 1C).
Molecular Plant
Rice Phenolamide Profiling and Natural Variation
ID
Compound
Abbreviations
RT (min)
m/z
Main fragments
HCA01
N-p-Coumaroyl agmatine
Cou-Agm
4.88
277.1
147.2, 119.1, 91.1
HCA02
N-Feruloyl agmatine
Fer-Agm
5.16
307.1
177.2, 291.1, 145.2, 117.3, 89.1
HCA03
N-Caffeoyl agmatine
Caf-Agm
4.47
293.1
163.2, 233.1, 135.1, 89.1
HCA04
N-Sinapoyl agmatine
Sin-Agm
5.32
337.1
207.2, 175.2, 147.2, 119.1
HCA05
N-p-Coumaroyl putrescine
Cou-Put
3.81
235.1
147.2, 119.1, 91.1
HCA06
N-Feruloyl putrescine
Fer-Put
4.58
265.1
177.2, 145.2, 117.2, 89.1
HCA07
N-Caffeoyl putrescine
Caf-Put
2.75
251.1
163.2, 135.1, 117.3, 89.1
HCA08
N-Sinapoyl putrescine
Sin-Put
4.80
295.1
207.2, 175.2, 147.2, 119.1
HCA09
N-p-Coumaroyl spermidine
Cou-Spd
2.28
292.1
147.1, 275.1, 204.2, 119.1, 91.1
HCA10
N-Feruloyl spermidine
Fer-Spd
2.35
322.1
177.2
HCA11
N-Caffeoyl spermidine
Caf-Spd
1.66
308.3
163.2
0
00
HCA12
N ,N -p-Coumaroyl feruloyl spermidine
Cou,fer-Spd
6.48
468.3
204.2, 234.1, 147.2, 177.2, 292.2, 275.2
HCA13
N0 ,N00 -Di-p-coumaroyl spermidine
Dicou-Spd
6.01
438.1
147.2, 292.1, 204.2, 119.1
0
00
HCA14
N ,N -Diferuloyl spermidine
Difer-Spd
6.63
498.1
177.2, 323.1, 234.2, 145.2, 117.1
HCA15
N0 ,N00 -Disinapoyl spermidine
Disin-Spd
7.36
558.1
207.2, 189.2, 177.3, 137.2
HCA16
N0 ,N00 ,N%-Diferuloyl sinapoyl spermidine
Difer,sin-Spd
10.33
704.1
177.2, 145.1, 109.1
Table 1. A List of the 16 Phenolamides Identified in Rice.
Developmentally Controlled Accumulation of
Phenolamides in Rice
To investigate the accumulation patterns of phenolamides
at different developmental stages, a total of 21 tissues/
organs covering 10 developmental stages of rice were used
(Supplemental Table 4). Quantification of major phenolamides
was subsequently performed using two rice varieties, indica
rice Zhenshan 97 (ZS97) and japonica rice Zhonghua 11 (ZH11),
to uncover the range of variations observed in seedling, leaf,
culm, panicle, and root during the growing stages (Figure 2).
The results showed differentially temporal accumulation
patterns for different phenolamides during development.
The level of N-feruloyl agmatine peaked at an early stage
of both the vegetative and reproduction stages of leaf growth
(Figure 2A), while the level of N-p-coumaroyl agmatine was
constant (Figure 2B). We observed an increased level of
N-feruloyl putrescine in the leaves at the early tillering stage
(L2), followed by a sharp decrease during the later stages
(L2–L4), and N-feruloyl putrescine was barely detected in the
leaves at the reproductive stages (Figure 2C). There was a
sharp decrease in the levels to about zero for N-p-coumaroyl
and N-feruloyl putrescine in the culm from the tillering
to booting transition (Figure 2C and 2D). Both N0 ,N00 -pcoumaroyl feruloyl spermidine and N0 ,N00 -diferuloyl spermidine
showed leaf-specific accumulation in ZH11; their levels
decreased during vegetative leaf development remained at a
constant low level at the reproductive stage (Figure 2E and
2F). Notably, we detected nearly no accumulation of these
two phenolamides in ZS97 (Figure 2E and 2F). Sinapoylated
spermidines such as N0 ,N00 -disinapoyl and N0 ,N00 ,N%-diferuloyl
sinapoyl spermidine showed a distinct accumulation pattern
compared with other phenolamides with their levels slightly
fluctuating during development in various tissues (Figure 2G
and 2H).
Natural Variation for Phenolamides in Rice Leaf
We then used a collection of 156 rice accessions (Supplemental
Table 5), representing the mini–core collection of Chinese rice
varieties, to study the naturally occurring variation of the levels
of phenolamides in rice. We collected the samples from leaves
at the vegetative stage for metabolic profiling where the highest
accumulations of most phenolamides occurred in rice shoots.
Evaluation of the phenolamide contents in japonica and indica
by analysis of variance (ANOVA) revealed significant variation in
10 phenolamides (P < 0.01) (Figure 3A). Based on their relative
differences in accumulation in different accessions, HCA
clearly grouped these phenolamides into three main clusters
(Figure 3A), disclosing the natural variation of phenolamides in
rice. Four spermidine conjugates were grouped into cluster I
and could be further divided into two subclusters according to
their number (mono- or di-) of aromatic substitutions, indicating
a similar accumulation pattern of these spermidine conjugates.
Phenolamides in cluster II consisted of N-sinapoyl agmatine/
putrescine, while N0 ,N00 -disinapoyl spermidine standing out
from other phenolamides. In contrast to cluster I, which was
exclusively represented by phenolamides with spermidine
moieties, cluster III was represented by a mixture of coumaroyl/
feruloyl agmatine and putrescine, with the two coumaroylated
phenolamides lying closer to each other, suggesting
coordinated regulation of agmatine and putrescine conjugates
in rice. To examine the subspecies accumulations of
phenolamides, the median content and the variation of each
phenolamide within the two subspecies were calculated
(Figure 3B). Coumaroylated and/or feruloylated spermidines
showed highly subspecies-specific accumulations in japonica,
with trace amounts detected in indica subspecies. A similar
pattern was observed for N-sinapoyl putrescine. Different
to japonica-specific accumulation of spermidine conjugates,
the overall content of coumaroyl or feruloyl agmatine
and putrescine was much higher in indica than in japonica,
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
113
Molecular Plant
Flag leaf
A
Culm
Rice Phenolamide Profiling and Natural Variation
Panicle
Grain
Root
Cou−Put
Difer,sin−Spd
Fer−Spd
Fer−Agm
Cou−Agm
Disin−Spd
Difer−Spd
Fer−Put
Cou−Spd
Sin−Put
Cou,fer−Spd
Dicou−Spd
Caf−Spd
Sin−Agm
Caf−Agm
Caf−Put
Content (μg/g DW)
B
Content (μg/g DW)
C
800
400
80
Flag leaf
Culm
Panicle
Grains
Root
40
0
Cou-Agm
60
40
Fer-Agm
Cou-Put
Difer-Spd
Disin-Spd
Fer-Put
Flag leaf
Culm
Panicle
Grains
Root
20
0
Cou,fer-Spd
Difer,sin-Spd
Figure 1. Distribution of Phenolamides in Rice Flag Leaf, Culm,
Panicle, Grain, and Root.
(A) Heat map visualization of the relative differences in phenolamides in
five tissues of rice. Each tissue type is visualized in a single column and
each phenolamide is represented by a single row. Red indicates high
abundance, whereas low relative phenolamides are green (color key scale
above the heat map).
(B and C) Content of eight major phenolamides in the five tissues of rice.
DW, dry weight.
The full names for the abbreviations of the metabolites are given in Table 1.
although substantial intrasubspecies variation exists in both
subspecies.
Genetic Control of Natural Variation for Spermidine
Conjugates in Rice
In order to investigate the possible genetic control of the natural
variation in the major spermidine conjugates in rice, a metabolic
GWAS (mGWAS) was performed using the collection of 156 rice
accessions from the mini–core collection of Chinese rice varieties.
The single nucleotide polymorphisms (SNPs) and the imputed
genotypes of the association panel were downloaded from
our website, RiceVarMap (http://ricevarmap.ncpgr.cn/), which
are pre-released and are available for query. A linear mixed model
(LMM), which results in fewer false-positive results by taking into
account the genome-wide patterns of genetic relatedness, was
used for the analysis and the genome-wide significance
threshold, PLMM, was set to 8.9E8 after Bonferroni correction
(Supplemental Table 6, see Methods section). mGWASs of
114
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
the spermidine conjugates are visualized in Manhattan plots
(Figure 4) with genomic coordinates (chromosome 1–12)
displayed along the x axis, and the negative logarithm of the
associated P value for each single nucleotide polymorphism
displayed on the y axis (Gibson, 2010).
We detected a total of 26 lead SNPs (PLMM < 8.9E8), corresponding to 19 loci (L1–L19, as indicated in the Manhattan plot)
for the seven major spermidine conjugates tested (Figure 4,
Table 2, and Supplemental Table 7). Six of seven spermidine
conjugates had at least one significant associated locus, with
an average of 2.7 associated loci per metabolite. These loci, in
general, show large effects, up to 42.9%, with an average of
20.3% (Table 2). In contrast to complex traits such as flowering
time and grain yield of rice, which are controlled by many loci,
with most of them having small effects (Zhao et al., 2011;
Huang et al., 2012), the levels of most hydroxycinnamoyl
spermidines were controlled by a few major loci with large
effects. For example, natural variation of the content of N-pcoumaroyl spermidine was controlled by three major loci, L1,
L2, and L12, which explain 43%, 21%, and 18% of the
observed variation within the population, respectively (Figure 4
and Supplemental Table 7). Similar results were also obtained
from the content of variation of N0 ,N00 -coumaroyl feruloyl
spermidine, in which the three major loci, L1, L2, and L18,
explained nearly 50% of the total variation (Figure 4 and
Supplemental Table 7). N0 ,N00 -Di-p-coumaroyl and N0 ,N00 diferuloyl spermidine were controlled by more loci with
relatively smaller effects (Figure 4 and Supplemental Table 7).
We detected at least one common locus, L1 on chromosome
12, among coumaroyl, caffeoyl, and feruloyl spermidine,
indicating overlapping genetic control of these phenolamides.
However, N0 ,N00 -disinapoyl spermidine showed a distinct
profile in the Manhattan plot compared with the other
hydroxycinnamoyl spermidines, suggesting different genetic
control for sinapoylation to other hydroxycinnamoylations of
spermidines in rice. This result is consistent with the separate
grouping of N0 ,N00 -disinapoyl spermidine, as revealed in the
HCA (Figure 3A).
To find out whether there are interactions between the significant
loci, the pairwise epistatic interactions between the significant
loci against the average accumulation of the individual spermidine conjugate were calculated and a total of eight significant
interactions (P < 0.05) for the six phenolamides tested were detected, ranging from zero to four epistatic interactions for a single
phenolamide (Supplemental Table 8). We observed a significant
interaction between the two major loci L1 and L2 in determining
the level of N-p-coumaroyl spermidine, in which the effect
of the A and T alleles at L12 was dependent on the T allele at
L1 (Figure 5A). Similarly, the effect of L12 was found to be
dependent on both L2 (Figure 5B and 5D) and L1 (Figure 5D)
for N-p-coumaroyl spermidine. In addition, accessions with
genotypes of C at L1 hardly accumulate any N-p-coumaroyl
spermidine (Figure 5A and 5D). Based on these interactions
and the accumulation pattern (Figure 5A–5D), a sequential
model was suggested for the genetic control of the level of Np-coumaroyl spermidine (Figure 5E). Using the same approach
(Figure 5F–5I), a triangular model was proposed for the
accumulation of N0 ,N00 -diferuloyl spermidine, taking account of
the three loci (L1, L2, and L19; Figure 5J). However, four
Molecular Plant
Rice Phenolamide Profiling and Natural Variation
Fer-Agm_ZH11
Fer-Agm_ZS97
B
120
80
40
Cou-Agm_ZH11
3
2
1
0
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
0
Seedling
Leaf
Fer-Put_ZH11
Seedling
Culm Panicle Root
Fer-Put_ZS97
D
Content (μg/g DW)
Content (μg/g DW)
C
900
450
300
150
150
100
(A) N-Feruloyl agmatine.
(B) N-p-Coumaroyl agmatine.
(C) N-Feruloyl putrescine.
(D) N-p-Coumaroyl putrescine.
(E) N0 ,N00 -p-Coumaroyl feruloyl spermidine.
(F) N0 ,N00 -Diferuloyl spermidine.
(G) N0 ,N00 -Disinapoyl spermidine.
(H) N0 ,N00 ,N%-Diferuloyl sinapoyl spermidine.
The full names for the abbreviations of the
metabolites and tissues are given in Table 1 and
Supplemental Table 4, respectively.
50
Seedling
F
Content (μg/g DW)
Culm Panicle Root
Cou,fer-Spd_ZS97
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
Content (μg/g DW)
Leaf
Cou,fer-Spd_ZH11
15
10
5
Leaf
Difer-Spd_ZH11
Culm Panicle Root
Difer-Spd_ZS97
30
20
10
0
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
0
Seedling
Leaf
Disin-Spd_ZH11
Culm Panicle Root
Disin-Spd_ZS97
Seedling
H
Content (μg/g DW)
Content (μg/g DW)
Culm Panicle Root
Cou-Put_ZS97
Figure 2. Accumulation Patterns of Different
Phenolamides in Seedling, Leaf, Culm,
Panicle, and Root at Various Developmental
Stages of Rice.
0
Seedling
G
Leaf
Cou-Put_ZH11
400
0
E 20
Cou-Agm_ZS97
40
Content (μg/g DW)
Content (μg/g DW)
160
40
20
0
Leaf
Difer,sin-Spd_ZH11
40
20
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
0
Seedling
Leaf
Culm Panicle Root
Culm Panicle Root
Difer,sin-Spd_ZS97
and Os12g27254 indeed belong to the
BAHD acyltransferase gene family. The
separated clustering of the spermidine
acyltransferases, including the two new
candidates (Figure 6B; phylogenetic tree
of the BAHD family protein), is consistent
with the independent evolution of some
members of the BAHD family proteins,
especially spermidine acyltransferases.
In searching for possible functional polymorphism(s) underlying the natural variation of
spermidine conjugates, we found a number
of highly significant associations between
non-synonymous SNPs in Os12g27220 and
Os12g27254 and the levels of spermidine
Culm Panicle Root
conjugates. Two allelic mutations (SNP
sf1215970581 and SNP sf1215968382,
occurring at position 217 and 676 from
ATG, respectively) in the Os12g27220 coding region were significantly associated with levels of phenolamides such as N0 ,N00 -pcoumaroyl feruloyl spermidine, N0 ,N00 -diferuoyl spermidine, and
N0 ,N00 -di-p-coumaroyl spermidine. These mutations resulted in
significant polarity changes of amino acids (Glu-Lys, Thr-Ala) between the two groups (alleles I and II; Supplemental Table 9). The
mean N0 ,N00 -p-coumaroyl feruloyl spermidine and N0 ,N00 -diferuoyl
spermidine content in group I (allele I), which are mainly japonica,
was more than 100 times higher when compared with that in group
II (indica; allele II), with P values up to 10100 (Supplemental
Table 10). In addition, we found that two SNPs (sf1215968402
and sf1215967910) in Os12g27220 were significantly associated
with the content of N0 ,N00 -p-coumaroyl feruloyl spermidine (P <
10200). Each of these SNPs introduced a stop codon, which
resulted in a truncated protein in about 90 rice accessions
(allele II), and the levels of N0 ,N00 -p-coumaroyl feruloyl
spermidine in these accessions were significantly lower than
those in the remaining 60 accessions (allele I; Supplemental
Table 10). Similarly, we also observed strong associations
between three SNPs (sf1216002392, sf1215996415, and
sf1215996366) in the Os12g27254 coding region and the levels
of phenolamides such as N0 ,N00 -p-coumaroyl feruloyl spermidine
(P values up to 10231; Supplemental Table 10). Of these,
sf1216002392 and sf1215996415 allelic mutations resulted
in significant changes of amino acids, while sf1215996366
S1
S2
S3
S4
S5
L1
L2
L3
L4
L5
L6
L7
L8
C1
C2
C3
P1
P2
P3
R1
R2
A
Seedling
Leaf
significant pairwise interactions were detected among the five
significant loci, indicating more complex genetic control of this
metabolite.
Candidate Genes Underlying the Natural Variation for
Spermidine Conjugates in Rice
As an initial attempt to mine the candidate genes underlying the
significant loci, we focused on the locus on the long arm of chromosome 12 that showed the highest significance and was associated with a number of polyamine conjugates (Figure 4). One of
the last steps of the biosynthesis of most polyamine conjugates
is the formation of an ester bond between polyamine and
hydroxycinnamoyl-CoA catalyzed by members of the BAHD acyltransferases. Closer investigation revealed that the leading SNP
1215988027 representing this locus (P = 6.0E23 for coumaroyl
spermidine) lies 11 kb and 39 kb upstream of genes Os12g27220
and Os12g27254, respectively, encoding two putative transferase family proteins, suggesting that these genes may encode
spermidine hydroxycinnamoyl transferases. Furthermore, both
the putative transferase proteins have the two typical conserved
domains shared by BAHD family enzymes; HXXXDG, located
near the center portion of the enzyme, and the DFGWG
motif, located near the carboxyl terminus (Figure 6A).
Subsequent phylogenetic analysis suggests that Os12g27220
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
115
Molecular Plant
Rice Phenolamide Profiling and Natural Variation
Indica
Japonica
Content (μg/g DW)
B
3 5 7
Japonica
Indica
60
Cou-Spd
L1
Cou,fer-Spd
L2
L18
15
L12
10
L1,2
5
15
Dicou-Spd
10
L16
L17
L10 L1,2
L11
Caf-Spd
L14
L1,13
L6
5
15 Difer-Spd
L8 L15
10
40
L9
L1,2
Disin-Spd
L19
L3
L4 L7 L5
5
20
Chr. 1 2 3 4 5 6 7 8 9 10 11 12
0
Co
u−
d
Sp
u
Co
,fe
r−
d
Sp
er−
Dif
d
Sp
Sin
u
−P
t
Co
u
u
−P
t
r−
Fe
m
Ag
Figure 3. Natural Variation of Phenolamides with and within
Rice Subspecies.
(A) A heat map of the natural variation of phenolamides in 156 rice varieties. The content value of each phenolamide was normalized to complete
linkage hierarchical clustering. Each rice variety is visualized in a single
column and each phenolamide is represented by a single row. Red
indicates high abundance, whereas low relative flavonoids are green.
(B) Box plot for the content of six phenolamides between japonica (gray)
and indica (white). The horizontal line represents the mean and the vertical
lines mark the range from the 5th to the 95th percentile of the total data.
The full names for the abbreviations of the metabolites are given in Table 1.
introduced a stop codon, which resulted in a truncated protein
(Supplemental Table 9). Thus, we assign Os12g27220 and
Os12g27254 as the candidate spermidine acyltransferases
and propose that genetic variants within their coding regions
might contribute to the natural variation in the levels of
N-hydroxycinnamoyl spermidines in rice (Figure 6).
High sequence similarity between Os12g27220 and Os12g27254
suggests that they may function redundantly with each other.
In addition, the close linkage between the two genes makes
it difficult to create double knockout mutant lines. To establish
their functions in vivo, a gain-of-function strategy was adopted
by overexpressing each of them individually in ZH11 under
the control of the maize ubiquitin promoter. The overexpression
of Os12g27220 and Os12g27254 in the leaves of the transgenic lines was confirmed by RT–PCR (Figure 7A and 7D).
The phenolamides in the leaves of the transgenic lines were
then analyzed and compared with those of wild-type by
LC/MS/MS. Overexpression of Os12g27220 or Os12g27254
in rice led to a substantial increase in the levels of these
N-hydroxycinnamoyl spermidines compared with the control,
whereas a slight decrease in both N-hydroxycinnamoyl
agmatine and putrescine was observed in the transgenic
lines (Figure 7B, 7C, 7E, and 7F), suggesting that Os12g27220
and Os12g27254 encode spermidine hydroxycinnamoyl
acyltransferases.
116
20
-log10 (p)
Difer-Spd
Cou,fer-Spd
Caf-Spd
Cou-Spd
Sin-Agm
Sin-Put
Disin-Spd
Cou-Agm
Cou-Put
Fer-Agm
A
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
1
2
3
4 5 6 7 8 9 10 11 12
Figure 4. Manhattan Plot Displaying the GWAS Results of the
Content of Six Phenolamides.
The strength of association for phenolamides is indicated as the negative
logarithm of the P value for the LMM model. All metabolite–SNP associations with P values below 8.88E08 (horizontal dotted lines) are plotted
against the genome location in intervals of 1 Mb.
The full names for the abbreviations of the metabolites are given in Table 1.
To understand the relationship between expression of candidate
genes with the accumulation of spermidine derivatives, microarray data for gene expression patterns were downloaded from our
website, CREP (http://crep.ncpgr.cn/crep-cgi/home.pl), which
are available for query (Wang et al., 2010). Gene expression
profilings of the candidate gene Os12g27254 in 28 tissues
covering the entire life cycle of the rice plant from two
varieties, Zhenshan 97 and Minghui 63 (MH63), were compared
(Supplemental Figure 1). A leaf-specific expression of
Os12g27254 could be observed in MH63 at the vegetative stage
(three-leaf stage, seedling with two tillers stage), which was
followed by sharply decreased levels in the leaves at the reproductive stage (leaf at the young panicle stage, flag leaf at 5/
14 days before heading). In contrast, almost no transcripts of
Os12g27254 could be detected in various tissues of ZS97
(Supplemental Figure 1), both coinciding with the accumulation
pattern of spermidine derivatives, such as N0 ,N00 -p-coumaroyl
feruloyl spermidine and N0 ,N00 -diferuloyl spermidine, in different
tissues of ZH11 and ZS97, respectively (Figure 2E and 2F).
DISCUSSION
Although ubiquitously found in plants, phenolamides display
qualitative and quantitative differences in various species with
respect to the chain length of amine and the type and degree of
phenolic acid substitution. Despite the implications of the importance of phenolamides in many aspects of physiological and biological processes such as plant development and defense, no
systematic profiling of spatiotemporal accumulation of phenolamides has been carried out in rice, one of the most important
crops and the model plant for monocots. By applying a targeted
metabolic profiling method, we revealed tissue-specific and
developmentally controlled accumulation of phenolamides in
rice. Higher levels of most phenolamides, including hydroxycinnamoyl spermidines, were detected in root than in other tissues,
Molecular Plant
Rice Phenolamide Profiling and Natural Variation
Items
Population
Number of metabolites
7
Number of lead SNPsa
26
Number of locib
19
Average loci per metabolite
2.7
SNPs above 20% of variation
12
Maximum explained variation (%)
42.9
Explained variation per SNP (%)
20.3
Table 2. Summary of Significant LociMetabolites Associations
Identified by GWAS.
a
The SNPs with the lowest P value in a region (significant
P value = 8.88E08); bAdjacent lead SNPs <300 kb were considered
as a cluster.
which is in contrast to the absence of spermidine conjugates in
Arabidopsis root (Luo et al., 2009). Flavonoids that are highly
accumulated in the Arabidopsis root were barely detected in
rice root (Dong et al., 2014). The negative correlation between
phenolamides and flavonoids in the root of both species might
be explained by the fact that they are in different branches of
the phenylpropanoid pathway, which compete with each other;
the hydroxycinnamoyl CoAs are generated at the early steps
of the pathway. However, the genetic basis underlying their
distinct accumulation patterns and the biological significance of
this distinction need further investigation.
Phenolamides in rice are mainly represented by coumaroyl,
feruloyl, or sinapoyl substitutions, while only a small amount of
N-caffeoyl putrescine was detected in the root (Figure 1A).
Similar results were obtained in the dicot plant, Arabidopsis,
in which N0 ,N00 ,N%-diferuloyl sinapoyl spermidine and N0 ,N00 disinapoyl spermidine proved to be the major spermidine
conjugate in flower (Grienenberger et al., 2009; Handrick
et al., 2010) and seed (Luo et al., 2009), respectively. However,
caffeoyl and coumaroyl putrescine represent the major
phenolamides in Nicotiana plants (Galis et al., 2006; Kaur et al.,
2010; Onkokesung et al., 2012). The differential accumulation
of different hydroxycinnamoylated polyamines might be the
combined effects of the selectivity of the enzymes that
catalyzed the formation of the corresponding phenolamides
and the availability of the different hydroxycinnamoyl CoAs.
Most phenolamides displayed an increase in their levels or accumulated at higher contents at the early stage of development
followed by rapid decrease in various tissues, except in root, in
which relatively stable accumulation was observed (Figure 2).
This developmentally controlled pattern is clearly different to
the increase in N0 ,N00 -disinapoyl spermidine and its derivative
during silique maturation in Arabidopsis, which results in high
accumulation of this spermidine conjugate in the mature seed.
However, only trace amounts of phenolamides were detected
in rice grain. Turnover and translocation of conjugates were
described early (Martin-Tanguy, 1985; Havelange et al., 1996;
Martin-Tanguy, 1997) and by the recent support of the
interconversion between free and conjugated precursors (Luo
et al., 2009). So, the degradation of phenolamides could be the
way to regulate the pools of both phenolics and bioactive
polyamines.
In addition to their spatiotemporal distributions, some phenolamides, such as coumaroylated and feruloylated spermidines,
also exhibited subspecies-specific accumulation, with the levels
much higher in japonica than in indica. C-glycosylated and
malonylated flavonoids showed an opposite subspeciesspecific accumulation pattern (Dong et al., 2014). Natural
variation analysis disclosed distinct clustering of spermidine
conjugates, while a similar accumulation pattern between
hydroxycinnamoyl agmatine and putrescine was observed
(Figure 3), suggesting some overlapping regulation of the latter
two types of phenolamides. This notion was supported by
the finding that enzymes involved in spermidine conjugate
biosyntheses show high specificity toward spermidine as an
acyl acceptor (Grienenberger et al., 2009; Luo et al., 2009),
while broader substrate specificity toward both agmatine
and putrescine was found for PHT, which is involved
in putrescine conjugate formation (Wen et al., 2014). The
separated clustering of sinapoylated spermidines suggests
unique regulation of these phenolamides, which is also
reflected by the distinct pattern of the Manhattan plot from the
mGWAS analysis (Figure 4). Different to other hydroxycinnamic
acid substitutions, which are transferred exclusively by BAHD
acyltransferases, substitution of sinapic acid could use either
its O-glucose ester (Baumert et al., 2005; Mugford et al., 2009)
or sinapoyl CoA as acyl donor (Luo et al., 2009), which is
catalyzed by serine carboxypeptidase-like and BAHD acyltransferases, respectively. Spermidine disinapoyl transferase has
been identified as a BAHD acyltransferase, but the gene
responsible for monosinapoylation of polyamine still needs to
be identified.
Co-expression analysis integrating transcriptomics and metabolic profiling followed by reverse genetic approaches has
been applied extensively for the characterization of genes encoding both enzymes and transcription factors controlling various
metabolic pathways (Hirai et al., 2004; Hirai et al., 2007;
Yonekura-Sakakibara et al., 2008). A combination of metabolic
profiling and genetics is a promising alternative to dissect
the genetic basis and identify novel genes underlying
phytochemicals (Fu et al., 2009; Matsuda et al., 2012; Gong
et al., 2013). With the recent advances in the next generation of
sequencing (Han and Huang, 2013), GWAS has become a
popular approach in plant genetics and functional genomics
studies in plants (Huang and Han, 2014; Wen et al., 2014). The
high resolution and large effects of mGWAS, together with the
chemical structure of the metabolite and the annotated genome
sequence, facilitate the assignment of candidate genes, as
demonstrated in our study. Systematic investigation of natural
variation at the metabolite level will lead to construction of the
biosynthetic pathways of bioactive metabolites (Gong et al.,
2013; Wen et al., 2014), which in turn will provide new targets
for metabolic engineering of crops with enhanced stress
resistance and health-promoting effects.
METHODS
Chemicals
All chemicals were of analytical reagent grade. Gradient grade
of methanol, acetonitrile, and acetic acid were purchased from Merck,
Germany (http://www.merck-chemicals.com). Water was doubly deionized with a Milli-Q water purification system (Millipore, Bedford, MA).
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
117
Molecular Plant
2
0
T_L12
A_L12
Cou-Spd
4
G_L1
A_L1
2
0
G_L19
A_L19
Difer-Spd
G
T_L1
C_L1
2
1
0
T_L2
A_L2
Cou-Spd
T_L12
A_L12
Cou-Spd
Content (μg/g DW)
Content (μg/g DW)
F
C
H
4
2
0
T_L2
C_L2
G_L19
A_L19
Difer-Spd
G_L1
A_L1
4
2
0
C_L2
T_L2
Difer-Spd
D6
Content (μg/g DW)
0
A_L2
T_L2
Cou-Spd
E
L1
4
L2
2
L12
0
Cou-Spd
L12: T A A T A T A
L2 : A A T A A T T
L1 : T T T C C C C
I 6
Content (μg/g DW)
2
4
Content (μg/g DW)
B
T_L1
C_L1
Content (μg/g DW)
4
Content (μg/g DW)
Content (μg/g DW)
A
Rice Phenolamide Profiling and Natural Variation
Difer-Spd
J
L1
4
L2
2
L19
0
L19: A
L2 : T
L1 : G
G
T
G
A
C
G
G
C
G
A
T
A
G
C
A
Difer-Spd
Figure 5. Locus Interactions among Significant Loci Controlling Phenolamides Accumulation.
Interactions between two major loci controlling N-p-coumaroyl spermidine (A–C) and N0 ,N00 -diferuloyl spermidine (F–H) accumulation. Three loci act in
controlling the accumulation of N-p-coumaroyl spermidine (D) and N0 ,N00 -diferuloyl spermidine (I). Proposed models for the interaction of the three loci
controlling N-p-coumaroyl spermidine (E) and N0 ,N00 -diferuloyl spermidine (J) accumulation.
The full names for the abbreviations of the metabolites are given in Table 1.
The internal standard lidocaine used in this study was from our laboratory
collection (Chen et al., 2013). An authentic standard of p-coumaric acid
was purchased from Sigma-Aldrich, USA (http://www.sigmaaldrich.
com/united-states.html). The standard stock solution was prepared using
methanol as a solvent and stored at 20 C. A standard solution of p-coumaric acid was prepared just before use by diluting the stock solution with
aqueous methanol.
Plant Materials
The rice core germplasms used in this study were from a collection of
Chinese cultivated rice germplasm (Dong et al., 2014). Flag leaf, culm,
panicle, grain, and root at different developmental stages were
collected from 12 japonica and 12 indica rice varieties (Supplemental
Table 5), resulting in 144 individual samples. For each tissue, a mixture
of samples from 24 rice varieties was obtained. To investigate
developmentally controlled accumulation of phenolamides, a total of 21
tissues or organs were collected from two regular rice varieties,
Zhonghua 11 (ZH11) and Zhenshan 97 (ZS97; Supplemental Table 4).
To study the natural variation of the phenolamides, leaves of 156 rice
varieties (Supplemental Table 5) were harvested from the plant at the
vegetative stage (60 days after sowing, around the five-leaf stage). Two
biological replicate leaf samples obtained from the same accessions
were used for the GWAS.
Flag leaf, culm, and panicle were harvested at the grain-filling stage, and
mature grains were collected at 30 days after pollination of the plant. For
the samples from ZH11 and ZS97, leaves, culms, and panicles at 10
developmental stages were harvested. These samples were taken from
the plant examined under natural field conditions as described previously
(Chen et al., 2013). Root at 40 days was collected from the plants cultured
hydroponically as described previously (Dong et al., 2014). Seedlings
were collected from ZH11 and ZS97 plants grown in a greenhouse. All
the samples were harvested at 10:00–12:00 h, placed in liquid N2
immediately, and stored at 70 C until vacuum freeze-drying. Samples
were taken from three different plants per line and pooled for each biological replicate.
118
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
Sample Preparation and Extraction
The freeze-dried leaf was crushed using a mixer mill (MM 400, Retsch)
with a zirconia bead for 1.5 min at 30 Hz. Fifty milligrams of powder was
weighed and extracted overnight at 4 C with 1.0 ml of 70% aqueous
methanol containing 0.1 mg/l lidocaine for water-soluble metabolites.
Following centrifugation at 10 000 g for 10 min, the extracts were absorbed (CNWBOND Carbon-GCB SPE Cartridge, 250 mg, 3 ml; ANPEL,
Shanghai, China, http://www.anpel.com.cn/cnw) and filtrated (SCAA104, 0.22 mm pore size; ANPEL, Shanghai, China, http://www.anpel.
com.cn/) before LC–MS analysis (Chen et al., 2013).
LC–MS/MS Analysis of Phenolamides
Samples were analyzed using LC-ESI-QTRAP-MS/MS, and quantification
of phenolamides was carried out in MRM mode. The analytical conditions
were as described previously (Chen et al., 2013). Data acquisition, peak
integration, and the calculations were performed using Analyst 1.5
software (AB Sciex). Phenolamides were quantified by calculating the
area of each individual peak and comparing this with the standard curve
obtained from p-coumaric acid authentic standard. Calibration curves
were drawn by plotting at six different concentrations of the authentic
standard according to the peak area.
Statistical Analysis
Metabolite data were log2 transformed to improve normality and normalized. A total of 16 phenolamides were used for hierarchical clustering analysis by R (http://www.r-project.org/) to study phenolamide tissue-specific
accumulation and natural variations.
Genotyping of Rice Germplasm
Total genomic DNA was isolated from leaf tissues of each plant and pairedend sequencing libraries with insert sizes of 450–500 bp were constructed
for each variety according to standard instructions provided by Illumina.
Paired-end 90-bp reads were obtained using the Illumina HiSeq 2000 platform, and the raw sequences were further processed to remove adapter
pollutions and low-quality reads (base quality of more than 50% bases
%5). All library construction, sequencing, and sequence cleaning were
Molecular Plant
Rice Phenolamide Profiling and Natural Variation
HXXXD
A
B
88
32
0.2
46 33
100
51
97 72
99
99
97
100
C
100 bp
*
1 2
3
4
5
6
7
8
YFGNC DFGWG
MpAAT1
AMAT
CbBEBT
HMT/HLT
CmAAT1
CHAT
Os10g01650
Os10g04400
100 Os10g04429
Os12g27330
Os12g27350
Os12g27254
Os12g27220
At2g23510 AtSDT
At2g25150 AtSCT
CRE2
**
100 bp
1 2 3 4 5 6 7
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
**
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
r2
Os12g27254
Genome-Wide Association Analyses
Only SNPs with minor allele frequency R 0.05 and the number of varieties
with minor alleles R6 in a (sub)population were used to carry out the
GWASs. Population structure was modeled as a random effect in LMM using the kinship (K) matrix, and we found that it is enough to control
spurious associations, because the genomic inflation factor is near one
in all GWASs. We performed GWASs using LMM in the FaST-LMM program (Lippert et al., 2011). The genome-wide significance thresholds
of the GWASs were determined using a modified Bonferroni correction
as described previously (Li et al., 2012), in which the total SNPs (M)
for threshold calculation were replaced by the effective number of
independent SNPs (Me).
Analysis of Two-Locus Interactions
*
Os12g27220
*
carried out by BGI-Shenzhen, China. Reads were then aligned to rice reference genomes (Nipponbare, MSU version 6.1). After imputation, SNPs
with a missing rate less than 20% were selected for further analysis. The
SNPs and imputed genotypes can be queried on our website RiceVarMap
(http://ricevarmap.ncpgr.cn).
1
0.8
0.6
0.4
0.2
0
Figure 6. Analysis of Candidate Genes Underlying the Natural
Variation of Spermidine Conjugates.
(A) Conserved amino acid sequences of the BAHD family of
acyltransferases.
(B) Phylogenetic analysis of BAHD acyltransferases. The neighbor-joining
tree was constructed using aligned full-length amino acid sequences.
Bootstrap values from 1000 replicates are indicated at each node. (Bar,
0.2 amino acid substitutions per site). GenBank accession numbers are
given: MpAAT1 (AAU14879), AMAT (AAW22989), CbBEBT (AAN09796),
HMT/HLT (BAD89275), CmAAT1 (CAA94432), CHAT (AAN09797), AtSDT
(At2g23510), AtSCT (At2g25150), the rice gene amino acids sequences
from the Rice Genome Annotation Project (http://rice.plantbiology.msu.
edu/cgi-bin/gbrowse/rice/), CRE2 (AAM64817).
(C) A representation of the pairwise r2 value (a measure of linkage
disequilibrium) among polymorphic sites in Os12g27220 and
Os12g27254, where the darkness of the color of each box corresponds to
the r2 value according to the legend. For the gene model, a filled black box
represents the coding sequence. The stars represent the proposed
functional sites.
According to the mGWAS results, six of the metabolites detected have
multiple significant loci. To identify epistatic interactions between significant loci, we calculated the pairwise epistatic interactions between the
significant loci against the average accumulation of six metabolites,
respectively, within the full population using two-way ANOVA (Zhou
et al., 2012). The calculation was based on unweighted cell means, and
the sums of squares were multiplied by the harmonic means of the cell
sizes to form the test criteria. Those that showed significant interactions
at P % 0.001 were subjected to permutation tests, in which
the positions of the phenotype scores in the dataset were randomized
to perform the two-way ANOVA again. This process was repeated 1000
times. If no more than 1% of the random F value was larger than the
F value from the real data, it was regarded as significant at P % 0.001.
Constructs and Transformation
The overexpression vector (pJC034) for rice was constructed from the
Gateway overexpression vector pH2GW7 and the 35S promoter of
pH2GW7 was replaced by maize ubiquitin promoter. The Os12g27220
and Os12g27254 overexpression constructs were constructed by directionally inserting the full cDNA sequence first into the entry vector
pDONR207 and then into the destination vector pJC034, using the
Gateway recombination reaction (Invitrogen). The constructs were independently introduced into the Agrobacterium strain EHA105, and transformation was performed as described previously (Hiei et al., 1994). A total of
52 and 24 transgenic plants were generated for Os12g27220 (T0) and
Os12g27254 (T0), respectively, of which 39 and 19 were transgenic
positive. After co-segregation tests, T1 progeny from three independent
transgenic positive T0 plants for Os12g27220 and Os12g27254 were
used for further analysis.
Expression Analyses
We isolated total RNA from rice using an RNA extraction kit (TRIzol reagent;
Invitrogen) according to the manufacturer’s instructions. The PCR primers
were designed using Primer Premier V (Supplemental Table 11). The firststrand cDNA was synthesized using 3 mg of RNA and 200 U of M-MLV
reverse transcriptase (Invitrogen) according to the manufacturer’s protocol. Real-time PCR was performed on an optical 96-well plate in an ABI
StepOne plus PCR system (Applied Biosystems) using SYBR Premix
reagent F-415 (Thermo Scientific). The relative expression level of the
Os12g27220 and Os12g27254 genes was determined with the rice Actin1
gene as an internal control, and the expression measurements were obtained using the relative quantification method as described previously
(Gong et al., 2013).
SUPPLEMENTAL INFORMATION
Supplemental Information is available at Molecular Plant Online.
Molecular Plant 8, 111–121, January 2015 ª The Author 2015.
119
Molecular Plant
1
30
20
10
0
WT 1 2 3
Over expression
WT 1 2 3
Os12g27220-OE
WT
Os12g27220-OE
80
6
4
2
0
t
pd Spd Spd pd Spd Spd Agm -Pu
u-S Fer- cou- ifer-Sr,sin- isin- Fer- Fer
o
C
D
D ife
Di
D
D
Os12g27254
E
Relative content
50
Cou,fer-Spd
40
Relative expression
150
0
B
30
18
6
1
0
WT 1 2 3
Over expression
F
Relative content
250
Relative content
C
Os12g27220
Relative content
Relative expression
A
Rice Phenolamide Profiling and Natural Variation
Cou,fer-Spd
Figure
7. Functional
Validation
Os12g27220 and Os12g27254 In Vivo.
WT 1 2 3
Os12g27254-OE
Box plot for the mRNA level of Os12g27220
(A) and Os12g27254 (D) in Os12g27220 and
Os12g27254 transgenic individuals, respectively.
Box plot for the content of spermidine and
agmatine/putrescine conjugates in Os12g27220
(B and C) and Os12g27254 (E and F) transgenic
individuals, respectively. WT indicates the transgenic background variety ZH11. The metabolic
data of phenolamides are means ± standard error
of the mean.
The full names for the abbreviations of the
metabolites are given in Table 1.
30
20
10
0
WT
Os12g27254-OE
12
8
4
of
0
u
Co
d pd pd pd pd pd gm ut
-Sp er-S ou-S er-S sin-S sin-S er-A Fer-P
F
F Dic Dif fer,
Di
Di
FUNDING
This work was supported by the Major State Basic Research Development
Program of China (973 Program) (No. 2013CB127001), the National High
Technology R&D Program of China (863 Program) (No. 2012AA10A304),
the National Natural Science Foundation of China (No. 31070267), and
the Program for New Century Excellent Talents in University of Ministry
of Education in China (NCET-09-0401).
ACKNOWLEDGMENTS
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Received: June 2, 2014
Accepted: September 2, 2014
Published: September 29, 2014
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