































Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202307

Research Article

Open Access

1 Bioinformatics Department, Agriculture Ge-

netic Engineering Research Institute, Agricul-

ture Research Center, Giza, Egypt.
2 Biotechnology/Biomolecular Chemistry De-

partment, Faculty of Science, Cairo University,

Egypt.
3 Wheat Res. Dept., Field Crops Res. Inst., ARC,

Giza, Egypt.
4 Genome Mapping Department, Molecular

Genetics and Genome Mapping Laboratory,

Agricultural Genetic Engineering Research

Institute, Giza, Egypt.

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

Editor: Aladdin Hamwieh, International Center
for Agricultural Research in the Dry Areas
(ICARDA), Giza, Egypt.

Reviewer(s):
Ayed M. Al-Abdallat, aculty of Agriculture, The
University of Jordan, Jordan.

Tawffiq Istanbuli, International Center for
Agricultural Research in the Dry Areas (ICARDA),
Beirut, Lebanon

Received: October 20, 2023

Accepted: December 20, 2023

Published: December 26, 2023

Citation: Ibrahim MS, Ibrahim SM. Unlocking the
Genetic Basis of Abiotic Stress Tolerance in Wheat:
Insights from Differential Expression Analysis and
Machine Learning. 2023 Dec 26;6:bs202307

Copyright: © 2023 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 reproduc-
tion 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.

Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat: In-
sights from Differential Expression Analysis and Machine Learning

Saifeldeen M. Ibrahim *1
><, Manar S. Ibrahim 1

>< ,Radwa Khaled2
>< , Ahmed

Fawzy Elkot 3
>< , Shafik D. Ibrahim4

><

Abstract

Abiotic stresses such as heat and cold temperatures, salinity, and drought are threat-
ening global food security by affecting crop quality and reproductivity. Wheat is the
most essential staple crop in the world, its complex genome is the main barrier to find-
ing valuable genes responsive to different stresses. Thus, in our study we conducted
differential RNA-seq analysis to identify Differentially Expressed Genes (DEGs)
involved in 4 different stresses such as drought, heat, freeze resistance, and water-
deficit stress, then applied two machine learning models; the "Extra-tree regressor"
and LIME algorithms to accurately predict and select the highly significant genes.
Our findings identified a set of 36 significant genes, many of which play important
roles in various molecular functions, cellular components, and biological processes
related to the response or resistance to abiotic stress in wheat. For example, Hsp101b
is a member of the heat shock protein family, which protects cells against stress by
stabilizing proteins. BADH, an enzyme involved in the synthesis of stress hormones,
is important for the plant’s response to different stresses. AGL14 is a member of the
AGL protein family, which regulates gene expression and is involved in the plant’s re-
sponse to drought, cold, and salinity stresses. This study demonstrates the prospects
of the integration of bioinformatics tools as well as machine learning models to as-
sess the genes responsible for wheat stress resistance, genes’ regulatory networks, and
their functions in order to save time and cost to improve wheat productivity.

Keywords: Wheat, Abiotic stress, Differential Gene Expression, Machine Learning.

Introduction
Wheat (Triticum spp.) is one the most strategically important crops for high proportions of the

world population, supplying merely 55% of carbohydrates and 20% of dietary proteins of the world’s

consumed food [1; 2]. Moreover, it has high nutritional value as it is a rich source of vitamins and

minerals [3]. In terms of economic importance, wheat is the third most widely grown crop in the

world after rice and maize, the annual production of wheat reached approximately 778.6 million

tons in the 2021-2022 season [4; 5]. In addition to being a major staple food, wheat is also used in

the production of various other products, such as flour, pasta, bread, and cereals [6]. It is a versatile

grain that can be used in a wide variety of food products, making it an important commodity in the

global food industry.

Climate change endangers plant productivity by increasing the intensity and extent of numer-

ous abiotic stresses, such as heat, salinity, and drought [7]. For instance, drought stress decreased

wheat yields globally by 32%, it is estimated that more than half of the world’s cultivated area will

experience water scarcity by 2050. Wheat yields are also being reduced by 40% due to soil salinity

contamination[8; 9]. Plants suffer from abiotic stresses differently, to withstand this threat plants

have developed a variety of biochemical, physiological, and metabolic responses. For instance, nu-

merous stress-responsive genes are activated, which are involved in producing many proteins that

aid in activating and adjusting the physiological and biochemical pathways in stress tolerance [10].

Highlights in BioScience Page 1 of 10 December 2023|Volume 6

https://doi.org/10.36462/H.BioSci.202307
https://creativecommons.org/licenses/by/4.0/
saifeldeenmib99@gmail.com
https://orcid.org/0000-0003-0143-2596
manar.samir249@science.helwan.edu.eg
https://orcid.org/0000-0001-8575-2372
radwa.khaled3rafa@gmail.com
https://orcid.org/0000-0002-4566-9803
elkot1982@gmail.com
https://orcid.org/0000-0003-0143-2596
shafikdarwish@ageri.sci.eg
https://orcid.org/0000-0001-8152-1999
http://bioscience.highlightsin.org/


Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

Understanding the molecular pathways and mechanisms un-
derlying stress tolerance in crops through genomics, transcrip-
tomics, and proteomics techniques pave the way for the identifi-
cation of genetic biomarker hallmarks involved with high-stress
tolerance and the recognition of the processes behind adaptation
to stresses [11].

The contributions of the plant proteome have become more
relevant for understanding gene activity and networks in response
to an external stimulus [7]. Micro-arrays and RNA-seq tech-
niques are dramatically evolving transcriptomics studies by pro-
viding a massive amount of data about the genes involved in
stress response, downstream signaling, and the synthesis of stress
response molecules at a specific time point and under particular
conditions in plants in order to create crop types that can endure
biotic and abiotic stress and produce a greater yield [12]. The
comparative transcriptome study between cultivars that are resis-
tant to drought and those that are vulnerable identifies possible
genes and processes of adaptation to drought stress [13].

Several CIPK genes are elevated in rice, especially under
drought-stressed situations, according to earlier comparative anal-
ysis investigations [14]. Le et al. demonstrated a large number
of kinase-encoding genes that are drought-inducible, including
PP2C proteins, hormone-signaling-related proteins, MAP and
CIPK kinases, that were thought to be important in the control of
the drought response in soybean leaves through microarray anal-
ysis. While the over-expression of the ANAC019, ANAC055,
and ANAC072 genes in Arabidopsis provided the first evidence
of the roles of NAC TFs in the enhancement of drought tolerance
in plants[15].

The crop genomics field cannot simply interpret molecular
complex phenotypes, due to the large, diverse, and heteroge-
neous data-sets, so leveraging strong data mining algorithms and
bioinformatics techniques to anticipate and interpret these phe-
notypes is crucial [16].The recent advances in Machine learning
Algorithms (MLA) integrated with omics data analysis helped in
the recognition, categorization, measurement, and early predic-
tion of plant stress responses in addition to the extensive metabolic
description for the target plant species[17]. Machine learning
has been applied to a variety of fields, including medicine, en-
gineering, biology, and genomics, it has the potential to revolu-
tionize the way we analyze and interpret gene expression data.
One application of machine learning in gene expression analy-
sis is the identification of genes that are deferentially expressed
between different samples or conditions. By analyzing gene ex-
pression data in the context of other genomic data, such as DNA
sequence data or protein-protein interaction data, machine learn-
ing algorithms can identify patterns and relationships that can
provide insights into gene function and the underlying biologi-
cal processes [18; 19].

The MLAs have recently introduced successful models for
the agriculture field. Osco et al.[20] successfully applied the
Artificial Neural Networks (ANN) to differentiate between the
hyper-spectral response of water-stressed lettuce from the non-

stressed group with an accuracy reached 93%. Another study
found that it was possible to evaluate water stress in winter wheat
crops over time in connection to other factors including disease
and nitrogen accumulation by employing continuous wavelet
analysis, Fisher’s linear discrimination analysis, and support vec-
tor machines [21].

Many studies and research projects have used machine learn-
ing to identify genes that are differentially expressed between
different samples or conditions. In a study of the molecular
markers of drought stress in wheat, Priya et al. [22] used ML
algorithms to identify differentially expressed genes that were as-
sociated with drought tolerance. They found that the genes iden-
tified by the ML analysis were significantly enriched for func-
tions related to drought tolerance, highlighting the potential of
ML for identifying key genes involved in stress responses. An-
other study used differential gene expression analysis and ML to
identify genes associated with rust resistance in wheat [23]. The
genes identified by the ML analysis were significantly enriched
for functions related to rust resistance, and they concluded that
the use of bioinformatics approaches, including ML, can be an
effective way to identify genes associated with stress responses
in wheat. Another study that applied machine learning to gene
expression data in wheat used a decision tree algorithm to iden-
tify genes that were differentially expressed between different
varieties of wheat and to predict their potential functions [24].
Thanmalagan et al. [25] applied machine learning to gene ex-
pression data from rice (Oryza sativa) to predict gene function
and identify pathways that were important for drought tolerance.
They used a machine learning algorithm to identify patterns in
the gene expression data and predict gene function based on
those patterns. Also, they found that the machine learning al-
gorithm was able to accurately predict gene function and iden-
tify pathways that were involved in drought tolerance, including
those related to stress response and carbohydrate metabolism.
Based on the previous investigations, the current study aimed to
use both approaches (DE analysis and ML algorithms) to iden-
tify the molecular markers related to the most significant genes
associated with abiotic stress.

Methodology
Data gathering

The Gene Expression Omnibus (GEO) was utilized to re-
trieve four distinct wheat dataset experiments related to various
abiotic stresses using the terms Such as drought and heat stress
resistance, freeze resistance, and water deficit response. The
experiment name of each accession is supplied for all GSE ac-
cessions in Table 1.

Differential gene expression analysis
The statistical tool GEO2R [26] was used to analyse the raw

four gene expression data sets of wheat under different abiotic
stresses using the R/Bioconductor and Limma packages. Each
sample has been divided into two categories (control and treat-
ment). The differentially expressed genes (DEGs) were then

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Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

Table 1. Information for the four GEO datasets for wheat.

accession experiment name abbreviation

GSE45563 Drought, heat and combined stress in durum wheat DHSW

GSE14697 Freeze resistance basis of winter wheat mutant lines FRBW

GSE48620 Long-term growth under elevated CO2 differentially suppresses biotic stress genes GUCO

GSE45262 Transcriptomics of water-deficit Stress Responses in TAM Wheat Cultivars TAMW

visualised using R packages by creating a volcano plot high-
lighting all significant genes and a heatmap of the top differen-
tially expressed genes based on each group in all samples using
log2FC (fold change) ≥ 1 and an adjusted p-value of 0.05 as
the DEG threshold. Whereas up-regulated DEGs were consid-
ered when the logFC (fold change) ≥ 1, down-regulated DEGs
were considered when the logFC (fold change) ≤ -1. The vol-
cano plot displays statistical significance (P value) in accordance
with the rate of fold change. It makes it possible to quickly vi-
sualise genes with significant fold changes. These genes may be
the most important in terms of biology. After reading the data
and filtering it according to the adjusted p-value and logFC, this
figure was created using the R package (ggplot2) (blue: down-
regulated, red: upregulated). Following that, a heatmap of the
top differentially expressed genes in the RNA-seq data set was
created using the pheatmap package after filtering the data. Then,
a Venn diagram was used to show all of the genes that were
shared by all four wheat experiments using the interactiVenn
website [27].

Protein-protein interaction and functional enrichment analyses
To assess the link between genes associated with wheat gene

expression, the STRING database was used for protein-protein
interaction analysis including functional and physical interac-
tions [28]. For each GSE dataset, all information regarding GO,
annotated keywords, and protein domains has been presented in
Table 2.

Machine Learning Model
The "Extra-tree regressor" and "local interpretable model-

agnostic explanation" algorithms were used in this study to iden-
tify the significant genes related to the response or resistance to
abiotic stress in wheat. The "Extra-tree regressor" is a machine
learning model that utilizes decision trees to make predictions.
Decision trees are constructed by considering the characteristics
of a given dataset and dividing it into smaller subsets based on
the values of certain features. The "Extra-tree regressor" model
differs from traditional decision tree models in that it uses ran-
dom thresholds for feature selection, rather than using the mean
value of each feature [29]. This allows the model to capture
non-linear relationships in the data, making it well-suited for
the analysis of gene expression data. The "local interpretable
model-agnostic explanation" algorithm, or LIME, is a technique
for explaining the predictions made by machine learning models.

It works by approximating the complex, non-linear relationships
learned by the model with a simpler, interpretable model that is
specific to a particular prediction [30]. This allows researchers
to understand the factors that contributed to a particular predic-
tion, and can be useful for identifying the underlying mecha-
nisms behind the model’s results.

Results and Discussion
Identification of DEGs

Identification of molecular mechanisms, biological processes,
and cellular components for both up-and down-regulated genes
through the different environmental stresses is an essential step
toward enhancing wheat survival rates. The GSE14697 dataset,
which highlights the genes that might confer and maintain freeze
resistance in winter wheat, had the highest number of expressed
genes (14,484 genes), according to our analyses. This dataset
also found multiple cold-responsive (Cor)/late-embryogenesis-
abundant (Lea) genes; the proteins of accumulating COR/LEA
genes are thought to promote and sustain the development of
freezing tolerance [31] [32]. On the other hand, the GSE45563
dataset had the fewest expressed genes, with just 1,297.

The volcano plot, shown in Figure 1, illustrates which genes
are up-regulated (red) and which are down-regulated (blue) in
Triticum aestivum samples. The fold change (log2FC) is repre-
sented by the horizontal axis, while the adjusted p-values are
represented by the vertical axis. The GSE14697 dataset pro-
duced 6,987 up-regulated and 7,497 down-regulated DEGs, ac-
cording to the volcano plot, whereas the GSE45563 dataset pro-
duced 540 up-regulated and 757 down-regulated DEGs while,
GSE48620 produced 2,693 up-regulated and 3,164 down-regulated
DEGs, and GSE45262, which examined the drought-responsive
genes in wheat, produced 1,221 up-regulated and 803 down-
regulated DEGs.

For a more in-depth analysis of the DEGs, a heatmap was
generated for the top DE genes based on the adjusted p-value
ranking. The heatmap divides the samples into two groups: con-
trol (blue) and treatment (red). Red cells indicate high gene ex-
pression (upregulated), whereas blue cells indicate low gene ex-
pression
(downregulated). Lighter tones and white are used to depict
genes with stable expression levels. The samples and genes were
reordered using dendrogram hierarchical clustering, as shown in
Figure 2. This heatmap gives a deeper insight into the changes in

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Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

Table 2. The STRING database table contains information on GO, annotated keywords, and protein domains, for each GSE dataset.

GSE Category ID Description FDR

GSE14697 Cellular Component GO:0110165 Cellular anatomical entity 6.24E-18

GSE45262 Cellular Component GO:0110165 Cellular anatomical entity 2.88E-12

GSE45563

Molecular Function
GO:0004363 Glutathione synthase activity 0.003

GO:0043295 Glutathione binding 0.0464

Cellular Component GO:0110165 Cellular anatomical entity 3.54E-12

Subcellular localization
GOCC:0017109 Glutamate-cysteine ligase complex 0.003

GOCC:0036087 Glutathione synthase complex 0.003

Annotated Keywords
KW-0317 Glutathione biosynthesis 0.00026

KW-0460 Magnesium 0.019

Protein Domains
PF03199 Eukaryotic glutathione synthase 0.0036

PF03917 Eukaryotic glutathione synthase, ATP binding domain 0.0036

Protein Domains and Features

IPR004887 Glutathione synthase, substrate-binding domain 0.0034

IPR005615 Glutathione synthase 0.0034

IPR014042 Glutathione synthase, alpha-helical 0.0034

IPR014049 Glutathione synthase, N-terminal, eukaryotic 0.0034

IPR014709 Glutathione synthase, C-terminal, eukaryotic 0.0034

IPR037013 Glutathione synthase, substrate-binding domain superfamily 0.0034

IPR016185 Pre-ATP-grasp domain superfamily 0.004

GSE48620

Molecular Function GO:0005200 Structural constituent of cytoskeleton 0.0293

Cellular Component GO:0110165 Cellular anatomical entity 1.76E-38

Annotated Keywords

KW-0342 GTP-binding 0.00015

KW-0963 Cytoplasm 0.00015

KW-0206 Cytoskeleton 0.0062

KW-0493 Microtubule 0.0073

KW-1015 Disulfide bond 0.0076

KW-0326 Glycosidase 0.0241

KW-0732 Signal 0.0241

KW-0325 Glycoprotein 0.0279

KW-0624 Polysaccharide degradation 0.0355

KW-0547 Nucleotide-binding 0.0426

KW-0597 Phosphoprotein 0.0426

KW-0809 Transit peptide 0.0426

Protein Domains
PF03953 Tubulin C-terminal domain 0.0035

PF00091 Tubulin/FtsZ family, GTPase domain 0.0079

Protein Domains and Features

IPR000217 Tubulin 0.00016

IPR002453 Beta tubulin 0.00016

IPR003008 Tubulin/FtsZ, GTPase domain 0.00016

IPR008280 Tubulin/FtsZ, C-terminal 0.00016

IPR013838 Beta tubulin, autoregulation binding site 0.00016

IPR017975 Tubulin, conserved site 0.00016

IPR018316 Tubulin/FtsZ, 2-layer sandwich domain 0.00016

IPR023123 Tubulin, C-terminal 0.00016

IPR037103 Tubulin/FtsZ, C-terminal domain superfamily 0.00016

IPR036525 Tubulin/FtsZ, GTPase domain superfamily 0.00017

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Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance 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.

gene expression at the cellular level and shows which genes are
most affected by abiotic stress. This can help to identify key ge-
netic markers for the development of improved stress tolerance
wheat varieties.

Figure (3) illustrates the Venn diagrams of the DEGs be-
tween the four integrated GEO data sets. The numbers within
each circle represent the number of differently expressed genes
across the various comparisons. The overlapping numbers refer
to the common DEGs shared between the experiments, whilst
the non-overlapping numbers refer to genes that are unique to
each GSE. The GSE14697 and GSE45262 shared 19 genes, the
GSE45262 and GSE45563 shared 9 genes, and the GSE14697
and GSE45563 shared 5 genes. All the common and shared
genes are listed in the Table 4. It is believed that all of these
genes are related to abiotic stress, as they play key roles in the

response to various types of stress, including drought and salt
stress.

One example is Wrab15, a member of the WRKY transcrip-
tion factor family, which plays a key role in the response to var-
ious types of stress, including abiotic stress. Wrab15 has been
shown to be upregulated in response to drought and salt stress in
wheat [33]. Another example is CCoAMT, a copper-containing
amine oxidase that is involved in the detoxification of reactive
oxygen species (ROS) generated during stress conditions [34].
Tatil is a member of the aquaporin family, which plays a role
in the transport of water and other small molecules across cell
membranes [35]. Rab is a member of the Rab GTPase family,
which regulates vesicle transport and is involved in the response
to stress [36]. TLK1 is a member of the protein kinase family,
which plays a role in the regulation of gene expression and cell
division [37]. Wcor18 and LEA are members of the LEA (late
embryogenesis abundant) protein family, which is involved in
the protection of cells against stress conditions [38]. NAC693 is
a member of the NAC transcription factor family, which plays a
role in the regulation of gene expression and is involved in the
response to stress [39]. P5CR is a member of the pyrroline-5-
carboxylate reductase enzyme family, which is involved in the
synthesis of Proline, a compound that plays a role in the protec-
tion of cells against stress [40]. GS3 is a member of the grain
softness protein family, which is involved in the development of
grain hardness and is upregulated in response to abiotic stress
in wheat [41]. HSP101c, Hsp16.9B, Hsp16.913LC1, Tahsp17.3
and Hsp 26.6B are members of the heat shock protein family,
which plays a role in the protection of cells against stress by sta-
bilizing proteins and preventing their aggregation [42]. TAc41 is
a member of the TAC (transcription activator-like) protein fam-
ily, which plays a role in the regulation of gene expression and is
involved in the response to stress [43]. Rbcl is a member of the
ribulose-1,5-bisphosphate carboxylase/oxygenase enzyme fam-
ily, which is involved in photosynthesis and is upregulated in
response to abiotic stress. [44]. PP2C is a member of the pro-
tein phosphatase 2C enzyme family, which plays a role in the
regulation of protein phosphorylation and is involved in the re-
sponse to stress [45]. Wcor413 is a member of the COR protein
family, which is involved in the protection of cells against cold
stress [46]. FKBP77 is a member of the FK506-binding protein
family, which is involved in the regulation of protein folding and
is upregulated in response to abiotic stress in wheat [47].

However, a deeper understanding of the mechanisms by which
these genes function and interact with each other is still needed
to develop effective strategies for improving the stress tolerance
of wheat. This requires further research, including functional
characterization and genetic manipulation of these genes, in or-
der to fully understand the underlying mechanisms and to apply
this knowledge to improve wheat breeding and crop manage-
ment practices. Additionally, it will be important to investigate
the interactions between these genes and the broader genetic and
molecular networks that govern the plant’s response to stress.

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Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

Figure 2. Heatmap of top DE genes data.

Figure 3. Venn diagram of DEGs.

Protein Protein Interaction (PPI) network
Proteins have greater diversity as biomarkers, exhibit a more

direct and dynamic reaction, and have excellent application prospects
in cultivar screening. In this study, we utilized the STRING
tool to create protein-protein interaction (PPI) networks and to
make Gene Ontology (GO) annotation that spans cellular compo-
nents, biological processes, and molecular functions of our stud-
ied genes that contribute to the plants response to stress. Four
modules were identified in this constructed network, which con-
sists of 90 nodes and 38 edges. The most significant module
was GSE48620, which had 40 nodes and 30 edges and a PPI en-
richment p-value of 6.95e-5, this module has some functions ex)
protein domain, and features. GSE45563, the smallest module,

had only 12 nodes and 1 edge and a PPI enrichment p-value of
0.333. (Figure 4) and this module has some functions ex) pro-
tein domains and features, molecular function (a structural con-
stituent of the cytoskeleton), cellular components, and annotated
keywords.

Machine learning model
In this study, the Extra-tree regressor and LIME algorithms

were employed to examine gene expression data in wheat in or-
der to identify key genes involved in the plant’s response or re-
sistance to abiotic stress. The results of this analysis revealed a
total of 40 markers, 36 genes, and 36 gene titles, as shown in Ta-
ble 3. Each GSE accession consisted of ten markers, with each
marker displaying a positive or negative value. These values rep-
resent the prediction probabilities of negative and positive out-
comes achieved from the classifiers and indicate the extent to
which the corresponding genes are affected by the specific abi-
otic stress of each GSE. Blue represents negative values, while
orange represents positive values (as shown in Figure 5).

The model generated a set of genes that respond to biotic
stress as the most significant, including hypothetical LOC89+3077,
probable light-induced protein, gstf5, PRO3, Tad1, ETT-L alpha,
rab2, imidazoleglycerolphosphate dehydratase, PP2C, triticain
beta, U2AF small subunit, immature spike ubiquitin-conjugating
enzyme 2, Ta-RE, rab, delta tonoplast intrinsic protein TIP2;3,
Hsp101b, BADH, AGL14, CDPK2, WNdr1D, cold acclimation
protein COR413-TM1, gs3, ZF001, ethylene receptor-like pro-
tein, Mlo2, elongation factor, SH6.2, GS2, ribosomal protein
L11, metallothionein, PR4, CPK2B, ald1 myosin, protein H2A,

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Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

Figure 4. PPI networks of DEGs: (a) GSE14697, (b) GSE45262, (c)

GSE45563, (d) GSE48620

and Waox1a.
It is important to note that these genes interact with each

other and with genes from other families to coordinate the plant’s
response to stress. Further research is needed to fully understand
the mechanisms by which these genes function and interact with
one another in order to develop effective strategies for improv-
ing the stress tolerance of wheat. Additionally, more research
is needed to explore the impact of these identified genes on the
plant’s overall growth and productivity and to validate the results
of this study using other data sets and methods.

These genes are involved in a variety of molecular functions,
cellular components, and biological processes that are related
to the response or resistance to abiotic stress in wheat. For ex-
ample, BADH is an enzyme involved in the synthesis of stress
hormones, which are important for the plant’s response to stress
[48]. AGL14 is a member of the AGL (AGAMOUS-like) pro-
tein family, which plays a role in the regulation of gene expres-
sion and is involved in the plant’s response to stress [49]. rab2 is
a member of the Rab family of GTPases, which play a role in the
regulation of vesicle trafficking and are involved in the plant’s
response to stress [50]. GS2 is a member of the glutamine syn-
thetase family, which is involved in the synthesis of amino acids
and is upregulated in response to abiotic stress in wheat [51].

Additionally, heat shock proteins, such as Hsp101b, play
an important role in the plant’s response to stress by protect-
ing cellular proteins from damage [52]. Similarly, aquaporins,
such as Ta-RE, are integral membrane proteins that play a role

Figure 5. Machine learning results: (a) GSE14697, (b) GSE45563, (c)

GSE45262, (d) GSE48620

in the transport of water and other small molecules and are in-
volved in the plant’s response to drought stress [53]. Further-
more, transcription factors, such as CDPK2, are responsible for
the regulation of gene expression and play a critical role in the
plant’s response to stress by modulating the expression of stress-
responsive genes [54].

Interestingly, the transcription factor CDPK2 may interact
with the gene GS2, which is involved in the synthesis of amino
acids, to modulate its expression in response to abiotic stress.
This highlights the complexity of the plant’s response to stress
and the need for further research to fully understand the mecha-
nisms by which these genes function and interact with each other
in order to develop effective strategies for improving the stress
tolerance of wheat.

Overall, the results of this study provide a valuable starting
point for further research on the genetic basis of the response
or resistance to abiotic stress in wheat. By identifying key genes
that play a role in this process, researchers can focus their efforts
on understanding the mechanisms by which these genes func-
tion and interact with each other in order to develop effective
strategies for improving the stress tolerance of wheat. This can
ultimately lead to the development of new wheat varieties that
are more resilient to abiotic stress, which is crucial for ensuring
the food security of the world’s population.

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Ibrahim and Ibrahim, 2023 Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat

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 Gene.title

GSE14697

Ta.14183.1.S1_at positive 10.62 LOC543077 hypothetical LOC543077

Ta.3424.1.S1_at positive 9.12 LOC543347 probable light-induced protein

Ta.3418.2.S1_at positive 14.57 gstf5 glutathione transferase F5

Ta.5057.2.S1_x_at positive 8.74 PRO3 profilin

Ta.14281.1.S1_at positive 15.75 Tad1 defensin

Ta.25087.1.S1_at positive 9.49 ETT-L alpha ETTIN-like auxin response factor

Ta.6227.1.S1_at positive 12.77 rab2 small GTP-binding protein

Ta.46.1.S1_at positive 7.12 LOC543224 imidazoleglycerolphosphate dehydratase

Ta.23679.1.S1_at positive 14.02 PP2C protein phosphatase 2C

Ta.14289.1.S1_at positive 9.27 LOC100037638 triticain beta

GSE45262

Ta.5818.1.S1_at negative 945.07 LOC780664 U2AF small subunit

Ta.5818.1.S1_a_at negative 2522.32 LOC780664 U2AF small subunit

Ta.23834.1.S1_at negative 11596.3 LOC780682 immature spike ubiquitin-conjugating enzyme 2

Ta.5818.3.S1_x_at negative 619.12 LOC780664 U2AF small subunit

Ta.277.1.S1_at negative 574.07 Ta-RE pullulanase

Ta.2704.1.S1_at negative 41.07 rab rab protein

Ta.1082.1.S1_a_at negative 1364.3 LOC100037645 delta tonoplast intrinsic protein TIP2;3

Ta.256.1.S1_at negative 212.45 Hsp101b heat shock protein 101

Ta.435.1.S1_at positive 6753.69 BADH betaine-aldehyde dehydrogenase

Ta.6411.1.S1_at negative 235.62 AGL14 MADS-box transcription factor TaAGL14

GSE45563

Ta.6301.2.S1_a_at positive 248.32 CDPK2 calcium-dependent protein kinase 2

Ta.5011.1.S1_at positive 1254.5 WNdr1D protein kinase

Ta.19248.1.S1_x_at positive 12151 LOC543089 cold acclimation protein COR413-TM1

Ta.6301.1.S1_at positive 3090.3 CDPK2 calcium-dependent protein kinase 2

Ta.5307.1.S1_at positive 1491.8 gs3 glutathione synthetase

Ta.13961.1.S1_at positive 241.58 ZF001 GATA-type zinc finger protein

Ta.19248.1.S1_at positive 14096 LOC543089 cold acclimation protein COR413-TM1

Ta.10139.1.S1_s_at positive 1084.6 LOC543446 ethylene receptor-like protein

Ta.280.2.S1_x_at positive 201.15 Mlo2 seven transmembrane-spanning protein

Ta.2576.1.S1_at positive 22922 LOC542923 elongation factor

GSE48620

Ta.1258.2.S1_x_at positive 11.12 SH6.2 S-adenosyl-L-homocysteine hydrolase

TaAffx.105423.1.S1_at positive 4.15 GS2 plastid glutamine synthetase isoform GS2a

Ta.28712.1.S1_at positive 5.47 LOC606335 ribosomal protein L11

Ta.28695.6.S1_at positive 3.65 LOC542898 metallothionein

Ta.9226.1.S1_at positive 11.57 PR4 pathogenesis-related protein 4

Ta.6350.2.S1_x_at positive 5 CPK2B calcium-dependent protein kinase

Ta.304.1.S1_at positive 6.27 ald ald protein

Ta.29355.1.S1_at positive 1.41 LOC542906 1 myosin

Ta.644.1.S1_at positive 6.14 LOC543185 protein H2A

Ta.233.1.S1_at positive 5.89 Waox1a alternative oxidase

Table 4. The common genes that were shared between the GSEs.

GSE14697 and GSE45262 GSE45262 and GSE45563 GSE14697 and GSE45563

Wrab15 CCoAMT Tatil

rab:1 CCoAMT:1 TLK1

Wcor18:1 NAC693 RcaB

Wcor18 P5CR gs3

LEA3 Tahsp17.3 TAc41

rab,rab 15B hsp 26.6B

Wrab18 Wcor518

wpi6 WRKY

HSD11BL rbcl

LEA2

Wcs66

WTABAPM

PP2C

Wcor413

FKBP77

HSP101c

hsp16.9B:1

hsp16.913LC1

Cht2,hsp16.9B

Conclusion
In conclusion, our study has discovered a group of key genes

that are connected to the response or resistance to abiotic stress
in wheat by leveraging differential gene expression and machine
learning models. The response or resilience of wheat plants to
abiotic stress situations is greatly influenced by these genes. The
discovered genes also come from a variety of gene families, in-
cluding heat shock proteins, aquaporins, protein kinases, and
protein oxidases. To coordinate the plant’s response to stress,
these genes interact with one another and other genes. To com-
pletely understand the mechanisms by which these genes oper-
ate and interact with one another, additional study is required in
order to create effective strategies for improving the stress toler-
ance of wheat.

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	Abstract
	Introduction
	Methodology
	Data gathering
	Differential gene expression analysis

	Results and Discussion
	 Identification of DEGs
	Protein Protein Interaction (PPI) network
	Machine learning model

	Conclusion

