Transcriptome Dynamics during Spike Differentiation of Wheat Reveal Amazing Changes in Cell Wall Metabolic Regulators

Abstract

Coordinated cell proliferation and differentiation result in the complex structure of the inflorescence in wheat. It exhibits unique differentiation patterns and structural changes at different stages, which have attracted the attention of botanists studying the dynamic regulation of its genes. Our research aims to understand the molecular mechanisms underlying the regulation of spike development genes at different growth stages. We conducted RNA-Seq and qRT-PCR evaluations on spikes at three stages. Our findings revealed that genes associated with the cell wall and carbohydrate metabolism showed high expression levels between any two stages throughout the entire process, suggesting their regulatory role in early spike development. Furthermore, through transgenic experiments, we elucidated the role of the cell wall regulator gene in spike development regulation. These research results contribute to identifying essential genes associated with the morphology and development of wheat spike tissue.

Full text

Citation: Han, J.; Liu, Y.; Shen, Y.;
Zhang, D.; Li, W. Transcriptome
Dynamics during Spike
Differentiation of Wheat Reveal
Amazing Changes in Cell Wall
Metabolic Regulators. Int. J. Mol. Sci.
2023,24, 11666. https://doi.org/
10.3390/ijms241411666
Academic Editor: Ivan Kreft
Received: 24 June 2023
Revised: 12 July 2023
Accepted: 17 July 2023
Published: 19 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Molecular Sciences
Article
Transcriptome Dynamics during Spike Differentiation of Wheat
Reveal Amazing Changes in Cell Wall Metabolic Regulators
Junjie Han , Yichen Liu, Yiting Shen, Donghai Zhang and Weihua Li *
College of Agriculture, The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction
Group, Shihezi University, Shihezi 832000, China; hanjunjie1208@sina.com (J.H.); lyc162710@163.com (Y.L.);
15662712664@163.com (Y.S.); xjzhangdh@126.com (D.Z.)
*Correspondence: lwh_agr@shzu.edu.cn
Abstract:
Coordinated cell proliferation and differentiation result in the complex structure of the
inflorescence in wheat. It exhibits unique differentiation patterns and structural changes at different
stages, which have attracted the attention of botanists studying the dynamic regulation of its genes.
Our research aims to understand the molecular mechanisms underlying the regulation of spike
development genes at different growth stages. We conducted RNA-Seq and qRT-PCR evaluations on
spikes at three stages. Our findings revealed that genes associated with the cell wall and carbohydrate
metabolism showed high expression levels between any two stages throughout the entire process,
suggesting their regulatory role in early spike development. Furthermore, through transgenic
experiments, we elucidated the role of the cell wall regulator gene in spike development regulation.
These research results contribute to identifying essential genes associated with the morphology and
development of wheat spike tissue.
Keywords:
wheat inflorescence; cell wall; gene expression; xyloglucan endotrans glucose/hydroxylase
(XTH); transgenosis
1. Introduction
Wheat is a crucial food crop, providing nutrition and energy for one-third of the
global population [
1
]. With the challenge of rapid population growth, increasing wheat
yield has become a primary goal for breeders worldwide. The inflorescence structure
of wheat plays a vital role in seed yield, encompassing panicle development, spikelet
formation, and floret development [
2
]. Therefore, conducting comprehensive research on
the regulatory mechanisms governing wheat spike development is essential for improving
yield and quality.
Understanding the development and regulation of inflorescences is of great importance
to plant biologists and crop breeders, as it is closely linked to the reproductive processes
and grain production in flowering plants. Wheat’s compound spike consists of multiple
sessile spikelets interlacing along the spike axis. Each axis node gives rise to several
spikelet meristems (SMs). These SMs typically produce two to four fertile, small flowers
that develop into seeds [
3
]. Unlike other crops, wheat lacks branches, and the central flower
axis directly produces spikelets. The number of SMs per flower axis influences the number
of small flowers produced, and occasional variations in spikelet excess may occur [
4
]. While
critical genes involved in inflorescence initiation and development are conserved in higher
plants [
5
,
6
], there is still a need for a comprehensive understanding of the diversity and
behavior of inflorescences.
Previous studies in rice and maize have identified multiple genes associated with
spike development using functional genomics, bioinformatics, and genetic resources [
7
,
8
].
However, our understanding of wheat spike development’s molecular mechanisms remains
relatively limited. Genes and quantitative trait loci (QTLs) related to spike development
Int. J. Mol. Sci. 2023,24, 11666. https://doi.org/10.3390/ijms241411666 https://www.mdpi.com/journal/ijms

Int. J. Mol. Sci. 2023,24, 11666 2 of 19
are dispersed across wheat’s 21 chromosomes [
9
]. For example, the Qgene on chromosome
5A is closely linked to spike compactness and affects spike length, plant height, and spike
emergence time [
10
]. The TaSnRK2 gene, found on chromosomes 4A/4B/4D, encodes a
protein kinase that influences spike length and thousand-grain weight [
11
,
12
]. Mutations
in the ARGONAUTE1d gene in durum wheat result in shorter spikes and fewer grains
per spike [
13
]. The compact (C) gene on chromosome 2D plays a critical role in spike
compactness, grain size, shape, and grain number per spike [
14
]. Additionally, the Photope-
riod1 (Ppd1) gene on chromosome 2D inhibits paired spikelet formation by regulating the
expression of the FT gene [
15
]. The polyploid nature of wheat, its large genome size (approx-
imately 17 Gb), and low transformation efficiency have posed challenges in understanding
the complex genetic network governing spike development. Nevertheless, these studies
have provided insights into the molecular mechanisms involved in spike development.
The differentiation of wheat spikes is a complex biological process influenced by
multiple genetic and physiological factors. Transcriptome dynamics, which refer to changes
in gene expression patterns over time, play a crucial role in wheat spike differentiation and
yield formation [
16
]. By studying transcriptome dynamics, we can unveil the temporal and
spatial distribution patterns of gene expression in wheat panicles at different developmental
stages. This helps us understand the molecular mechanisms underlying wheat panicle
differentiation. Recent advancements in high-throughput sequencing and metabolomics
technologies have allowed for a comprehensive investigation of dynamic changes in the
wheat transcriptome during spike differentiation. Extensive genome sequencing efforts
have been undertaken to construct reference sequences for wheat [
17
,
18
], culminating in
the latest version of the wheat reference genome, RefSeq v2.1, available on the IWGSC
website (http://www.wheatgenome.org/ (accessed on 5 January 2023)). It provides a
unique opportunity to systematically unravel the gene expression regulatory network and
the metabolic regulatory network during wheat spike differentiation. We can enhance
our understanding of wheat spikes’ formation and development mechanisms by studying
transcriptome dynamics, providing a valuable scientific basis for improving wheat yield
and quality. Significant differences exist in the composition, developmental process, and
function of cell walls (CWs).
In addition to cellulose, wood glucan (a hemicellulose polysaccharide), pectin, lignin,
and other significant components [
19
], additional components are involved. CWs have
multiple functions, including providing mechanical support, regulating intercellular flow,
and serving as a barrier to environmental stress. During the development of inflores-
cences, the formation, differentiation, and shape of CWs are usually influenced by physical,
chemical, and biological reactions. These developmental processes are regulated by a
variety of function-specific enzymes, including glycosyltransferases (such as cellulose-
like synthetase CslC) and xyloglucan endoglycosyltransferases/hydrolases (XTH) [
20
].
Meanwhile, cell wall-modifying enzymes such as
β
-1,4-glucanase and xylanase are also
involved in reconstructing and modifying cell walls. By regulating the composition and
structure of cell walls, these enzymes affect the morphological establishment and func-
tional functioning of flower organs [
21
]. During inflorescence development, changes in
the morphology and composition of cell walls are crucial for forming floral organs. By
increasing or decreasing the activity of specific enzymes, the synthesis and degradation
of cell walls are balanced, thereby affecting the plasticity and stability of cell walls. This
regulatory mechanism enables the cell wall to adapt to different environmental conditions
and biological processes.
This study aims to elucidate the dynamic changes in gene expression during the
development of wheat spikes. We assessed temporal variations in transcriptional abun-
dance throughout spike development. We aimed to unravel the molecular mechanisms
underlying wheat apical meristem development. We specifically focused on describing
stage-specific differences in cell wall development and metabolism within the transcrip-
tome. Furthermore, we investigated the subcellular localization of TraesCS7A02G426700.

Int. J. Mol. Sci. 2023,24, 11666 3 of 19
Notably, we generated transgenic wheat lines that overexpressed the TraesCS7A02G426700
gene and performed evaluations of spike traits in these transgenic lines.
2. Results
2.1. Analysis of Transcriptome Expression Profile and Differentially Expressed Genes during Wheat
Spike Development
After conducting RNA-Seq analysis, we detected differentially expressed genes (DEGs)
at different stages of wheat spike development: pistil and stamen primordium differentia-
tion stage (S1), anther separation stage (S2), and tetrad formation stage (S3). After removing
low-quality and adapter sequences, we obtained 49,552,576, 49,254,077, and 46,632,094
clean reads, respectively. We conducted a comparison between S2 and S1, and S3 and S2,
and then identified many DEGs. A total of 12,688 DEGs were identified from these two
libraries using Cufflinks software (2.2.1), considering the absolute value of fold change
(log2FC > 1 or log2FC <
−
1) and statistical significance (p-value < 0.05) for each gene.
Through volcano plots, we visualized the transcriptome dynamics of S2 vs. S1 (Figure 1A)
and S3 vs. S2 (Figure 1B). After removing duplicate genes, there were 9493 remaining DEGs,
with 7312 genes upregulated (Figure 1C) and 2181 genes downregulated (Figure 1D).
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 3 of 21
specific differences in cell wall development and metabolism within the transcriptome.
Furthermore, we investigated the subcellular localization of TraesCS7A02G426700. Nota-
bly, we generated transgenic wheat lines that overexpressed the TraesCS7A02G426700
gene and performed evaluations of spike traits in these transgenic lines.
2. Results
2.1. Analysis of Transcriptome Expression Profile and Differentially Expressed Genes during
Wheat Spike Development
After conducting RNA-Seq analysis, we detected differentially expressed genes
(DEGs) at different stages of wheat spike development: pistil and stamen primordium
differentiation stage (S1), anther separation stage (S2), and tetrad formation stage (S3).
After removing low-quality and adapter sequences, we obtained 49,552,576, 49,254,077,
and 46,632,094 clean reads, respectively. We conducted a comparison between S2 and S1,
and S3 and S2, and then identified many DEGs. A total of 12,688 DEGs were identified
from these two libraries using Cufflinks software (2.2.1), considering the absolute value of
fold change (log2FC > 1 or log2FC < −1) and statistical significance (p-value < 0.05) for each
gene. Through volcano plots, we visualized the transcriptome dynamics of S2 vs. S1 (Fig-
ure 1A) and S3 vs. S2 (Figure 1B). After removing duplicate genes, there were 9493 remain-
ing DEGs, with 7312 genes upregulated (Figure 1C) and 2181 genes downregulated (Fig-
ure 1D).
Figure 1. The volcano plot displays the correlation between expressed genes. (A) S2 vs. S1 transition.
(B) S3 vs. S2. Red spots, log2 fold change > 1 and p-value < 0.05; blue spots, log2 fold change < −1
Figure 1.
The volcano plot displays the correlation between expressed genes. (
A
) S2 vs. S1 transition.
(
B
) S3 vs. S2. Red spots, log2 fold change > 1 and p-value < 0.05; blue spots, log2 fold change <
−
1
and p-value < 0.05. The Venn plot depicts the overlapping differentially expressed genes (DEGs) that
are upregulated (
C
) and downregulated (
D
) at different stages of wheat spike development. The
accompanying bar chart displays the total number of DEGs across different comparative groups.

Int. J. Mol. Sci. 2023,24, 11666 4 of 19
Genome-wide transcriptome analysis demonstrates widespread gene expression dur-
ing spike development. We employed a strict 5% false discovery rate (FDR) screening to
compare the number of upregulated and downregulated DEGs between consecutive time
points. Significance analysis (Figure 1) reveals that, in the comparison of S2 vs. S1, we de-
tected 4049 significant DEGs, with 2952 showing upregulation and the rest demonstrating
downregulation. However, in comparing S3 vs. S2, we identified 8639 significant DEGs,
with 6452 upregulated and 2187 downregulated. The number of DEGs in S3 vs. S2 is twice
that of S2 vs. S1, indicating that the latter period may hold more importance in biosynthesis
and energy metabolism.
2.2. STC and GO Annotation Analysis of DEGs
We observed eight distinct gene expression profiles after conducting an STC analysis
of the DEGs (Figure 2). Out of these eight profiles, we identified four significant expression
profiles and ranked them based on their respective p-values (Figure 2A). Further statistical
analysis of these four significant profiles revealed that upregulated and downregulated
genes accounted for 81.45% of the total DEGs (see Supplementary Table S4 for details).
Moreover, among the remaining four insignificant expression profiles, we observed that the
expression level of the Profile 1 gene decreased during the S1–S2 transition and stabilized
during the S2–S3 transition. Conversely, the expression level of the Profile 6 gene increased
during the S1–S2 transition. Notably, Profile 2 and Profile 5 exhibited contrasting expression
patterns (Figure 2A). Regarding the four significant expression profiles, Profile 4 and Profile
0 contained the highest number of DEGs, with 4167 and 811 genes, respectively (Figure 2B,
Supplementary Table S4).
We conducted GO annotation and enrichment analysis to understand the gene profiles
of four statistically significant differential expression patterns involved in spike devel-
opment. Supplementary Table S5 presents the results of GO annotations. Based on the
annotated p-values, the profiles belong to upregulated Profiles 4 and 7 and downregulated
Profiles 0 and 3. Figure 3shows the distribution in the enrichment analysis (p< 0.05).
Profile 4’s most crucial enrichment term is GO: 0003824 (catalytic activity). Additionally,
GO: 0016787 (hydroxylase activity), GO: 0016491 (oxidoreductase activity), GO: 0005975
(carbon metabolic process), GO: 0071554 (cell wall organization or biogenesis), and GO:
0042546 (cell wall biogenesis) are also significantly enriched (Figure 3A). In Profile 0, the
first three significantly overexpressed terms are GO: 0043226 (organelle), GO: 0043229
(internal organelle), and GO: 0003676 (nucleic acid binding) (Figure 3B). In Profile 7, the
most significant enrichment term is GO: 0016787 (hydrolase activity), while GO: 0005975
(carbohydrate metabolic process) and GO: 0015979 (photosynthesis) are also significantly
enriched (Figure 3C). In Profile 3, the first three enriched terms are GO: 0035639 (purine
ribonucleotide triphosphate binding), GO: 0032559 (adenyl ribonucleotide binding), and
GO: 0005524 (ATP binding) (Figure 3D). Functional annotations related to carbohydrate
and cell wall metabolism were frequently detected in all four profiles, indicating that both
functions play essential roles in spike development.
In addition, we performed GO analysis on all DEGs, and the results are listed in
Supplementary Table S5 and displayed in Figure 3E (p< 0.05). We observed significant
enrichment of items related to DNA replication (GO: 00044815, GO: 0032993, GO: 0004427,
GO: 0005694, GO: 0000785, GO: 0006323, GO: 0031497, GO: 0006333, and GO: 0065004). This
enrichment suggests that these genes are involved in cell division and tissue development
by regulating the cell cycle and gene expression during panicle development. Furthermore,
hydrolase activity (GO: 0016798, GO: 0016798, GO: 004553, GO: 0016818, and GO: 0016817),
carbohydrate metabolism (GO: 0005975, GO: 0044262, GO: 008643, GO: 0034637, and GO:
0016051), and cell wall metabolism (GO: 0071554, GO: 0042546, GO: 0070592, GO: 0044038,
and GO: 0009834) were also significantly enriched. These findings further support the
importance of carbohydrate and cell wall metabolism in panicle development and indicate
that DNA replication and hydrolase activity also play vital roles in this process.

Int. J. Mol. Sci. 2023,24, 11666 5 of 19
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 4 of 21
and p-value < 0.05. The Venn plot depicts the overlapping differentially expressed genes (DEGs) that
are upregulated (C) and downregulated (D) at different stages of wheat spike development. The
accompanying bar chart displays the total number of DEGs across different comparative groups.
Genome-wide transcriptome analysis demonstrates widespread gene expression
during spike development. We employed a strict 5% false discovery rate (FDR) screening
to compare the number of upregulated and downregulated DEGs between consecutive
time points. Significance analysis (Figure 1) reveals that, in the comparison of S2 vs. S1,
we detected 4049 significant DEGs, with 2952 showing upregulation and the rest demon-
strating downregulation. However, in comparing S3 vs. S2, we identified 8639 significant
DEGs, with 6452 upregulated and 2187 downregulated. The number of DEGs in S3 vs. S2
is twice that of S2 vs. S1, indicating that the laer period may hold more importance in
biosynthesis and energy metabolism.
2.2. STC and GO Annotation Analysis of DEGs
We observed eight distinct gene expression profiles after conducting an STC analysis
of the DEGs (Figure 2). Out of these eight profiles, we identified four significant expression
profiles and ranked them based on their respective p-values (Figure 2A). Further statistical
analysis of these four significant profiles revealed that upregulated and downregulated
genes accounted for 81.45% of the total DEGs (see Supplementary Table S4 for details).
Moreover, among the remaining four insignificant expression profiles, we observed that
the expression level of the Profile 1 gene decreased during the S1–S2 transition and stabi-
lized during the S2–S3 transition. Conversely, the expression level of the Profile 6 gene
increased during the S1–S2 transition. Notably, Profile 2 and Profile 5 exhibited con-
trasting expression paerns (Figure 2A). Regarding the four significant expression pro-
files, Profile 4 and Profile 0 contained the highest number of DEGs, with 4167 and 811
genes, respectively (Figure 2B, Supplementary Table S4).
Figure 2. Gene expression paerns were analyzed using model maps, and two significant paerns
(Profiles 4 and 0) were displayed. (A) Each box in the figure represents a model expression profile.
Figure 2.
Gene expression patterns were analyzed using model maps, and two significant patterns
(Profiles 4 and 0) were displayed. (
A
) Each box in the figure represents a model expression profile.
The upper number in each profile box indicates the model number, while the lower number represents
the p-value associated with different gene expression patterns. Importantly, all four gene expression
patterns exhibited significant p-values (p< 0.05) and were labeled with the same color when they
shared the same expression pattern. (
B
) Profile 4 demonstrates an increasing expression level during
spike development, whereas Profile 0 shows a decreasing expression level during the same period.
The horizontal axis represents the developmental stage, while the vertical axis represents the time
series of gene expression levels after log
2
-normalization transformation. The numbers near the profile
represent the utilized trend models, while each line represents a gene in the sample.
2.3. MapMan Analysis Uncovered the Involvement of Active Cell Walls and Carbohydrate
Metabolism during Spike Development
We analyzed the transcriptome data using MapMan and presented the results through
the following steps: Firstly, we defined the functional categories. Secondly, significantly
overexpressed functional groups were identified and displayed across different stages of
spike development (refer to Figure 4and Supplementary Table S6). During the early stage
of spike differentiation, carbohydrate metabolism becomes active, providing energy for
spike differentiation and elongation. Furthermore, the widespread expression of genes as-
sociated with cell wall metabolism indicates their crucial involvement in cell wall synthesis,
remodeling, intercellular communication, cell division, and expansion.

Int. J. Mol. Sci. 2023,24, 11666 6 of 19
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 6 of 21
Figure 3. GO annotation analysis of the four significant expression profiles. Panels (A–D) represent
the GO analysis results for the four significant expression profiles, while Panel (E) represents the
GO analysis results for all DEGs. In the figure, the outer circle represents the GO classification,
where the size of each circle indicates the scale of the number of genes, and different colors represent
different classifications. The second circle displays the number of genes in each GO classification
within the background gene set and the corresponding p-value. The length of the bar represents the
number of genes, and the color becomes redder as the p-value decreases. The third circle represents
the number of differentially expressed genes. The inner circle indicates the RichFactor values for
each category, with each small grid on the gridline representing 0.1.
Figure 3.
GO annotation analysis of the four significant expression profiles. Panels (
A
–
D
) represent
the GO analysis results for the four significant expression profiles, while Panel (
E
) represents the GO
analysis results for all DEGs. In the figure, the outer circle represents the GO classification, where the
size of each circle indicates the scale of the number of genes, and different colors represent different
classifications. The second circle displays the number of genes in each GO classification within the
background gene set and the corresponding p-value. The length of the bar represents the number of
genes, and the color becomes redder as the p-value decreases. The third circle represents the number
of differentially expressed genes. The inner circle indicates the RichFactor values for each category,
with each small grid on the gridline representing 0.1.

Int. J. Mol. Sci. 2023,24, 11666 7 of 19
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 8 of 21
Figure 4. MapMan metabolism overview maps show differences in transcript levels (S2 vs. S1 and
S3 vs. S2) during spike development. log2 ratios for average transcript abundance were based on
three replicates of RNA-seq. The resulting file was loaded into the MapMan Image Annotator mod-
ule to generate the metabolism overview map. Blue represents downregulated transcripts on the
logarithmic color scale, and red represents upregulated transcripts.
Figure 4.
MapMan metabolism overview maps show differences in transcript levels (S2 vs. S1 and
S3 vs. S2) during spike development. log2 ratios for average transcript abundance were based
on three replicates of RNA-seq. The resulting file was loaded into the MapMan Image Annotator
module to generate the metabolism overview map. Blue represents downregulated transcripts on the
logarithmic color scale, and red represents upregulated transcripts.

Int. J. Mol. Sci. 2023,24, 11666 8 of 19
During spike development, the activation of glycolysis, the tricarboxylic acid (TCA)
cycle, and mitochondrial electron transport are essential energy sources [
22
,
23
]. Our gene
expression data demonstrate that most sucrose degradation and glycolysis genes are up
or downregulated during the S1–S2 stage (Figure 4). Key enzymes, including sucrose
invertase, hexokinase, phosphofructokinase (PPFK), and pyruvate kinase (PK), exhibit
notable changes in expression. For instance, from the S1 to S3 stages, the expression of
genes TraesCS7D02G008700 (encoding sucrose invertase) and TraesCS1D02G123700 (encod-
ing hexokinase) decreased by 23 and 2 times, respectively. In contrast, the expression of
genes TraesCS3D02G109600 (related to PPFK) and TraesCS4A02G129900 (involved in PK)
was strongly activated and upregulated during the S1–S2 and S2–S3 stages. In addition,
sucrose synthase (SUSY) plays a crucial role in starch synthesis by converting sucrose
into fructose and glucose. Following that, UDPG pyrophosphorylase (UGPase) facili-
tates the conversion of fructose and glucose into uridine diphosphate glucose (UDPG).
UDPG acts as a glucose donor in synthesizing glycosides, oligosaccharides, polysaccha-
rides, and related molecules. Our analysis revealed significant upregulation of genes
encoding SUSY (TraesCS7D02G036600,TraesCS4A02G446700, and TraesCS7A02G040900)
and UGPase (TraesCS7B02G319900 and TraesCS7A02G419300) during spike development.
Previous studies have also demonstrated that the interaction between UGPase and ADPG
pyrophosphorylase (AGPase), another key enzyme in starch synthesis, generates ADPG
for starch synthesis [
24
,
25
]. Our data indicate dynamic changes in the expression of mul-
tiple genes related to AGPase, such as TraesCS5D02G182600,TraesCS5D02G484500, and
TraesCS7B02G183300. The upregulation of these genes contributes to the gradual accumula-
tion of carbohydrates.
Our findings reveal a significant expression of genes associated with cell wall metabolism
during spike development. The synthesis of plant cell walls involves two consecutive
steps that play a fundamental role in shaping and strengthening cells. We observed
changes in the expression of cell wall-related genes throughout spike development, with
significant differences at different growth stages. During the transition phase from S1–
S2, we observed notable upregulation of specific genes, including glycosyltransferases
(TraesCS4B02G323100,TraesCS4D02G320100, and TraesCS3B02G474200), endotransglu-
cosylase/hydrolases (TraesCS7A02G426700 and TraesCS7A02G427000), endoglucanases
(TraesCS4A02G248200,TraesCS4D02G065600, and TraesCS4B02G066700), and cellulose syn-
thases (TraesCS2A02G102600,TraesCS6B02G104600, and TraesCS5D02G517200). These genes
are crucial for forming primary cell walls in rice [
26
–
28
]. The high expression levels of
these genes suggest that forming primary cell walls involves synergistic degradation,
biosynthesis, and remodeling assembly of xylan, glucan, and xylan.
In the S2–S3 stage, we observed significant upregulation of fatty acyl CoA reduc-
tases (TraesCS4B02G283200,TraesCS4A02G304400, and TraesCS4D02G282000), ATP-binding
cassette transporters (TraesCS1D02G053800 and TraesCS1A02G051800), phosphatidyl dia-
cylglycerol acyltransferase 1 (TraesCS5A02G207500 and TraesCS5D02G213600) involved in
an acyl-coenzyme-independent pathway to generate triacylglycerol, and BAHD acyltrans-
ferase 1 (TraesCS1B02G354000,TraesCS1A02G341300, and TraesCS1D02G343400) involved in
secondary wax/keratin biosynthesis [
29
]. These genes are associated with fatty acid synthe-
sis and the assembly of secondary cell walls, including suberin and keratin synthesis from
fatty acids and lignin synthesis from Phenylalanine [
30
,
31
]. These findings indicate that
primary and secondary cell wall assembly occurs at different stages of spike development.
During spike development, we used MapMan cell response visualization tools to
analyze the reactions associated with the cell wall and carbohydrate metabolism. We
extracted genes involved in these two processes separately and conducted an enrichment
analysis to determine if these transcriptional changes were specific to developmental
stages (Supplementary Table S7). Most transcripts associated with the cell wall and
glucose metabolism exhibited upregulation during the S2–S3 stage (Figure 5A). During
the S1–S2 stage, there was enrichment of significant DEGs (p< 0.05) in critical metabolic
pathways, including “Cutin, suberin, and wax biosynthesis”, “Fatty acid degradation”,

Int. J. Mol. Sci. 2023,24, 11666 9 of 19
and “Carbohydrate digestion and absorption” (Figure 5B). In the S2–S3 stage, a greater en-
richment of significant DEGs related to these two metabolic pathways was observed (Fig-
ure 5C). It is noteworthy that the 4-Coumarate:CoA ligase (such as TraesCS6B02G294100,
TraesCS2B02G291100, and TraesCS4B02G269200) is involved in the synthesis of ferulic
acid in the phenylpropane metabolic pathway and displays upregulation (Supplementary
Table S6). Previous studies have highlighted the crucial role of ferulic acid oligomers
in building the extensive molecular network of the cell wall through cross-linking with
hemicellulose chains and hemicellulose–lignin complexes in the cell walls of Poaceae
plants [32,33].
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 10 of 21
pathways, including “Cutin, suberin, and wax biosynthesis,” “Fay acid degradation,”
and “Carbohydrate digestion and absorption” (Figure 5B). In the S2–S3 stage, a greater
enrichment of significant DEGs related to these two metabolic pathways was observed
(Figure 5C). It is noteworthy that the 4-Coumarate:CoA ligase (such as
TraesCS6B02G294100, TraesCS2B02G291100, and TraesCS4B02G269200) is involved in the
synthesis of ferulic acid in the phenylpropane metabolic pathway and displays upregula-
tion (Supplementary Table S6). Previous studies have highlighted the crucial role of ferulic
acid oligomers in building the extensive molecular network of the cell wall through cross-
linking with hemicellulose chains and hemicellulose–lignin complexes in the cell walls of
Poaceae plants [32,33].
Figure 5. GO classification and pathway enrichment of genes related to the cell wall and carbohy-
drate metabolism. (A) Gene expression heatmap and classification. The left side shows that the tran-
scriptome data are divided into different clusters, and the heat map is generated according to
log
2
FPKM. The right side displays different GO classifications. In S1–S2 (B) and S2–S3 (C), pathways
corresponding to significant DEGs were demonstrated. The horizontal direction represents the pro-
portion of genes, while the vertical direction represents specific pathways. The color of the dot rep-
resents the p-value, and the size of the dot represents the number of genes.
Figure 5.
GO classification and pathway enrichment of genes related to the cell wall and carbohy-
drate metabolism. (
A
) Gene expression heatmap and classification. The left side shows that the
transcriptome data are divided into different clusters, and the heat map is generated according to
log
2
FPKM. The right side displays different GO classifications. In S1–S2 (
B
) and S2–S3 (
C
), pathways
corresponding to significant DEGs were demonstrated. The horizontal direction represents the
proportion of genes, while the vertical direction represents specific pathways. The color of the dot
represents the p-value, and the size of the dot represents the number of genes.

Int. J. Mol. Sci. 2023,24, 11666 10 of 19
2.4. Validation of Differentially Expressed Genes by qRT-PCR
To validate the accuracy of RNA-Seq analysis results, we employed quantitative
real-time polymerase chain reaction (qRT-PCR) to confirm the expression of 15 DEGs
(Figure 6). These DEGs consist of two oxidation-related genes (SOD and POD), eight genes
related to cell wall metabolism (CSI1,Ces,XTH,MUR3,XI,GLC1,EG, and BGL), one gene
associated with fatty acid metabolism (FAR), and four genes involved in glycometabolism
(SS,HK,PK, and SST). The expression patterns of these 15 DEGs exhibited consistent trends
between RNA-Seq and qRT-PCR results, indicating the accuracy of the transcriptome
analysis. The details of the gene-specific primers used in this study can be found in
Supplementary Table S1.
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 11 of 21
2.4. Validation of Differentially Expressed Genes by qRT-PCR
To validate the accuracy of RNA-Seq analysis results, we employed quantitative real-
time polymerase chain reaction (qRT-PCR) to confirm the expression of 15 DEGs (Figure
6). These DEGs consist of two oxidation-related genes (SOD and POD), eight genes related
to cell wall metabolism (CSI1, Ces, XTH, MUR3, XI, GLC1, EG, and BGL), one gene associ-
ated with fay acid metabolism (FAR), and four genes involved in glycometabolism (SS,
HK, PK, and SST). The expression paerns of these 15 DEGs exhibited consistent trends
between RNA-Seq and qRT-PCR results, indicating the accuracy of the transcriptome
analysis. The details of the gene-specific primers used in this study can be found in Sup-
plementary Table S1.
Figure 6. Perform qRT-PCR using 15 randomly selected DEGs. The bar chart and line chart represent
qRT-PCR and RNA-seq data, respectively. The data are expressed as the mean ± standard error (SE).
2.5. Experimental Verification of Candidate Spike Regulator Gene
The transcriptome analysis results have provided abundant gene resources for
screening genes related to wheat spike development, particularly those associated with
cell wall development. These findings hold significant implications for enhancing wheat
grain production capacity. To validate this hypothesis, we selected an essential gene in-
volved in cell wall generation, xyloglucan endotransglucosylase/hydrolase
(TraesCS7A02G426700), for experimental validation. Notably, we observed the expression
Figure 6.
Perform qRT-PCR using 15 randomly selected DEGs. The bar chart and line chart represent
qRT-PCR and RNA-seq data, respectively. The data are expressed as the mean
±
standard error (SE).
2.5. Experimental Verification of Candidate Spike Regulator Gene
The transcriptome analysis results have provided abundant gene resources for screen-
ing genes related to wheat spike development, particularly those associated with cell wall
development. These findings hold significant implications for enhancing wheat grain
production capacity. To validate this hypothesis, we selected an essential gene involved in
cell wall generation, xyloglucan endotransglucosylase/hydrolase (TraesCS7A02G426700),
for experimental validation. Notably, we observed the expression of TraesCS7A02G426700
throughout all three stages of spike development, with continuous upregulation in the S1
to S3 stages (Supplementary Table S7). We proceeded to overexpress this gene in the locally
cultivated wheat “Xinchun 11” in Xinjiang, generating over 20 independent transgenic
lines. We conducted phenotype analysis on two of these lines.

Int. J. Mol. Sci. 2023,24, 11666 11 of 19
The TraesCS7A02G426700 gene encodes a protein sequence that exhibits high homol-
ogy to AtXTH25. In Arabidopsis, researchers have shown that this protein influences
inflorescence and seed development by regulating cell wall metabolism [
34
]. In our study,
we discovered that the transgenic plant line carrying TraesCS7A02G426700 exhibited longer
spikes (Figure 7A), indicating the potential role of this gene in regulating spike elongation.
To investigate the cellular localization and potential functions of the TraesCS7A02G426700
protein, we fused its coding region with the N-terminal of the GFP gene. We expressed
it under the control of the CaMV35S promoter. Microscopic observation revealed that
the TraesCS7A02G426700-GFP fusion protein was present on the cell wall (Figure 7B),
suggesting that TraesCS7A02G426700 is a cell wall protein. Furthermore, we observed
a significant prolongation of the development time for the S1, S2, and S3 stages in the
TraesCS7A02G426700 overexpressing strain (Figure 7C), accompanied by a significant
increase in the number of spikelets and spike length (Figure 7D,E). In conclusion, we
hypothesize that the TraesCS7A02G426700 gene plays a crucial regulatory role in spike
development by modulating cell wall degradation processes.
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 12 of 21
of TraesCS7A02G426700 throughout all three stages of spike development, with continu-
ous upregulation in the S1 to S3 stages (Supplementary Table S7). We proceeded to over-
express this gene in the locally cultivated wheat “Xinchun 11” in Xinjiang, generating over
20 independent transgenic lines. We conducted phenotype analysis on two of these lines.
The TraesCS7A02G426700 gene encodes a protein sequence that exhibits high homol-
ogy to AtX TH25. In Arabidopsis, researchers have shown that this protein influences in-
florescence and seed development by regulating cell wall metabolism [34]. In our study,
we discovered that the transgenic plant line carrying TraesCS7A02G426700 exhibited
longer spikes (Figure 7A), indicating the potential role of this gene in regulating spike
elongation. To investigate the cellular localization and potential functions of the
TraesCS7A02G426700 protein, we fused its coding region with the N-terminal of the GFP
gene. We expressed it under the control of the CaMV35S promoter. Microscopic observa-
tion revealed that the TraesCS7A02G426700-GFP fusion protein was present on the cell
wall (Figure 7B), suggesting that TraesCS7A02G426700 is a cell wall protein. Furthermore,
we observed a significant prolongation of the development time for the S1, S2, and S3
stages in the TraesCS7A02G426700 overexpressing strain (Figure 7C), accompanied by a
significant increase in the number of spikelets and spike length (Figure 7D,E). In conclu-
sion, we hypothesize that the TraesCS7A02G426700 gene plays a crucial regulatory role in
spike development by modulating cell wall degradation processes.
Figure 7. Functional verification and subcellular localization of wheat transgenic plants. (A) Spike
morphology of WT and T2 transgenic plants. (B) Subcellular localization of TraesCS7A02G426700 in
Figure 7.
Functional verification and subcellular localization of wheat transgenic plants. (
A
) Spike
morphology of WT and T2 transgenic plants. (
B
) Subcellular localization of TraesCS7A02G426700
in N. benthamiana leaves. The control vector (GFP) and fusion construct (TraesCS7A02G426700-GFP)
were separately transiently expressed in five-week-old N. benthamiana leaves by agroinfiltration, and
all images were collected under the Zeiss confocal microscope after agroinfiltration for 48 h. The bar
is 50
µ
m. (
C
) Comparing the developmental duration between the wild type (WT) and transgenic
lines (OE1 and OE2) at each stage, as well as examining the number of spikelets per spike (
D
) and
spike length (
E
) in T2 transgenic plants. Data are the mean
±
SD of 10 plants for each line. Student’s
t-test, * p< 0.05, ** p< 0.01.

Int. J. Mol. Sci. 2023,24, 11666 12 of 19
3. Discussion
The development of wheat spikes directly affects yield, as a healthy and well-developed
wheat spike typically produces more grains. Previous studies have conducted large-scale
transcriptome analyses on young inflorescences of maize [
35
], rice [
36
], and barley [
37
]. This
identifies modules that regulate spike development. However, we still need to improve our
understanding of genes related to wheat spike development [
38
,
39
]. Therefore, this study
aims to explore the molecular pathways that regulate the development of wheat panicles
through dynamic transcriptome analysis of young wheat panicles at the stages of pistil and
stamen primordium differentiation, anther separation stages, and tetrad formation. We
will compare differentially expressed genes (DEGs) and identify highly expressed genes
during spike development, focusing on DEGs involved in cell wall metabolism. We aim to
provide new insights into the gene regulation of inflorescence development in wheat by
analyzing the transcriptome data from critical stages in wheat inflorescence development.
The complexity of ear development determines its production potential. Therefore,
regulating the complexity of ear development is an essential strategy for improving yield
potential. We found that the increased gene types strongly depend on ear maturity by
comparing differentially expressed genes and identifying highly expressed genes during
wheat ear development (Figures 3and 4). Upon investigating the core set of differentially
expressed genes, we discovered that these highly expressed genes mainly involve catalytic
enzyme activity, hydrolase activity, carbohydrate metabolism, cell wall tissue, and biosyn-
thesis (see Figure 3). It is worth noting that during the three stages of spike development,
the number of upregulated differential genes (7312) was significantly higher than the
number of downregulated differential genes (2181). It may reflect a general enhancement
of the apical meristem before the complete termination of spike formation [
40
]. Most
DEGs exhibited upregulation during the S1–S2 stage in the transcriptome data. This stage,
characterized by the emergence and differentiation of stamen meristems, is crucial in wheat
inflorescence development. Previous studies have highlighted the significance of this stage,
as it ultimately influences the nutrition of tiny flowers and the grain count per spike [
41
].
Our research has identified several protein genes associated with cell wall metabolism
during this stage, including 4-collate: coenzyme A (4CL). 4CL acts as a critical enzyme in
the lignin biosynthesis pathway, providing precursors for lignin synthesis by catalyzing the
esterification of phenylpropanoic acid and coenzyme A. We observed an upregulation of
4CL gene expression, thus promoting lignin biosynthesis. Furthermore, the transcriptome
data revealed heightened activity of genes related to other cell wall components such as
suberin and keratin (derived from fatty acids) and those involved in the lignin pathway (de-
rived from Phenylalanine). Previous research has shown that flower organs’ formation and
flowering processes involve the regulation of various enzymes, including lignin enzymes.
These enzymes’ activity and gene expression levels exhibit changes at different stages of
flower development. Upregulation of cellulase and lignin enzyme expression promotes cell
wall degradation, which is beneficial for elongating flower organs [
42
]. This study presents
many related gene expression patterns. Based on these findings, we propose a regulatory
process that appears to dominate spike development as the stages progress (Figure 8). It
suggests that the synthesis and degradation of cell walls are extensively involved in wheat
spike development, governing their differentiation and elongation.
Cell walls are the main components of plant morphology and contribute to the biome-
chanics of organs through their rigid or viscoelastic properties. The cell wall contributes
to the shape and function of plant organs by defining the organ-strengthening effect of
vascular and fibrous cells with thickened cell walls on the surface of individual cells and
inside. Cell wall synthesis is a necessary process in the development of panicles or inflores-
cences. Cellulose synthase, hemicellulose synthase, and lignin synthase regulate cell wall
synthesis. Research has shown that these enzyme genes’ expression levels and activities
exhibit spatiotemporal specificity at different stages of spike or inflorescence develop-
ment [
43
]. The growth and development of flowers require regulating cell size and shape
to control changes in cell wall plasticity. Studies on Mirabilis jalapa [
44
], Rosa sp. [
45
,
46
],

Int. J. Mol. Sci. 2023,24, 11666 13 of 19
and Sandersonia aurantiaca [
47
] have shown that the development and opening of flowers
involve cell wall metabolism. Another study suggests that inflorescence growth mainly
depends on cell expansion, and the cell wall is the main limiting factor for cell expansion [
48
].
Therefore, genes related to cell wall metabolism may contribute to cell wall modifications
related to inflorescence development. This result is supported by the high expression of cell
wall-related protein genes detected in gladiolus’s stamen, petal, and tea [49].
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 14 of 21
Figure 8. A simplified coordination process of cell wall organization during wheat spike develop-
ment. Data were obtained using the log
2
FPKM of each gene. Red and green represent up- and down-
regulated genes, respectively. PAL(Phenylalanine ammonialyase); 4CL(4-coumarate:coenzyme A
ligase); CCoAOMT(Caffeoyl-CoA 3-O-methyltransferase); CCR(Cinnamoyl-CoA reductase);
CAD(Cinnamyl alcohol dehydrogenase); CHS(Chalcone synthase); CHI(Chalcone-flavonone iso-
merase); FNS(Flavone synthase); KCS(3-ketoacyl-CoA synthase); HSD17B12(Very-long-chain 3-
oxoacyl-CoA reductase); HACD(Very-long-chain (3R)-3-hydroxyacyl-CoA dehydratase);
ALDH(Aldehyde dehydrogenase); ADH(Alcohol dehydrogenase); GPAT(Glycerol-3-phosphate
acyltransferase).
Cell walls are the main components of plant morphology and contribute to the bio-
mechanics of organs through their rigid or viscoelastic properties. The cell wall contrib-
utes to the shape and function of plant organs by defining the organ-strengthening effect
of vascular and fibrous cells with thickened cell walls on the surface of individual cells
and inside. Cell wall synthesis is a necessary process in the development of panicles or
inflorescences. Cellulose synthase, hemicellulose synthase, and lignin synthase regulate
cell wall synthesis. Research has shown that these enzyme genes’ expression levels and
activities exhibit spatiotemporal specificity at different stages of spike or inflorescence de-
velopment [43]. The growth and development of flowers require regulating cell size and
shape to control changes in cell wall plasticity. Studies on Mirabilis jalapa [44], Rosa sp.
[45,46], and Sandersonia aurantiaca [47] have shown that the development and opening of
flowers involve cell wall metabolism. Another study suggests that inflorescence growth
mainly depends on cell expansion, and the cell wall is the main limiting factor for cell
expansion [48]. Therefore, genes related to cell wall metabolism may contribute to cell wall
modifications related to inflorescence development. This result is supported by the high
expression of cell wall-related protein genes detected in gladiolus’s stamen, petal, and tea
[49].
Figure 8.
A simplified coordination process of cell wall organization during wheat spike development.
Data were obtained using the log
2
FPKM of each gene. Red and green represent up- and downregu-
lated genes, respectively. PAL(Phenylalanine ammonialyase); 4CL(4-coumarate:coenzyme A ligase);
CCoAOMT(Caffeoyl-CoA 3-O-methyltransferase); CCR(Cinnamoyl-CoA reductase); CAD(Cinnamyl
alcohol dehydrogenase); CHS(Chalcone synthase); CHI(Chalcone-flavonone isomerase); FNS(Flavone
synthase); KCS(3-ketoacyl-CoA synthase); HSD17B12(Very-long-chain 3-oxoacyl-CoA reductase);
HACD(Very-long-chain (3R)-3-hydroxyacyl-CoA dehydratase); ALDH(Aldehyde dehydrogenase);
ADH(Alcohol dehydrogenase); GPAT(Glycerol-3-phosphate acyltransferase).
It is particularly noteworthy that xyloglucan endotransglucosylase/hydrolase (XTH)
participates in the dissociation of cell walls [
50
,
51
]. Our results support the critical role
of xyloglucan as a primary cell wall component in the transition of wall tissue during
spike development in monocotyledons, such as wheat. Interestingly, we observed high
expression of some genes involved in two different pathways of plant cell wall biosynthesis,
and the functions of these enzymes have been confirmed in previous reports. These genes
are directly involved in the biosynthesis of cork and keratin precursors in multiple plant
organs, including panicles. They include CHS [
52
], CCR [
53
], 4CL [
54
], GPAT [
55
], ADH [
56
],
KCS [
57
], and keratin/cork transport processes, as well as ATP binding cassette (ABC)
transport proteins located in the plasma membrane [
58
]. In addition, during the S2–S3
stage, we also observed significant upregulation of genes encoding acyltransferases (Ats)

Int. J. Mol. Sci. 2023,24, 11666 14 of 19
and peroxidases (POD), which play essential roles in the synthesis of cork, keratin, and
lignin (Supplementary Figure S1). Recent transcriptome data have also shown that gene
expression in cell wall metabolism exhibits stage dependence during spike differentia-
tion [
16
]. In this study, we observed the upregulation of genes related to the biosynthesis
and remodeling of xyloglucan during the S2–S3 conversion. Since xyloglucan endotrans-
glucosylase (XET) is directly involved in the dissociation of cell walls, our research results
further support the essential role of xyloglucan as the main component of cell walls during
the transformation of spike differentiation in monocotyledons, such as wheat. Based on
our research findings, a possible inference is that the active synthesis process of cell walls
predominates in the later stages of spike development.
Our results indicate that the overexpression of the TraesCS7A02G426700 gene, which
encodes the XTH protein, can influence spike development in wheat. Wheat lines overex-
pressing TraesCS7A02G426700 exhibit long spikes. To evaluate whether changes in cell wall
structure cause differences in spike traits, we compared the microstructure of wild-type
and transgenic plants. The transgenic plants displayed irregular and distorted shapes in
the spike axis, stem under the spike, small flowers, and ovary cells, resulting in a more
expansive gap space and less compact cells (Supplementary Figure S2). Previous studies
have reported the impact of XTH genes on tissue development and morphogenesis. For
instance, overexpression of the XTH9 gene promotes the elongation of reproductive meris-
tem and inflorescence cells in Arabidopsis [
59
]. XTH1, another studied gene, enhances cell
wall flexibility by relaxing the cell wall and degrading the cellulose wood glucan matrix,
facilitating rapid expansion and maturation of fruit [
60
]. XTH1 also affects the cell wall
structure of transgenic Arabidopsis by altering the cellulose structure by encoding a corn
cell wall binding protein [
61
]. The TraesCS7A02G426700 gene shares a high sequence simi-
larity with these genes, suggesting its potential role in cell wall-related processes. Changes
in the cell wall structure, possibly influenced by the overexpression of TraesCS7A02G426700,
may lead to increased interstitial space and less compact cells. These changes can impact
the reorganization and reconstruction of cell walls, potentially affecting cell division and
expansion. Consequently, they can influence the duration of the three stages (Figure 7C)
and the number of spikelets (Figure 7D).
In conclusion, our study employed transcriptome data analysis and transgenic verifi-
cation to identify genes associated with spike development, offering potential avenues for
enhancing spike traits in wheat. By effectively regulating the expression of genes involved
in cell wall metabolism, it is possible to augment both the length and number of spikelets,
thereby maximizing the spikelet production potential.
4. Materials and Methods
4.1. Plant Materials and Growth Conditions
On the experimental farm of Shihezi University, we cultivated the spring wheat
“Xinchun 11”, which is primarily grown in Xinjiang. The experiment comprised three bio-
logical replicates, with each replicate covering an area of 10 square meters. The cultivation
management followed field production conditions. To conduct transcriptome analysis,
we randomly selected 50 spikelets from the main branches at different developmental
stages, namely, the pistil and stamen primordium differentiation stage (S1), the anther
separation stage (S2), and the tetrad formation stage (S3). We manually removed the leaves
surrounding the young spikes to collect the samples. We carefully excised the reproduc-
tive tissue without stems using a sharp blade under a stereomicroscope (S8 APO, Leica
Microsystems) to identify the developmental stage [
62
,
63
]. Simultaneously, we collected
approximately 30 spikes at each stage for RNA extraction. The collected samples were
immediately submerged in liquid nitrogen and stored at −80 ◦C for future use.
4.2. Gene Function Annotation and Enrichment Analysis
Supplementary materials File S1 provides detailed information on RNA extraction,
RNA-Seq library preparation and sequencing, and bioinformatics analysis. We obtained the

Int. J. Mol. Sci. 2023,24, 11666 15 of 19
reference genome sequences and gene annotations from the Ensemble plant database (https:
//ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-56/ (accessed on 5 January 2023)).
The genome sequences underwent quality filtering to remove low-quality bases, and short
reading segments were filtered out. Subsequently, we mapped the RNA sequencing reads
to the annotated gene assembly (Supplementary Tables S2 and S3). To identify DEGs
between the control and experimental groups, we employed the F-test of the Random
Variance Model (RVM) in a small sample scenario to ensure higher degrees of freedom.
We conducted a significance analysis and applied a False Discovery Rate (FDR) threshold
to screen for statistically significant DEGs [
64
]. We used the Cufflinks program v2.2.1
(http://cole-trapnell-lab.github.io/cufflinks/ (accessed on 5 January 2023)) to determine
the expression levels of all DEGs, with specific details provided in the reference [
65
].
The expression level of each gene was normalized using Fragments Per Kilobase of exon
per Million mapped reads (FPKM). Furthermore, we utilized the DAVID bioinformatics
resources (v2022q4) (https://david.ncifcrf.gov/home.jsp (accessed on 5 January 2023)) to
classify the identified differentially expressed genes into Gene Ontology (GO) categories.
4.3. Series Test of Cluster (STC) Analysis of DEGs
By applying the ANOVA corrected by the RVM, we employed the series test of the
cluster (STC) to identify DEGs. Our analysis revealed a unique set of expression pattern
trends based on the distinct changes in signal density among genes under different circum-
stances. We transformed the original expression values into log2 ratios. We further defined
specific patterns using a clustering strategy with short-term sequence gene expression data.
The expression model characterizes the observed or predicted number of genes involved
in each expression trend within the model. To assess the significance of the identified
patterns, we employed Fisher’s exact test and multiple comparison tests to determine if
their occurrence exceeded the expected probability [66].
4.4. MapMan Analysis
For the MapMan analysis, we created the input file by calculating the ratio of the
natural logarithm of three control samples to the average detection value of the pro-
cessed samples. Genes with two missing values out of three replicates were deemed
unexpressed under the corresponding experimental conditions. We employed MapMan
version 3.6.0RC1 [
67
] for the final analysis, which automatically applied the Wilcoxon rank
sum test.
4.5. RNA Extraction and Real-Time Quantitative PCR
RNA extraction from 100–200 mg of frozen tissue was conducted following the guide-
lines provided by the manufacturer, utilizing the TransZol Up Plus RNA Kit (Lot# Q41020,
TransGen, Beijing, China). We assessed the quality of the extracted RNA using the Nan-
odrop 8000 (Thermo Fisher Scientific Inc., Logan, UT, USA). At the same time, the quantity
was determined using the Agilent Bioanalyzer 2100 (Agilent Technologies Inc., Santa Clara,
CA, USA). We employed the EasyScript One-Step gDNA Removal and cDNA Synthe-
sis Super Mix (Lot# P20708, TransGen, China) for reverse transcription. The Actin gene
(GenBank accession number: KC775782.1) was the reference gene. The control group
comprised plants of the same age. Following the manufacturer’s protocol, we conducted
three independent biological replicates using the PerfectStartTM Green qPCR SuperMix
(TransGen Biotech, Beijing, China). We performed the qRT-PCR analysis using the ABI
QuantStudio
™
6 system (ABI, Carlsbad, CA, USA). The 2
−∆∆CT
method was employed
to normalize the relative gene expression levels. Supplementary Table S1 lists the primers
used for quantitative PCR.

Int. J. Mol. Sci. 2023,24, 11666 16 of 19
4.6. Construction of pCAMBIA1301-TraesCS7A02G426700 Vector for Agrobacterium-Mediated
Wheat Transformation
We designed specific primers for the full-length coding sequence (CDS) of the
TraesCS7A02G426700 gene. The obtained PCR product, 870 bp in length, was cloned
into the pMD18-T vector. Subsequently, we inserted the TraesCS7A02G426700 gene into
the pAHC25 vector containing the Ubi promoter. Finally, we transferred the entire gene
construct into the pCAMBIA1301 vector. After sequencing to confirm its integrity, we
used the resulting pCAMBIA1301-TraesCS7A02G426700 vector for the Agrobacterium-
mediated transformation of wheat. To verify the presence of the target gene and selective
marker genes in the transgenic plants, we selected a minimum of 10 individual plants
from the T0 generation for GUS staining and resistance analysis using hygromycin
(150
µ
g/mL, Vetec, Sigma, China). Similar analyses were performed on the T1 and T2
generations to support subsequent research.
4.7. Subcellular Localization of The TraesCS7A02G426700
The PCR product of the TraesCS7A02G426700 gene was cloned into the pBWA (V)
HS-gfp vector (Biorun Biosciences Co., Ltd., Wuhan, China) to create a fusion vector called
CaMV35S-TraesCS7A02G426700-GFP. After sequencing, we transferred the fusion and
control vector (pBWA (V) HS-gfp) into Agrobacterium tumefaciens strain GV3101. We
transformed tobacco leaves using the method Yang et al. [
68
] described. We cultured the
transformed leaves on MS medium for 48 h and performed live cell imaging using inverted
confocal microscopy (Zeiss LSM 780, Jena, Germany).
4.8. Microscopic Observation of WT and Transgenic Plants
During the heading stage of WT and T2 transgenic wheat plants, we collected samples
from specific regions: the middle of the spike axis (0.5 cm), the stem 1–2 cm below the spike,
small flowers, and the ovary before pollination. The samples were promptly fixed in FAA
solution (5% formaldehyde, 6% acetic acid, and 45% ethanol) under vacuum conditions
for 1 h. Afterward, a sequential process was conducted, including dehydration in 70%,
85%, 95%, and 100% ethanol (1 h each), followed by a transition from 100% ethanol to 100%
xylene. Subsequently, the samples were permeated and embedded in paraffin. Using a
slicing machine (JY202A, Beijing, China), 6
µ
m thickness sections were cut and mounted
onto microscope slides. Safranine staining was applied, and the samples were examined
using an optical microscope and imaged using a confocal laser scanning microscope (Zeiss
LSM 800 with Airyscan, Jena, Germany).
4.9. Data Analysis
We employed the statistical software SPSS V20 (SPSS, Inc., Chicago, IL, USA) to
perform all analyses. An ANOVA was employed to investigate gene expression differences,
followed by Fisher’s LSD tests. Additionally, differences between means were detected
using Duncan’s multiple range tests with a significance level set at p< 0.05.
Supplementary Materials:
The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/ijms241411666/s1.
Author Contributions:
Conceptualization, J.H.; data curation, J.H.; formal analysis, J.H.; funding
acquisition, W.L.; investigation, Y.L. and Y.S.; project administration, D.Z. and W.L.; resources, Y.S.;
software, Y.L.; supervision, D.Z. and W.L.; validation, J.H.; visualization, J.H.; writing—original draft,
J.H. All authors have read and agreed to the published version of the manuscript.
Funding:
This work received partial support from the International Science and Technology Coop-
eration Project of the Science and Technology Bureau of the Xinjiang Production and Construction
Corps (2019BC003).
Informed Consent Statement: Not applicable.

Int. J. Mol. Sci. 2023,24, 11666 17 of 19
Data Availability Statement:
We have archived the raw sequence data mentioned in this paper in the
Genome Sequence Archive (GSA) at the National Genomics Data Center, China National Center for
Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences. The accession number
for the data is GSA: PRJCA009595, and it can be accessed publicly at https://ngdc.cncb.ac.cn/gsa
(accessed on 5 January 2023).
Acknowledgments:
We thank our colleagues from Oasis Eco-agriculture, University of Shihezi,
China, for generously providing the seed stocks.
Conflicts of Interest:
We affirm that we have no financial or personal affiliations with individuals or
organizations that could unduly influence our work. We have no professional or personal interests
whatsoever in any product, service, or company that could influence the views expressed in the
manuscript or its review.
References
1. Shewry, P.R. Wheat. J. Exp. Bot. 2009,60, 1537–1553. [CrossRef] [PubMed]
2.
Luo, X.; Yang, Y.; Lin, X.; Xiao, J. Deciphering spike architecture formation towards yield improvement in wheat. J. Genet. Genom.
2023. [CrossRef] [PubMed]
3.
Shitsukawa, N.; Kinjo, H.; Takumi, S.; Murai, K. Heterochronic development of the floret meristem determines grain number per
spikelet in diploid, tetraploid and hexaploid wheats. Ann. Bot. 2009,104, 243–251. [CrossRef] [PubMed]
4.
Gao, X.-Q.; Wang, N.; Wang, X.-L.; Zhang, X.S. Architecture of Wheat Inflorescence: Insights from Rice. Trends Plant Sci.
2019,24, 802–809. [CrossRef]
5. Theißen, G.; Saedler, H. Floral quartets. Nature 2001,409, 469–471. [CrossRef]
6.
Zhang, D.; Yuan, Z. Molecular Control of Grass Inflorescence Development. Annu. Rev. Plant Biol.
2014
,65, 553–578. [CrossRef]
7.
Sreenivasulu, N.; Schnurbusch, T. A genetic playground for enhancing grain number in cereals. Trends Plant Sci.
2012
,17, 91–101.
[CrossRef]
8.
Tanaka, W.; Pautler, M.; Jackson, D.; Hirano, H.-Y. Grass Meristems II: Inflorescence Architecture, Flower Development and
Meristem Fate. Plant Cell Physiol. 2013,54, 313–324. [CrossRef]
9.
Liu, K.; Sun, X.; Ning, T.; Duan, X.; Wang, Q.; Liu, T.; An, Y.; Guan, X.; Tian, J.; Chen, J. Genetic dissection of wheat panicle traits
using linkage analysis and a genome-wide association study. Theor. Appl. Genet. 2018,131, 1073–1090. [CrossRef]
10. Simons, K.J. Molecular Characterization of the Major Wheat Domestication Gene Q. Genetics 2005,172, 547–555. [CrossRef]
11.
Miao, L.; Mao, X.; Wang, J.; Liu, Z.; Zhang, B.; Li, W.; Chang, X.; Reynolds, M.; Wang, Z.; Jing, R. Elite Haplotypes of a
Protein Kinase Gene TaSnRK2.3 Associated with Important Agronomic Traits in Common Wheat. Front. Plant Sci. 2017,08, 368.
[CrossRef] [PubMed]
12.
Zhang, Z.-G.; Lv, G.-D.; Li, B.; Wang, J.-J.; Zhao, Y.; Kong, F.-M.; Guo, Y.; Li, S.-S. Isolation and characterization of the TaSnRK2.10
gene and its association with agronomic traits in wheat (Triticum aestivum L.). PLoS ONE
2017
,12, e0174425. [CrossRef] [PubMed]
13.
Feng, N.; Song, G.; Guan, J.; Chen, K.; Jia, M.; Huang, D.; Wu, J.; Zhang, L.; Kong, X.; Geng, S.; et al. Transcriptome Profiling of
Wheat Inflorescence Development from Spikelet Initiation to Floral Patterning Identified Stage-Specific Regulatory Genes. Plant
Physiol. 2017,174, 1779–1794. [CrossRef] [PubMed]
14.
Johnson, E.B.; Nalam, V.J.; Zemetra, R.S.; Riera-Lizarazu, O. Mapping the compactum locus in wheat (Triticum aestivum L.) and its
relationship to other spike morphology genes of the Triticeae. Euphytica 2007,163, 193–201. [CrossRef]
15.
Boden, S.A.; Cavanagh, C.; Cullis, B.R.; Ramm, K.; Greenwood, J.; Finnegan, E.J.; Trevaskis, B.; Swain, S.M. Ppd-1 is a key
regulator of inflorescence architecture and paired spikelet development in wheat. Nat. Plants
2015
,1, 14016. [CrossRef] [PubMed]
16.
Li, Y.; Fu, X.; Zhao, M.; Zhang, W.; Li, B.; An, D.; Li, J.; Zhang, A.; Liu, R.; Liu, X. A Genome-wide View of Transcriptome
Dynamics During Early Spike Development in Bread Wheat. Sci. Rep. 2018,8, 15338. [CrossRef]
17.
The International Wheat Genome Sequencing Consortium (IWGSC); Appels, R.; Eversole, K.; Feuillet, C.; Keller, B.; Rogers, J.;
Stein, N.; Pozniak, C.J.; Stein, N.; Choulet, F.; et al. Shifting the limits in wheat research and breeding using a fully annotated
reference genome. Science 2018,361, eaar7191. [CrossRef]
18.
Shi, X.; Cui, F.; Han, X.; He, Y.; Zhao, L.; Zhang, N.; Zhang, H.; Zhu, H.; Liu, Z.; Ma, B.; et al. Comparative genomic and
transcriptomic analyses uncover the molecular basis of high nitrogen-use efficiency in the wheat cultivar Kenong 9204. Mol. Plant
2022,15, 1440–1456. [CrossRef]
19. Reiter, W.-D. Biosynthesis and properties of the plant cell wall. Curr. Opin. Plant Biol. 2002,5, 536–542. [CrossRef]
20.
Meents, M.; Watanabe, Y.; Samuels, A.L. The cell biology of secondary cell wall biosynthesis. Ann. Bot.
2018
,121, 1107–1125.
[CrossRef]
21.
Perrot, T.; Pauly, M.; Ramírez, V. Emerging roles of
β
-glucanases in plant development and adaptative responses. Plants
2022,11, 1119. [CrossRef] [PubMed]
22.
Guo, G.; Lv, D.; Yan, X.; Subburaj, S.; Ge, P.; Li, X.; Hu, Y.; Yan, Y. Proteome characterization of developing grains in bread wheat
cultivars (Triticum aestivum L.). BMC Plant Biol. 2012,12, 147. [CrossRef] [PubMed]
23.
Fernie, A.R.; Carrari, F.; Sweetlove, L.J. Respiratory metabolism: Glycolysis, the TCA cycle and mitochondrial electron transport.
Curr. Opin. Plant Biol. 2004,7, 254–261. [CrossRef] [PubMed]

Int. J. Mol. Sci. 2023,24, 11666 18 of 19
24.
Eimert, K.; Villand, P.; Kilian, A.; Kleczkowski, L.A. Cloning and characterization of several cDNAs for UDP-glucose pyrophos-
phorylase from barley (Hordeum vulgare) tissues. Gene 1996,170, 227–232. [CrossRef]
25.
Ap Rees, T.; Leja, M.; Macdonald, F.; Green, J. Nucleotide sugars and starch synthesis in spadix of Arum maculatum and
suspension cultures of Glycine max. Phytochemistry 1984,23, 2463–2468. [CrossRef]
26.
Hara, Y.; Yokoyama, R.; Osakabe, K.; Toki, S.; Nishitani, K. Function of xyloglucan endotransglucosylase/hydrolases in rice. Ann.
Bot. 2013,114, 1309–1318. [CrossRef]
27. Hazen, S.P.; Scott-Craig, J.S.; Walton, J.D. Cellulose Synthase-Like Genes of Rice. Plant Physiol. 2002,128, 336–340. [CrossRef]
28.
Oraby, H.; Venkatesh, B.; Dale, B.; Ahmad, R.; Ransom, C.; Oehmke, J.; Sticklen, M. Enhanced conversion of plant biomass into
glucose using transgenic rice-produced endoglucanase for cellulosic ethanol. Transgenic Res. 2007,16, 739–749. [CrossRef]
29.
Stahl, U. Cloning and Functional Characterization of a Phospholipid: Diacylglycerol Acyltransferase from Arabidopsis. Plant
Physiol. 2004,135, 1324–1335. [CrossRef]
30.
Joo, J.-Y.; Kim, M.-S.; Sung, J. Transcriptional Changes of Cell Wall Organization Genes and Soluble Carbohydrate Alteration
during Leaf Blade Development of Rice Seedlings. Plants 2021,10, 823. [CrossRef]
31.
Hu, R.; Xu, Y.; Yu, C.; He, K.; Tang, Q.; Jia, C.; He, G.; Wang, X.; Kong, Y.; Zhou, G. Transcriptome analysis of genes involved
in secondary cell wall biosynthesis in developing internodes of Miscanthus lutarioriparius.Sci. Rep.
2017
,7, 9034. [CrossRef]
[PubMed]
32.
Lam, T.; Kadoya, K.; Iiyama, K. Bonding of hydroxycinnamic acids to lignin: Ferulic and p-coumaric acids are predominantly
linked at the benzyl position of lignin, not the
β
-position, in grass cell walls. Phytochemistry
2001
,57, 987–992. [CrossRef]
[PubMed]
33.
Fry, S.C.; Willis, S.C.; Paterson, A.E.J. Intraprotoplasmic and wall-localised formation of arabinoxylan-bound diferulates and
larger ferulate coupling-products in maize cell-suspension cultures. Planta 2000,211, 679–692. [CrossRef] [PubMed]
34.
Yokoyama, R.; Nishitani, K. A comprehensive expression analysis of all members of a gene family encoding cell-wall enzymes
allowed us to predict cis-regulatory regions involved in cell-wall construction in specific organs of Arabidopsis. Plant Cell Physiol.
2001,42, 1025–1033. [CrossRef] [PubMed]
35.
Eveland, A.L.; Goldshmidt, A.; Pautler, M.; Morohashi, K.; Liseron-Monfils, C.; Lewis, M.W.; Kumari, S.; Hiraga, S.; Yang,
F.; Unger-Wallace, E.; et al. Regulatory modules controlling maize inflorescence architecture. Genome Res.
2013
,24, 431–443.
[CrossRef]
36.
Furutani, I.; Sukegawa, S.; Kyozuka, J. Genome-wide analysis of spatial and temporal gene expression in rice panicle development.
Plant J. 2006,46, 503–511. [CrossRef]
37.
Digel, B.; Pankin, A.; von Korff, M. Global Transcriptome Profiling of Developing Leaf and Shoot Apices Reveals Distinct
Genetic and Environmental Control of Floral Transition and Inflorescence Development in Barley. Plant Cell
2015
,27, 2318–2334.
[CrossRef]
38.
Wang, Y.; Yu, H.; Tian, C.; Sajjad, M.; Gao, C.; Tong, Y.; Wang, X.; Jiao, Y. Transcriptome Association Identifies Regulators of Wheat
Spike Architecture. Plant Physiol. 2017,175, 746–757. [CrossRef]
39.
Dobrovolskaya, O.; Pont, C.; Sibout, R.; Martinek, P.; Badaeva, E.; Murat, F.; Chosson, A.; Watanabe, N.; Prat, E.; Gautier, N.; et al.
FRIZZY PANICLE Drives Supernumerary Spikelets in Bread Wheat. Plant Physiol. 2014,167, 189–199. [CrossRef]
40.
Hagemann, W. Comparative morphology of acrogenous branch systems and phylogenetic considerations. II. Angiosperms. Acta
Biotheor. 1990,38, 207–242. [CrossRef]
41.
Guo, Z.; Schnurbusch, T. Variation of floret fertility in hexaploid wheat revealed by tiller removal. J. Exp. Bot.
2015
,66, 5945–5958.
[CrossRef] [PubMed]
42.
Mollet, J.C.; Leroux, C.; Dardelle, F.; Lehner, A. Cell wall composition, biosynthesis and remodeling during pollen tube growth.
Plants 2013,2, 107–147. [CrossRef] [PubMed]
43.
Verhertbruggen, Y.; Marcus, S.E.; Chen, J.; Knox, J.P. Cell Wall Pectic Arabinans Influence the Mechanical Properties of Arabidopsis
thaliana Inflorescence Stems and Their Response to Mechanical Stress. Plant Cell Physiol.
2013
,54, 1278–1288. [CrossRef] [PubMed]
44.
Gookin, T.E.; Hunter, D.A.; Reid, M.S. Temporal analysis of alpha and beta-expansin expression during floral opening and
se-nescence. Plant Sci. 2003,164, 769–781. [CrossRef]
45.
Takahashi, R.; Fujitani, C.; Yamaki, S.; Yamada, K. Analysis of the cell wall loosening proteins during rose flower opening. In
Proceedings of the International Conference on Quality Management in Supply Chains of Ornamentals 755, Bangkok, Thailand,
3–6 December 2007; pp. 483–488.
46.
Yamada, K.; Takahashi, R.; Fujitani, C.; Mishima, K.; Yoshida, M.; Joyce, D.C.; Yamaki, S. Cell wall extensibility and effect of
cell-wall-loosening proteins during rose flower opening. J. Jpn. Soc. Hortic. Sci. 2009,78, 242–251. [CrossRef]
47.
O’Donoghue, E.M.; Somerfield, S.D.; Heyes, J.A. Organization of cell walls in Sandersonia aurantiaca floral tissue. J. Exp. Bot.
2002,53, 513–523. [CrossRef]
48.
Watanabe, Y.; Niki, T.; Norikoshi, R.; Nakano, M.; Ichimura, K. Soluble carbohydrate concentration and expression of expansin
and xyloglucan en-dotransglucosylase/hydrolase genes in epidermal and parenchyma cells during lily flower opening. J. Plant
Physiol. 2022,270, 153615. [CrossRef]
49.
Azeez, A.; Sane, A.P.; Tripathi, S.K.; Bhatnagar, D.; Nath, P. The gladiolus GgEXPA1 is a GA-responsive alpha-expansin gene
expressed ubiquitously during expansion of all floral tissues and leaves but repressed during organ senescence. Postharvest Biol.
Technol. 2010,58, 48–56. [CrossRef]

Int. J. Mol. Sci. 2023,24, 11666 19 of 19
50.
Franková, L.; Fry, S.C. Biochemistry and physiological roles of enzymes that ‘cut and paste’ plant cell-wall polysaccharides. J. Exp.
Bot. 2013,64, 3519–3550. [CrossRef]
51.
Van Sandt, V.S.T.; Suslov, D.; Verbelen, J.-P.; Vissenberg, K. Xyloglucan Endotransglucosylase Activity Loosens a Plant Cell Wall.
Ann. Bot. 2007,100, 1467–1473. [CrossRef]
52.
Shi, J.; Cui, M.; Yang, L.; Kim, Y.-J.; Zhang, D. Genetic and Biochemical Mechanisms of Pollen Wall Development. Trends Plant Sci.
2015,20, 741–753. [CrossRef] [PubMed]
53.
Li, X.; Bonawitz, N.D.; Weng, J.-K.; Chapple, C. The Growth Reduction Associated with Repressed Lignin Biosynthesis in
Arabidopsis thaliana Is Independent of Flavonoids. Plant Cell 2010,22, 1620–1632. [CrossRef] [PubMed]
54.
Cao, Y.; Han, Y.; Li, D.; Lin, Y.; Cai, Y. Systematic Analysis of the 4-Coumarate:Coenzyme A Ligase (4CL) Related Genes and
Expression Profiling during Fruit Development in the Chinese Pear. Genes 2016,7, 89. [CrossRef] [PubMed]
55. Schmid, K.M.; Ohlrogge, J.B. Lipid metabolism in plants. Biochem. Lipids Lipoproteins Membr. 2008, 97–130. [CrossRef]
56.
Llewellyn, D.; Finnegan, E.; Ellis, J.; Dennis, E.; Peacock, W. Structure and expression of an alcohol dehydrogenase 1 gene from
Pisum sativum (cv. “Greenfeast”). J. Mol. Biol. 1987,195, 115–123. [CrossRef]
57.
Millar, A.A.; Kunst, L. Very-long-chain fatty acid biosynthesis is controlled through the expression and specificity of the
condensing enzyme. Plant J. 1997,12, 121–131. [CrossRef]
58.
Chen, G.; Komatsuda, T.; Ma, J.F.; Nawrath, C.; Pourkheirandish, M.; Tagiri, A.; Hu, Y.-G.; Sameri, M.; Li, X.; Zhao, X.; et al. An
ATP-binding cassette subfamily G full transporter is essential for the retention of leaf water in both wild barley and rice. Proc.
Natl. Acad. Sci. USA 2011,108, 12354–12359. [CrossRef]
59.
Hyodo, H.; Yamakawa, S.; Takeda, Y.; Tsuduki, M.; Yokota, A.; Nishitani, K.; Kohchi, T. Active gene expression of a xyloglucan
endotransglucosylase/hydrolase gene, XTH9, in inflorescence apices is related to cell elongation in Arabidopsis thaliana.Plant Mol.
Biol. 2003,52, 473–482. [CrossRef]
60.
Miedes, E.; Lorences, E.P. Xyloglucan endotransglucosylase/hydrolases (XTHs) during tomato fruit growth and ripening. J. Plant
Physiol. 2009,166, 489–498. [CrossRef]
61.
Genovesi, V.; Fornalé, S.; Fry, S.C.; Ruel, K.; Ferrer, P.; Encina, A.; Sonbol, F.-M.; Bosch, J.; Puigdomènech, P.; Rigau, J.; et al.
ZmXTH1, a new xyloglucan endotransglucosylase/hydrolase in maize, affects cell wall structure and composition in Arabidopsis
thaliana*. J. Exp. Bot. 2008,59, 875–889. [CrossRef]
62. Bonnett, O.T. The development of the wheat spike. J. Agric. Res. 1936,53, 445–451.
63.
Fisher, J.E. Developmental morphology of the inflorescence in hexaploid wheat cultivars with and without the cultivar Norin 10
in their ancestry. Can. J. Plant Sci. 1973,53, 7–15. [CrossRef]
64.
Wright, G.W.; Simon, R.M. A random variance model for detection of differential gene expression in small microarray experiments.
Bioinformatics 2003,19, 2448–2455. [CrossRef] [PubMed]
65.
Trapnell, C.; Roberts, A.; Goff, L.; Pertea, G.; Kim, D.; Kelley, D.R.; Pimentel, H.; Salzberg, S.L.; Rinn, J.L.; Pachter, L. Differential
gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc.
2012
,7, 562–578.
[CrossRef]
66.
Ramoni, M.F.; Sebastiani, P.; Kohane, I.S. Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA
2002
,99, 9121–9126.
[CrossRef]
67.
Thimm, O.; Bläsing, O.; Gibon, Y.; Nagel, A.; Meyer, S.; Krüger, P.; Selbig, J.; Müller, L.A.; Rhee, S.Y.; Stitt, M. Mapman: A
user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J.
2004,37, 914–939. [CrossRef]
68. Yang, Y.; Li, R.; Qi, M. In vivo analysis of plant promoters and transcription factors by agroinfiltration of tobacco leaves. Plant J.
2000,22, 543–551. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.