































Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202203

Research Article

Open Access

1 Biotechnology Department, Science Faculty,

Helwan university, Helwan, Egypt.
2 Biotechnology Department, Agriculture Faculty,

Al-Azhar university, Cairo, Egypt.

* To whom correspondence should be
addressed: saifeldeenmib99@gmail.com

Editor: Hatem Zayed, College of Health and
Sciences, Qatar University, Doha, Qatar.

Reviewer(s):
Jean Legeay, Université de Lorraine, Inra, UMR
IAM - Interactions Arbres-Microorganismes, F-
54000 Nancy, France.

Vivek Chaudhary, Motilal Nehru National Institute
of Technology, Allahabad, Prayagraj, Uttar
Pradesh 211004, India.

Received: September 20, 2022

Accepted: December 3, 2022

Published: December 15, 2022

Citation: Ibrahim MS, Ibrahim SM. Identifying
biotic stress-associated molecular markers in wheat
using differential gene expression and machine
learning techniques. 2022 Dec 15;5:bs202203

Copyright: © 2022 Ibrahim and Ibrahim. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.

Data Availability Statement: All relevant data are
within the paper and supplementary materials.

Funding: The authors have no support or
funding to report.

Competing interests: The authors declare
that they have no competing interests.

Identifying biotic stress-associated molecular markers in wheat using
differential gene expression and machine learning techniques

Manar S. Ibrahim1
><, Saifeldeen M. Ibrahim*2

><

Abstract
Wheat is an important crop for global food security and a key crop for many devel-

oping countries. Thanks to next-generation sequencing (NGS) technologies, researchers
can analyze the transcriptome of wheat and reveal differentially expressed genes (DEGs)
responsible for essential agronomic traits and biotic stress tolerance. In addition, machine
learning (ML) methods have opened new avenues to detect patterns in expression data and
make predictions or decisions based on these patterns. We used both techniques to identify
potential molecular markers in wheat associated with biotic stress in six gene expression
studies conducted to investigate powdery mildew, blast fungus, rust, fly larval infection,
greenbug aphid, and Stagonospora nodorum infections. A total of 24,152 threshold genes
were collected from different studies, with the highest threshold being 7580 genes and
the lowest being 1073 genes. The study identified several genes that were differentially
expressed in all comparisons and genes that were present in only one data set. The study
also discussed the possible role of certain genes in plant resistance. The Ta-TLP, HBP-
1, WRKY, PPO, and glucan endo-1,3-beta-glucosidase genes were selected by the inter-
pretable model-agnostic explanation algorithm as the most important genes known to play
a significant role in resistance to biotic stress. Our results support the application of ML
analysis in plant genomics and can help increase agricultural efficiency and production,
leading to higher yields and more sustainable farming practices.

Keywords: Wheat, gene expression, machine learning, biotic stress, disease

Introduction
Agriculture is the primary source of food production on a global scale. In the new era, sus-

tainable agriculture is used to preserve or improve food quality while protecting the environment

[1]. wheat (Triticum aestivum) is a member of the Poaceae family and is considered the oldest and

most commonly cultivated crop [2], with an annual cultivation area of 217 million hectares. It is

the most commonly grown crop in the world [3]. Also, it is one of three primary cereal crops with

the greatest impact on global food security, second only to maize (Zea mays) and third only to rice

[4]. Most experts assume that wheat was the first crop to be produced between 10,000 and 8,000

B.C. [2], and this is considered evidence of the importance of wheat from ancient times. Wheat

is one of the most essential crops for ensuring global food security [5]. Considering the position

of wheat in the world trade, wheat accounts for over 50% of the worldwide grain trade and 30%

of the global grain yield [6]. And for its role in nutrition, wheat is a staple crop in more than 40

nations worldwide, feeding 82% to 85% of the world’s population [6]. According to the estimations,

the annual wheat production is expected to reach 750.4 million metric tonnes in the next few years.

Africa would consume 76.5 MMT of wheat, with imported grain accounting for 48.3 MMT, or 63.3

percent of total consumption [7]. Powerful yet user-friendly bioinformatics tools are required by

the expanding field of gene expression profiling in order to facilitate systems-level data comprehen-

sion [8]. Studies on transcriptomics have a significant potential for unique and exploratory research

aimed at new insights into molecular pathways [9].

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https://creativecommons.org/licenses/by/4.0/
manar.samir249@science.helwan.edu.eg
https://orcid.org/0000-0003-0143-2596
saifeldeenmib99@gmail.com
https://orcid.org/0000-0002-6593-378X
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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Next-generation sequencing (NGS) technologies greatly im-
prove studies on genome-wide mRNA expression data. In com-
parison to microarray technology, NGS provides higher reso-
lution data and more precise transcript level measurements for
gene expression studies [10]. By studying the wheat transcrip-
tome, it is possible to identify the differentially expressed genes,
genomic annotations, regulatory elements, molecular markers,
and expression quantitative trait loci (eQTLs), as well as their
sequence variants, which are responsible for significant charac-
teristics [11].

With the advancement of next-generation sequencing tech-
nology, the genetic diversity of wheat has recently been studied
using a high-throughput, low-cost molecular technique [12].The
significant genetic variation seen in the primary, secondary, and
tertiary wheat gene pools provides the starting point for the breed-
ing of nutrient-rich wheat genotypes [13]. A variety of molec-
ular markers have been used to study the genetic diversity of
wheat [12] to measure biological population divergence’s magni-
tude and each component character’s proportionate contribution
to the overall divergence [14].

Machine learning (ML) is a rapidly growing field of com-
puter science that has the potential to revolutionize many indus-
tries, including plant science. Machine learning algorithms use
statistical techniques to identify patterns in data and make pre-
dictions or decisions based on those patterns. This makes them
well-suited to solving complex problems that are difficult for hu-
mans to tackle, such as analyzing vast amounts of data from
experiments or monitoring the health of crops. In plant science,
machine learning can be used to predict the best time to plant
crops, optimize irrigation systems, identify pests and diseases,
and even breed new varieties of plants. This could help im-
prove the efficiency and productivity of agriculture, leading to
better yields and more sustainable farming practices. Overall,
the use of machine learning in plant science has the potential to
significantly advance the field and improve our understanding of
plants.

Consequently, in this study, the aim of our investigation is
to perform expression profiling analysis of wheat to identify un-
known molecular markers. We aim to use bioinformatics tools
to ;(a) Study the gene expression of wheat under various envi-
ronmental and developmental conditions. (b) Study the genetic
diversity of these genes across the wheat genome. (c) Identify
important genes and their interactions. (d) Use machine learn-
ing methods to identify genes associated with wheat response
to biotic stresses. By doing so, we hope to gain a better under-
standing of the genetic basis of wheat response to biotic stresses,
which could ultimately lead to the development of more resilient
and productive varieties of wheat. This is important because bi-
otic stresses, such as pests and diseases, can cause significant
yield losses and can threaten the global food supply. By identi-
fying molecular markers that are associated with wheat response
to biotic stresses, we can improve our ability to breed new vari-
eties of wheat that are better able to withstand these stresses.

This could help to improve the efficiency and productivity of
agriculture, leading to better yields and more sustainable farm-
ing practices.

Methodology
Data acquisition

The Gene Expression Omnibus (GEO) database, first estab-
lished in 2005 [15], has continued to be a vital resource for the
global scientific community. With over 20 years of experience,
GEO remains the most widely used public repository for high-
throughput gene expression and other functional genomics data
sets. This valuable resource allows researchers to quickly and
easily access a wide range of raw and processed data, along
with detailed experimental descriptions, at no cost. The con-
venience and accessibility of GEO make it an invaluable tool
for advancing scientific research. In this study, GEO has been
used to download the data of six different experiments on wheat
for further analysis. These experiments have been conducted un-
der different biotec stresses. such as the response to powdery
mildew infection, the response to blast fungus magnaporthe, the
resistance pathways of transgenic wheat lines, the response to a
Hessian fly larval attack, the response to greenbug aphid feed-
ing, and the response to the necrotrophic effector SnTox3. All
GSE accessions are provided with the experiment name of each
accession in (table 1).

Differential gene expression analysis
With the help of the R/Bioconductor and Limma package,

the online statistical tool GEO2R [16] was used to analyze the
raw six gene expression data sets of wheat under different bi-
otic stresses from GEO datasets [17] (table1). The samples have
been clustered into two groups (control and infected / treatment).
Differential expression genes were analyzed using R packages
to create a volcano plot highlighting all significant genes and a
heatmap of the top differentially expressed genes based on each
group in all samples according to log2FC (fold change) ≥ 1 and
adjusted p-value < 0.05 as the threshold for DEGs. Whereby
up-regulated DEGs were considered if the logFC (fold change)
≥ 1 and down-regulated DEGs were considered if the logFC ≤
-1. A volcano plot shows statistical significance (P value) versus
magnitude of change (fold change). It enables quick visual iden-
tification of genes with large fold changes that are also statisti-
cally significant. These may be the most biologically significant
genes [18]. First, a volcano plot was generated using the R pack-
age (ggplot2) after reading the data and filtering it according to
the adjusted p-value and logFC (blue: down-regulated, red: up-
regulated). Second, a heatmap of the top differentially expressed
genes in the RNA-seq data set was then generated. To do this,
we need to extract the differentially expressed genes from the
DE results file. The heatmap for the gene expression data was
generated using R package (pheatmap) after filtering the top dif-
ferential gene expression from the data. Then, a Venn diagram
was used to show all the interrelationships of the six wheat exper-
iments. The Venn diagram of the overlapping DEGs was output

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

by the interactiVenn website [19].

Protein-protein interaction and and functional enrichment analyses
The STRING database was utilised for protein-protein inter-

action analysis [20] to evaluate the link between genes associ-
ated with wheat gene expression. The STRING database was
chosen because this database seeks to integrate all known and
predicted relationships between proteins, including functional
and physical interactions [20]. All information about GO, an-
notated keywords, and protein domains for each GSE dataset
has been provided in (table 2)

Machine Learning Model
The results of the differential gene expression study of pu-

tative biotic-associated gene biomarkers were employed as at-
tribute values for machine learning (ML) analysis. We retrieved
gene expression data from genes that were strongly expressed in
gene expression. Their expression data were utilised as training
and validation sets for the ML analysis. The gene expression
data were adjusted before to the ML analysis. The sklearn and
lime0.2.0.1 Python libraries were used to run the "Extra-tree re-
gressor" and local interpretable model-agnostic explanations al-
gorithms.

Results and Discussion
Identification of DEGs

A total of 24,152 threshold genes were collected from dif-
ferent studies. The highest threshold of genes was collected by
GSE27320, with 7580 genes, while GSE31760 had the lowest
threshold of genes, with only 1073 genes. The volcano plot
indicates the upregulated (red color) and downregulated (blue
color) genes in Triticum aestivum samples. The horizontal axis
represents the fold change (log2FC), and the vertical axis rep-
resents the adjusted p-values. A total of 3701 upregulated and
3879 downregulated DEGs were identified from the GSE27320
dataset, while 241 upregulated and 832 downregulated DEGs
were identified from the GSE31760 dataset.
GSE32151 yielded 3533 upregulated and 5138 downregulated
DEGs, GSE34445 yielded 658 upregulated and 1442 downreg-
ulated DEGs, GSE45995 yielded 2054 upregulated and 1459
downregulated DEGs, and GSE59723 yielded 74 upregulated
and 1141 downregulated DEGs. The volcano plot of each gene
expression profile data is shown in (Figure 1).

Users can use heatmaps to examine the expression of a sub-
set of genes. This can provide helpful insight into the expression
of various groups and samples without losing sight of the larger
study or losing clarity when evaluating patterns averaged over
hundreds of genes at once [18]. According to the adjusted p-
value ranking, a heatmap is created for the top DE genes.The
heatmap clusters the samples into control (blue) and infected /
treatment (red) groups. Red indicates samples with relatively
high gene expression, while blue indicates samples with rela-
tively low gene expression. Genes with intermediate expression
levels are represented by lighter tones and white. A dendrogram

hierarchical clustering has been used to reorder the samples and
genes (Figure 2).

Venn diagrams of the DEGs between the integrated six GEO
data sets are shown in (Figure 2). The number in each circle
indicates the quantity of differentially expressed genes across the
various comparisons. The overlapping number refers to genes
that are differently expressed across all comparisons, while the
non-overlapping number refers to genes that are unique to each
sample. Genes were identified as being up- or down-regulated.

The GSE32151 and GSE45995 data sets shared 12 genes,
while the GSE45995 and GSE59723 data sets shared 6 genes
(PDI2, pepc, gstf5, gstf5:1, and ltp9.2). According to the study
of Guo-Tian Liu et al.[21], gstf5 is a member of the (GSTs)
family, which is responsible for catalysing the conjugation of
the reduced form of glutathione to a number of electrophilic
substrates, they also reported that GSTs contribute to resistance
against powdery mildew.
The GSE31760, GSE45995, and GSE59723 data sets shared 3
genes (rgp, PR4, and pox3). The relationship between methyl
jasmonate (MeJA) and the expression profiles of nine pathogenesis-
related protein genes (PR genes) was examined in the Zongbiao
Duan et al. [22] study to determine the interactive role in pow-
dery mildew resistance. This investigation revealed that PR4 is
one of the pathogenesis-related protein genes (PR genes), and
the expressions of PR4 and other PR genes were most signif-
icantly activated by MeJA and showed a significant resistance
to powdery mildew. The GSE32151 and GSE59723 datasets
shared 1 gene (Xip-R1). Xip-R1 is one of the xylanase inhibitors.
R.-J. SUN et al. [23], xylanase inhibitors (XIs) are plant cell
wall proteins found mostly in monocots that limit microbial xy-
lanases’ hemicellulose degrading ability. Silvio Tundo et al.
[24]. The GSE34445 and GSE59723 data sets shared 4 genes
(WRKY45, Cht2, WRKY, and ccd1), and the GSE31760 and
GSE59723 shared 2 genes (TaAOS and tamdr1). The allene
oxide synthase (TaAOS) gene has been identified as being en-
gaged in the JA signalling system, which increases plant resis-
tance to Fusarium head blight (FHB) [25], Tamdr1 has also been
identified as a Fusarium head blight resistance gene [26]. The
GSE27320 and GSE31760 shared 2 genes (ald and pSBGer4),
and the GSE27320 and GSE32151 data sets shared 2 genes (
hsp16.9-3LC2 and S276 ) . The GSE32151 and GSE34445
data sets shared 1 gene (TaAML15), and the GSE27320 and
GSE34445 data sets shared 3 genes (Ss1, wpi6, and gstf6b). The
GSE34445 and GSE45995 data sets shared 4 genes (TaGlb2b,
TaAKT1, TaMRP2, and omet). Pratiksha Singh et al. [27] re-
ported that TaGlb2b showed a significant response to Erysiphe
graminis and Fusarium graminearum.
The GSE27320, GSE31760, and GSE34445 data sets shared 1
gene (HSP101c) which has an important role in heat tolerance
in hexaploid wheat [28]. The GSE27320 and GSE45995 data
sets shared 13 genes (Tra2, Tra2:1, TAc23, zip1, TaGI1, PsbP,
ctpA, Wcor726, pre-FBPase, lgul, GAPN, 6-FEH, and lgul:1).
In a study by Yu Liu et al. to examine the response of PsbP to

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Table 1. Information for the Six GEO datasets for wheat. Unique identifier for the dataset within GEO (accession), full name of the experiment (experiment

name), shortened version of the experiment name (short name), ans the abbreviated version of the experiment name (abbrev).

accession experiment name short name abbrev

GSE27320 Expression data in wheat (T. aestivum L.) near isogenic lines in response to powdery mildew infection wheat responses to powdery mildew WRPM

GSE31760 Transcription profiling wheat responses to adapted and non-adapted isolates of the blast fungus, Magnaporthe wheat responses to blast fungus WRBF

GSE32151 Lr1-mediated leaf rust resistance pathways of transgenic wheat lines mediated leaf rust resistance MLRR

GSE34445 Expression data from wheat following Hessian fly larval attack. wheat following fly larval WFFL

GSE45995 Transcriptomics of induced defense responses to Greenbug aphid feeding in near isogenic wheat lines wheat responses to Greenbug aphid WRGA

GSE59723 Transcriptional analyses of wheat responses to the necrotrophic effector SnTox3 wheat responses to SnTox3 WRST

Table 2. The SRING database table provides information about GO, annotated

keywords, and protein domains for each GSE dataset.

GSE Category ID Description FDR

GSE27320

Molecular Function GO:0005200 Structural constituent of cytoskeleton 0.0046

Cellular Component GO:0110165 Cellular anatomical entity 2.86E-24

KEGG Pathways map04145 Phagosome 0.0343

Annotated Keywords

KW-0963 Cytoplasm 0.00025

KW-0206 Cytoskeleton 0.0015

KW-0493 Microtubule 0.0016

KW-0342 GTP-binding 0.0065

KW-0809 Transit peptide 0.0256

Protein Domains
PF03953 Tubulin C-terminal domain 0.00055

PF00091 Tubulin/FtsZ family, GTPase domain 0.0012

Protein Features

IPR000217 Tubulin 2.43E-05

IPR002453 Beta tubulin 2.43E-05

IPR003008 Tubulin/FtsZ, GTPase domain 2.43E-05

IPR008280 Tubulin/FtsZ, C-terminal 2.43E-05

IPR013838 Beta tubulin, autoregulation binding site 2.43E-05

IPR017975 Tubulin, conserved site 2.43E-05

IPR018316 Tubulin/FtsZ, 2-layer sandwich domain 2.43E-05

IPR023123 Tubulin, C-terminal 2.43E-05

IPR037103 Tubulin/FtsZ, C-terminal superfamily 2.43E-05

IPR036525 Tubulin/FtsZ, GTPase domain superfamily 2.71E-05

IPR000877 Proteinase inhibitor I12, Bowman-Birk 0.0046

Protein Domains SM00269 Bowman-Birk type proteinase inhibitor 0.004

GSE31760 Cellular Component GO:0110165 Cellular anatomical entity 0.001

GSE32151

Cellular Component GO:0110165 Cellular anatomical entity 6.46E-37

Annotated Keywords

KW-0325 Glycoprotein 3.38E-05

KW-1015 Disulfide bond 0.00015

KW-0732 Signal 0.00024

KW-0809 Transit peptide 0.0023

KW-0963 Cytoplasm 0.0023

KW-0326 Glycosidase 0.0028

KW-0378 Hydrolase 0.0062

KW-0676 Redox-active center 0.0206

KW-0119 Carbohydrate metabolism 0.0217

KW-0624 Polysaccharide degradation 0.0386

Protein Features IPR000877 Proteinase inhibitor I12, Bowman-Birk 0.00055

Protein Domains SM00269 Bowman-Birk type proteinase inhibitor 4.17E-05

GSE34445 Cellular Component GO:0110165 Cellular anatomical entity 1.42E-07

GSE45995

Cellular Component GO:0110165 Cellular anatomical entity 1.53E-19

Subcellular localization GOCC:0005576 Extracellular region 0.0056

Annotated Keywords

KW-0809 Transit peptide 0.0011

KW-0150 Chloroplast 0.0022

KW-0732 Signal 0.0039

KW-0676 Redox-active center 0.0081

KW-0597 Phosphoprotein 0.0241

KW-0963 Cytoplasm 0.0338

Protein Domains SM00269 Bowman-Birk type proteinase inhibitor 0.004

GSE59723

Cellular Component GO:0110165 Cellular anatomical entity 1.75E-08

Annotated Keywords

KW-1015 Disulfide bond 0.00015

KW-0732 Signal 0.00097

KW-0325 Glycoprotein 0.0088

KW-0326 Glycosidase 0.0214

Protein Features IPR000877 Proteinase inhibitor I12, Bowman-Birk 0.0199

Protein Domains SM00269 Bowman-Birk type proteinase inhibitor 0.0016

Table 3. The results of the machine learning model represent the most signifi-

cant genes with their corresponding values.

Accsession Marker P/N Value Gene.Symbol

GSE27320

Ta.85.1.S1 positive 28944.5 PsbP
Ta.1842.1.S1_a positive 309.3 WPEAMT
Ta.1848.2.S1 positive 4463.8 pip1:2
Ta.2907.1.S1 positive 4340.4 LOC543101
Ta.10.1.S1_a positive 47759.5 LOC543334

TaAffx.120000.1.S1 positive 465.7 TLK1
Ta.10.2.S1 positive 63930.8 LOC543334

Ta.21350.2.S1 positive 216.2 wrsi5-1
Ta.21348.1.S1 positive 292.1 LOC543233
Ta.23758.1.S1 positive 265.9 Wcor518

GSE31760

Ta.28.1.S1 negative 16942 LOC543330
Ta.27762.1.S1 negative 3687.7 Ta-TLP
Ta.24501.1.S1 negative 2852.3 LOC543292
Ta.2788.1.A1 negative 768.65 1-SST

Ta.82.1.S1 negative 8065.5 LOC543287
TaAffx.115935.1.S1 negative 375.52 LOC543157

Ta.87.1.S1 negative 654.58 pSBGer4
Ta.24254.3.S1 negative 333.75 LOC606342
Ta.25053.1.S1 negative 5220.3 LOC542887

Ta.22673.1.S1_s negative 14994 LOC543498

GSE32151

Ta.9320.1.S1 negative 11.7 CCoAMT
Ta.9320.1.S1 negative 11.81 CCoAMT
Ta.9402.1.S1 negative 8.97 LOC542918
Ta.8629.1.A1 negative 5.9 WLTP1

TaAffx.39351.2.A1 negative 9.64 HrBP1-1
Ta.9402.1.S1 negative 8.92 LOC542918

Ta.24806.1.S1 negative 10.79 WLIP19
Ta.28700.1.S1 negative 4.55 CHS
Ta.2907.1.S1 negative 11.76 LOC543101

Ta.28734.1.S1 negative 15.08 TaGRP2

GSE34445

Ta.8614.1.S1 negative 46.58 WRKY45
Ta.27312.1.S1 negative 63.75 AMT1
Ta.4050.1.S1 negative 136.86 LOC100037581
Ta.203.1.S1 negative 86.99 LOC543416
Ta.1058.1.S1 negative 11290.8 SAMDC1
Ta.192.1.S1 negative 79 LOC543235
Ta.4678.2.S1 negative 61.95 WRKY71
Ta.217.1.S1 negative 1033.27 LOC543244
Ta.1058.3.S1 negative 15078.5 SAMDC1

TaAffx.129134.2.S1 negative 281.44 a2b

GSE45995

Ta.28.1.S1 negative 37166.6 LOC543330
Ta.9226.1.S1 negative 41349.6 PR4
Ta.2784.1.A1 negative 42724.1 Chi 1
Ta.14183.1.S1 negative 304.86 LOC543077
Ta.21342.1.S1 negative 34995.6 Chi 3

Ta.22871.1.S1_s negative 13472.6 gamma-TIP
TaAffx.39351.2.A1 negative 947.06 HrBP1-1

Ta.27762.1.S1 negative 31690.6 Ta-TLP
Ta.278.1.S1 negative 41378.7 LOC543422
Ta.278.1.S1 negative 41179.1 LOC543422

GSE59723

Ta.5428.1.S1 negative 7.41 acT3
Ta.8228.1.S1 negative 4.69 LOC543097

TaAffx.128418.102.S1 negative 2.53 ltp9.2
Ta.706.1.S1_s negative 7.5 g6pdh

Ta.81.1.S1 negative 6.86 pepc
Ta.24501.1.S1 negative 10.2 LOC543292
Ta.4725.1.S1 negative 5.08 WRKY53-b

Ta.27762.1.S1 negative 7.64 Ta-TLP
Ta.234.1.S1 negative 4.48 LCT1

TaAffx.115935.1.S1 negative 5.11 LOC543157

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Figure 1. Visualization of DEGs volcano plots.The representations are as follows: x-axis, log2FC; y-axis, -log10 of an adjusted p-value.

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Figure 2. Heatmap and Venn diagram of the top DE genes data

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Figure 3. PPI networks show the interaction of DEGs.

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Figure 4. Machine learning results and venn diagrams of the markers, genes, and gene titles that were shared between GSEs.

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

wheat dwarf virus (WDV), they noticed that the PsbP gene was
down-regulated under WDV infection [29].
The GSE27320, GSE45995, and GSE59723 data sets shared 3
genes (pox4, wrsi5-1, and WAS-2). The GSE27320, GSE31760,
GSE45995, and GSE59723 data sets shared 2 genes (Ta-TLP
and Chi 3).
The GSE27320, GSE32151, and GSE45995 data sets shared
14 genes, while the GSE27320, GSE32151, GSE45995 , and
GSE59723 data sets shared 5 genes (Taxi-III, Taxi-III:1, wali6,
Taxi-IV, and POX2). The GSE27320, GSE34445, and GSE45995
data sets shared 1 gene (Wcab) which was highly significant
in the response to stripe rust in wheat [30]. The GSE27320,
GSE31760, GSE34445, and GSE45995 data sets shared 1 gene
(rbcl), in an investigation by Ceyda Ozfidan -Konakci et al.,
the rbcl gene was observed at higher transcription levels in the
chloroplasts of wheat as a response to hydrogen sulphide (H2S)
and nitric oxide (NO) alleviate cobalt toxicity. The GSE31760,
GSE34445, and GSE45995 data sets shared 1 gene (prx), and
the GSE27320, GSE34445, and GSE59723 data sets shared 1
gene (FKBP77) which has a high expression under heat stress
[31].
The GSE27320, GSE32151, GSE34445, and GSE59723 data
sets shared 1 gene (taxi-I).
GSE32151, GSE34445, GSE45995, and GSE59723 data sets
shared 3 genes (xipI:1, Tamyb7, and xipI). The Tamyb7 is one of
the MYB family, which is considered to have a significant role
in resisting stripe rust [32]. GSE27320, GSE34445, GSE45995,
and GSE59723 data sets shared 1 gene (WRN2).
GSE31760, GSE34445, GSE45995, and GSE59723 data sets
shared 1 gene (S85). The GSE31760, GSE32151, GSE34445,
and GSE59723 data sets shared 1 gene (WCK-1), WCK-1 ex-
pression is reported to be induced by the fungal elicitor cal-
cium ionophore A23187 as well as drought [33]. GSE31760,
GSE34445, and GSE59723 data sets shared 2 genes (Cht4 and
gstu2), Cht-4 expression was substantially faster in the resistant
cultivar than in the susceptible one, in the response to Fusar-
ium graminearum in wheat [34]. GSE32151, GSE45995, and
GSE59723 data sets shared 4 genes (Tamyb7:2, wali5, Chi 2,
and Tamyb7:1), Guozhang Kang et al. [35] discovered that
freezing stress increased the expression of the wali5 protein gene.
GSE31760, GSE32151, GSE45995, and GSE59723 data sets
shared 2 genes (WRKY53-b and Chi 1).
The GSE31760, GSE32151, and GSE45995 data sets shared 1
gene (DBP), and the GSE27320, GSE32151, and GSE34445
data sets shared 2 genes (AMT1 and AMT1:1).

PPI network construction
Protein-protein interaction (PPI) networks were created us-

ing the STRING tool. Six modules were identified in this con-
structed network, which was made up of 115 nodes and 56 edges.
The top significant module was GES32151, which was com-
posed of 44 nodes and 30 edges, and had a PPI enrichment p-
value of 0.0565. The lowest module was GSE34445, which was

made up of only 11 nodes, 1 edge, and had a PPI enrichment
p-value of 0.174 (Figure 3).

Machine learning
The machine learning model identified 53 markers, 48 genes,

and 46 gene titles, as shown in (table 3). Also see (Figure 4).
Each GSE accession consisted of ten markers. Only GSE27320
had positive values, ranging from 216.2 to 63930.8. The rest of
the GSEs had negative values, as the table shows. Some of the
GSEs shared the same markers, genes, and gene titles. Accord-
ing to the venn diagram (figure 4) of the markers, GSE32151 and
GSE45995 shared TaAffx.39351.2.A1. GSE31760, GSE45995,
and GSE59723 shared Ta.27762.1.S1. GSE31760 and GSE59723
shared Ta.24501.1.S1 and TaAffx.115935.1.S1. GSE27320 and
GSE32151 shared Ta.2907.1.S1. GSE31760 and GSE45995 shared
Ta.28.1.S1.

For genes, GSE32151 and GSE45995 shared HrBP1-1. GSE31760
, GSE45995 , and GSE59723 shared Ta-TLP. GSE31760 and
GSE59723 shared LOC543292 and LOC543157. GSE27320
and GSE32151 shared LOC543101. GSE31760 and GSE45995
shared LOC543330. For gene titles, GSE32151 and GSE45995
shared harpin binding protein 1. GSE31760, GSE45995, and
GSE59723 shared thaumatin-like protein. GSE34445 and GSE59723
shared WRKY transcription factor. GSE31760 and GSE59723
shared polyphenol oxidase. GSE27320 and GSE32151 shared
methylmalonate semialdehyde dehydrogenase. GSE31760 and
GSE45995 shared glucan endo-1 and 3-beta-D-glucosidase.

Danielle et al. [36] mention in their study that the Ta-TLP
gene is a member of pathogenesis-related proteins and has an
important role in plant resistance to powdery mildew. Another
study by Zhi-Hui He et al. [37] reported that harpin-binding
protein 1 is thought to be involved in plant disease resistance and
drought resilience because it is responsible for plastid glutamine
synthase (GS). It was shown to be down-regulated in wheat’s
single seed descent line 10 (SSDL 10). Furthermore, Thaumatin-
like proteins (TLPs), as reported by Weibo Sun et al. [38], play
roles in plant resistance to pathogen stress by acting as a positive
factor in transgenic poplars with enhanced resistance to spots
disease.

For WRKY transcription factors, Patel P et al [39] men-
tioned in their study that WRKY transcription factors are thought
to have a role in contributing to the level of thermotolerance. It
was reported [39] that polyphenol oxidase (PPO) has a role in
plant resistance to osmotic stress-tolerant bacteria. They found
that bacterized seedlings showed a slight improvement in wheat
roots and shoots, and the polyphenol oxidase was greater in the
roots and shoots. Hegedus et al. [40] reported that the overex-
pression of glucan endo-1,3-beta-glucosidase has been linked to
a variety of physiological and developmental processes, as well
as resistance to biotic and abiotic stress. This suggests that these
genes may play an important role in plant resistance to various
stresses and diseases.

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Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

Reference
1. Gimenez E, Salinas M, Manzano-Agugliaro F. World-

wide research on plant defense against biotic stresses as
improvement for sustainable agriculture. Sustainability.
2018;10(2):391.

2. Igrejas G, Branlard G. The importance of wheat. In:
Wheat quality for improving processing and human health.
Springer; 2020. p. 1-7.

3. Erenstein O, Jaleta M, Mottaleb KA, Sonder K, Donovan J,
Braun HJ. Global Trends in Wheat Production, Consump-
tion and Trade. In: Wheat Improvement. Springer; 2022. p.
47-66.

4. Hayta S, Smedley MA, Clarke M, Forner M, Harwood WA.
An efficient Agrobacterium-mediated transformation proto-
col for hexaploid and tetraploid wheat. Current Protocols.
2021;1(3):e58.

5. Zhou Y, Zhao X, Li Y, Xu J, Bi A, Kang L, et al. Triticum
population sequencing provides insights into wheat adapta-
tion. Nature genetics. 2020;52(12):1412-22.

6. Poudel PB, Poudel MR. Heat stress effects and tolerance
in wheat: A review. Journal of Biology and Today’s World.
2020;9(3):1-6.

7. Jasrotia P, Kashyap PL, Bhardwaj AK, Kumar S, Singh G.
Scope and applications of nanotechnology for wheat pro-
duction: A review of recent advances. Wheat Barley Res.
2018;10(1):1-14.

8. Zhou G, Soufan O, Ewald J, Hancock RE, Basu N, Xia J.
NetworkAnalyst 3.0: a visual analytics platform for com-
prehensive gene expression profiling and meta-analysis. Nu-
cleic acids research. 2019;47(W1):W234-41.

9. Kumar Kushwaha S, Vetukuri RR, Odilbekov F, Pareek N,
Henriksson T, Chawade A. Differential gene expression
analysis of wheat breeding lines reveal molecular insights
in yellow rust resistance under field conditions. Agronomy.
2020;10(12):1888.

10. Wang T, Li B, Nelson CE, Nabavi S. Comparative analysis
of differential gene expression analysis tools for single-cell
RNA sequencing data. BMC bioinformatics. 2019;20(1):1-
16.

11. Paul S, Duhan JS, Jaiswal S, Angadi UB, Sharma R, Raghav
N, et al. RNA-Seq Analysis of Developing Grains of Wheat
to Intrigue Into the Complex Molecular Mechanism of the
Heat Stress Response. 2022.

12. Yang X, Tan B, Liu H, Zhu W, Xu L, Wang Y, et al. Ge-
netic diversity and population structure of Asian and Eu-
ropean common wheat accessions based on genotyping-by-
sequencing. Frontiers in Genetics. 2020;11:580782.

13. Velu G, Crespo Herrera L, Guzman C, Huerta J, Payne T,
Singh RP. Assessing genetic diversity to breed competitive
biofortified wheat with enhanced grain Zn and Fe concen-
trations. Frontiers in Plant Science. 2019;9:1971.

14. Devesh P, Moitra P, Shukla R, Pandey S. Genetic diversity
and principal component analyses for yield, yield compo-
nents and quality traits of advanced lines of wheat. Journal
of Pharmacognosy and Phytochemistry. 2019;8(3):4834-9.

15. Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC,
Ledoux P, et al. NCBI GEO: mining millions of expres-
sion profilesdatabase and tools. Nucleic acids research.
2005;33(suppl_1):D562-6.

16. Zhang X, Wang Z, Hu L, Shen X, Liu C. Identification
of potential genetic biomarkers and target genes of peri-
implantitis using bioinformatics tools. BioMed Research
International. 2021;2021.

17. Udhaya Kumar S, Thirumal Kumar D, Bithia R, Sankar
S, Magesh R, Sidenna M, et al. Analysis of differentially
expressed genes and molecular pathways in familial hy-
percholesterolemia involved in atherosclerosis: a system-
atic and bioinformatics approach. Frontiers in Genetics.
2020;11:734.

18. Liu S, Wang Z, Zhu R, Wang F, Cheng Y, Liu Y. Three
differential expression analysis methods for RNA sequenc-
ing: limma, EdgeR, DESeq2. JoVE (Journal of Visualized
Experiments). 2021;(175):e62528.

19. Sivasakthi P, Sabarathinam S, Vijayakumar TM. Network
pharmacology and in silico pharmacokinetic prediction of
Ozanimod in the management of ulcerative colitis: A com-
putational study. Health Science Reports. 2022;5(1).

20. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R,
Pyysalo S, et al. The STRING database in 2021: customiz-
able protein–protein networks, and functional characteriza-
tion of user-uploaded gene/measurement sets. Nucleic acids
research. 2021;49(D1):D605-12.

21. Liu GT, Wang BB, Lecourieux D, Li MJ, Liu MB, Liu RQ,
et al. Proteomic analysis of early-stage incompatible and
compatible interactions between grapevine and P. viticola.
Horticulture research. 2021;8.

22. Duan Z, Lv G, Shen C, Li Q, Qin Z, Niu J. The role of jas-
monic acid signalling in wheat (Triticum aestivum L.) pow-
dery mildew resistance reaction. European journal of plant
pathology. 2014;140(1):169-83.

23. Sun RJ, Xu Y, Hou CX, Zhan YH, Liu MQ, Weng XY. Ex-
pression and characteristics of rice xylanase inhibitor Os-
XIP, a member of a new class of antifungal proteins. Biolo-
gia plantarum. 2018;62(3):569-78.

Highlights in BioScience Page 10 of 11 December 2022|Volume 5

http://bioscience.highlightsin.org/


Ibrahim and Ibrahim, 2022 Identifying biotic stress associated molecular markers in wheat

24. Tundo S, Mandalà G, Sella L, Favaron F, Bedre R,
Kalunke RM. Xylanase Inhibitors: Defense Players in
Plant Immunity with Implications in Agro-Industrial Pro-
cessing. International Journal of Molecular Sciences.
2022;23(23):14994.

25. Hu C, Chen P, Zhou X, Li Y, Ma K, Li S, et al. Arms Race
between the Host and Pathogen Associated with Fusarium
Head Blight of Wheat. Cells. 2022;11(15):2275.

26. Wang Q, Shao B, Shaikh FI, Friedt W, Gottwald S. Wheat
resistances to Fusarium root rot and head blight are both
associated with deoxynivalenol-and jasmonate-related gene
expression. Phytopathology. 2018;108(5):602-16.

27. Singh P, Song QQ, Singh RK, Li HB, Solanki MK, Yang LT,
et al. Physiological and molecular analysis of sugarcane (Va-
rietiesF134 and NCo310) during Sporisorium scitamineum
interaction. Sugar Tech. 2019;21(4):631-44.

28. Erdayani E, Nagarajan R, Grant NP, Gill KS. Genome-wide
analysis of the HSP101/CLPB gene family for heat toler-
ance in hexaploid wheat. Scientific reports. 2020;10(1):1-
17.

29. Liu Y, Liu Y, Spetz C, Li L, Wang X. Comparative transcrip-
tome analysis in Triticum aestivum infecting wheat dwarf
virus reveals the effects of viral infection on phytohormone
and photosynthesis metabolism pathways. Phytopathology
Research. 2020;2(1):1-13.

30. Lata C, Prasad P, Gangwar O, Adhikari S, Thakur RK,
Savadi S, et al. Temporal behavior of wheat–Puccinia
striiformis interaction prompted defense-responsive genes.
Journal of Plant Interactions. 2022;17(1):674-84.

31. Vishwakarma H, Junaid A, Manjhi J, Singh GP, Gaikwad
K, Padaria JC. Heat stress transcripts, differential expres-
sion, and profiling of heat stress tolerant gene TaHsp90 in
Indian wheat (Triticum aestivum L.) cv C306. PloS one.
2018;13(6):e0198293.

32. Al-Attala MN. The role of Some MYB genes in defense
response of wheat against stripe rust pathogen. Egyptian
Journal of Desert Research. 2019;69(3):113-29.

33. Liu Y, Yu TF, Li YT, Zheng L, Lu ZW, Zhou YB, et al.
Mitogen-activated protein kinase TaMPK3 suppresses ABA
response by destabilising TaPYL4 receptor in wheat. New
Phytologist. 2022;236(1):114-31.

34. Sorahinobar M, Niknam V, Jahedi A, Ebrahimzadeh H,
Moradi B, Behmanesh M, et al. Application of sodium
salicylate up-regulates defense response against Fusar-
ium graminearum in wheat spikes. Biologia plantarum.
2019;63:690-8.

35. Kang G, Li G, Yang W, Han Q, Ma H, Wang Y, et al. Tran-
scriptional profile of the spring freeze response in the leaves
of bread wheat (Triticum aestivum L.). Acta physiologiae
plantarum. 2013;35(2):575-87.

36. Ekom DCT, Benchekroun MN, Udupa SM, Iraqi D, Ennaji
MM, et al. Wheat Genetic Transformation as Efficient Tools
to Fight against Fungal Diseases. Journal of Agricultural
Science and Technology A. 2015;5(3):153-61.

37. He ZH, Li HW, Shen Y, Li ZS, Mi H. Comparative analysis
of the chloroplast proteomes of a wheat (Triticum aestivum
L.) single seed descent line and its parents. Journal of plant
physiology. 2013;170(13):1139-47.

38. Sun W, Zhou Y, Movahedi A, Wei H, Zhuge Q. Thaumatin-
like protein(Pe-TLP)acts as a positive factor in transgenic
poplars enhanced resistance to spots disease. Physiological
and Molecular Plant Pathology. 2020;112:101512.

39. Patel P, Patil T, Maiti S, Paul D, Amaresan N. Screening
of osmotic stress-tolerant bacteria for plant growth promo-
tion in wheat (Triticum aestivum L.) and brinjal (Solanum
melongena L.) under drought conditions. Letters in Applied
Microbiology. 2022;75(5):1286-92.

40. Hegedus G, Kutasy B, Kiniczky M, Decsi K, Juhász Á,
Nagy Á, et al.. Liposomal Formulation of Botanical Ex-
tracts may Enhance Yield Triggering PR Genes and Phenyl-
propanoid Pathway in Barley (Hordeum vulgare). Plants
2022, 11, 2969. s Note: MDPI stays neutral with regard
to jurisdictional claims in published ; 2022.

Highlights in BioScience Page 11 of 11 December 2022|Volume 5

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	Abstract
	Introduction
	Methodology
	Data acquisition
	Differential gene expression analysis

	Results and Discussion
	 Identification of DEGs
	PPI network construction 
	Machine learning


