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Highlights in BioScience                                                                   
  

 

 

Highlights in BioScience                DOI:10.36462/H.BioSci.20185                November 2018 | Volume1 
        

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Citation: Nassar A.E., Mousa K.H., 

Madbouly A.A., Ibrahim S.D. and  

Alsamman A.M. (2018). Identification of 

genes for wheat fungal resistance using 

bioinformatics techniques. Highlights in 

BioScience, Volume 1. Article ID 20185, 

dio:10.36462/ H.BioSci.20185 

 

 

 

 
Received: September  5, 2018 

 

Accepted: October  25, 2018 

 

Published: November 10, 2018 

 

 
Copyright:© 2018 Nassar et al. 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 

 

Identification of Genes for Wheat Fungal 

Resistance Using Bioinformatics Techniques 

Ahmed E. Nassar
1
, Khaled H. Mousa

1
, Ahmed A. Madbouly

2
, 

Shafik D. Ibrahim
3
 and Alsamman M. Alsamman

3
*

 

 
 

1 
Genetics Department, Faculty of Agriculture, Mansoura University. 

2 Plant Tissue Culture Department, Date Palm Research Institute, Agricultural  

Research Institute 
3 MGGM lab., Agricultural Genetic Engineering Research Institute, Agricultural 

Research Center, Egypt. 

 

* To whom correspondence should be addressed: smahmoud@ageri.sci.eg 

 

Abstract 

For the majority of world populations, wheat (Triticum aestivum L.) 

would be the first essential and economic cereal grain crop.  Pests and 

pathogens in both rich and developing countries are constantly 

threatening wheat production and sustainable development. Multiple 

gene pathways were recorded to share an association with fungal 

pathogens with wheat biological resistance. Our aim to use such tools in 

order to detect and classify fungal resistance genes in wheat through 

sequence alignment, protein domain identification and phylogenetic 

analysis. In addition the introduction for restriction fragment length 

polymorphism (RFLP) for such genes in the new primer database. 

Approximately 138 sequences of DNA were recovered from the wheat 

genome by aligning 3845 anti-fungal amino acids through tblastn tool. 

The NCBI blastn online tool used to detect sequences with functional 

genes, where 92 genes have been detected. The total number of 

nucleotides was 48385, where the smallest DNA sequence have 302 bp 

and the longest contains 977 bp with an average length of 525.9 bp per 

sequence. The wheat chromosomes 3D, and 4B have the highest number 

of sequences (9) followed by chromosomes 3B (7) and 3A(6), where 

wheat genomes A, B and D have 30, 35 and 27 genes, respectively. Five 

different amino acids motifs have been revealed among studied wheat 

amino acid sequences. The gene annotation tools used to infer studied 

amino acid gene annotation. Amino acid sequences belongs to lectin, 

kinase, tyrosine-protein kinase (STK), thaumatin, and cysteine-rich 

repeats  representing 2, 9, 8, 19, 23 genes respectively, in addition to 31 

hypothetical genes. The proteins chemical content have been assessed 

through 16 different amino acid chemical and physical characteristics.   

 

 

Keywords: Wheat, Pathogens, Pathways, Fungal, Phylogenetic, RFLP.  

 

Research Article                                                                                           Open Access   



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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Introduction  

For the majority of world populations, wheat 

(Triticum aestivum L.) would be the first essential and 

economic cereal grain crop. Its 29 percent share shows the 

world economy's significance of wheat production from 

world's 730 million hectares of crops cultivated lands. 

Depending on FAO figures for 2017, this percentage is 

comparable to 218 million hectares of wheat region (1). In 

most of the world, wheat is an key ingredient of human 

diet. Worldwide, 735 million tons of wheat were produced 

in 2015/2016, worth around US$ 145 billion (2). Wheat 

accounts for almost 55% of the carbohydrates and 20% of 

the world's food calorie intake (3).  

In Egypt, the significance of wheat crops returns to 

its strategic importance in Egyptian dietary commodities 

as it offers more than one-third of Egyptian consumers ' 

daily calorie intake and 45% of their daily total protein 

intake (4). Wheat is Egypt's largest winter grain and is 

cultivated across the Nile delta and along the Nile Valley 

length. Wheat is usually planted after summer maize, 

cotton, or rice crops in early November and collected in 

late April or early May. Germination happens after 

planting at 19 to 22 weeks (5). While Egypt's wheat 

productivity has increased over the past few years, wheat 

production provides only 45% of its yearly domestic 

demand. Egypt is still one of the largest importing wheat 

countries. Wheat imports were approximately 9.8 million 

tons in 2011, costing approximately US$ 3.2 billion  (6).  

Pests and pathogens in both rich and developing 

countries are constantly threatening wheat production and 

sustainable development (7). A key component for trying 

to meet this challenge is proper management of fungal 

diseases that may be accountable for yield losses of 15–20 

percent per year. The rusts, blotches and head blight/scab 

are among the major wheat diseases that make a 

contribution to these losses(2). The rusts, blotches and 

head blight/scab are among the major wheat diseases that 

currently contribute to these losses. There are three 

diseases of wheat rust, namely stem, stripe and leaf rust, 

all caused by members of the Basidiomycete family, genus 

Puccinia, named P. graminis f. SP. tritici, P. striiformis f. 

sp. tritici (PST) and P. triticina (Pt), respectively (2). 

Multiple gene pathways were recorded to share an 

association with fungal pathogens with wheat biological 

resistance. The resistance is attributable to the additive 

impacts of multiple resistance metabolites and proteins 

generated through a network of multiple plant R gene 

structures. Plants recognize pathogenic elicitors or 

receptors and then stimulate downstream genes to 

ultimately produce resistance metabolites and proteins that 

suppress plant pathogen progression (8). Following the 

view of pathogens and microbes, reactive oxygen species 

(ROS) were consistently identified to accumulate in the 

plant and over the years, ROS was postulated as an 

essential part of the plant's defense response (9). 

The connection between genetic variability identified 

using distinct molecular marker assays and genes 

responsible for morphological and physiological 

characteristics could be identified through sophisticated 

bioinformatics analysis tools (10–12). These tools could 

be used to identify and reveal new genes in wheat related 

to resistance to fungal diseases. Our aim to use such tools 

in order to detect and classify fungal resistance genes in 

wheat through sequence alignment, protein domain 

identification and phylogenetic analysis. In addition the 

introduction for restriction fragment length polymorphism 

(RFLP) for such genes in the new primer database. 

Materials and Methods 

Anti fungal gene sequences have been downloaded 

from NCBI database (13). We have downloaded 3845  

antifungal protein sequences from NCBI. The draft 

genome sequence of wheat has been downloaded from 

Ensemble database (14). The local NCBI blast package 

(15) was used to build sequence databases using the wheat 

draft genome and blast all anti-fungal genes with 

TBLASTN against wheat database. The online NCBI blast 

tblatn was used to annotate sequences recovered from 

previous step. MEME suite was used to discover amino 

acid motifs in the sequence (16). MegaX program was 

used to construct phylogenetic analysis through maximum 

likelihood algorithm (17). clustalo (18) tool was used to 

construct sequence similarity matrix through multiple 

sequence alignment. Clust-Vis was applied to create 

Principal Component Analysis (PCA) plots and heatmaps 

depicting genes similarities. Pepstat (19) was used through 

in-home per scrips to assess proteins chemical and 

physical characteristics. The perl script RestrictionDigest 

was used to detect restriction enzymes recognition sites in 

DNA sequences (20). BatchPrimer3 online tool was used 

for designing PCR primers could be used to target studied 

wheat genes (21). 

Result and Discussion 

 

Chromosomal distribution of wheat anti-fungal genes 

Approximately 138 sequences of DNA were 

recovered from the wheat genome by aligning 3845 anti-

fungal amino acids through tblastn tool. The NCBI blastn 



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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online tool used to detect sequences with functional genes, 

where 92 genes have been detected (Supplementary file 1). 

The total number of nucleotides was 48385, where the 

smallest DNA sequence have 302 bp and the longest 

contains 977 bp with an average length of 525.9 bp per 

sequence. The wheat chromosomes 3D, and 4B have the 

highest number of sequences (9) followed by 

chromosomes 3B (7) and 3A(6), where wheat genomes A, 

B and D have 30 , 35 and 27 genes, respectively (Figure 

1).    

                        . 

 

Figure 1 : The Circos diagram for the position of predicated anti-fungal genes and their proposed gene description, where the 

inner connections represent similar genes on separate chromosomes with sequence similarity over 75%. 

 

Wheat anti-fungal proteins chemical and physical 

properties 

The proteins chemical content have been assessed 

through 16 different amino acid chemical and physical 

characteristics (Figure 2 and Table 1). The total molecular 

weight (MW) of studied fungal amino acid sequences was 

1913 KDa with an average of 20 KDa, where 

1A:551827789-551828139 has the minimum MW (10.6) and 

5D:59043196-59044206 generated the maximum MW 

(71.911.68) Extinction coefficients are a measure of how 

much light the protein can be measured at a certain 

wavelength from the extinction factor. Approximation of this 

component is needed to be able to track the protein within the 

spectrophotometer. It is necessary to know the amino acid 

content to assess the molar extinction coefficient of the 

protein (22). 

 

Table 1: The highest and lowest proteins characteristics and their corresponding proteins. 

Protein Characteristic Total Mean Min. Min. protein Max. Max. protein 

A280 Molar Extinction 

Coefficients cystine bridges 
2261000 24576.09 1740 3B 716295639-716296412 104570 5D 59043196-59044206 

A280 Molar Extinction 

Coefficients reduced 
2214500 24070.65 1490 3B 716295639-716296412 103820 5D 59043196-59044206 

Improbability of expression 

in inclusion bodies 
65.124 0.794 0.504 7D 45432639-45432947 0.977 3D 462838207-462838962 

Average Residue Weight 9829.348 106.841 98.498 7A 164401571-164402272 116.631 3D 550382548-550383990 

Isoelectric Point 588.9429 6.402 4.0084 1A 20888904-20889590 10.3971 7B 40654848-40655408 

Molecular weight 1913642.24 20800.46 10658.89 1A 551827789-551828139 71911.68 5D 59043196-59044206 

Non-polar 5471.072 59.468 48.81 3A 719941535-719942353 69.919 5B 461292418-461292831 

Polar 3728.357 40.526 30.081 5B 461292418-461292831 51.19 3A 719941535-719942353 



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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Protein Characteristic Total Mean Min. Min. protein Max. Max. protein 

Residues 17857 194.098 101 7D 45432639-45432947 644 5D 59043196-59044206 

Small 5629.919 61.195 40.488 6A 610941935-610943086 77.027 5B 518810105-518810797 

Tiny 3673.776 39.932 20.27 2B 634173118-634173630 54.054 5B 518810105-518810797 

Acidic 858.259 9.329 5.172 3A 20936815-20937576 17.593 4B 41828978-41829304 

Aliphatic 2702.584 29.376 19.048 5A 14266438-14266881 41.27 3D 8508332-8509102 

Aromatic 952.739 10.356 3.604 3B 716295639-716296412 16.292 4D 29673605-29674213 

Basic 932.965 10.141 4.082 7D 634805112-634805630 19.672 3B 176321011-176321379 

Charge 24.5 0.266 -9.5 3B 690358283-690358846 22 7B 40654848-40655408 

Charged 1791.221 19.47 10.204 7D 634805112-634805630 32.258 3D 550382548-550383990 

The A280 molar extinction coefficients cystine bridges 

(A280-MECc ) and reduced (A280-MECr) are two different 

measurements of  extinction coefficient, where salt bridges 

are important motifs of the tertiary protein structure and are 

mostly correlated with the structural influence force which 

maintains the stability of the protein. Commonly found on the 

solvent system and particularly vulnerable to solvent-solute 

interactions typically with water as well as other cosolvents 

(23). The minimum scores of A280-MECc and A280-MECr 

were 1740 and 1490, generated by 3B:716295639-

716296412, while the highest scores were 104570 and 

103820, revealed by 5D:59043196-59044206 with a mean of 

24576 and 24070, respectively. Expression improbability in 

bodies of inclusion (IEIB) is a form of measurement of 

solubility. For instance, recombinant protein in Escherichia 

coli could be expressed either as soluble throughout the 

cytosol or as insoluble in the bodies of inclusion (24). The 

IEIB of wheat amino acids showed a mean of 0.794, where it 

ranges from 0.504 (7D:45432639-45432947) to 0.977 (3D: 

462838207-462838962).

 

 

Figure 2: The protein chemical composition and physical properties for the predicated anti-fungal genes.  



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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The average residue weight (ARW) stands for the 

average collective weight for all amino acid sequences 

according to its length. The total ARW for all amino acid 

sequences was 9829.3 Da, where 7A:164401571-164402272 

revealed the minimum ARW (98.498 Da). 3D:550382548-

550383990 generated the maximum  (116.631 Da) and the 

mean was 106.841 Da. The isoelectric point (IP) is the pH 

level at which protein's net charge is positive and is 

associated with its production of amino acids and protein 

conformation (25). The collective wheat fungal genes amino 

acids was 588.9 with a mean of 6.402, where it ranges from 4  

(1A:20888904-20889590) to 10.4 (7B:40654848-40655408).  

In addition, a protein's folded structure becomes 

thermodynamically less desirable because it decreases the 

protein's disorder or entropy.  Nonpolar (water hating) side 

chains tend to push themselves inside a protein whereas side 

chains of polar (water loving) prefer to put themselves 

outside of the molecule (26). The non-polar and polar amino 

acid scores were ranged from 48.81 and 30.081 

(3A:719941535-719942353 and 5B:461292418-461292831) 

to 69.919 and 51.19 (5B:461292418-461292831 and 

3A:719941535-719942353), respectively.  

Acidic amino acids have an acidic group in side chain 

while basic amino acids have some basic group in side chain.  

Acidic amino acids have low pKa while basic amino acids 

have high pKa. The score of acidic and basic amino acids 

range from 5.172 and 4.082 (3A:20936815-20937576 and 

7D:634805112-634805630 ) to 17.593 and 19.672 

(4B:41828978-41829304 and 3B:176321011-176321379 ). 

The studied fungal amino acids charges ranged from -9.5 

(3B:690358283-690358846) to 22 (7B:40654848-40655408) 

(Figure 2 and Table 1). 

Detection of protein domain and phylogenetic analysis 

A motif for the protein sequence is a brief pattern that 

nature retains. For proteins, a motif can refer to an enzyme's 

active site or a structural unit required for proper protein 

folding. Therefore, sequence motifs are among molecular 

evolution's basic functional components (16). Five different 

amino acids motifs have been revealed among studied wheat 

amino acid sequences (Motif-1: YGLAQCTPDL,  Motif-2: 

CYLRYSNKNF, Motif-3: PNRVWGITWC, Motif-4: YDVS 

VVDGFN, and Motif-5: NPGDCGQCLQ) (Figure 3 and 4). 

Figure 3: The identified protein domains / motifs in predicated wheat genes.  

 

The Motif-1 and Motif-3 has a match to ELME000249 

in motif database with p-value of 9.64e-04. This motif is a 

TRFH domain docking motifs wich are coordinating the 

telomeres with other proteins. Not only do they form 

homodimers with their TRFH domains, they also provide 

specific protein-binding surfaces for interaction (27). TRFHs 

and other proteins have been proposed to have important 

implications for the biology and evolution of telomeres. In 

general, this offers a basis for recognizing and controlling the 

hierarchical assembly and stoichiometry of telomere subunits 

during the cell cycle, cell division and senescence (28).  

Motif-2 has an ELME000084 match which is ligands of 

phosphotyrosine bound by domains SH2. Src Homology 2 

(SH2) domains identify tiny patterns comprising a residue of 

phosphorylated tyrosine. Up to four positions after the pTyr 

was found mainly to assess additional specificity. In plants 

SH2 dominates new signaling scenarios and participates in 

metazoan signal transduction, serving as key mediators of 

controlled protein-protein interactions with tyrosine-

phosphorylated substrates (29, 30).  

Moreover, the database motif of ELME000377 was 

highly similar to Motif-4, this motif codes for Pex14 ligand 

motif, which belongs to peroxisomes. Peroxisomes are 

subcellular organelles present in eukaryotes that are single-

membrane spherical. Peroxisomes relate together with 

glyoxysomes found in plants and glycosomes found in 

trypanosomes to microbody group of organelles.   



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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Figure 4: The phylogenetic tree developed using  multiple sequence alignment of predicted anti-fungal genes as compared to 

the expected positions of protein domains/motifs. 



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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Import into the peroxisome of peroxisomal matrix 

enzyme proteins (PTS1 cargo) includes the identification of 

the PTS1 cargo by the cytosol Pex5 receptor, the docking of 

the PTS1-Pex5 complex at the peroxisomal membrane, and 

the translocation of the PTS1 cargo through the peroxisomal 

membrane into the matrix. This is preceded by recycling the 

Pex5 receptor back into the cytosol for another round of 

export of PTS1 stock. Imported into peroxisome are 

unfolded, folded oligomeric or cofactor-bound proteins (31).  

Additionally, Motif-5 was with high similarity with 

ELME000159, which is MAPK Phosphorylation Site. 

Cascades of mitogen-activated protein kinase (MAPK) are 

highly conserved downstream signaling modules of 

receptors/sensors that transform extracellular stimuli in 

eukaryotes into intracellular responses. Plant MAPK 

cascades play crucial functions against pathogen invasion in 

signaling plant defense (32). The NCBI-blastx tool used to 

infer studied amino acid gene annotation. Amino acid 

sequences belongs to lectin, kinase, tyrosine-protein kinase 

(STK), thaumatin, and cysteine-rich repeats  representing 

respectively, 2, 9, 8, 19, 23 genes, in addition to 31 

hypothetical genes (Figure 1).  

Lectins are non-immune proteins which attach 

carbohydrates directly and reversibly. The lectin's 

biochemical functions are really diverse. A common theme 

arising from the identified functions of many plant and 

animal lectins is their involvement in communicating with 

other organisms whether symbiosis or defense, as effectors or 

regulators (33). New types of nucleocytoplasmic plant lectins 

have been described and defined over the past decade, 

especially lectins expressed within the nucleus and the 

cytoplasm of plant cells, much as part of a particular plant 

response when exposed to different stressors or shifting 

environmental conditions (34). Lectins include proteins 

containing at least one non-catalytic domain which helps 

them to selectively identify and reversibly attach to different 

glycans that are either freely available or are member of 

glycoproteins or glycolipids.  

Plants release an overwhelming number of highly 

complex lectins with various molecular structures and 

attaching specificities to endogenous (plant) glycans as well 

as exogenous (non-plant) glycans  (35). The role of plant 

lectins in plant defense againt different pathogens including 

fungi have been reported in previous researches (36,37). 

There are several receptor-like kinases among the plant 

proteins proposed to engage in immunity pathways (38). For 

example, multiple kinase proteins have been recorded for 

wheat resistance to fungal diseases, which provides 

temperature-dependent resistance to wheat stripe rust (39), 

and fungal resistance in Arabidopsis (40).  

In this regards , various thaumatin-like proteins have 

been reported to generate tolerance to fungal pathogens 

Arabidopsis (41), potato (42) and grape (43). Moreover,  

cysteine-rich proteins have been informed to confer 

resistance for fungal diseases such as maize fungal pathogens 

(44) and legume modules (45-47).  

Using maximum likelihood analysis phylogenetic 

analysis was effective in differentiating between anti-fungal 

wheat proteins according to their protein domains. Four 

classes grouped the phylogenetic tree. Group A comprises 

only Motif-3 domain protein fragments, group B has Motif-3, 

Motif-4 and Motif-5 domain proteins.  In addition some 

group C contains genes with Motif-1, Motif-2 and Motif-5 

domains, and eventually group D contains genes with all 

protein domains (Figure 4). 

 

PCR primers and restriction enzymes analysis 

About 85 PCR primers pairs have been designed to 

target most of the predicted anti-fungal genes 

(Supplementary file 2). The PCR predicated product size 

ranged from 300 bp to 708 pb, where the GC content ranges 

from 36.4 to 66.7% and the primers annealing temperature 

ranges from 58.05 oC to  62.55 oC. A total of 40 different 

restriction enzymes (RE) were used to identify potential 

locations for future RFLP tests for genetic polymorphism 

inside predicted anti-fungal wheat genes. Figure (5) 

demonstrates the site redundancy ratio for RE identification 

within genes. Figure (5) shows that some RE such as BanII, 

NlaIII and BaeGI have high probability to produce fragment 

length polymorphism isnide most of predicated anti-fungal 

genes. 

 

Conclusion  
 

Predicting using available fungal resistance 

genes in the public database was very helpful and 

suggested that several wheat genes could be used 

to restrict genetic research of genes that hold the 

key to fungal resistance in wheat. Most of the 

identified protein domains clarify the genetic 

structure of anti-fungal genes and suggest a 

potential role for the MAPK gene family in such 

pathways. Providing restriction enzyme 

information and gene-specific PCR primers could 

be useful to wheat scientists and breeders, saving 

time and effort. 

 

 



 
 

Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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Figure 5: The ratio heat map for redundancy site identification of different restriction enzymes, where red and blue squares 

represent high and low rate of RE digestion sites, respectively. 

 

Supplementary Files 

The Supplementary Material for this article can be found 

online at: http://bioscience.highlightsin.org/2018/20185.php  

  

Supplementary file 1: The nucleotide sequences of 

identified fungal resistance genes in wheat genome. 

Supplementary file 2: The information of PCR primers 

designed to target identified fungal resistance genes in wheat. 

 

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Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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Nassar at al., 2018                                                                                            Identification of Genes for Wheat Fungal Resistance 

 

 

 

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