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Citation: Li, H.; Zhou, T.; Chong, X.;
Lu, X.; Li, Y.; Zheng, B.; Wang, X.;
Chen, H. Transcriptome and
Expression Analysis of Genes Related
to Regulatory Mechanisms in Holly
(Ilex dabieshanensis) under Cold Stress.
Forests 2022,13, 2150. https://
doi.org/10.3390/f13122150
Academic Editors: Bin Dong, Cuihua
Gu and Guirong Qiao
Received: 16 October 2022
Accepted: 13 December 2022
Published: 15 December 2022
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Article
Transcriptome and Expression Analysis of Genes Related to
Regulatory Mechanisms in Holly (Ilex dabieshanensis) under
Cold Stress
Huihui Li 2, †, Ting Zhou 1, †, Xinran Chong 1, Xiaoqing Lu 1, Yunlong Li 1, Bingsong Zheng 3, Xiaolong Wang 1
and Hong Chen 1, *
1Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany,
Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
2Fuyang Academy of Agricultural Sciences, Fuyang 236065, China
3Zhejiang Provincial Key Laboratory of Forest Aromatic Plants-Based Healthcare Functions,
Zhejiang A & F University, Hangzhou 311300, China
*Correspondence: chenhong@cnbg.net
† These authors contributed equally to this work.
Abstract:
Ilex dabieshanensis (K. Yao and M. B. Deng) is not only an important economic tree species,
but also has the characteristics of evergreens in all seasons, as well as strong cold resistance. In order
to understand the molecular mechanism of holly’s response to cold stress, we used transcriptome
analysis to identify the main signaling pathways and key genes involved in cold stress. The result
showed that 5750 differentially expressed genes (DEGs) were identified under different cold treatment
times compared with the control (cold—0 h). The Kyoto Encyclopedia of Genes and Genomes
(KEGG) analysis of DEGs showed that seven phytohormone signal transduction were the most highly
enriched, including abscisic acid (ABA), ethylene (ET), cytokinin (CK), auxin (IAA), gibberellin (GA),
jasmonate (JA), and brassinosteroids (BR). In addition, proline metabolism, arginine metabolism,
flavonoid biosynthesis, and anthocyanin biosynthesis were also implicated in response to cold
stress. The weighted gene co-expression network analysis (WGCNA) showed that the genes in two
modules were significantly up-regulated after 12 h and 24 h treatments, suggesting these two module
genes may participate in the cold stress. The gene ontology (GO) results of the two module genes
showed that calcium, scavenging reactive oxygen species, and nitric oxide might act as signaling
molecules to regulate cold tolerance in holly. By calculating the connectivity and function prediction
of genes in the two modules, five genes (evm.TU.CHR2.244,evm.TU.CHR1.1507,evm.TU.CHR1.1821,
evm.TU.CHR2.89, and evm.TU.CHR2.210) were identified as the key hub genes of I. dabieshanensis
response to cold stress. These results provided candidate genes and clues for further studies on the
molecular genetic mechanism of cold stress in holly.
Keywords: cold stress; transcriptome; WGCNA; DEGs; expression analysis
1. Introduction
Ilex L. (holly) is the largest woody dioecious genus of angiosperms, with approximately
600 species and wide geographical distribution. Many holly species are economically
important trees used in various ways, including manufacturing beverages, medicines, and
wood [
1
–
3
]. Ilex dabieshanensis (K. Yao and M. B. Deng) (I. dabieshanensis) is a species of
Ilex, which was first discovered and named by the Institute of Botany, Chinese Academy of
Sciences in Jiangsu Province. This plant is a precious native tree species with multiple uses
in medicine, ornamental landscaping, and tea. I. dabieshanensis also has unique ecological
characteristics, such as pruning resistance, wide adaptability, cold resistance (Figure 1),
pollution resistance, and fewer pests and diseases [
4
]. Most of the research conducted on
holly involves processed products, extract functions, origin and distribution, physiological
Forests 2022,13, 2150. https://doi.org/10.3390/f13122150 https://www.mdpi.com/journal/forests
Forests 2022,13, 2150 2 of 22
characteristics, cultivation methods, and ornamental applications [
5
–
9
]. A recent article has
reported the whole genome of I. polyneura, which provides a necessary basis for advancing
research on Ilex to the molecular level [
7
]. As a perennial woody plant, I. dabieshanensis
undergoes seasonal changes and is evergreen throughout the year. Due to the cyclical
changes in seasons, plants are inevitably affected by the adversity of low temperatures.
Low temperatures are one of the most harmful environmental stressors encountered by
vascular plants [
10
,
11
]. Throughout long-term evolution, plants have gradually developed
unique biological characteristics, physiological functions, and other adaptive mechanisms,
which have given them the ability to adapt to and resist low-temperature conditions. These
adaptations vary by species, geographic distribution, and even developmental stage. Each
plant has different enrichment pathways during different cold stress periods, and every
gene has a unique mechanism in response to cold stress [
12
]. Currently, there are no reports
on the cold tolerance mechanism of holly.
Forests 2022, 13, x FOR PEER REVIEW 2 of 23
unique ecological characteristics, such as pruning resistance, wide adaptability, cold re-
sistance (Figure 1), pollution resistance, and fewer pests and diseases [4]. Most of the re-
search conducted on holly involves processed products, extract functions, origin and dis-
tribution, physiological characteristics, cultivation methods, and ornamental applications
[5–9]. A recent article has reported the whole genome of I. polyneura, which provides a
necessary basis for advancing research on Ilex to the molecular level [7]. As a perennial
woody plant, I. dabieshanensis undergoes seasonal changes and is evergreen throughout
the year. Due to the cyclical changes in seasons, plants are inevitably affected by the ad-
versity of low temperatures. Low temperatures are one of the most harmful environmen-
tal stressors encountered by vascular plants [10,11]. Throughout long-term evolution,
plants have gradually developed unique biological characteristics, physiological func-
tions, and other adaptive mechanisms, which have given them the ability to adapt to and
resist low-temperature conditions. These adaptations vary by species, geographic distri-
bution, and even developmental stage. Each plant has different enrichment pathways dur-
ing different cold stress periods, and every gene has a unique mechanism in response to
cold stress [12]. Currently, there are no reports on the cold tolerance mechanism of holly.
Figure 1. I. dabieshanensis in the snow.
When plants are subjected to low-temperature stress, they undergo a series of phys-
iological and biochemical reactions and gene expression regulation from sensing low-tem-
perature signals, which produce cold resistance. At the physiological level, many sub-
stances or protective proteins are synthesized in the plant, such as soluble sugars, proline,
and cold resistance proteins [13,14]. These substances or proteins regulate osmotic poten-
tial, ice crystal formation, cell membrane stability, and scavenging reactive oxygen species
(ROS) [15,16]. At the molecular level, according to the function of low temperature-in-
duced expression genes, genes can be divided into two categories: regulatory genes,
which play a role in regulating gene expression and signal transduction, including tran-
scription factors (TFs) such as C-repeat binding factors (CBF) and protein kinases that
sense and transmit signals such as mitogen-activated protein kinase (MAPKs) [17–21]; and
cold-regulated (COR) genes, which are early responsive to dehydration (ERD) genes, re-
sponsive to dehydration (ED) genes, low-temperature induced (LTI) genes, and cold in-
ducible (KIN) genes [22,23]. Previous studies found that the inducer of CBF expression
(ICE)-CBF-COR pathway plays a crucial role in plant cold tolerance and is one of the most
widely reported pathways [24]. In most plant species, the ICE-CBF-COR pathway is in-
duced by cold stress, activating the expression of downstream genes encoding osmotic
regulators [25]. In detail, ICE1 can be released from DELLA-binding Jasmonate ZIM-do-
main (JAZ) to induce the expression of CBF3 under low-temperature stress. CBFs, also
Figure 1. I. dabieshanensis in the snow.
When plants are subjected to low-temperature stress, they undergo a series of phys-
iological and biochemical reactions and gene expression regulation from sensing low-
temperature signals, which produce cold resistance. At the physiological level, many
substances or protective proteins are synthesized in the plant, such as soluble sugars, pro-
line, and cold resistance proteins [
13
,
14
]. These substances or proteins regulate osmotic
potential, ice crystal formation, cell membrane stability, and scavenging reactive oxygen
species (ROS) [
15
,
16
]. At the molecular level, according to the function of low temperature-
induced expression genes, genes can be divided into two categories: regulatory genes,
which play a role in regulating gene expression and signal transduction, including tran-
scription factors (TFs) such as C-repeat binding factors (CBF) and protein kinases that
sense and transmit signals such as mitogen-activated protein kinase (MAPKs) [
17
–
21
];
and cold-regulated (COR) genes, which are early responsive to dehydration (ERD) genes,
responsive to dehydration (ED) genes, low-temperature induced (LTI) genes, and cold
inducible (KIN) genes [
22
,
23
]. Previous studies found that the inducer of CBF expression
(ICE)-CBF-COR pathway plays a crucial role in plant cold tolerance and is one of the
most widely reported pathways [
24
]. In most plant species, the ICE-CBF-COR pathway is
induced by cold stress, activating the expression of downstream genes encoding osmotic
regulators [
25
]. In detail, ICE1 can be released from DELLA-binding Jasmonate ZIM-
domain (JAZ) to induce the expression of CBF3 under low-temperature stress. CBFs, also
known as dehydration-responsive element-binding proteins (DREBs), are members of the
APETALA2/ethylene-responsive factor (AP2/ERF) family that activates cold-responsive
(COR) gene expression by binding to cis-elements on the COR gene promoter [
26
]. Winter
Forests 2022,13, 2150 3 of 22
barley has a vernalization gene (VRN1) that can synergize with the CBF gene, inducing a
stronger cold resistance than spring barley [
27
]. ICE acts upstream to induce and regulate
the expression of CBF [
28
–
30
]. Two homologs of the ICE gene (ICE1 and ICE2) have been
characterized in multiple plant species, and their role in cold tolerance has been eluci-
dated [
31
]. In Arabidopsis, many COR genes are activated or repressed by the action of
CBF1/2/3 and have been shown to directly or indirectly increase cold stress tolerance in
plants [
32
]. Many TFs that regulate cold signaling and stress have been identified, such as
CBF1-3, ICE1-2, CAMTA3, MYB15, COR15a, COR15b, etc. [
33
]. In addition, some protein
kinases (MEKK1-MKK1/2-MPK4) were found to induce the expression of CBF, especially
CBF2, which is involved in the mitogen-activated protein kinase (MAPK) cascade [
34
,
35
].
In detail, cold stress receptor kinase-like 1 (CRKL1) is regulated by calcium/calmodulin
binding, and CRLK1/2 interacts with MEKK1, a MAPK module that responds to lower
temperatures [
19
,
20
]. MEKK1 then phosphorylates MKK2, which activates MPK4/6, form-
ing an upstream pathway, CRLK1-MEKK1-MKK2-MPK4-MPK3/6, that enhances CBF gene
expression [21,36].
Plant hormones play an important role in plant growth and development and protect
plants from abiotic stresses, such as cold stress. It has been reported that many hormones
regulate cold stress by regulating CBF. One of the most important hormones in cold
stress is abscisic acid (ABA) [
37
]. Previous studies have shown that ABA can induce the
expression of CBF and COR genes [
38
]. Gibberellin (GA) plays a powerful role in the
ICE-CBF-COR pathway [
39
]. It is also understood that anthocyanin content in plants is
higher under cold stress conditions. Brassica napus transformed with the AtGA2ox8 gene
has a reduced amount of bioactive GA and can produce more anthocyanins in winter [
40
].
Ethylene (ET), ABA, and jasmonate (JA) induce the expression of ethylene-responsive
factor (ERF) genes. ERFs can bind to the GCC box and DRE elements at low temperatures
to enhance plant tolerance to cold stress [
41
]. In addition, methyl jasmonate can induce
the expression of guaiacol peroxidase (POD), catalase (CAT), and ascorbate peroxidase
(APX)-related genes that reduce reactive oxygen species, enhancing bell pepper tolerance
to low-temperature stress [
33
]. Two brassinosteroids (BR)-responsive transcription factors,
Brassinazole-resistant 1 (BZR1) and CES (CESTA), are also direct regulators of CBF [
39
,
42
].
Due to these differences in signal transduction pathways and metabolisms, the plant
mechanisms associated with cold stress are complex [
43
]. Studying transcriptional changes
in plants during cold stress is crucial for understanding and identifying plants’ molecular
mechanisms under cold stress. In this study, we designed cold treatments at different time
markers to investigate the transcriptome changes of I. dabieshanensis. A comprehensive
comparative analysis identified the major signaling pathways and key genes involved
or shared by cold stress. These results broadened our understanding of the molecular
mechanisms by which holly responds to low temperatures and provided information for
further studies on key gene functions.
2. Materials and Methods
2.1. Plant Materials and Experimental Design
The research material is the annual cutting seedlings of I. dabieshanensis, and the
cuttings are all derived from the same mother plant. This ensures the consistency of the
genetic background and physiological state of the experimental material. The seedlings
with consistent growth (about 10 leaves) were selected and placed in an artificial climate
room at 25
◦
C for 4 weeks. After 4 weeks, they were treated at a low temperature of 4
◦
C,
and samples were taken at 0 h, 3 h, 6 h, 9 h, 12 h, and 24 h, respectively [
44
], and the leaves
of 6 plants were mixed as a sample in each time period, and three biological replicates were
set for each treatment. Samples were immediately stored in liquid nitrogen. A part was
directly used for transcriptome sequencing, and the rest was stored in a
−
80
◦
C ultra-low
temperature freezer for later use.
Forests 2022,13, 2150 4 of 22
2.2. Total RNA Extraction and mRNA Libraries Construction
Total RNA was extracted from leaves using a plant RNA purification reagent. We used
the Illumina TruseqTM RNA sample prep Kit method to construct the library. Detailed
experimental steps are as follows. The starting amount of 1
µ
g total RNA was used to build
the library; after the mRNA was isolated by magnetic beads, the mRNA was interrupted by
ions (TruseqTM RNA sample prep Kit); double-stranded cDNA synthesis, filling, adding
A at the 3
0
end, and connecting the index adapter (TruseqTM RNA sample prep Kit)
were performed; library enrichment and PCR amplification for 15 cycles were performed;
2% agarose gel recovery of the target band (Certified Low Range Ultra Agarose); TBS380
(Picogreen) quantification was performed, mixed according to the data ratio; cBot Bridge
PCR amplification was performed on the top to generate clusters; and the Illumina Novaseq
sequencing platform was used for 2×150 bp sequencing.
2.3. mRNA Sequence Data Processing
The original image data obtained by Illumina sequencing was converted into sequence
data through base calling, and the results are stored in the FASTQ file format. The FASTQ
file is the most original data file, and the file contains the sequence information of the
sequencing read and the sequencing quality information. The raw sequencing data were
first filtered by using trimmomatic (ILLUMINACLIP:adapters.fa:2:30:10 SLIDINGWIN-
DOW:4:15 MINLEN:75) (http://www.usadellab.org/cms/index.php?page= trimmomatic)
(accessed on 12 August 2021), so as to obtain high-quality sequencing data (clean data)
to ensure the smooth progress of subsequent analysis (Bioproject accession number: PR-
JNA897070). Next, the high-quality sequencing sequences obtained after quality control
were compared with the designated I. polyneura reference genome (https://ngdc.cncb.
ac.cn/search/?dbId=gwh&q=GWHBDNW00000000&page=1) (accessed on 7 April 2022),
using Hisat2 (http://://ccb.jhu.edu/software/hisat2/faq.shtml) (accessed on 7 April 2022)
with default parameters. The FPKM value (fragments per kilobase of exon model per mil-
lion mapped reads) was calculated using featureCounts (http://subread.sourceforge.net/)
(accessed on 7 April 2022).
2.4. Screening of Differentially Expressed Genes
Use edgeR software was used to analyze differentially expressed genes, and it was
used to compare different cold treatment groups in pairs to obtain Cold-3 h vs. Cold-ck
(0 h), Cold-6 h vs. Cold-ck, Cold-9 h vs. Cold-ck, Cold-12 h vs. Cold-ck, Cold-24 h vs.Cold-
ck, Cold-6 h vs. Cold-3 h, Cold-9 h vs. Cold-3 h, Cold-12 h vs.Cold-3 h, Cold-24 h vs.
Cold-3 h, Cold-9 h vs. Cold-6 h, Cold-12 h vs. Cold-6 h, Cold-24 h vs. Cold-6 h, Cold-12 h
vs. Cold-9 h, Cold-24 h vs. Cold-9 h, and Cold-24 h vs. Cold-12 h as differentially expressed
genes. The fold change (FC) and false discovery rate (FDR) were used as the screening
conditions for differentially expressed genes, and the screening threshold was FDR
≤
0.05
and FC ≥2. The differential expression was analyzed visually.
2.5. Differentially Expressed Gene Enrichment Analysis
The software Goatools (https://github.com/tanghaibao/GOatools) (accessed on
7 April 2022) was used for GO enrichment analysis, and the method was Fisher’s ex-
act test. The p-values were corrected using the FDR (false discovery rate) multiple testing
method to control for the calculated false positive rate. Typically, this GO function was
considered significantly enriched when the p-value
≤
0.05. Generally, the top 30 results of
the GO enrichment analysis were selected as the main nodes of the directed acyclic graph,
and the associated GO term was displayed together through the inclusion relationship.
The depth of the color represents the degree of enrichment. In our project, we drew DAG
diagrams of biological process, molecular function, and cellular component, respectively.
The software KOBAS (http://kobas.cbi.pku.edu.cn/kobas3/?t=1) (accessed on 7 April
2022) was used for KEGGPATHWAY enrichment analysis. The calculation principle was
the same as that of GO functional enrichment analysis, and Fisher’s exact test was used
Forests 2022,13, 2150 5 of 22
for calculation. In order to control the calculated false positive rate, the BH (FDR) method
was used for multiple testing. The calculation formula was the same as the previous
section, and the pvalue was 0.05 as the threshold. The KEGG pathway that met this
condition was defined as the KEGG pathway that was significantly enriched in differentially
expressed genes.
2.6. Construction of Weighted Gene Co-Expression Network
The WGCNA algorithm is a common algorithm for constructing gene co-expression
networks, and the R language package [
45
] was used for analysis. The WGCNA algorithm
first assumes that the gene network obeys a scale-free distribution and defines the gene co-
expression correlation matrix and the adjacency function formed by the gene network, and
then it calculates the dissimilarity coefficients of different nodes and builds a hierarchical
clustering tree accordingly. Different branches of the clustering tree represent different
gene modules. To identify biologically meaningful modules, module features are used to
calculate correlation coefficients. Principal component analysis (PCA) was performed on all
genes in each co-expression module, and the principal component 1 (PC1) was called the
eigengene of this module (Module eigengene, ME) in order to screen the cold stress-related
specificity module, as well as to calculate the correlation coefficient r and pvalue of ME
values of each module and different traits (here, different cold stress time). In this study, a
module was considered to be a specific module if the correlation coefficient between its ME
value and the trait |r| > 0.70 and p< 0.05. The top 30 genes connected in the module were
selected as hub genes (highly connected genes). In this study, the connection relationship
with edge weight > 0.3 was selected, and the hub gene interaction network was visualized
using Cytoscape_3.3.0.
2.7. Validation of Gene Expression by qRT-PCR
To verify the RNA-seq results, we selected five DEGs for quantitative reverse tran-
scription PCR (RT-qPCR) analysis. Total RNA of 18 samples was isolated by Trizol reagent
following the protocol. The cDNA was generated by Primerscript RT reagent Kit with
gDNA Erase (Takara), according to the manufacturer’s protocol. Quantitative PCR (qPCR)
was performed using SYBR Premix Ex Tag (Takara), with actin as an internal reference
gene. The relative expression levels of five DEGs normalized to the expression level of the
internal reference control were calculated using the 2
−∆∆Ct
method [
46
]. The primers are
listed in Table S1.
3. Results
3.1. Differential Analysis of Gene Expression in I. dabieshanensis under Cold Stress
Taking the genome annotation file as a reference, the FPKM value of each transcript in
each sample was counted, and this value was used as the transcript’s expression level. The
significant differences in expression were analyzed for different cold treatments [0 h (h),
3 h, 6 h, 9 h, 12 h, and 24 h] to study the gene expression of I. dabieshanensis under cold
stress. The significantly different gene (DEG) screening conditions were FDR
≤
0.05 and
FC
≥
2. A total of 5750 DEGs were obtained, and differential expression was visualized
(Table S2). Compared to no cold stress (CK), gene expression was significantly up-regulated
or down-regulated during different cold stress time markers. As the period of stress in-
creased, the number of up-regulated DEGs and down-regulated DEGs increased
(Figure 2a–e). Compared to 3 h, the number of DEGs under stress for 6 h, 9 h, 12 h,
and 24 h were 241, 555, 1859, and 2228, respectively (Figure 2f–i). Compared to 6 h, the
number of DEGs under stress for 9 h, 12 h, and 24 h were 238, 1737, and 1937, respectively
(Figure 2j–l). Compared to 9 h, the number of DEGs under stress for 12 h and 24 h was 1330
and 1636, respectively (Figure 2m,n). Compared to 12 h, the number of DEGs under stress
for 24 h was 1566 (Figure 2o). These results indicated that the expression of some genes
was significantly different between different stress treatments, and the number of DEGs
showed an upward trend as the treatment time gap increased.
Forests 2022,13, 2150 6 of 22
Forests 2022, 13, x FOR PEER REVIEW 6 of 23
under stress for 9 h, 12 h, and 24 h were 238, 1737, and 1937, respectively (Figure 2j–l).
Compared to 9 h, the number of DEGs under stress for 12 h and 24 h was 1330 and 1636,
respectively (Figure 2m,n). Compared to 12 h, the number of DEGs under stress for 24 h
was 1566 (Figure 2o). These results indicated that the expression of some genes was sig-
nificantly different between different stress treatments, and the number of DEGs showed
an upward trend as the treatment time gap increased.
Figure 2. Volcano-plots of cold-induced DEGs. (a) 3 h vs. CK (0 h); (b) 6 h vs. CK; (c) 9 h vs. CK; (d)
12 h vs. CK; (e) 24 h vs. CK; (f) 6 h vs. 3 h; (g) 9 h vs. 3 h; (h) 12 h vs. 3 h; (i) 24 h vs. 3 h; (j) 9 h vs. 6
h; (k) 12 h vs. 6 h; (l) 24 h vs. 6 h; (m) 12 h vs. 9 h; (n) 24 h vs. 9 h; (o) 24 h vs. 12 h. The abscissa is the
fold change value of the gene or transcript expression difference between two samples (sample
1/sample 2), and the ordinate is the statistical test value of the difference in gene or transcript ex-
pression change; that is, the higher the p-value, the more significant the difference in expression.
The values of the abscissa and ordinate are all logarithmic. Each dot in the figure represents a spe-
cific gene or transcript; the red dots represent significantly up-regulated genes, the blue dots repre-
sent significantly down-regulated genes, and the black dots represent non-significant differential
genes. The dots on the left represent differentially down-regulated genes, and the dots on the right
are genes whose expression is differentially up-regulated.
3.2. GO Enrichment Analysis of DEGs
To fully understand the gene function classification of holly in response to cold stress,
GO annotation was first performed on DEGs. GO annotation includes three aspects: bio-
logical process (BP), cellular component (CC), and molecular function (MF). The results
showed that, compared to 0 h, DEGs under different cold treatments were annotated in
BP, CC, and MF, with differences. From the analysis of level 2, the DEGs of 3 h vs. 0 h
were mainly annotated to 8, 3, and 17 GO terms for MF, CC, and BP, respectively (Figure
S1). The DEGs of 6 h vs. 0 h were mainly annotated to 8, 3, and 19 GO terms for MF, CC,
and BP, respectively (Figure S1). The DEGs of 9 h vs. 0 h were mainly annotated to 8, 3,
and 19 GO terms for MF, CC, and BP, respectively (Figure S1). The DEGs of 12 h vs. 0 h
were mainly annotated to 9, 3, and 18 GO terms for MF, CC, and BP, respectively (Figure
S1). The DEGs of 24 h vs. 0 h were mainly annotated to 8, 3, and 20 GO terms for MF, CC,
and BP, respectively (Figure S1). Although the GO terms were different, they were mainly
Figure 2.
Volcano-plots of cold-induced DEGs. (
a
) 3 h vs. CK (0 h); (
b
) 6 h vs. CK; (
c
) 9 h vs. CK;
(
d
) 12 h vs. CK; (
e
) 24 h vs. CK; (
f
) 6 h vs. 3 h; (
g
) 9 h vs. 3 h; (
h
) 12 h vs. 3 h; (
i
) 24 h vs. 3 h;
(
j
) 9 h vs. 6 h; (k) 12 h vs. 6 h; (
l
) 24 h vs. 6 h; (
m
) 12 h vs. 9 h; (
n
) 24 h vs. 9 h; (
o
) 24 h vs. 12 h. The
abscissa is the fold change value of the gene or transcript expression difference between two samples
(sample 1/sample 2), and the ordinate is the statistical test value of the difference in gene or transcript
expression change; that is, the higher the p-value, the more significant the difference in expression.
The values of the abscissa and ordinate are all logarithmic. Each dot in the figure represents a specific
gene or transcript; the red dots represent significantly up-regulated genes, the blue dots represent
significantly down-regulated genes, and the black dots represent non-significant differential genes.
The dots on the left represent differentially down-regulated genes, and the dots on the right are genes
whose expression is differentially up-regulated.
3.2. GO Enrichment Analysis of DEGs
To fully understand the gene function classification of holly in response to cold stress,
GO annotation was first performed on DEGs. GO annotation includes three aspects:
biological process (BP), cellular component (CC), and molecular function (MF). The results
showed that, compared to 0 h, DEGs under different cold treatments were annotated in BP,
CC, and MF, withdifferences. From the analysis of level 2, the DEGs of 3 h vs. 0 h were mainly
annotated to 8, 3, and 17 GO terms for MF, CC, and BP, respectively (Figure S1). The DEGs of
6 h vs. 0 h were mainly annotated to 8, 3, and 19 GO terms for MF, CC, and BP, respectively
(Figure S1). The DEGs of 9 h vs. 0 h were mainly annotated to 8, 3, and 19 GO terms for
MF, CC, and BP, respectively (Figure S1). The DEGs of 12 h vs. 0 h were mainly annotated
to 9, 3, and 18 GO terms for MF, CC, and BP, respectively (Figure S1). The DEGs of 24 h
vs. 0 h were mainly annotated to 8, 3, and 20 GO terms for MF, CC, and BP, respectively
(Figure S1). Although the GO terms were different, they were mainly annotated to catalytic
activity, intracellular entities, cellular anatomical entities, cellular processes, biological
regulation, response to stimuli, and metabolic processes (Figure S1).
Canonically, GO functions are considered to be significantly enriched when the
p-value
≤
0.05. According to the GO enrichment analysis, compared to 0 h, the num-
ber of GO-enriched terms of DEGs under stress for 3 h, 6 h, 9 h, 12 h, and 24 h were 160
(BP: 124, CC: 17, MF: 19), 240 (BP: 184, CC: 17, MF: 39), 239 (BP: 197, CC: 10, MF: 32), 329
Forests 2022,13, 2150 7 of 22
(BP: 271, CC: 6, MF: 52), and 319 (BP: 224, CC: 10, MF:76), respectively (Table 1). With
prolonged cold-treatment time, the terms of DEGs related to BP and MF were increasingly
abundant, while the terms related to CC decreased.
Table 1. GO enrichment analysis statistics of DEGs (p-value ≤0.05).
Sample DEG-Type GO Term_
Enrich_Num. BP CC MF
Cold-3_vs_Cold-ck DEG-all 160 124 17 19
Cold-3_vs_Cold-ck DEG-up 162 127 18 17
Cold-3_vs_Cold-ck DEG-down 37 32 5 0
Cold-6_vs_Cold-ck DEG-all 240 184 17 39
Cold-6_vs_Cold-ck DEG-up 215 165 18 32
Cold-6_vs_Cold-ck DEG-down 107 90 1 16
Cold-9_vs_Cold-ck DEG-all 239 197 10 32
Cold-9_vs_Cold-ck DEG-up 225 173 13 39
Cold-9_vs_Cold-ck DEG-down 70 65 1 4
Cold-12_vs_Cold-ck DEG-all 329 271 6 52
Cold-12_vs_Cold-ck DEG-up 339 274 10 55
Cold-12_vs_Cold-ck DEG-down 60 56 0 4
Cold-24_vs_Cold-ck DEG-all 310 224 10 76
Cold-24_vs_Cold-ck DEG-up 395 303 19 73
Cold-24_vs_Cold-ck DEG-down 79 44 5 30
In terms of MF, DEGs under cold stress at different times were significantly en-
riched in “DNA-binding transcription factor activity” (Figure 3) and “calcium ion binding”
(Tables S2–S7). According to statistics, there are 425 transcription factors in DEGs, which
are distributed in 42 transcription factor families. The larger transcription factor families
are ERF (58), MYB (33), NAC (32), bHLH (28), WRKY (28), C2 H2 (24), GRAS (21), MYB_
Related (21), HD-ZIP (14), G2-like (13), Trihelix (11), bZIP (10), Dof (10), HSF (10), etc.
(Figure S2). In addition, the activities of some enzymes may be affected differently in
different stress stages (Tables S3–S7). For example, “galactosidase activity” and “trehalose-
phosphatase activity” were mainly affected in the early cold treatment stage, while “ubiquitin-
protein transferase activity” and “UDP-glucosyltransferase activity” were mainly affected
in the later stages. In terms of CC, GO terms are few, and DEGs were mainly enriched
in “plant-type cell wall”, “plasmodesma”, “amyloplast”, “intrinsic component of plasma
membrane”, and “anchored component of membrane” (Figure 3a–c).
The most abundant GO terms for BP were cold-treatment-induced processes related
to hormone metabolism and signal transduction pathways. Under cold stress at different
time markers, DEGs were significantly enriched in “response to hormone” (Figure 3). Some
DEGs under cold stress were also significantly enriched in “hormone transport”, “hormone
metabolic process”, “Regulation of hormone levels”, “regulation of hormone metabolic
process”, and “regulation of hormone biosynthetic process” (Tables S3–S7). In more detail,
cold-induced DEGs were significantly enriched in a variety of hormone-related terms.
For example, cold-induced DEGs were involved in ten IAA-related GO terms, includ-
ing “response to auxin”, “auxin-activated signaling pathway”, “auxin transport”, “auxin
homeostasis”, and “auxin efflux”, etc. (Tables S3–S7). Six ABA-related GO terms were
significantly enriched, including “response to abscisic acid”, “abscisic acid-activated signal-
ing pathway”, and “abscisic acid metabolic process”, etc. (Tables S3–S7). Four JA-related
GO terms were significantly enriched, including “response to jasmonic acid”, “jasmonic
acid mediated signaling pathway”, and “regulation of jasmonic acid mediated signaling
pathway”, etc. (Tables S3–S7). Two ET-related GO terms were significantly enriched, in-
cluding “response to ethylene” and “ethylene-activated signaling pathway” (Tables S3–S7).
Two GA-related GO terms were significantly enriched, including “response to gibberellin”
and “regulation of gibberellic acid mediated signaling pathway”) (Tables S3–S7). Two
CK-related GO terms were significantly enriched, including “response to cytokine” and
Forests 2022,13, 2150 8 of 22
“cytokinein-activated signaling pathway” (Tables S3–S7). Three salicylic acid-related GO
terms were significantly enriched, including “response to salicylic acid”, “regulation of
salicylic acid mediated signaling pathway”, and “regulation of salicylic acid metabolic
process” (Tables S3–S7). Additionally, three BR-related GO terms were significantly en-
riched, including “response to brassinosteroid”, “brassinosteroid biosynthetic process”, and
“brassinosteroid homeostasis” (Tables S3–S7). In addition, “response to larrikin”, “response
to water”, “response to temperature stimulus”, “response to cold”, and “response to chitin”
were also highly enriched (Figure 3b–e).
Forests 2022, 13, x FOR PEER REVIEW 8 of 23
Figure 3. GO enrichment map of cold-induced DEGs. (a) 3 h vs. CK (0 h); (b) 6 h vs. CK; (c) 9 h vs.
CK; (d) 12 h vs. CK; and (e) 24 h vs. CK. The horizontal axis represents the enrichment factor, and
the ratio of differential genes enriched to a GO term to the number of background genes obtained
by sequencing is presented. The vertical axis represents the enriched function of the GO term: the
larger the circle, the more enriched. The number of differential genes for this function is relatively
greater. The color spectrum from blue to red represents uncorrected p-values.
The most abundant GO terms for BP were cold-treatment-induced processes related
to hormone metabolism and signal transduction pathways. Under cold stress at different
time markers, DEGs were significantly enriched in “response to hormone” (Figure 3).
Some DEGs under cold stress were also significantly enriched in “hormone transport”,
“hormone metabolic process”, “Regulation of hormone levels”, “regulation of hormone
metabolic process”, and “regulation of hormone biosynthetic process” (Tables S3–S7). In
more detail, cold-induced DEGs were significantly enriched in a variety of hormone-re-
lated terms. For example, cold-induced DEGs were involved in ten IAA-related GO terms,
including “response to auxin”, “auxin-activated signaling pathway”, “auxin transport”,
Figure 3.
GO enrichment map of cold-induced DEGs. (
a
) 3 h vs. CK (0 h); (
b
) 6 h vs. CK; (
c
) 9 h vs.
CK; (
d
) 12 h vs. CK; and (
e
) 24 h vs. CK. The horizontal axis represents the enrichment factor, and the
ratio of differential genes enriched to a GO term to the number of background genes obtained by
sequencing is presented. The vertical axis represents the enriched function of the GO term: the larger
the circle, the more enriched. The number of differential genes for this function is relatively greater.
The color spectrum from blue to red represents uncorrected p-values.
Forests 2022,13, 2150 9 of 22
3.3. KEGG Enrichment Analysis of DEGs
The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment anal-
ysis showed that the DEGs induced by cold treatment were significantly enriched in various
metabolic pathways, including carbohydrate metabolism, biosynthesis of other secondary
metabolites, energy metabolism, amino acid metabolism, xenobiotics biodegradation and
metabolism, lipid metabolism, glycan biosynthesis and metabolism, and metabolism of
terpenoids and polyketides (Tables S8–S12). Among them, “Pentose and glucuronate inter-
conversions” and “Starch and sucrose metabolism” were significantly enriched in the early
stages of cold stress, but not during later stages. In contrast, some metabolic pathways
were significantly enriched in the middle and late stages of cold stress and not signifi-
cantly enriched in earlier periods, such as “Arginine and proline metabolism”, “Carotenoid
biosynthesis”, “Cysteine and methionine metabolism”, “Galactose metabolism”, “Inositol
phosphate metabolism”, “Photosynthesis—antenna proteins”, “Fatty acid elongation”, and
“Flavone and flavonol biosynthesis” (Tables S8–S12).
In terms of cellular processes, DEGs were significantly enriched in “Cellular community—
eukaryotes”, “involving Gap junction” and “Tight junction” (Figure 4a–d). In organis-
mal systems, DEGs were significantly enriched in the “Toll-like receptor signaling path-
way”, “NOD-like receptor signaling pathway”, “Toll and Imd signaling pathway”, “Plant-
pathogen interaction”, “Mineral absorption”, and “Neurotrophin signaling pathway”
(Figure 4). In organismal systems, cold stress only induced significant enrichment of
“Mineral absorption” (Figure 4e) and “Plant-pathogen interaction” (Figure 4d,e) in the
middle and late stages.
Notably, signal transduction pathways in environmental information processing were
significantly enriched, including “Plant hormone signal transduction”, “MAPK signaling
pathway”, “NF-kappa B signaling pathway”, and “Two-component system” (Figure 4).
In particular, “Plant hormone signal transduction” was a significantly enriched pathway
of DEGs in each cold stress stage and was also the most significantly enriched pathway,
except for during the 3 h time marker (Figure 4). The “MAPK signaling pathway” and
the “NF-kappa B signaling pathway” were significantly enriched in four of the cold stress
stages (Figure 4a–e).
3.4. The Expression Patterns of DEGs Response to Cold Tolerance in I. dabieshanensis
To understand the gene expression profile of I. dabieshanensis in response to cold stress,
we used Delight Hwarari’s figure [
24
] as a reference to show the expression pattern of DEGs
under cold stress. The results showed that various hormone signaling pathways, MAPK
signaling pathways, and some “cold-responsive” genes played essential roles during cold
stress in holly. We found that seven hormone (IAA, ABA, CK, ET, GA, BR, and JA) signal
transduction-related genes were up-regulated or down-regulated to varying degrees by
cold stress (Figure 5, Table S13). Some MAPK signaling-related genes were up-regulated,
such as four serine/threonine protein kinase genes (evm.TU.CHR18.1145,evm.TU.CHR2.571,
evm.TU.CHR8.589, and evm.TU.CHR9.656), one HSP gene (evm.TU.CHR2.705), and one
mitogen-activated protein kinase gene (evm.TU.CHR13.25) (Figure 5, Tables S14 and S15).
The ICE-CBF-COR pathway-related gene ICE1 (evm.TU.CHR20.178) gene and two CBF
genes (evm.TU.CHR4.1892 and evm.TU.CHR8.33) were shown to be up-regulated by cold
stress (Figure 5, Table S16). Among some “response to cold” genes, one ERF gene
(evm.TU.CHR11.908) and one peroxidase gene (evm.TU.CHR17.727) were up-regulated
(Figure 5, Table S15). In contrast, more “response to cold” genes were significantly down-
regulated, such as C-repeat/DRE binding factor 1 (evm.TU.CHR19.317), dehydrin, and
dehydration-responsive element-binding protein 1D (Figure 5, Table S15).
Forests 2022,13, 2150 10 of 22
Forests 2022, 13, x FOR PEER REVIEW 10 of 23
Figure 4. KEGG enrichment map of cold-induced DEGs. (a) 3 h vs. CK (0 h); (b) 6 h vs. CK; (c) 9 h
vs. CK; (d) 12 h vs. CK; (e) 24 h vs. CK. The horizontal axis represents the enrichment factor, that is,
the ratio of the number of differential genes enriched to a certain KEGG pathway to the number of
background genes obtained by sequencing; the vertical axis represents the enriched function of the
KEGG pathway: the larger the circle, the more enriched. The number of differential genes for this
function is relatively more. The color spectrum from blue to red represents uncorrected p-values.
Notably, signal transduction pathways in environmental information processing
were significantly enriched, including “Plant hormone signal transduction”, “MAPK sig-
naling pathway”, “NF-kappa B signaling pathway”, and “Two-component system” (Fig-
ure 4). In particular, “Plant hormone signal transduction” was a significantly enriched
pathway of DEGs in each cold stress stage and was also the most significantly enriched
pathway, except for during the 3 h time marker (Figure 4). The “MAPK signaling pathway”
Figure 4.
KEGG enrichment map of cold-induced DEGs. (
a
) 3 h vs. CK (0 h); (
b
) 6 h vs. CK; (
c
)9h
vs. CK; (
d
) 12 h vs. CK; (
e
) 24 h vs. CK. The horizontal axis represents the enrichment factor, that is,
the ratio of the number of differential genes enriched to a certain KEGG pathway to the number of
background genes obtained by sequencing; the vertical axis represents the enriched function of the
KEGG pathway: the larger the circle, the more enriched. The number of differential genes for this
function is relatively more. The color spectrum from blue to red represents uncorrected p-values.
Forests 2022,13, 2150 11 of 22
Forests 2022, 13, x FOR PEER REVIEW 12 of 23
Figure 5. Expression of genes related to response to cold stress in I. dabieshanensis. The original figure
is from Hwarari’s figure, and it was modified according to our results. The squares in each column
of the heatmap represent cold treatment at 0 h (CK), 3 h, 6 h, 9 h, 12 h, and 24 h, respectively, from
left to right.
3.5. Construction and Analysis of Weighted Gene Co-Expression Network
We performed a weighted gene co-expression network analysis (WGCNA) to iden-
tify genes associated with cold stress. All the DEGs obtained by cold treatment of holly
during 0 h (CK), 2 h, 6 h, 12 h, and 24 h were selected, and the WGCNA package in the R
software environment was used to construct the weighted gene co-expression network.
According to Figure S3, a soft threshold β = 6 was chosen to construct the co-expression
network. The modules were divided by the dynamic shearing method and merged into
11 co-expression modules by choosing a cutting height of 0.25. Different colors repre-
sented different modules, and the grey modules represented genes that could not be clas-
sified into modules (Figure 6a). Two modules were significantly associated with cold traits
(showing regular changes with increasing cold treatment time). The black module (r =
0.86, p = 5 × 10−6) was significantly positively correlated with cold stress for 12 h, and the
blue module (r = 0.91, p = 1 × 10−7) was significantly positively correlated with cold stress
Figure 5.
Expression of genes related to response to cold stress in I. dabieshanensis. The original figure
is from Hwarari’s figure, and it was modified according to our results. The squares in each column of
the heatmap represent cold treatment at 0 h (CK), 3 h, 6 h, 9 h, 12 h, and 24 h, respectively, from left
to right.
3.5. Construction and Analysis of Weighted Gene Co-Expression Network
We performed a weighted gene co-expression network analysis (WGCNA) to iden-
tify genes associated with cold stress. All the DEGs obtained by cold treatment of holly
during 0 h (CK), 2 h, 6 h, 12 h, and 24 h were selected, and the WGCNA package in the
R software environment was used to construct the weighted gene co-expression network.
According to Figure S3, a soft threshold
β
= 6 was chosen to construct the co-expression
network. The modules were divided by the dynamic shearing method and merged into
11 co-expression modules by choosing a cutting height of 0.25. Different colors represented
different modules, and the grey modules represented genes that could not be classified
into modules (Figure 6a). Two modules were significantly associated with cold traits
(showing regular changes with increasing cold treatment time). The black module (r = 0.86,
p= 5 ×10−6
) was significantly positively correlated with cold stress for 12 h, and the blue
module (r = 0.91, p= 1
×
10
−7
) was significantly positively correlated with cold stress for
24 h (Figure 6b). We found that black and blue modules’ gene expression profiles signif-
Forests 2022,13, 2150 12 of 22
icantly differed between cold-stressed and unstressed samples. The genes of the black
module were significantly up-regulated by cold stress for 12 h, while the genes of the
blue module were significantly up-regulated by cold stress for 24 h (Figure S4). These
results suggested that genes in these two modules may trigger and participate in the cold
mediation process within the plant.
Forests 2022, 13, x FOR PEER REVIEW 13 of 23
for 24 h (Figure 6b). We found that black and blue modules’ gene expression profiles sig-
nificantly differed between cold-stressed and unstressed samples. The genes of the black
module were significantly up-regulated by cold stress for 12 h, while the genes of the blue
module were significantly up-regulated by cold stress for 24 h (Figure S4). These results
suggested that genes in these two modules may trigger and participate in the cold medi-
ation process within the plant.
Figure 6. WGCNA analysis of DEGs. (a) The clustering dendrogram of DEGs identifying the
WGCNA modules. (b) The correlation of the identified modules with the different cold treatments.
To find the key genes involved in low-temperature stress, GO and KEGG enrichment
analyses were performed on the genes identified in the black and blue modules. GO en-
richment analysis showed that some GO terms were common to the black and blue mod-
ules and were arranged in the front, such as “response to acid chemical”, “response to
oxygen-containing compound”, “response to organic substance”, and “response to acid
chemical”, etc. (Figure 7a,c). In addition, some GO terms were also significantly enriched
in the black and blue modules, such as “response to cold”, “calcium ion binding”, and
some hormone-related terms (“response to abscisic acid”, “cellular response to jasmonic
acid stimulus”, and “response to ethylene”) (Tables S17 and S18). Some GO terms, such
as “UDP-glycosyltransferase activity”, “UDP-glucosyltransferase activity”, “glucosyl-
transferase activity”, “quercetin 3-O-glucosyltransferase activity”, and “quercetin 7-O-
glucosyltransferase activity”, were unique to the blue module and had a high degree of
enrichment (Figure 7a,c). KEGG pathway enrichment analysis showed that “Plant hor-
mone signal transduction” and “Plant-pathogen interaction” were significantly enriched
pathways for both black and blue modules (Figure 7b,d). “Toll-like receptor signaling
pathway”, “NF-kappa B signaling pathway”, “Toll and Imd signaling pathway”, “MAPK
signaling pathway”, and “NOD-like receptor signaling pathway” were significantly en-
riched in the black module, but were not significantly enriched in the blue module (Tables
S19 and S20). Conversely, some pathways were significantly enriched in the blue module
but not significantly or not enriched in the black module, such as “Fatty acid elongation”,
“Flavone and flavonol biosynthesis”, and “Flavonoid biosynthesis” (Tables S19 and S20).
Figure 6.
WGCNA analysis of DEGs. (
a
) The clustering dendrogram of DEGs identifying the WGCNA
modules. (b) The correlation of the identified modules with the different cold treatments.
To find the key genes involved in low-temperature stress, GO and KEGG enrichment
analyses were performed on the genes identified in the black and blue modules. GO
enrichment analysis showed that some GO terms were common to the black and blue
modules and were arranged in the front, such as “response to acid chemical”, “response
to oxygen-containing compound”, “response to organic substance”, and “response to
acid chemical”, etc. (Figure 7a,c). In addition, some GO terms were also significantly
enriched in the black and blue modules, such as “response to cold”, “calcium ion bind-
ing”, and some hormone-related terms (“response to abscisic acid”, “cellular response
to jasmonic acid stimulus”, and “response to ethylene”) (Tables S17 and S18). Some GO
terms, such as “UDP-glycosyltransferase activity”, “UDP-glucosyltransferase activity”,
“glucosyltransferase activity”, “quercetin 3-O-glucosyltransferase activity”, and “quercetin
7-O-glucosyltransferase activity”, were unique to the blue module and had a high degree of
enrichment (Figure 7a,c). KEGG pathway enrichment analysis showed that “Plant hormone
signal transduction” and “Plant-pathogen interaction” were significantly enriched path-
ways for both black and blue modules (Figure 7b,d). “Toll-like receptor signaling pathway”,
“NF-kappa B signaling pathway”, “Toll and Imd signaling pathway”, “MAPK signaling
pathway”, and “NOD-like receptor signaling pathway” were significantly enriched in the
black module, but were not significantly enriched in the blue module (
Tables S19 and S20
).
Conversely, some pathways were significantly enriched in the blue module but not signifi-
cantly or not enriched in the black module, such as “Fatty acid elongation”, “Flavone and
flavonol biosynthesis”, and “Flavonoid biosynthesis” (Tables S19 and S20).
Forests 2022,13, 2150 13 of 22
Forests 2022, 13, x FOR PEER REVIEW 14 of 23
Figure 7. Function enrichment analysis of black and blue module genes. (a) GO enrichment map of
black module genes. (b) KEGG enrichment map of black module genes. (c) GO enrichment map of
blue module genes. (d) KEGG enrichment map of blue module genes.
Next, the highly connected genes of the black and blue modules were used as hub
genes to construct gene co-expression networks for holly cold stress, in which each node
represented a gene and the lines between genes represented the co-expression relation-
ship. The internal connectivity of a gene in a module reflected the network location and
importance of the gene to the module. In the hub gene co-expression network of the black
module, the connectivity and edge weight of nine genes were high, such as
evm.TU.CHR2.70, evm.TU.CHR1.2647, evm.TU.CHR1.2184, evm.TU.CHR1.1707,
evm.TU.CHR2.566, evm.TU.CHR2.244, evm.TU.CHR2.177, evm.TU.CHR1.1507, and
evm.TU.CHR2.210 (Figure 8a). Through functional annotation of the genes, we found that
evm.TU.CHR2.244 and evm.TU.CHR1.1507 were transcription factors (Table S21). In con-
trast, evm.TU.CHR2.227, a transcription factor, had low connectivity and edge weight val-
ues in this network. Additionally, three genes (evm.TU.CHR1.1351, evm.TU.CHR1.1079,
and evm.TU.CHR2.696) had higher connectivity but lower edge weight values (Figure 8a).
Eleven genes (evm.TU.CHR1.1761, evm.TU.CHR1.2160, evm.TU.CHR2.63,
evm.TU.CHR1.1762, evm.TU.CHR2.89, evm.TU.CHR1.1567, evm.TU.CHR1.2325,
evm.TU.CHR2.974, evm.TU.CHR1.30, evm.TU.CHR1.2240, and evm.TU.CHR1.2003) in the
blue module had high connectivity and edge weights (Figure 8b), and evm.TU.CHR1.2003
was functionally annotated as a transcription factor (Table S22). Another transcription fac-
tor, evm.TU.CHR1.1821, was found to have the highest number of connections in this net-
work, but the edge weights were all low (Figure 8b, Table S22). The identified hub genes
had close interactions or indirect cross-regulation, suggesting that these genes may play
crucial roles in regulating cold stress.
Figure 7.
Function enrichment analysis of black and blue module genes. (
a
) GO enrichment map of
black module genes. (
b
) KEGG enrichment map of black module genes. (
c
) GO enrichment map of
blue module genes. (d) KEGG enrichment map of blue module genes.
Next, the highly connected genes of the black and blue modules were used as hub
genes to construct gene co-expression networks for holly cold stress, in which each
node represented a gene and the lines between genes represented the co-expression
relationship. The internal connectivity of a gene in a module reflected the network lo-
cation and importance of the gene to the module. In the hub gene co-expression net-
work of the black module, the connectivity and edge weight of nine genes were high,
such as evm.TU.CHR2.70,evm.TU.CHR1.2647,evm.TU.CHR1.2184,evm.TU.CHR1.1707,
evm.TU.CHR2.566,evm.TU.CHR2.244,evm.TU.CHR2.177,evm.TU.CHR1.1507, and
evm.TU.CHR2.210 (Figure 8a). Through functional annotation of the genes, we found
that evm.TU.CHR2.244 and evm.TU.CHR1.1507 were transcription factors (Table S21). In
contrast, evm.TU.CHR2.227, a transcription factor, had low connectivity and edge weight
values in this network. Additionally, three genes (evm.TU.CHR1.1351,evm.TU.CHR1.1079,
and evm.TU.CHR2.696) had higher connectivity but lower edge weight values (Figure 8a).
Eleven genes (evm.TU.CHR1.1761,evm.TU.CHR1.2160,evm.TU.CHR2.63,evm.TU.CHR1.1762,
evm.TU.CHR2.89,evm.TU.CHR1.1567,evm.TU.CHR1.2325,evm.TU.CHR2.974,evm.TU.CHR1.30,
evm.TU.CHR1.2240, and evm.TU.CHR1.2003) in the blue module had high connectivity and
edge weights (Figure 8b), and evm.TU.CHR1.2003 was functionally annotated as a tran-
scription factor (Table S22). Another transcription factor, evm.TU.CHR1.1821, was found
to have the highest number of connections in this network, but the edge weights were
all low (Figure 8b, Table S22). The identified hub genes had close interactions or indi-
rect cross-regulation, suggesting that these genes may play crucial roles in regulating
cold stress.
Forests 2022,13, 2150 14 of 22
Forests 2022, 13, x FOR PEER REVIEW 15 of 23
Figure 8. Co-expression regulatory network analysis of the black and blue module. Co-expression
regulatory network analysis of the black and blue module. (a) Co-expression network constructed
by 30 hub genes in black modules; (b) Co-expression network constructed by 30 hub genes in blue
modules. Each circle represents a gene. The red circles represent transcription factors. The larger the
circle, the higher the number of connections. The darker the black line connecting two nodes, the
higher the edge weight value.
3.6. Validation of RNA-Seq Data by qRT-PCR Analysis
In order to confirm our calculation and analysis results, we selected five important
hub genes from the black and blue modules and performed a quantitative reverse-tran-
scription PCR (qRT-PCR) analysis of their expression levels under cold treatment (0 h (ck),
3 h, 6 h, 9 h, 12 h, and 24 h). The results showed that the change trend of transcription
level based on qRT-PCR was basically consistent with RNA-Seq (Figure 9). Compared
with the control, the expression levels of five genes were significantly up-regulated by
cold stress for 12 h or 24 h (Figure 9). This indicates that the RNA-Seq data obtained in
this study are reliable and can support the above transcriptome analysis.
Figure 9. The expression analysis of five genes from the I. dabieshanensis core cold stress responsive
genes. The relative expression of five genes was obtained by qRT-PCR based on 2−∆∆Ct method [46],
Figure 8.
Co-expression regulatory network analysis of the black and blue module. Co-expression
regulatory network analysis of the black and blue module. (
a
) Co-expression network constructed
by 30 hub genes in black modules; (
b
) Co-expression network constructed by 30 hub genes in blue
modules. Each circle represents a gene. The red circles represent transcription factors. The larger the
circle, the higher the number of connections. The darker the black line connecting two nodes, the
higher the edge weight value.
3.6. Validation of RNA-Seq Data by qRT-PCR Analysis
In order to confirm our calculation and analysis results, we selected five important hub
genes from the black and blue modules and performed a quantitative reverse-transcription
PCR (qRT-PCR) analysis of their expression levels under cold treatment (0 h (ck), 3 h, 6 h,
9 h, 12 h, and 24 h). The results showed that the change trend of transcription level based
on qRT-PCR was basically consistent with RNA-Seq (Figure 9). Compared with the control,
the expression levels of five genes were significantly up-regulated by cold stress for 12 h or
24 h (Figure 9). This indicates that the RNA-Seq data obtained in this study are reliable and
can support the above transcriptome analysis.
Forests 2022, 13, x FOR PEER REVIEW 15 of 23
Figure 8. Co-expression regulatory network analysis of the black and blue module. Co-expression
regulatory network analysis of the black and blue module. (a) Co-expression network constructed
by 30 hub genes in black modules; (b) Co-expression network constructed by 30 hub genes in blue
modules. Each circle represents a gene. The red circles represent transcription factors. The larger the
circle, the higher the number of connections. The darker the black line connecting two nodes, the
higher the edge weight value.
3.6. Validation of RNA-Seq Data by qRT-PCR Analysis
In order to confirm our calculation and analysis results, we selected five important
hub genes from the black and blue modules and performed a quantitative reverse-tran-
scription PCR (qRT-PCR) analysis of their expression levels under cold treatment (0 h (ck),
3 h, 6 h, 9 h, 12 h, and 24 h). The results showed that the change trend of transcription
level based on qRT-PCR was basically consistent with RNA-Seq (Figure 9). Compared
with the control, the expression levels of five genes were significantly up-regulated by
cold stress for 12 h or 24 h (Figure 9). This indicates that the RNA-Seq data obtained in
this study are reliable and can support the above transcriptome analysis.
Figure 9. The expression analysis of five genes from the I. dabieshanensis core cold stress responsive
genes. The relative expression of five genes was obtained by qRT-PCR based on 2−∆∆Ct method [46],
Figure 9.
The expression analysis of five genes from the I. dabieshanensis core cold stress responsive
genes. The relative expression of five genes was obtained by qRT-PCR based on 2
−∆∆Ct
method [
46
],
while FPKM values of five genes were obtained by RNA-Seq. Data are from three biological replicates
and three technical replicates. Results are the means±standard error of the three.
Forests 2022,13, 2150 15 of 22
4. Discussion
Many previous studies have investigated and described the molecular mechanisms of
plant cold tolerance [
47
–
52
]. The current understanding of the complex mechanisms of cold
resistance in holly is limited. This study used transcriptome sequencing to elucidate the
cold tolerance mechanisms of holly undergoing cold stress by comparing and analyzing
the DEGs of holly under cold treatment (3 h, 6 h, 9 h, 12 h, 24 h, and untreated control
conditions).
4.1. Expression Patterns of Plant Hormone Signal Transduction-Related Genes in the Regulation of
Cold Stress in I. dabieshanensis
Cold signaling and other signaling pathways that coordinate stress adaptation re-
sponds to plant growth and development. Hormonal signals, such as BR, JA, ET, IAA, GA,
and ABA, have been reported to regulate cold tolerance [
24
]. For example, low temperature
induces the expression of CBF to inhibit growth, reducing the GA content by stimulating
the expression of the GA 2-oxidase gene, thereby enhancing the accumulation of DELLA
proteins [
53
]. The GRETCHEN HAGEN 3 (GH3) family gene OsGH3-2 encodes an enzyme
that catalyzes the binding of IAA to amino acids. OsGH3-2 overexpression can enhance
cold resistance and reduce ABA levels in rice. F-box protein transport inhibitor response 1
(TIR1) is a receptor for auxin [
54
], and as putative orthologs of TIR1, the transcript levels of
OsAFB2 and OsTIR1 decreased under cold stress [
55
]. Small auxin-up RNAs (SAURs) are
negative regulators of auxin synthesis and transport [
56
], and nine OsSAUR genes were
significantly down-regulated under cold stress in Oryza sativa, and two OsSAUR genes
were induced by cold stress [
55
]. In Arabidopsis thaliana, the cytokinin receptor histidine
kinases AHK2-AHK4 and Arabidopsis histidine phosphor-transferase (AHP) may play neg-
ative roles under adverse environmental conditions [
57
]. Most type A response regulators
(A-ARRs) are partially redundant negative regulators of cytokinin signaling, induced by
cytokinins and cold stress [
58
]. Ethylene negatively regulates cold signaling through direct
transcriptional control of the COR, CBF, and A-ARR genes [
59
]. Overexpression of ethylene-
responsive factor 13 (ERF13) can significantly increase the cold tolerance of birch [
60
].
Drought stress leads to higher expression levels of SIMKK (salt stress-induced MAPK ki-
nase) genes in Medicago sativa L. and Medicago arborea L. When plants encounter cold stress,
they trigger a series of intracellular responses that help them tolerate stress by producing
BRs. Activated BZR 1 can induce cold tolerance in plants through a CBF-dependent or
CBF-independent pathway [
61
]. The Arabidopsis Tetracycline hydrochloride (TCH) gene
encoding a calmodulin-related protein and xyloglucan endotransglycosylase is known
to be up-regulated after cold shock [
62
]. The basic helix-loop-helix (bHLH) transcription
factor MYC2, a master regulator of the core JA signaling machinery, is involved in methyl
jasmonate (MeJA)-induced cold tolerance in banana fruit in synergy with MaICE1 [63].
Plant hormone signal transduction was this study’s most highly enriched pathway,
excluding cold stress at 3 h (Figure 3). Expression results of differentially expressed genes in-
dicated that seven hormones (ABA, ET, CK, IAA, GA, JA, and BR) were involved in response
to cold stress (Figure 5). Under cold stress, the expression of a large number of DELLA genes
in holly increased significantly (Figure 5, Table S13). In particular, evm.TU.CHR17.303 was
up-regulated 5.9-fold under cold stress at 24 h, and evm.TU.CHR4.1916 was up-regulated
7.2-fold under cold stress at 9 h (Figure 5, Table S13). The expression levels of two ABA
receptor genes, pyrabactin resistance (PYR)/PYR1-like (PYL) genes (evm.TU.CHR2.528 and
evm.TU.CHR12.725) were up-regulated by cold stress. The evm.TU.CHR2.528 gene was not
expressed at 0 h, but its FPKM values were 14.18 and 14.01 at 12 h and 24 h, respectively
(Figure 5, Table S13). The above results indicate that holly can also respond to cold stress
under low temperatures by regulating the accumulation of DELLA proteins. The expression
levels of the two GH3 genes (evm.TU.CHR4.280 and evm.TU.CHR5.340) in holly were signifi-
cantly changed at the later stage of cold stress (12 h and 24 h), but the expressions were op-
posite. Two putative TIR1s (evm.TU.CHR1.1393 and evm.TU.CHR4.2549_evm.TU.CHR4.2550)
genes were down-regulated by more than 1-fold at 24 h cold treatment (Figure 5, Table S13).
Forests 2022,13, 2150 16 of 22
Two SAUR genes (evm.TU.CHR5.1764 and evm.TU.CHR19.132) were up-regulated, and
one SAUR gene (evm.TU.CHR11.1410) was down-regulated by cold in I. dabieshanensis
(Figure 5, Table S13). Under cold stress, I. dabieshanensis adapted to unfavorable environ-
mental factors by regulating auxin homeostasis, signal transduction, and transport. One
putative AHP gene (evm.TU.CHR8.1461) was consistent with previous findings, and its
expression decreased gradually as cold stress time increased (Figure 5, Table S13). The pu-
tative B-ARR genes evm.TU.CHR10.112 and evm.TU.CHR5.1852 were slightly up-regulated
by cold stress (Figure 5, Table S13), which was consistent with previous studies. However,
the putative A-ARR genes evm.TU.CHR11.1113 and evm.TU.CHR15.84 in I. dabieshanensis
were up-regulated and down-regulated (Figure 5, Table S13). Therefore, further explo-
ration is needed to determine the exact role of the cytokinin signaling pathway in holly
freezing tolerance. The SIMKK gene (evm.TU.CHR13.25) was up-regulated by cold stress
at different times, especially at 24 (up-regulated by about 4 times) (Figure 5, Table S13).
The expression levels of two EBF1/2 genes (evm.TU.CHR16.373 and evm.TU.CHR 3.2027)
were higher than the control at 12 h and 24 h, especially evm.TU.CHR3.2027, which in-
creased by 1.28 times and 8.43 times, respectively (Figure 5, Table S13). One ERF gene,
evm.TU.CHR14.26, was slightly down-regulated by cold stress at 3 h, 6 h, and 9 h, but
not significantly. Then evm.TU.CHR14.26 was up-regulated by cold stress at 12 h and
24 h (4.36 times at 24 h). The other two ERF genes were not expressed at 0 h, 3 h, 6 h, and
9 h, but were expressed at 12 h or 24 h (Figure 5, Table S13). In conclusion, I. dabiesha-
nensis’s response to cold stress may involve the participation of ethylene signals, mainly
by up-regulating the expression of SIMKK, EBF1/2, and ERF genes. One BZR 1/2 gene
(evm.TU.CHR4.816) was induced by cold stress and was up-regulated by 1.23 times after
24 h of cold stress (Figure 5, Table S13). Consistent with previous findings, the expres-
sion of the four TCH 4 genes (evm.TU.CHR10.943,evm.TU.CHR3.2328,evm.TU.CHR3.1162,
and evm.TU.CHR3.1174) were all up-regulated to varying degrees (Figure 5, Table S13).
These results suggest that BR signaling was also involved in mediating the process of
low-temperature tolerance in I. dabieshanensis. Under cold stress, the expression levels
of four holly MYC2 genes (evm.TU.CHR16.819,evm.TU.CHR5.2033,evm.TU.CHR1.2022,
and evm.TU.CHR6.1409) increased to varying degrees at different stress times, especially
evm.TU.CHR16.819,evm.TU.CHR5.2033,evm.TU.CHR16.819, and evm.TU.CHR5.2033, which
increased by more than two times at 12 h or 24 h (Figure 5, Table S13). This result suggests
that JA signaling may positively regulate cold tolerance in I. dabieshanensis through the
up-regulation of MYC2 expression. Overall, seven hormonal signaling pathways were
involved in holly’s response and tolerance to cold stress. Crosstalk may exist between these
hormonal signals, so further research is needed to clarify these relationships.
4.2. Metabolic Pathways during Cold Stress Response in I. dabieshanensis
During cold stress, the oxidation reaction in chloroplasts becomes more intense, re-
sulting in an increase in glycolate content and conversion to glyoxylic acid, which is
accompanied by the accumulation of a reactive oxygen species (ROS), hydrogen peroxide
(H
2
O
2
) [
12
]. In this study, “Glyoxylate and dicarboxylate metabolism” (ko00630) were
highly enriched for DEGs at 9 h of cold stress (Figure 4c) and were also enriched (but
not significantly) at other stages of cold stress (Tables S8–S12). This observation indicated
that glyoxylic acid metabolism changed the most in I. dabieshanensis under cold stress for
9 h. Proline protects and stabilizes ROS scavenging enzymes and activates alternative
detoxification pathways when plants are exposed to various abiotic stresses [
64
]. Arginine
also significantly reduces chilling symptoms and H
2
O
2
accumulation [
65
]. The DEGs at
12 h and 24 h of I. dabieshanensis cold stress were significantly enriched in “Arginine and
proline metabolism” (ko00330) (Figure 4d,e). In particular, 11 genes were enriched at 24 h,
and all were up-regulated, of which two genes (evm.TU.CHR2.2709 and evm.TU.CHR5.126)
were up-regulated by more than four times (Tables S12 and S16). The red peel is associated
with the accumulation of anthocyanins and flavonoids, exhibiting increased resistance to
cold and pathogens [
66
]. In our study, the DEGs of each stage of cold stress were enriched
Forests 2022,13, 2150 17 of 22
in “Flavonoid biosynthesis” (ko00941) (Tables S8–S12) and were significantly enriched at
6 h, 9 h, and 24 h of cold stress (Figure 4b,c,e). “Anthocyanin biosynthesis” (ko00942) was
enriched at 6 h, 9 h, and 24 h (Tables S9, S10 and S12) and was significantly enriched at
6 h and 9 h (Figure 4b,c). This result suggests that the biosynthesis of anthocyanins and
flavonoids may play an important role in fruit reddening and low-temperature resistance in
I. dabieshanensis. The metabolism of glycolate, arginine, proline, flavonoid, and anthocyanin
may regulate the process by which holly responds and tolerates cold stress.
4.3. Peroxisome Pathway during Cold Stress Response in I. dabieshanensis
Usually, low temperatures induce oxidative stress in plants. Mitochondria (in the
Ubichinon (UQ)/Ubichinol (UQH2) cycle) activate the plant’s internal antioxidant system
consisting of scavenging enzymes and antioxidants to scavenge excess ROS [
67
,
68
]. In
this study, “Peroxisome” (ko04146) was enriched in each stage of I. dabieshanensis cold
stress, but not significantly (Tables S8–S12). The 24 h DEGs under cold stress were most
enriched in “Peroxisome” (ko04146) (Tables S8–S12), including six up-regulated genes
(two SOD genes, two MPV17 genes, one Epoxide Hydrolase 2 (EPHX2) gene, and one
long-chain acyl-coenzyme A synthase (ACSL) gene), and one was down-regulated Nudix
Hydrolase 7 (NUDT7) (Figure S5). MPV17 directly scavenges excess ROS [
69
]. Two MPV17
analogs, evm.TU.CHR18.801 and evm.TU.CHR5.351, were up-regulated by 1.8 and 2.1 times,
respectively, at 24 h of cold stress (Table S16). In Brassica rapa L., many cold-induced genes
were enriched in the “Peroxisome”, and MPV17 and ACSL were up-regulated [
69
]. Overall,
the peroxisomal pathway is an important cellular process for holly to respond to and
defend against cold rapidly.
4.4. Gene Co-Expression Network in the Regulation of Cold Stress in I. dabieshanensis
In this study, 11 co-expression network modules were obtained. Two black and blue
modules were significantly associated with cold stress in I. dabieshanensis. Cold-induced
film hardening is considered a primary cold-induced event [
70
]. Plasma membrane fluidity
correlates with the proportion of desaturated fatty acids [
71
]. In this study, “Fatty acid
elongation” (ko00062) and “Glycerolipid metabolism” (ko00561), as lipid metabolism-
related pathways, were significantly enriched in the blue module. This result suggests lipid
metabolism changes in I. dabieshanensis under cold stress, which is consistent with previous
studies. Sensing and intracellular transduction of stress signals are critical for the adaptation
and survival of organisms to environmental stressors [
72
]. The GO enrichment analysis
results of the black and blue module genes revealed that there were “cellular response
to stimulus” (GO:0051716), “cell communication” (GO:0007154), “cell surface receptor
signaling pathway” (GO:0007166), and “intracellular signal transduction” (GO:0035556)
terms (Tables S17 and S18). This result indicated that stress signaling was also an important
component of cold stress tolerance in I. dabieshanensis. Messenger molecules, such as
calcium (Ca
2+
), ROS, and nitric oxide (NO), are known to be involved in plant responses to
cold stress [
73
]. In this study, “response to oxygen-containing compound” (GO:1901700),
“response to nitrogen compound” (GO:1901698), and “calcium ion binding” (GO:0005509)
were enriched in two cold-related modules (Tables S17 and S18). This result shows that
Ca
2+
, ROS, and nitric oxide (NO) may regulate cold tolerance as signaling molecules in
I. dabieshanensis.
Gene regulatory networks mediated by transcription factors play an essential role in
plant resistance to various stresses. ERFs serve as a key regulatory center, integrating ET,
ABA, JA, and redox signaling in plant responses to many abiotic stressors [
41
]. HvWRKY1
was reported to play an important role during abiotic stress, possibly regulating cold and
drought stress in barley [
74
]. MYBS3 represses the well-known DREB1/CBF-dependent
cold signaling pathway in rice, and this repression appears to operate at the transcrip-
tional level [
75
]. MpMYBS3, as a key transcription factor in cold signaling, confers cold
tolerance in bananas [
76
]. This study found three genes with higher connectivity in the
two cold-related modules (evm.TU.CHR2.244,evm.TU.CHR1.1507, and evm.TU.CHR1.1821)
Forests 2022,13, 2150 18 of 22
were annotated as “ERF025-like”, “WRKY1b”, and “MYBS3-like”, respectively (Figure 9,
Tables S21 and S22). The prediction results of promoter elements of genes co-expressed
with these transcription factors show that many genes contain one or more transcription
factor action sites in their promoters, while some genes also have some hormone and
cold response sites. This result further proves the credibility of the co expression network
and the importance of these three key transcription factors (Figure S6). In addition to
transcription factors, many receptor-like protein kinases (RLKs) are involved in abiotic
stress responses, including ABA response, calcium signaling, and antioxidant defense,
implicated in drought, salt, and cold stress [
77
]. Previous studies have found that the
A-type ARR serves as a critical node that integrates ethylene and cytokinin signaling to
regulate plant responses to environmental stressors [
78
]. In addition, it has been reported
that cold-induced A-type ARRs play a negative regulatory role in cold stress signaling
by inhibiting ABA response, independent of the CBF/DREB pathway [
79
]. Two highly
connected hub genes (evm.TU.CHR2.210 and evm.TU.CHR2.89) in two cold-related modules
were functionally described as “a receptor-like serine/threonine- protein kinase”, and
“Two-component response regulator-like”, respectively (Figure 9, Tables S21 and S22). In
conclusion, five I. dabieshanensis genes (evm.TU.CHR2.244,evm.TU.CHR1.1507,evm.TU.
CHR1.1821,evm.TU.CHR2.89, and evm.TU.CHR2.210) have high connectivity and edge
weight value, and their analogs have also been shown to play important roles in cold stress.
These genes may be the key to holly responding to and tolerating cold stress.
5. Conclusions
Our current study shows that cold stress leads to dramatic changes in gene expression
profiles. There were differences in the reaction process of holly under different cold treat-
ment time. Proline metabolism, arginine metabolism, flavonoid biosynthesis, anthocyanin
biosynthesis, and various hormone signaling pathways may play an important role in the
response of holly to cold stress. The results of WGCNA analysis indicated that some holly
genes may be the key genes regulating cold tolerance. These results provide candidate
genes and insights for studying the regulatory mechanisms of holly in response to low
temperature conditions.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/f13122150/s1, Figure S1: GO annotations of cold-induced DEGs;
Figure S2: Analysis of transcription factors; Figure S3: Soft ower; Figure S4: Expression of black and
blue modules eigengene; Figure S5: Expression of genes associated with the peroxisomal pathway;
Figure S6: Analysis of promoter elements of genes co expressed with TF in black and blue mod-
ule co expression network; Table S1 Primers used for qRT-PCR analysis; Table S2: all.DEG.fpkm;
Table S3: Cold-3_vs_Cold-ck.DEG.Go.enrich; Table S4: Cold-6_vs_Cold-ck.DEG.Go.enrich;
Table S5: Cold-9_vs_Cold-ck.DEG.Go.enrich; Table S6: Cold-12_vs_Cold-ck.DEG.Go.enrich;
Table S7: Cold-24_vs_Cold-ck.DEG.Go.enrich; Table S8: Cold-3_vs_Cold-ck.DEG.Ko.enrich; Table S9:
Cold-6_vs_Cold-ck.DEG.Ko.enrich; Table S10: Cold-9_vs_Cold-ck.DEG.Ko.enrich; Table S11: Cold-
12_vs_Cold-ck.DEG.Ko.enrich; Table S12: Cold-24_vs_Cold-ck.DEG.Ko.enrich; Table S13: FPKM
values and annotations of genes related to Plant hormone signal transduction; Table S14: FPKM val-
ues and annotations of genes associated with the MAPK signaling pathway; Table S15: FPKM values
and annotations of genes associated with response to cold; Table S16: FPKM values and annotations
of other genes associated with cold stress; Table S17: black.Go.enrich; Table S18: blue.Go.enrich;
Table S19: black.Ko.enrich; Table S20: blue.Ko.enrich; Table S21: CytoscapeInput-edges-black;
Table S22: CytoscapeInput-edges-blue.
Author Contributions:
Methodology, H.L. and T.Z.; software, H.L. and X.L.; investigation, H.L., T.Z.,
X.C., X.L., Y.L., B.Z. and H.C.; resources, H.C.; data curation, H.L., X.W. and T.Z.; writing—original
draft preparation, H.L. and H.C.; writing—review and editing, H.C.; visualization, H.L. and X.C.;
supervision, H.C. and B.Z.; project administration, H.C. All authors have read and agreed to the
published version of the manuscript.
Forests 2022,13, 2150 19 of 22
Funding:
This research was funded by the Jiangsu Agricultural Science and Technology Innovation
Fund (grant number: CX(21)3020), the Opening Project of Zhejiang Provincial Key Laboratory of
Forest Aromatic Plants-based Healthcare Functions (SLFX202205), the Open Fund of Jiangsu Key
Laboratory for the Research and Utilization of Plant Resources (grant number: JSPKLB202209), and
the Independent Research Project of Jiangsu Province (grant number: JSPKLB202036).
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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