Abstract
Background
The bast fiber crop ramie can be used as high-quality forage resources,
especially in tropical or subtropical region where there is lack of
high-quality protein feed. Hongxuan No.1 (HX_1) is a unique ramie
variety with a light reddish brown leaf color, which is obviously
different from elite cultivar, Zhongzhu No.1 (ZZ_1, green leaf). While,
the regulatory mechanism of color difference or secondary metaboliates
synthesis between these two varieties have not been studied.
Results
In this study, phenotypic, transcriptomic and metabolomic analysis of
HX_1 and ZZ_1 were conducted to elucidate the mechanism of leaf color
formation. Chromaticity value and pigment content measuring showed that
anthocyanin was the main metabolites imparting the different leaf color
phenotype between the two varieties. Based on LC/MS, at least 14
anthocyanins were identified in leaves of HX_1 and ZZ_1, and the HX_1
showed the higher relative content of malvidin-, pelargonidin-,and
cyanidin-based anthocyanins. Transcriptome and metabolome co-analysis
revealed that the up-regulated expression of flavonoids synthesis gene
was positively correlated with total anthocyanins accumulation in ramie
leaf, and the differentfially expression of “blue gene” (F3’5’H) and
the “red gene” (F3’H) in leaves bring out HX_1 metabolic flow more
input into the cyanidin branch. Furthermore, the enrichment of
glycosylated modification pathway (UGT and AT) and the expression of
flavonoid 3-O-glucosyl transferase (UFGT), anthocyanidin reductase
(ANR), in leaves were significantly influenced the diversity of
anthocyanins between HX_1 and ZZ_1.
Conclusions
Phenotypic, transcriptomic and metabolomic analysis of HX_1 and ZZ_1
indicated that the expression levels of genes related to anthocyanin
metabolism contribute to the color formation of ramie variety.
Anthocyanins are important plant secandary metabilates with many
physiological functions, the results of this study will deepened our
understanding of ramie leaf color formation, and provided basis for
molecular breeding of functional forage ramie.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12864-021-08007-0.
Keywords: Ramie variety, Leaf, Color formation, Metabolites,
Anthocyanins
Background
Ramie is a perennial herb of Urticaceae widely grown in some Asian
countries such as southern China, Laos, Thailand and Indonesia [[41]1].
It is nicknamed “crop with the best fiber quality” in China and has
been cultivated for thousands of years. In addition to fiber
applications, ramie can be used as high-quality forage resources (rude
protein > 20%), especially in southern China where there is lack of
high-quality protein feed [[42]2]. With a wide range of medicinal value
including antioxidant, antibacterial, anti-inflammatory and
anti-diabetic proper-ties [[43]3–[44]6] ramie forage is valuable feed
that would enhance immunity of livestock and poultry, reduce the drug
residue problems caused by drug feed additives and promote healthy
development of animal husbandry.
In the national ramie germplasm resource garden nursery of Chinese
academy of agricultural sciences, there are more than 2800 kinds of
ramie varieties. The leaf color is one of the important phenotypic that
can distinguish these different varieties. For example, emerald green
color in the elite cultivar Zhongzhu No.1 (ZZ_1) and red leaves and
stems in Hongxuan No.1 (HX_1). Previous studies have shown that
anthocyanins are the main factor that determined plant color [[45]7].
Usually, the six kinds of common anthocyanins (cyanidin, pelargonidin,
delphinidin, peonidin, petunidin, malvidin) are very important for
plants [[46]8]. As a branch of phenylalanine pathway, anthocyanin
synthesis pathway has been well studied in many species. It is divided
into three stages. In the first stage, dihydroflavonols (DHK) is formed
by catalytic action of chalcone synthase (CHS), chalcone isomerase
(CHI) and flavanone 3-hydroxyxylase (F3H) enzymes. Flavonol synthase
(FLS), flavonoid 3′-hydroxylase (F3’H), flavonoid 3′,5′-hydroxylase
(F3’5’H), dihydroflavonol-4-reductase (DFR), anthocyanidins synthase
(ANS) and other enzymes catalyze the formation of flavonols and
cyanidins in the second stage. The third stage, is driven by
leucoanthocyanidin reductase (LAR), anthocyanidin reductase (ANR),
UDP-glucose: flavonoid 3-O-glucosyltransferase (UFGT) resulting into
procyanidins and anthocyanins of various colors.
Recently, the research on the color of leaves and petals was very hot
[[47]9, [48]10]. Transcriptional and metabolic studies were conducted
to understand flavonoid metabolism pathway and screen key genes to
provide basis for molecular breeding of blue waterlily [[49]11]. The
differences of leaf color and “Yin Rhyme” flavor of different tea
varieties were caused by differences of anthocyanins, catechins,
caffeine, limonene and so on. Their differentially expressed genes were
concentrated in the flavonoid metabolism pathway [[50]12]. In fleshy
roots, the difference in pigment accumulation in root bark of purple
radish and green radish at different developmental stages were linked
to anthocyanin biosynthesis rather than flavonol biosynthesis [[51]13].
At present, the effect of anthocyanins on plant color has been
extensively studied and some important structural and regulatory genes
from different species have been cloned. However, the synthesis and
regulation mechanism of anthocyanins in various crops are quite
different [[52]11, [53]12, [54]14]. The differences observed in leaf
color between the ramie varieties ZZ_1 and HX_1 remained at
morphological stages with no prior functional / regulatory genes
investigated to unravel the reason behind such differences as well as
the underlying metabolism pathway culminating in the production of the
green and red leaves which has greatly limits further development and
application of the new cultivar of HX_1, so the reason of this study.
In addition to play role in plant adaptation and resistance to
adversity stresses [[55]15], anthocyanins were also widely used in food
[[56]16], feed [[57]17] and pharmaceutical industries [[58]18]. In this
study, the color formation related anthocyanins in green leaf variety
(ZZ_1) and red leaf variety (HX_1) of ramie were identified and
measured. The differentially expressed genes and enrichments pathways
for these key components were co-analyzed by transcriptome and
metabolomics methods combined with biochemical analysis, fluorescence
quantitative PCR and so on. The results of this study will help to
provide a theoretical basis for the functional analysis of key genes
controlling the color components of ramie leaves, distinctive new
varieties breeding.
Result
Phenotypes difference between varieties ‘HX_1’ and ‘ZZ_1’
The HX_1 and ZZ_1 are two varieties of ramie that can be used as fiber
and feed, but their biological characteristics or phenotypes are quite
different. Although both of the two varieties have similar leaf
outlines, the leaf surface of HX_1 is smoother and the ZZ_1 leaf have
more wrinkles. The flower bud, stem and leaf of HX_1 is all red in
varying degrees, while the ZZ_1 is bright green (Fig. [59]1A).
Fig. 1.
[60]Fig. 1
[61]Open in a new tab
Characteristics of phenotype and pigment content in HX_1 and ZZ_1. (A)
Phenotypic characteristics of red leaf ramie and green leaf ramie. HX_1
represents the red leaf ramie while ZZ_1 represents the green leaf
ramie; (B) Variance analysis of leaf color parameters; (C) Pigment
(Carotenoid, Chlorophyll, Anthocyanin) content and variance analysis;
*, **, *** respectively indicates significant difference (P < 0.05,
P < 0.01, P < 0.01), and n. s. indicates no significant difference
To explore the phenotypic differences between HX_1 and ZZ_1, the leaf
color parameters (L^*, a^*, b^*, c^*, h^*) of the two varieties were
investigated and the indexes of chlorophyll, carotenoid and anthocyanin
were determined. The results showed that all the leaf color parameters
measured between the two cultivars were significantly different (Fig.
[62]1B). The L^* of HX_1 is significantly less than that of ZZ_1, and
as L^* represents brightness, it implies that ZZ_1 is more colorful.
The a^* represents red and green concentration of the color, positive
value of a^* means red, and the negative value green. In this study,
the a^* values of both samples were negative, and HX_1 showed extremely
significantly greater a^* value than ZZ_1. According to the a^* values,
both of the HX_1 and ZZ_1 showed a main color of green, while HX_1
contain more red elements. The b^* represents the concentration of
yellow and blue elements. Both HX_1 and ZZ_1 had a positive b^* value,
and the value of ZZ_1 is significantly higher than HX_1. The value of
c^* (saturation), h^* (chromaticity angle) were also positive with the
c^*, h^* value of ZZ_1 significantly greater than HX_1.
The quantitative analysis of various pigments showed that chlorophyll
is the most abundant pigment, and anthocyanin is the main difference
pigment between the two varieties (Fig. [63]1C). These may be the main
reason for the color difference between the two varieties. The
anthocyanin content of red variety was significantly (P < 0.01) higher
(5 times higher) than that of the green leaf ZZ_1. No differences were
obtained for chlorophyll and carotenoid content, and chlorophyll was
more than 140 times the amount of anthocyanin (mg/g) which signifies
that the color tone of ramie leaves as green.
RNA-Seq and de novo assembly
In order to identify candidate genes that cause leaf color differences,
the transcriptome sequencing and the de novo assembly of 8 samples
(HX1_1, HX1_2, HX1_3, HX1_4, ZZ1_ 1, ZZ1_ 2, ZZ1_3 and ZZ1_4) from
leaves of two varieties under the same conditions were conducted using
the ZZ_1 as reference genome [[64]19]. About 54.38 Gb total clean bases
were obtained by RNA-seq after cleaning and quality checking, with an
average of 6.80 Gb for each sample. The lowest value of Q30 (percentage
of bases with sequencing error rate lower than 1 ‰) was 89.96%. The
content of GC ranged from 48.76 to 50.02% (Table [65]1). These data
indicated that the sequencing quality is reliable and suitable for
further analysis.
Table 1.
Summary of sequencing data quality preprocessing results
Sample raw reads raw bases clean reads clean bases valid bases Q30 GC
HX1_1 48.04 M 7.21G 44.96 M 6.56G 91.01% 89.68% 49.37%
HX1_2 50.71 M 7.61G 46.96 M 6.81G 89.52% 88.96% 50.02%
HX1_3 49.53 M 7.43G 46.19 M 6.71G 90.25% 89.45% 49.25%
HX1_4 48.01 M 7.20G 44.89 M 6.57G 91.17% 89.28% 49.53%
ZZ1_1 51.12 M 7.67G 47.92 M 7.00G 91.25% 89.85% 48.76%
ZZ1_2 52.71 M 7.91G 49.40 M 7.19G 90.95% 89.93% 48.94%
ZZ1_3 47.44 M 7.12G 44.52 M 6.49G 91.15% 90.04% 49.10%
ZZ1_4 51.21 M 7.68G 48.17 M 7.05G 91.79% 89.93% 49.00%
[66]Open in a new tab
Subsequently, the filtered clean reads from each sample were mapped to
ramie ZZ_1 genome. The mapping rate varied from 91.98% ~ 93.07% (ZZ_1)
and 77.28% ~ 86.63% (HX_1), respectively. (Among them, 86.13% ~ 87.17%
(ZZ_1) and 71.60% ~ 81.29% (HX_1) were uniquely mapped (Additional file
[67]1: Table S1). The reads number of ‘+’ and ‘-'chains was less than
1% among different ramie varieties and samples (Additional file [68]1:
Table S1). These fragments were used to calculate the mRNA expression
level, expressed in FPKM (Fragments Per kb Per Million Reads), to
further compare the expression of different genes among different
samples. Statistical table of FPKM distribution of mRNA the expression
level of all samples tended to be stable (Additional file [69]2: Table
S2). Box plot, density map and regional distribution map of mRNA
expression level was shown in Fig. S1A&B&C (Additional file [70]3).
Principal component analysis confirmed the genetic differences in gene
expression between the two ramie varieties (Additional file [71]3: Fig.
S1E&F). The sample level clustering of FPKM divided eight samples into
two categories (Additional file [72]3: Fig. S1D).
GO and KEEG term classification of DEGs in HX_1 vs. ZZ_1
Based on the false discovery rate (FDR) ≤ 0.05 and fold change (FC) ≥2,
a total of 3248 differentially expressed genes (DEGs) were identified
(Fig. [73]2A). Among them, 1482 DEGs were up-regulated and 1766 DEGs
were down-regulated in red variety (HX_1) compared with green variety
(ZZ_1) (Fig. [74]2A& B). The overall distribution of DEGs were shown by
MA map (Additional file [75]4: Fig. S2A) and volcano map (Additional
file [76]4: Fig. S2B). Briefly, 2165 DEGs were annotated by 2559 GO
terms (Fig. [77]2C&D) and were divided into 3 groups (BP, CC, MF)
(Additional file [78]5: Table S3). Up regulation of DEGs in the Top10
GO term were enriched in growth and development, organogenesis and
biological defense. The expression of flavonoid synthesis related genes
was significantly up-regulated (Fig. [79]2D), which was consistent with
the phenotypic data. The directed acyclic graph for the enriched terms
based on top-GO showed that UDP glycosyltransferase related branches
(GO:0035251, GO0080043, GO0080044) were significantly enriched
(Fig. [80]3).
Fig. 2.
[81]Fig. 2
[82]Open in a new tab
Identification and GO functional enrichment analysis of DEGs. (A)
Screening of differential genes; (B) Cluster analysis of differential
genes by thermography; (C) Comparison of the distribution of
differentially expressed mRNA and all mRNA at GO Level2 level; (D) GO
enrichment analysis top30 (Up)
Fig. 3.
[83]Fig. 3
[84]Open in a new tab
The directed acyclic graph for the enriched terms based on Molecular
Function-GO
KEGG functional enrichment analysis was also carried out to clarify the
roles of these DEGs in HX_1 vs. ZZ_1 (Fig. [85]4A). The number of DEGs
in metabolism category was the largest, 181 metabolic pathways were
enriched, of which 153 were up-regulated and 157 were down-regulated.
Specifically, Phenylalanine, tyrosine and tryptophan biosynthesis
(ko00400), Glutathione metabolism (ko00480), Phenylpropanoid
biosynthesis (ko00940), Flavonoid biosynthesis (ko00941) and Flavone
and flavonol biosynthesis (ko00944) pathways were up-regulated and
enriched (Fig. [86]4B, Additional file [87]6: Fig. S3). The GO and KEEG
analysis of these DEGs provided clues for the comparison of the
differences between the two cultivars, especially the molecular events
related to leaf color development.
Fig. 4.
[88]Fig. 4
[89]Open in a new tab
KEGG enrichment analysis of differential mRNA. (A) KEGG classification
of differentially expressed mRNA; (B) KEGG enrichment top20 Bubble
Diagram. The larger the bubble size, the more the number of
differential mRNA. The color of the bubble changed from
purple-blue-green-red. The smaller the value of enrichment p-value was,
the greater the significance was
Anthocyanin metabolism pathway and color formation related genes analyses
between HX_1 and ZZ_1
Since anthocyanin (ko00942) and its precursor chemical (ko00940;
ko00941) synthesis pathways were significantly enriched, their involved
DEGs in HX_1 and ZZ_1 was identified and characterized. Most of the
related DEGs play role in anthocyanin biosynthesis, and more than half
of them had a higher expression level in HX_1 than ZZ_1. These genes
could be divided into two groups, one group is the early genes (EGs)
including PAL, 4CL, C4H, HCT, CHS, F3H, and the expression level of
these genes in HX_1 was higher. The other group is the late gene (LGs),
which included the synthetic genes (SGs) and modifying genes (MGs)
which play roles in the formation of diverse anthocyanin by group
modification such as malonyl, caffeoyl, coumaroyl, succinyl, galloyl,
or rhamnosyl at different positions of anthocyanin 3-glucoside. Among
these late genes, the UFGT and ANR were significantly up-regulated and
the expression level of UFGT gene in HX_1 was almost 26 times higher
than in ZZ_1. Another two anthocyanins related genes F3’H and F3’5’H
were respectively up- and down-regulated in HX_1 vs. ZZ_1, this may
bring out metabolic flow input into the different colored anthocyanins
synthesis branches. The modifying genes such as, AT, 3GGT and 3MaT have
low basic and negative differential expression levels (Fig. [90]5).
However, most of the UGT (glucosyl or sambubioside were added to the 5
‘end of the glucoside) and RT (rutinoside was added to the 5 ‘or 3’ end
of the glucoside) in the modified genes were significantly up-regulated
in HX_1 and ZZ_1 contrast. Fluorescence quantitative PCR (q-PCR) was
performed on the mentioned 16 genes to verify the reliability of
transcriptome data. As shown in Fig. [91]6, the expression trends of
these genes detected in q-PCR analysis were consistent with those
detected in RNA-seq data.
Fig. 5.
[92]Fig. 5
[93]Open in a new tab
Detailed part of flavonoid metabolic subnetwork showing the subset of
nodes or metabolites that constitute the process. The enzyme name,
unigene names and expression patterns of each step are placed next to
it. The square represents the difference multiple of ZZ_1 and HX_1
expression (log2 (HX_1 / ZZ_1)), the circle represents the FPKM value
of ZZ_1, and the depth of color indicates the level of expression and
difference. PAL: phenylalanine ammonia-lyase; 4CL: 4-coumarate CoA
ligase; C4H: trans-cinnamate 4-monooxygenase; HCT: shikimate
O-hydroxycinnamoyltransferase; CHS: chalcone synthase; CHI: chalcone
isomerase; F3H: flavanone 3-hydroxylase; F3′H: flavonoid
3′-hydroxylase; F3′5′H: flavonoid 3′5′-hydroxylase; FLS: flavonol
synthase; DFR: dihydroflavonol 4-reductase; ANS: anthocyanidin
synthase; GT: anthocyanidin 5,3-O-glucosyltransferase; ANR:
anthocyanidin reductase; UFGT: anthocyanidin 3-O-glucosyltransferase;
UGT: anthocyanidin 3-O-glucoside 5-O-glucosyltransferase; AT:
anthocyanidin acyltransferase; RT: rhamnosyl transferase; 3GGT:
anthocyanidin 3-O-glucoside 2″-O-glucosyltransferase; 3MaT: anthocyanin
3-O-glucoside-6″-O-malonyltransferase
Fig. 6.
[94]Fig. 6
[95]Open in a new tab
Expression patterns of differentially expressed genes (DEGs) in
anthocyanin biosynthesis pathway as determined by q-PCR. Red represents
HX_1 and green represent ZZ_1. Data are means ± standard deviations
(SD). *, **, *** respectively indicates significant difference
(P < 0.05, P < 0.01, P < 0.01), and n.s. indicates no significant
difference gene expression value. The outer circle represents the fold
change (log[2]FC) of gene expression in HX_1 vs. ZZ_1
Metabolomic data analysis (LC/MS)
The LC/MS non-targeted metabolomics were conducted for studying of
correlation between the differentially expressed genes and the content
of metabolites. Firstly, data-sets obtained from UPLC-Triple-TOF-MS
were subjected to PCA, PLS-DA (Additional file [96]7: Fig. S4A; Fig.
S4B) to compare the metabolites involved in the color formation of two
ramie varieties. The results showed that the two varieties were
obviously separated on the PCA score plot corresponding to HX_1and
ZZ_1, respectively. Furthermore, OPLS-DA analysis and 200 times
response sequencing test of OPLS-DA model were used for modeling the
differences between two varieties (Additional file [97]7: Fig. S4C;
Fig. S4D). The metabolites of the differences were selected by
statistical analysis (volcano plot, Additional file [98]7: Fig. S4E)
through statistical analysis as described in materials and methods. The
general pattern of metabolites is relatively similar in HX_1 and ZZ_1.
A total of 5815 metabolites were detected in 20,897 peaks. Among them,
649 metabolites such as flavonoids, anthocyanins and
glycerophospholipid were significantly different. According to the VIP
value, visual analysis of hierarchical clustering and correlation were
performed on the top 50 differential metabolites (Fig. [99]7), this
intuitively shows the interrelation between samples and metabolites
among different samples. Flavonoids such as anthocyanin are the most
important colorants in plants. As shown in Table [100]2, there were 15
kinds of modified anthocyanins in the two varieties, which could be
classified into four categories. All the identified pelargonidin-,
malvidin- and most cyanidin-based anthocyanins showed a high relative
content in HX_1 than ZZ_1, whereas delphinidin-based anthocyanins in
contrast have a high relative content in ZZ_1 compared to HX_1. We
found a total of 9 anthocyanin modifying groups among which the content
of MaT-related malonyl modified anthocyanins was found to be much
higher in ZZ_1 than in HX_1 (Table [101]2). The content of AT-related
acyl group modified anthocyanins including caffeoyl, coumaroyl,
succinyl, oxalyl and galloyl, however, was much higher in HX_1 compared
to ZZ_1.
Fig. 7.
[102]Fig. 7
[103]Open in a new tab
LC-MS: Visual clustering analysis (A) and correlation (B) analysis of
Top50 DEMs
Table 2.
The anthocyanins in HX_1 and ZZ_1
m/z Metabolites Compound ID Sub Class HX_1 ZZ_1 FC
Cyanidin 530.11 Cyanidin 3-(6”succinyl-glucoside) HMDB0029237 Flavonoid
glycosides 43.66 23.64 1.85
690.14 Cyanidin 3,5-diglucoside (6″,6″‘-malyl diester) LMPK12010207
Flavonoids 20.58 14.11 1.46
604.14 Cyanidin 3-sambubioside HMDB0037976 Flavonoid glycosides 40.97
26.51 1.55
592.07 Cyanidin 3-(6″-dioxalylglucoside) HMDB0039936 Flavonoid
glycosides 10.07 256.62 0.04
602.09 Cyanidin 3-(3″,6″-dimalonylglucoside) HMDB0030096 Flavonoid
glycosides 2.38 22.90 0.10
Pelargonidin 674.15 3,5-di-O-(beta-Glucopyranosyl) pelargonidin 6″-O-4,
6″‘-O-1-cyclic malate LMPK12010067 Flavonoids 16.24 0.79 20.57
Malvidin 332.09 Malvidin LMPK12010004 Flavonoids 82.16 24.30 3.38
673.2 Malvidin 3-(6″-p-caffeyglucoside) LMPK12010384 Flavonoids 28.46
9.85 2.89
800.22 Malvidin 3-(6-coumaroylglucoside) 5-glucoside HMDB0038011
Flavonoid glycosides 39.74 7.23 5.50
784.22 Malvidin 3-p-coumarylrutinoside LMPK12010385 Flavonoids 40.83
26.73 1.53
Delphinidin 682.14 Delphinidin 3-(6″-malonylsambubioside) LMPK12010305
Flavonoids 13.27 97.45 0.14
460.1 4′-O-Methyldelphinidin 3-O-beta-D-glucoside HMDB0041684 Flavonoid
glycosides 88.57 229.22 0.39
436.1 Delphinidin 3-arabinoside HMDB0037998 Flavonoid glycosides 4.04
12.44 0.33
598.15 Delphinidin 3-sambubioside HMDB0038003 Flavonoid glycosides
15.19 68.13 0.22
[104]Open in a new tab
Anthocyanin pathway is a branch of flavonoid metabolic pathway; thus,
we had also detected many other substances involved in the flavonoid
metabolism pathway. As shown in Table [105]3, a total of 14 flavonoids
had been identified, and the content of all flavonoids were found to be
higher in HX_1 than ZZ_1, in consistent with the transcriptome data.
The flavonol synthase encoded by FLS was the key enzyme leading to
flavonoids. The expression level of this gene in HX_1 is 27 times
higher than that of ZZ_1.
Table 3.
The main flavonoids in HX_1 and ZZ_1
m/z Metabolites Compound ID Sub Class HX_1 ZZ_1 FC
373.1 3′,4′-Methylenedioxy-2,4,6, beta-tetramethoxychalcone
LMPK12020254 Flavonoids 630.56 182.18 3.46
389.1 3-(3,4-dimethoxyphenyl)-5-hydroxy-7-methoxy-8
methyl-3,4-dihydro-2H-1-benzopyran-4-one HMDB0129573 O-methylated
isoflavonoids 1064.87 330.41 3.22
341.1 Brosimacutin D LMPK12140055 Flavonoids 332.41 41.46 8.02
665.2 Nirurin LMPK12140614 Flavonoids 2797.60 213.74 13.09
523.2 Dichotosinin LMPK12020273 Flavonoids 9362.60 4261.89 2.20
663.2 Flavaprenin 7,4′-diglucoside LMPK12140228 Flavonoids 2945.23
203.81 14.45
538.2 (7′S,8′S)-4,7′-Epoxy-3,8′-bilign-7-ene-3′,5
dimethoxy-4′,9,9′-triol 4′-glucoside HMDB0040636 2-arylbenzofuran
flavonoids 610.02 121.62 5.02
583.2 8-trans-[2-(6-Benzoyloxy-4-hydroxy-2-methoxy-3-methylphenyl)
ethenyl]-5-methoxyflavan-7-ol LMPK12020254 Flavonoids 993.88 278.95
3.56
903.3 Chamuvaritin LMPK12120473 Flavonoids 4859.48 2062.92 2.36
811.3 3-(3-hydroxyphenyl)-2-phenyl-4-[(E)-2-phenylethenyl]
2,3-dihydro-1-benzofuran-6-ol HMDB0129161 2-arylbenzofuran flavonoids
188.22 11.14 16.90
551.2 4,6,2′,4′-Tetramethoxychalcone 2′-beta-glucoside LMPK12120280
Flavonoids 582.56 161.13 3.62
451.1 Afzelechin 7-apioside HMDB0030824 Flavans 258.82 100.19 2.58
449.1 Koaburanin LMPK12020253 Flavonoids 540.35 119.89 4.51
739.4 Unanisoflavan LMPK12080009 Flavonoids 462.82 123.97 3.73
[106]Open in a new tab
Discussion
Ramie is traditionally used only as bast fiber resource and more than
80% of its biomass (leaves and young stems) is wasted [[107]2] even
though such wasted tender leaf and stem are rich in functional
compounds such as flavonoids, chlorogenic acid and many useful
bioactive constituents [[108]20]. To harness maximal benefits and
ensure multi-purpose utilization of ramie we previously bred two forage
ramie variety HX_1 and ZZ_1. Both of these varieties have shown strong
regenerative ability and can as well be harvested 4–7 times per year.
The main difference between HX_1 and ZZ_1 was their leaf color.
Previous studies have shown anthocyanins as the main factor determining
plant color, and also an important plant secondary metabolites with
many physiological functions [[109]21, [110]22]. This prompted us to
proposed that the variation in the color might not be unconnected to
the anthocyanins contents and might affect the quality of HX_1 and ZZ_1
leaves as forage.
Plant pigments determine the color of plants, and the differences in
leaf color among different cultivars are due to the relative contents
of three main pigment groups (chlorophylls, carotenoids, and
anthocyanin) [[111]23]. Analyses of biological characteristics of the
ramie varieties ZZ_1 and HX_1 have confirmed that the leaf color of
ZZ_1 is green, while that of HX_1 is red. However, precise phenotypic
characteristics and the main pigment components and their contents in
these varieties had not been determined. The comparison of leaf color
parameters showed that there were obvious color differences between the
two with the a^* value of ZZ_1 being significantly lower than HX_1
which indicated that ZZ_1 is greener. We found that the contents of
chlorophyll and carotenoid were similar in the two varieties whereas
the anthocyanin content of HX_1 was significantly higher than that of
ZZ_1. Anthocyanins are the reason why plant leaves appear red, blue and
purple [[112]24, [113]25]. Though HX_1 has higher anthocyanin content
its leaves are green. This is because the chlorophyll content is more
than 140 times the anthocyanin content (mg/g). Therefore, we inferred
that anthocyanin at low concentrations can’t mask the green color of
chlorophylls and color of plants is due to the different relative
content of the pigments. These explanations were consistent with those
of a previous study on tea [[114]12]. Together, these results indicated
that high contents of anthocyanin play critical roles in the redness of
leaf of HX_1.
Phenotypic analysis and metabolomics analysis showed that the content
of anthocyanins in HX_ 1 leaf was higher than that in ZZ_1. Therefore,
the identification of DEGs related to flavonoid and anthocyanin
metabolism is a necessary step to elucidating the mechanism of mottled
pigment formation. Structural genes involved in flavonoid and
anthocyanin biosynthesis have been found and reported in many plants
[[115]26]. In our study, PAL, C4H, CHS and other upstream genes were
up-regulated, which provided enough precursors for downstream
metabolism. Another key node is the bifurcation of anthocyanin
synthesis, anthocyanin 3-O glucose transferase (UFGT) encoded by
whole_GLEAN_10025009 is the key enzyme in anthocyanin synthesis, also
known as BZ1 protein. It is the key step for anthocyanin stability and
water solubility in plant [[116]27]. In the grapes, the expression
analysis of UFGT genes in white and red-skinned revealed that the UFGT
was present, but not expressed in the white skins [[117]28], also exist
in apples [[118]29], and tobacco flowers [[119]30]. The loss of
function or low expression of UFGT leads to loss or reduced
accumulation of anthocyanin [[120]31]. In addition, the expression
levels of UGT and RT in anthocyanin modified genes were significantly
higher in HX_1 compared to ZZ_1, this did not mean that the
transcriptome did not match the metabolome as these modifiers also play
a role in the synthesis of other compounds which require further
investigation.
The expression level of ramie UFGT gene in HX_1 was almost 26 times
than in ZZ_1, these coincides with the anthocyanin accumulation and the
leaf color phenotype between HX_1 and ZZ_1. Furthermore, the substrate
competitor of UFGT--ANR correlated with epicatechin biosynthesis was
strongly up-regulated, which make the synthesis of anthocyanin prior to
the biosynthesis of the other substances, leading to high accumulation
of anthocyanins in HX_1. Furthermore, F3′H can catalyze the
hydroxylation of B-ring at C3′ for various substrates such as
dihydrokaempferol and naringenin chalcone, while F3′5′H catalyze the
hydroxylation of B-ring at C3′ and C5′, resulting into the production
of delphinidin-based anthocyanins. Therefore, F3’5’H and F3’H were
known as the “blue gene” and the “red gene” [[121]32]. The two genes
were respectively down- and up-regulated in HX_1 vs. ZZ_1, this may
bring out HX_ 1 metabolic flow into the cyanidin-based anthocyanin
branch, and ZZ_1 into the delphinidin-based anthocyanin branch. Thus,
we conclude that the differential expression of UFGT, F3’5’H or F3’H
gene were the key factors for anthocyanin accumulation and the leaf
color formation in the two ramie varieties.
We also found that anthocyanin synthesis pathway was up-regulated and
lignin synthesis pathway significantly down-regulated in HX_1 which was
consistent with the metabolome data. Interestingly, the content of
lignin decreased and the content of anthocyanins increased, which made
the leaves of forage ramie more palatable. Anthocyanin accumulation in
ramie may be a determinant for its utilization as forage. We think the
results of our study will accelerate the breeding of functional forage
ramie variety and their application in tropical, subtropical regions
where there is lack of high-quality protein feed.
Materials and methods
Plant materials and experimental conditions
The green variety Zhongzhu No.1 and red variety Hongxuan No.1, bred by
the Institute of bast fiber crops, Chinese Academy of Agricultural
Sciences, were selected as experimental materials in Wangcheng
District, Changsha City, Hunan Province. From top to bottom, 4–5 leaves
and petioles were collected from ramie plants in the first season under
the same environment at the same time. After labeling, they were put
into self-sealed bags and put in liquid nitrogen for cryopreservation
immediately and later stored at − 80 °C until further experiments.
LC-MS analysis
The above experimental materials were selected for LC-MS metabolomics
analysis. All chemicals and solvents were analytical or HPLC grade.
Water, methanol, acetonitrile, formic acid was purchased from CNW
Technologies GmbH (Düsseldorf, Germany) and L-2-chlorophenylalanine
from Shanghai Hengchuang Bio-technology Co., Ltd. (Shanghai, China).
Metabolomics was detected by OE Bio-technology Co., Ltd. (Shanghai,
China). Firstly, 80 mg of the sample was weighed, and 20 μl of internal
standard (L-2-chlorophenylalanine, 0.3 mg/ml; Lyso PC17:0, 0.01 mg/ml,
all methanol configuration) and 1 ml methanol: water (V: V = 7:3) were
added. Two small steel balls were then inserted, adding, precooled to
− 20 °C for 2 min followed by grinding (60 Hz, 2 min). Following
centrifugation, the solution was filtered into LC bottle using 0.22 μ m
organic phase pinhole filter and stored at − 80 °C until LC-MS
analysis. Quality control samples (QC) were prepared by mixing aliquots
of the all samples and used as pooled sample.
The experimental instrument was a liquid chromatography-mass
spectrometry (LC-MS) system composed of WatersTM ACQUITY UPLC ultra
performance liquid phase tandem AB Triple TOF 5600 high resolution mass
spectrometers. The column temperature was 45 °C, mobile phase A-water
(containing 0.1% formic acid), B-acetonitrile / methanol (2 / 3) (V /
V) (containing 0.1% formic acid), 0.4 ml / min flow rate and 5 μl
injection volume. ESI was used as the ion source, and the positive and
negative ion scanning mode was used to collect the MS signal. The data
preprocessing was basically the same as GC / MS with a difference being
only in baseline filtering. Peak identification, integration, retention
time correction, peak alignment and normalization were performed by
Progenesis QI v2.3 software (Nonlinear Dynamics, Newcastle, UK) for
LC-MS raw data. The parameter settings were slightly different, and the
metabolites were annotated by the Human Metabolome Database and METLIN
metabolite database.
Identification of differentially expressed metabolites (DEM)
The data matrix was imported into SIMCA software package (14.0,
Umetrics, Umeå, Sweden). We used a variety of statistical analysis
methods (PCA, PLS-DA, OPLS-DA) to distinguish the overall difference of
metabolic profiles between groups and find the metabolites differences
between groups. The metabolites with VIP value greater than 1.0 and P
value less than 0.05 were identified as differentially expressed
metabolites (DEMs) by OPLS-DA model and t test of normalized peak area
respectively. The p value and fold change value obtained by t-test and
multiple variation analysis respectively were visualized and volcano
map was made to screen differential metabolites. To prevent over
fitting, seven cycles cross validation (7-fold cross validation) and
200 response permutation testing (RPT) were used to evaluate the
quality of the model.
The KEGG ID of different metabolites was used for pathway enrichment
analysis to obtain the enrichment results of metabolic pathway.
Compared with the whole background, the hypergeometric test was used to
find the pathway items which were significantly enriched in the DEMs.
With a p-value ≤0.05 set threshold, the smaller it is, the more
significant the difference of the metabolic pathway.
Transcriptome sequencing analysis
After extracting total RNA and digesting DNA with DNase, eukaryotic
mRNA was enriched with magnetic beads Oligo (dT). The cDNA was
synthesized with six base random primers using the interrupted mRNA as
template. The purified double-stranded cDNA was subjected to terminal
repair (3′ end plus A base), and then connected with sequencing
connector and PCR amplification carried out. After passing the quality
inspection with Agilent 2100 Bioanalyzer, the library was sequenced
using Illumina HiSeqTM 2500 sequencer.
We used Trimmmatic software to preprocess the quality of the original
data (to remove adapter, low-quality reads and low-quality bases).
Hisat2 was used to compare Clean Reads with the reference genome of
ramie (Boehmeria nivea (L.) Gaud.) to obtain the position information
on the reference genome or gene, as well as the specific sequence
characteristics of the sequencing samples. The reference transcripts
were used as the database, and the expression abundance of each
transcript in each sample was calculated by sequence similarity
comparison using Bowtie and eXpress software.
The expression of mRNA was calculated by FPKM (Fragments Per kb Per
Million Reads) method. We used DESeq software to standardize the number
of mRNA counts in each sample (using baseMean value to estimate the
expression level). Finally, differential mRNA was screened according to
the difference multiple (|log2 (foldchange) | ≥1) and difference
significance test results (P < 0.05).
We performed GO enrichment analysis of differentially expressed mRNA in
order to describe its function. The hypergeometric distribution was
used to test whether the GO function set was significantly enriched
(P < 0.05), and Fisher algorithm was used to analyze the difference
mRNA among samples by CC, BP and MF. We used KEGG database to analyze
the pathway of differential mRNA (combined with KEGG annotation
results), and calculate the significance of differential mRNA
enrichment in each pathway entry by hypergeometric distribution test,
so as to find out which cell pathway changes may be related to the
differential mRNA in different samples [[122]33]. The transcriptome
sequencing and analysis were conducted by OE biotech Co., Ltd.
(Shanghai, China).
Combined analysis of transcriptome and metabolome
According to the data of gene expression and metabolite response
intensity, Pearson correlation algorithm was used to calculate the
correlation between gene expression and metabolite response intensity
data. DEGs and DEMs were selected to draw correlation heat map and
correlation matrix. According to the results of association analysis
between different genes and different metabolites, association network
diagram was drawn. At the same time, the differential genes and
metabolites pathway were analyzed, and their common pathway information
was mapped to KEGG by using the program written by OE.
Determination of chlorophylls, carotenoids, and anthocyanin contents
Chlorophyll (Chl) and carotenoids (Car) were extracted from ramie
leaves with ethanol acetone mixture (1:1). Fresh ramie leaves (0.1 g)
were cut into 1 mm strips and put into the test tubes containing 20 ml
of the mixture. The tubes were sealed and extracted overnight at room
temperature (Shaking 3–4 times during the period). The next day, after
centrifugation at 10000 g for 10 min, absorbance was detected at
470 nm, 645 nm and 663 nm by spectrophotometer. Each sample was
analyzed in triplicate. The contents of Chl and Car were calculated
according to the following formula described by Porra [[123]34] and
Parsons and Strickland [[124]35]:
[MATH: Chlamg/g=12.72A663−2.69A645∗V/1000∗M :MATH]
[MATH: Chlbmg/g=22.88A645−4.68A633∗V/1000∗M :MATH]
[MATH: TotalChlmg/g=20.19A645+8.04A633∗V/1000∗M :MATH]
[MATH: Carmg/g=8.73A470+2.11A663-9.06A645∗V/1000∗M :MATH]
Where V is the volume of the extract (ml) and M is the mass of the
sample (g).
Anthocyanins were extracted from two samples according to the method
described by Guo [[125]12] with slight modification. The content of
anthocyanins was determined by extinction coefficient method [[126]36]:
0.1 g fresh leaves was ground into powder in liquid nitrogen and put it
into test tube, 10 ml of mixture (1.5 mol/l HCl: 95% Ethanol = 15:85)
was added, sealed, extraction performed in dark for 24 h, shaking
several times during the period. The mixture was later centrifuged at
4000 r/min for 10 min, supernatant filtered, and volume fixed to 20 ml.
Absorbance was measured at 535 nm, and 657 nm with water as a
reference. Anthocyanin was then computed using the formula described as
follows [[127]36]:
Total anthocyanin content (mg/100gFW) = 100A[max]V/98.2 M
[MATH: Amax=A530−0.25∗A657 :MATH]
Where V is the volume of the extract (ml) and M is the mass of the
sample (g).
Relative expression analyses of selected key DEGs
Total RNA was extracted using EasySpin Plus plant RNA rapid extraction
Kit (Aidlab Biotechnologies Co., Ltd) from different plants. To
determine the relative transcript levels of the differentially
expressed genes the RNA was used as templates and reverse-transcribed
using RevertAid First cDNA Synthesis Kit (Thermo Scientific) into cDNA.
qRT-PCR analyses were conducted using gene-specific primers and the
enzyme and fluorescent dye required for qRT-PCR were quantified by
mixed 2 × Sybr Green qPCR Mix (Aidlab Biotechnologies Co., Ltd).
The two-step qRT-PCR had a 25 μl reaction system consisting of 12.5 μl
2 × SYBR qPCR Mix, 1 μl gene-specific primers (forward primer 0.5 μl
and reverse primer 0.5 μl), 1.0 μl DNA template, and ddH2O up to 25 μl.
CFX96 Touch Deep Well Real-Time PCR System (Bio-Rad) was used to detect
gene transcripts. The qRT-PCR reaction procedure included initial
denaturation at 95 °C for 3 min, and a total of 39 cycles (95 °C for
10 s, 60 °C for 30 s) was used for denaturation and annealing. Finally,
the melt curve program was used to detect the specificity of primers.
18 s gene was used as internal control. Relative transcript levels were
calculated using the 2-ΔΔCt formula. The primer sequences were designed
using Primer 6 software (Additional file [128]8: Table S4). All qRT-PCR
analyses were performed with three biological and technical
replications. All histograms were drawn using GraphPad Prism 8 software
and merged using Adobe Photoshop (2020) software.
Statistical analyses
Each experiment was set up with three biological repeats, and all data
were expressed as mean ± standard deviation (SD). Difference between
varieties using one-way ANOVA (T Test) and Duncan’s test. The
difference between groups is indicated by *. The number of * represents
the degree of difference, n.s. represents no significant difference.
The data were analyzed using SAS9.4 software.
Conclusions
In this study, phenotypic, transcriptomic and metabolomic analysis of
Hongxuan No.1 and Zhongzhu No.1 revealed various pigments, metabolites
(flavonoids, anthocyanin), and metabolic pathways (anthocyanin
metabolism) responsible for the leaf color and secondary metabolite
profiles of HX_1 and ZZ_1. HX_1 leaf contains higher content of
anthocyanin, flavonoids than ZZ_1. High contents of anthocyanin and
flavonoids contribute to the deeper red leaf color of HX_1 than ZZ_1.
High transcript levels of these genes which involved in anthocyanin
metabolism pathways corresponded to increased accumulation of
anthocyanin in HX_1. This study will help for the functional analysis
of key genes controlling the color components of ramie leaves, and
provide a theoretical basis for the breeding of forage ramie and
multi-purpose utilization of ramie varieties.
Supplementary Information
[129]12864_2021_8007_MOESM1_ESM.xls^ (1.7KB, xls)
Additional file 1 Table S1: Statistical results of comparison rate
between reads and reference genome.
[130]12864_2021_8007_MOESM2_ESM.xls^ (712B, xls)
Additional file 2 Table S2: Statistical table of FPKM distribution of
mRNA.
[131]12864_2021_8007_MOESM3_ESM.docx^ (718.3KB, docx)
Additional file 3 Fig. S1: Analysis of mRNA expression level. A is
distribution of mRNA expression in 8 samples; B is Box plot.
[132]12864_2021_8007_MOESM4_ESM.docx^ (429.5KB, docx)
Additional file 4 Fig. S2: MA map and Volcano map. (A) MA map of
differential expression; (B) Volcano map of differential expression.
[133]12864_2021_8007_MOESM5_ESM.xls^ (3.4MB, xls)
Additional file 5 Table S3: GO functional analysis and enrichment
classification.
[134]12864_2021_8007_MOESM6_ESM.docx^ (191.5KB, docx)
Additional file 6 Fig. S3: Top20 enrichment analysis of KEGG. (A)
HX1-vs-ZZ1 (Up); (B) HX1-vs-ZZ1 (Down).
[135]12864_2021_8007_MOESM7_ESM.docx^ (430.8KB, docx)
Additional file 7 Fig. S4: LC-MS data analysis. (A) PCA; (B) PLS-DA;
(C) OPLS-DA analysis; (D) 200 times response sequencing test of OPLS-DA
model; (E) volcano plot.
[136]12864_2021_8007_MOESM8_ESM.xlsx^ (10.7KB, xlsx)
Additional file 8 Table S4: Primer sequences of qRT-PCR analyses.
Acknowledgements