Differential Gene Expression Analysis of Wheat Breeding Lines Reveal Molecular Insights in Yellow Rust Resistance under Field Conditions

Abstract

The evolution of pathogens in the changing climate raises new challenges for wheat production. Yellow rust is one of the major wheat diseases worldwide, leading to an increased use of fungicides to prevent significant yield losses. The enhancement of the resistance potential of wheat cultivars is a necessary and environmentally friendly solution for sustainable wheat production. In this study, we aimed to identify the differentially expressed genes induced upon yellow rust infection in the field. Reference and de novo based transcriptome analysis was performed among the resistant and susceptible lines of a bi-parental population to study the global transcriptome changes in contrasting wheat genotypes. Based on the analysis, the de novo transcriptome analysis approach was found to be more supportive for field studies. Expression profiles, gene ontology, KEGG pathway analysis and enrichment studies indicated the relation between differentially expressed genes of wheat and yellow rust infection. The h0igh expression of genes related to non-race specific resistance along with pathogen-specific resistance might be a reason for the better resistance ability of a resistant wheat genotype in the field. The targeted metagenomic analysis of wheat samples revealed that Puccinia striiformis tritici was the most dominant pathogen along with other pathogens on the collected leaf material and validating the disease scoring carried out in the field and transcriptomics analyses.

1. Introduction

Yellow (stripe) rust (YR) in wheat (Triticum aestivum L.) is caused by the biotrophic pathogen Puccinia striiformis f. sp. tritici (Pst) and is distinguished by the yellow pustules that occur on leaves. It is a major disease of wheat worldwide and if left uncontrolled, may cause 100% yield losses in highly susceptible wheat cultivars [1]. The disease is airborne and spread through urediniospores which can disperse over long distances by wind [2]. New evolving races are regularly identified in north-western Europe and have become a major concern in recent years [3]. Genetic resistance to yellow rust in wheat at the seedling stage is mainly through the major resistance (R) genes while at the adult-plant stage, the genetic resistance can be either from R genes or adult-plant resistance genes (APR) or both. R genes recognize and neutralize specific pathogen effectors in a gene-for-gene interaction resulting in effector-triggered immunity (ETI) in plants leading to complete disease resistance [4]. R genes are easy to incorporate in breeding programs, and they can provide high-level resistance to YR. However, they are not durable due to the evolution of new virulent races [5]. Several historic epidemics were also observed in the past due to the breakdown of resistance to YR [6]. On the contrary, adult-plant stage resistance is due to the additive effects of several genes leading to partial quantitative resistance. Adult plant quantitative resistance is more durable due to its complex genetic nature and varieties with high and durable resistance can be developed by incorporating both R and APR genes [7]. Resistance genes for yellow rust are designated as Yr, and several of these are responsive mainly at the seedling stage while some are effective at the adult plant stage [7]. To date, 82 Yr genes have been identified, and around 25 of them are related to APR or high temperature adult plant resistance, whereas the rest provide all stage resistance (ASR) [8]. Several of the ASR genes (Yr5, Yr15, Yr53, Yr61, Yr65 and Yr69) are still effective and can be used in breeding for YR resistance [9]. Although these Yr genes have been genetically mapped to wheat chromosomes, only a small number of them have been isolated to date [9]. More than 140 QTL (Quantitative trait locus) have been identified with partial quantitative resistance to yellow rust and are distributed throughout the wheat genome [10]. A daunting task is to identify the candidate resistance genes within these QTL regions.

The transcriptome profiling and quantification of the differential expression of genes and proteins play a key role in the elucidation of regulatory pathways and gene-networks through wide transcripts coverage, high sensitivity, allele-specific differential expression and novel transcript identification [10,11,12,13,14]. Transcriptome analysis has facilitated the fine mapping of the Yr genes and in understanding the underlying regulatory mechanisms in several studies. Coram et al. (2010) identified 28 transcripts commonly induced upon race-specific resistance conferred by eight different Yr genes [15]. Hulbert et al. (2007) identified 59 putative rust-induced transcripts expressed in the flag leaves of the spring wheat Thatcher-Lr34/Yr18 isoline [16]. Chen et al. (2013) identified 102 and 113 rust responsive wheat genes associated with Yr5 and Yr39 genes, respectively [17]. Comparative transcriptomics revealed distinct differences in the responsive genes upon yellow rust and powdery mildew infection [18,19]. Transcriptomics studies have a high potential for innovative and exploratory studies towards novel insights into molecular mechanisms. However, a large number of genes identified in large scale omics studies impose a hurdle to identify candidate genes for functional validation. A map-based cloning approach has been successful at cloning key Yr genes, and induced mutagenesis identified Lr34/Yr18 to be an ABC transporter [20]. Map-based cloning in 4500 F2 plants followed by chemical mutagenesis and the screening of 1536 M2 lines led to the identification of a gene, Yr36, with a kinase and START domain [21]. Yr10 was identified as a CC-NBS-LRR domain-containing protein from a mapping population of 874 BC2F3 individuals [22]. In this work, we evaluated the possibility to identify candidate genes for YR resistance at the adult plant stage in a winter wheat bi-parental population through transcriptomics approach and also performed the metagenomics analysis to identify major wheat pathogens in the field trial.

2. Materials and Methods

2.1. Field Experiments and Sampling

A winter wheat bi-parental population of 109 lines obtained from the cross (Nimbus/3/SW, 2081221/2/SW2-7/Kranich) was sown in the field in Svalöv, Sweden, in the autumn of 2013. Scoring for yellow rust resistance was done at the booting stage (Zadoks stage 41–49) in the spring of 2014 with a scale of 1 (high resistance) to 9 (highly susceptible). On the basis of scoring, leaf materials from resistant and susceptible lines were collected from penultimate leaves pooled from three plants from each breeding line of the segregating population at the booting stage (Zadoks stage 41–49). Leaves were flash-frozen in liquid nitrogen and stored at −80 °C until further processing. Total RNA was extracted with the RNeasy Plant Mini kit (Qiagen, Hilden, Germany) including DNase treatment (RNase-free DNase set, Qiagen Inc., Santa Clarita, CA, USA).

2.2. RNAseq Library Preparation Sequencing and Quality Control

The concentration and quality of the RNA were estimated in ExperionTM Automated Electrophoresis System (Bio-Rad Laboratories, Hercules, CA, USA) and RIN values above seven were used to construct separate cDNA libraries. Paired-end sequencing was performed for 25 wheat lines on Illumina HiSeq 2500 instrument at the SciLifeLab (Stockholm, Sweden) with 300 base-pairs (bps) average fragment length. The FastQC program was used to analyse the quality of the raw sequencing reads [23]. Adapter sequences and low-quality reads were removed using trimmomatic [24].

2.3. De Novo and Reference Transcriptome Assembly and Validation

All the clean reads were mapped to the 13 known wheat pathogens (Zymoseptoria tritici (Desmazières) Quaedvlieg & Crous, Pyrenophora tritici-repentis (Diedicke) Drechsler, Bipolaris sorokiniana (Ito & Kuribayashi) Dastur, Puccinia graminis Persoon, Puccinia striiformis Westendorp, Puccinia triticina Eriksson, Phaeosphaeria nodorum (Berkeley) Quaedvlieg, Verkley & Crous, Pyrenophora tritici (Diedicke) Drechsler, Fusarium culmorum (W.G.Smith) Saccardo, Fusarium graminearum Schwabe, Fusarium oxysporum Schlechtendal, Fusarium verticil (Saccardo) Nirenberg and Magnaporthe oryzae Cavar) and un-aligned reads derived from individual lines were pooled to construct reference and de novo assemblies. De novo assembly was generated by using the Trinity assembler (version 2.5.1) [25]. Trinity pipeline was followed for the detection of differentially expressed genes. To facilitate a more in-depth comparison of the two different genotypes, reference-based transcriptome assembly was also generated through the alignment of all clean reads from each line to the reference wheat genome by using HiSat2 and Stingtie software [26]. The BUSCO software (version 2.4) was used to evaluate the quality of the two transcriptome assemblies [27]. Generated transcripts were clustered on 100% sequence identity through CD-HIT software [28]. Quality check of samples were also performed through expression abundance estimation of lines, PCA and MDS plot and YR field scoring of lines were also compared. High scoring (susceptible) and low scoring (resistant) lines were used as replicate for differential gene expression analysis.

2.4. Identification of Differentially Expressed Genes (DEGs), Annotation and Gene Ontology (GO) Analysis

The de novo and reference assembled transcriptome was then used as a reference to map the individual reads using the Bowtie2 program [29]. The transcript abundance, raw, transcript per million (TPM) and fragments per kilobase (kb) of the transcript sequence per million mapped reads (FPKM) was measured by using RSEM version 1.1.1131 for each sequenced line [30]. The DESeq2 package was employed to identify of differentially expressed genes (DEGs) from raw read counts at a false discovery rate (FDR) of 0.05 [31]. The heatmap of DEGs was generated through Pheatmap R package by using euclidean distance and hierarchical clustering algorithm [32]. The annotation of the DEGs was performed using the BLAST search program. Initially, BLASTx was performed with an e-value threshold of 1e-10 against wheat coding sequences (CDS) and Uniprot database. The Gene Ontology (GO) classification of DEGs in the genotypes were generated using the WEGO program [33]. The GO enrichment analysis was performed through AgriGO webserver by using hypergeometric statistical test methods and Yekutieli multiple test correction at a p-value of 0.05 [34], and visualization was done through REVIGO [35]. A KEGG pathway analysis was performed with the KOBAS program [36].

2.5. Metagenomic Analysis of Selected Wheat Pathogens

Targeted metagenomics analysis was performed to explore wheat-associated pathogen species in the samples. The trimmed RNA libraries were mapped to 13 selected wheat pathogens with Bowtie2 mapping software. The statistical significance of mapped reads were evaluated for pathogens with respect to resistant and susceptible groups.

3. Results

3.1. RNAseq Data Analyses

The resistant and susceptible lines were sequenced and analysed by the reference and de novo transcriptome assembly. A total number of 307 million read pairs (2 × 150 bps) were generated from the 20 lines. After quality control, 207 million high-quality reads of average length 126 bps were used in the construction of transcriptome assemblies. Library size, gene expression in lines, principal component analysis (PCA) plot and multidimensional scaling (MDS) plots of the samples are given in Figure 1. All the high quality wheat reads were pooled for the construction of reference and de novo assemblies and generated assemblies were clustered at 100% sequence identity. After clustering, 219,435 (N50 of 1835 bps) and 511,926 (N50 of 1420 bps) contigs were found for reference and de novo assemblies respectively. The longest contig sizes were 22,462 and 15,387 bps, respectively (

Table 1. Assembly metric of the reference and de novo transcriptome assemblies.

Transcriptome	Contigs	N50	Avg. Length	Assembly Size	Longest Contig Size	Contig Size >10 KB	Contig Size >1 KB
Reference	219,435	1835	1477	324,000,551	22,462	58	140,664
De novo	511,926	1420	953	488,058,762	15,387	30	175,517

). In BUSCO analysis, reference and de novo transcriptome assemblies achieved the 94.7% and 78.5% completeness, respectively. The percentage of partially complete BUSCO ranged from 99.1 to 94.0%, while the percentage of missing BUSCO was 0.9 and 6.0%, respectively (

Table 2. Results of the BUSCO analysis for the transcriptome assemblies’ validation.

BUSCO Description	Reference	De Novo
Complete BUSCOs (S+D)	1302 (94.7%)	1079 (78.5%)
Complete and single-copy BUSCOs (S)	69 (5.0%)	229 (16.7%)
Complete and duplicated BUSCOs (D)	1233 (D: 89.7%)	850 (D: 61.8%)
Fragmented BUSCOs (F)	61 (4.4%)	213 (15.5%)
Missing BUSCOs (M)	12 (0.9%)	83 (6.0%)

). The wheat lines, disease scoring, read count per line, high quality read, and mapping percentage are given in Supplementary Table S1.

3.2. DEGs Identified by De Novo and Reference-Based Methods

To quantify the transcriptomic variations in a sequenced line, we aligned the clean reads from each sample against the wheat reference and de novo transcriptome assemblies by using Bowtie2 with default parameters and RSEM software was used to quantify the transcript abundance to compare the expression level within and between different samples. The DEGs identified in the reference and de novo assembled transcriptome by DESeq2 software in pairwise comparisons between the resistance and susceptible lines were 141 and 8680, respectively, with a false discovery rate (FDR) < 0.05 (Table S2). To find wheat genes in the reference and de novo based analysis, identified DEGs were searched against wheat CDS sequences through BLAST similarity search, including up and downregulated transcripts (Figure 2). To determine the sample relations, differential expression data from the DESeq2 program were used to generate heat maps. Resistant and susceptible lines were grouped together in a different order with respect to reference and de novo transcriptome. However, most of the susceptible and resistant lines were grouped in the same clusters in both transcriptomes (Figure 3A,B). The expression heatmap of differentially expressed genes was clearly indicating a fewer number of upregulated genes in resistant lines than susceptible lines in their respective assemblies.

3.3. Differentially Expressed Genes Annotation and GO Enrichment

A BLASTX similarity search was performed for all the identified DEGs against the Uniprot database. Approximately 53% of the DEGs had BLASTX hits. The majority of the top BLASTX hit species in the reference-based DEGs belonged to Arabidopsis thaliana, Oryza sativa subsp. Japonica, and were followed by other cereal crops. In the de novo based analysis, DEGs were identified in approximately 17% of plant species, mainly: Arabidopsis thaliana, Oryza sativa janonica, Zea mays, Triticum aestivium, and Nicotiana benthamiana; 35% Schizosaccharomyces prombe and cerevisiae; 20% of fungal species, such as Puccinia gramine, Ustilago maydis, Cryptococcus neoformans and neoformans sp., Dictyostelium discoideum, Neurospora crassa, Candida glabrata and albicans sp., Neosartorya fischeri, Pneumocystis carinii, Solanum bulbocastanum, Trichosporon cutaneum, Agaricus bisporus, Aspergillus niger, flavus, terreus and oryzae, Chaetomium thermophilum and globosum sp., Pseudomonas aeruginosa, Eremothecium gossypii, Debaryomyces hansenii, Phanerochaete chrysosporium, Uromyces fabae, Laccaria bicolor, Kluyveromyces sp., Bacillus subtilis, Yarrowia lipolytica, Emericella nidulans. All the reference and de novo assembled transcripts were annotated against wheat genome and wheat annotated differentially expressed transcripts were considered for further downstream analysis. Wheat annotation for transcripts can be found in Tables S3 and S4.

The Gene Ontology (GO) classification of DEGs’ was represented in three main GO categories, i.e., the cellular component, molecular function and biological process in a histogram (Figure 4). In the GO analysis, the cellular component, cell and cell part (80%), organelle (55%) and membrane (46%) were highly represented compared to other components. Binding (64%) and catalytic activity (61%) were most represented among the molecular functions. Cellular process (65%), metabolic process (56%) and response to stimulus (36%) were the most dominant subcategories of biological processing. Cellular biosynthetic process, metabolic process, i.e., macromolecule, organic substance, nitrogen compound, aromatic compound metabolic process, and organic cyclic compound metabolic process were highly enriched in GO analysis. The significantly enriched GO term of the biological process can be further subcategorized into response to stress, response to stimulus, signal, methylation, multicellular organismal processing, multi-organism process and reproductive process which can be seen clearly in Figure 5A, generated by REVIGO. In Figure 5A (Table S5), the X and Y coordinates were based on the multidimensional scaling of a matrix of the GO terms’ semantic similarities whereas bubbles closeness on the plot were reflecting their closeness in the GO graph structure. It is clear that response to stress, stimulus, signalling and molecular localization-related terms were among the enriched GO terms. The KEGG database in KOBAS webserver was used to explore the networks of enriched pathways and gene products (Table S6). An enriched pathway among differentially expressed genes was provided as a scatter diagram with a degree of enrichment by the rich factor, p-adjust, and the number of genes enriched in a pathway (Figure 5B). The richness factor represents the ratio of the quantity of genes belonging to the pathway among differentially expressed genes to the total number of genes belonging to the pathway among all annotated genes. The lower the p-value, the higher the significance of metabolic pathways, whereas the size of bubbles represents the number of enriched genes. Metabolic pathways, the biosynthesis of secondary metabolites, peroxisome, glycerolipid metabolism, the mRNA surveillance pathway, plant–pathogen interaction, cutin, suberine and wax biosynthesis, glycerophospholipid metabolism and ubiquitin-mediated proteolysis have appeared as highly enriched pathways. To explore global transcriptome expression, RNAseq data of wheat resistant and susceptible lines were mapped on wheat genome. In general, genes in susceptible lines have a higher average read depth per million base pairs than resistant lines and an approximately similar trend has also been found at the genomic location of differentially expressed genes (Figure 6). However, higher read depth in resistant lines for differentially expressed genes was also found on wheat genome 1A, 2A, 3A, 3D and 5A, compared to susceptible lines which might be helpful to understand the molecular mechanism of resistance among the resistance lines.

To explore the field pathogenic factor association with the gene expression of wheat lines, targeted metagenomic analysis was performed by read mapping of resistant and susceptible line samples to the 13 known wheat pathogens. The highest number of reads was mapped to P. striiformis genome in the susceptible group (Figure 7). Significant differences in the read counts in the susceptible and resistant groups were also found in between those of P. graminis and P. triticiana, however, none of the wheat samples had more than 0.5% of total reads belonging to either of these two pathogens. RNA sequences from the pathogens B. sorokiniana, F. culmorum and Z. tritici were also identified with read counts of at least 1% of total reads in at least one sample. However, the read counts from these pathogens were not significantly different between the resistant and susceptible groups.

4. Discussion

In this study, resistant, susceptible and control wheat lines were sequenced. Control lines, common filed trial lines were used to check disease pressure in field whereas resistant and susceptible lines were selected on the basis of field scoring. To compare the transcriptomic variations among wheat lines exposed to yellow rust disease in the field, leaf tissue samples were collected and used for the RNA-seq analysis. On average, approximately 69% of reads were mapped to the wheat reference genome, which indicates the sequencing of other field-related genetic resources such as wheat pathogens. We performed the quality check of sequenced samples through gene expression data. The library size of the sequenced wheat lines was observed to be variable. However, the logCPM normalisation plot (Figure 1B) and gene expression level distributions both showed that the wheat lines had similar expression patterns and variation ranges suggesting that the sequenced line and sequencing data were comparable and suitable for downstream transcriptome analysis.

To know the effect of different biological and ecological factors of wheat field on wheat leaf transcriptomes, we performed the PCA analysis of wheat lines through TMM normalized expression counts for the wheat genome (Figure 1C). The first PCA component accounted for 71.01% of the total variability, which in this case corresponds to the reference genome specific variance. The second PCA component accounted for 12.76% of the overall variance which highlighted how the difference between the wheat lines might have been caused by other field factors. For a better understanding of the variation among the transcriptome of wheat lines, the MDS plot was used to check variations among samples (Figure 1D). The MDS plot showed that the transcriptome of control lines were very close to susceptible lines transcriptome. In order to identify the candidate genes of wheat among the resistant and susceptible ones, the samples from the control lines were removed from further downstream analysis. Moreover, susceptible and control lines transcriptome resemblance were also supported by field scoring. Although it was clear from the PCA and MDS plots that wheat lines had a varying expression in the field, it is challenging to make direct comparisons of field factors for transcriptomic variation among wheat lines.

Reference-based transcriptome analysis has been considered more effective than a de novo approach when the reference genome of organism is available [37]. However, very few studies have compared the two strategies [38,39] to identify differentially expressed genes, and no similar comparison has been found for field diseases. Thus, it is more important and interesting for field disease studies to determine whether the de novo assembly can detect the same genes, isoforms and the molecular responses as a reference genome-based analysis, and what else it was captured in transcriptome. In the present transcriptome analysis of wheat, we compared both strategies. The trinity de novo assembly has approximately twice the number of contigs than the reference assembly, which may be due to allelic variation among wheat genotypes, the lack of strand-specific information of genotypes or the sequencing of other unknown eukaryotic field environmental materials. Several genomes and transcriptome assembly studies have used BUSCO for evaluating the quality of assemblies. BUSCO detected the presence of 1347 extremely conserved core eukaryotic genes (CEGs) and their coverage in transcriptome assemblies for the evaluation of the completeness of the assembly. The BUSCO analysis showed that both assemblies were very close to complete in terms of gene content, together with the identified fragments of core-genes. Overall, both assemblies captured high percentages of ultra-conserved core eukaryotic genes (reference: 99.1%, de novo: 93.0%). However, the number of complete and single copy BUSCO were found more in de novo assemblies (Table 2). In the transcriptome analysis of wheat lines, the expression abundance and differential expression were different in the de novo and the reference-based analyses. However, there is a larger number of differentially expressed transcripts observed in the susceptible lines compared to the resistant lines (Figure 2 and Figure 3) in both assemblies. De novo and reference-based analysis indicated that the genes expressed in the resistant and susceptible lines might play key roles in their differential resistance abilities. This might be due to the presence of several external factors associated with susceptible lines in the field.

To understand the global trend of genomic variation among wheat lines exposed to different biological and ecological factors in the field, we identified and analysed DEGs from both resistant and susceptible lines in our study through generated reference-based and de novo transcriptome assemblies. The number of identified DEGs in the reference and de novo assembled transcriptome was very low in comparison to the total assembled transcripts, 141 and 8680, respectively, in the resistance and susceptible groups. Gene expression is the primary source to know concerning genetic variation among a population generated during a plant breeding process, and gene expression difference in the population could contribute and represent some of the observed differences in plant phenotype such as susceptibility to diseases. However, a low number of differentially expressed genes might indicate the least genomic variation among resistance and susceptible lines [40] and directly indicate the expression of key genes involved in disease resistance. To identify the potential genes involved in resistance, we investigated upregulated DEGs generated by both transcriptome assemblies. A BLAST similarity search was performed against wheat CDS sequences with the value 1e-10 to determine the commonly expressed genes and unique genes expressed between assemblies. A large proportion of transcript sequences failed to find homologous sequences in the wheat genome. With the used criteria, 126,669 and 107,651 transcript sequences were annotated in de novo and reference assemblies, respectively, in the similarity search of wheat CDS sequences (Tables S3 and S4). A large proportion of transcripts sequences generated from wheat lines were failed to find homologous sequences in wheat genome from both the assemblies. Huge number of un-annotating transcripts might be due to inclusion of lot of non-coding RNA content which are missing in wheat gene model or mixing of reads from other eukaryotic organisms.

Approximately 41% of sequences were found to be common between the de novo and reference assemblies, and approximately 29% of sequences were unique in assemblies which might indicate the difference generated by the used assembly approaches, tools and the inclusion of genomic material of other organisms in the wheat transcriptome. Furthermore, both methods identified many similar candidate genes putatively involved in resistant and susceptible genotypes. Thus, this also demonstrated the potential of the de novo method for the capturing of key genes even in the absence of a reference genome which can be useful for researchers working with field diseases with orphan species. In order to access expression difference in a wheat genotype, a list of upregulated differentially expressed genes was generated by both assemblies (

Table 3. List of upregulated differentially expressed genes (p-value < 0.05) in de novo and reference assemblies that can be considered as potential candidate genes involved in resistance. The table is sorted according to p-values. FC: fold change. IWGSC: International Wheat Genome Sequencing Consortium

Reference/De Novo	log2FC	Annotation	UniprotIds	IWGSC	Signal
Transcript				Chromosome Location
TRINITY_DN51078_c2_g4_i2	805.981	Cysteine-rich receptor-like protein kinase	CRK6_ORYSJ	2D:189356990:189359834:1	Y
TRINITY_DN46223_c0_g1_i25	98.303	Cysteine-rich receptor-like protein kinase	CRK6_ORYSJ	5A:546234176:546238163:−1	Y
TRINITY_DN46223_c0_g1_i31	82.836	Cysteine-rich receptor-like protein kinase	CRK6_ORYSJ	5A:546238637:546241834:1	Y
TRINITY_DN46223_c0_g1_i17	52.416	Cysteine-rich receptor-like protein kinase	CRK6_ORYSJ	5A:546238637:546241834:1	Y
TRINITY_DN38661_c1_g1_i1	38.481	Cysteine-rich receptor-like protein kinase	CRK6_ORYSJ	2B:245784381:245787768:1	Y
TRINITY_DN45544_c0_g1_i24	16.282	Cysteine-rich receptor-like protein kinase	CRK6_ORYSJ	2D:20432763:20439635:1	Y
TRINITY_DN37687_c0_g2_i5	11.964	Cysteine-rich receptor-like protein kinase	CRK7_ARATH	5A:546306021:546309203:−1	Y
TRINITY_DN49928_c0_g1_i18	11.545	Diacylglycerol kinase 5 OS = Arabidopsis thaliana	DGK5_ARATH	2A:748963953:748967651:−1	N
TRINITY_DN35364_c0_g1_i3	2.971	Disease resistance protein RGA2 OS = Solanum	RGA2_SOLBU	1D:455752931:455755683:1	N
TRINITY_DN44057_c1_g5_i1	7.574	Disease resistance protein RPP8 OS = Arabidopsis	RPP8_ARATH	6B:662028638:662032091:−1	N
TRINITY_DN39548_c1_g3_i2	11.392	Leaf rust 10 disease-resistance locus receptor-like	LRL28_ARATH	3B:29721933:29735756:1	Y
TRINITY_DN35553_c1_g1_i1	5.456	Leaf rust 10 disease-resistance locus receptor-like	LRL28_ARATH	6D:2047133:2050237:−1	Y
TRINITY_DN38577_c0_g1_i1	12.655	LRR receptor-like serine/threonine-protein	FLS2_ARATH	1D:466386615:466388390:−1	Y
TRINITY_DN38835_c1_g4_i13	4.145	LRR receptor-like serine/threonine-protein	FLS2_ORYSJ	6D:380568621:380572398:1	Y
TraesCS2B02G608600.1	59.366	Probable LRR receptor-like serine/threonine-protein	Y3475_ARATH	2B:788840706:788842030:−1	N
TRINITY_DN41813_c2_g5_i3	28.015	Probable LRR receptor-like serine/threonine-protein	Y3475_ARATH	2D:646488160:646491914:1	N
TRINITY_DN51069_c0_g2_i8	2.348	Protein TIFY 6b OS = Oryza sativa subsp.	TIF6B_ORYSJ	5B:369635031:369638011:−1	N
TRINITY_DN35330_c0_g3_i8	3.364	Putative disease resistance protein RGA4	RGA4_SOLBU	Un:47532810:47545586:1	N
TRINITY_DN51664_c0_g1_i11	3.269	Putative disease resistance protein RGA4	RGA4_SOLBU	Un:95706705:95715329:1	N
TRINITY_DN35364_c0_g4_i1	3.209	Putative disease resistance protein RGA4	RGA4_SOLBU	Un:234394428:234397967:1	N
TRINITY_DN51887_c1_g1_i1	4.127	Putative disease resistance RPP13-like	R13L2_ARATH	7D:11663563:11672717:1	N
TRINITY_DN52456_c0_g2_i15	8.426	Rust resistance kinase Lr10 OS = Triticum	LRK10_WHEAT	1A:9359231:9363721:1	N
TraesCS6B02G091700.1	4.309	S-(+)-linalool synthase, chloroplastic OS = Oryza	LINS_ORYSJ	6B:67408283:67411671:−1	N
TRINITY_DN51301_c1_g1_i10	11.432	Vesicle-associated protein 1-1 OS = Arabidopsis	VAP11_ARATH	7B:717630687:717634406:−1	N
TRINITY_DN46935_c4_g2_i7	5.042	Vesicle-associated protein 1-1 OS = Arabidopsis	VAP11_ARATH	7B:717785488:717796197:1	N
TRINITY_DN52084_c1_g1_i8	19.697	Wall-associated receptor kinase 1 OS = Arabidopsis	WAK1_ARATH	5A:464158592:464170977:−1	Y
TRINITY_DN50322_c1_g1_i11	5.429	Wall-associated receptor kinase 3 OS = Arabidopsis	WAK3_ARATH	2B:657850507:657854763:−1	Y
TRINITY_DN39541_c5_g2_i2	11.016	Wall-associated receptor kinase 5 OS = Arabidopsis	WAK5_ARATH	6D:467856066:467860898:1	Y
TRINITY_DN38381_c2_g3_i6	5.209	Wall-associated receptor kinase 5 OS = Arabidopsis	WAK5_ARATH	6B:713458733:713462914:1	Y
TRINITY_DN52648_c2_g2_i1	7.132	Wall-associated receptor kinase-like 9	WAKLH_ARATH	6D:467856066:467860898:1	Y
TRINITY_DN51423_c2_g1_i4	12.889	Receptor-like cytoplasmic kinase 185 OS = Oryza	RK185_ORYSJ	2B:104817628:104821546:−1	N
TRINITY_DN50690_c0_g4_i1	5.46	G-type lectin S-receptor-like serine/threonine-protein	Y1130_ARATH	2D:642197592:642202812:1	N
TRINITY_DN35935_c1_g1_i3	5.033	G-type lectin S-receptor-like serine/threonine-protein	CE101_ARATH	5B:690469413:690490270:−1	N

). Differentially expressed genes from reference and de novo assemblies were annotated by the wheat genome and Uniprot database through BLAST similarity search. It is clearly visible in Table 3 that most of the differentially expressed transcripts related to the disease were identified through de novo transcriptome analysis approach. Cysteine-rich receptor-like protein kinases (CRKs) are most highly differentially expressed in resistant lines among all potential candidates. CRKs are transmembrane, and are involved in a wide range of receptor-like protein kinase-dependent signalling networks including pathogen detection [41]. Seven different isoforms of CRKs; six as CRK6 and one as CRK7 (Table 3) were expressed among resistant lines on different genome locations. The Level of expression varies with respect to the genomic location. However, all the CRKs possess a secretory signal. CRKs have shown a significant role in disease resistance such as NPR1 and NH1 mediated immunity in Arabidopsis thaliana and Oryza sativa subsp. Japonica against bacterial blight pathogen Xanthomomas oryzae pv. oryzae. Absence or low CRKs expression can make plants more susceptible to infection [42]. Diacylglycerol kinase 5 (DAG5) is another gene among other significant DEGs. DAG5 phosphorylates diacylglycerol (DAG) to produce a signalling molecule of phosphatidic acid as a second messenger. Diacylglycerol kinases are key signalling enzymes which are involved in phosphorylating diacylglycerol (DAG) to yield phosphatidic acid (PA). The biosynthesis of PA plays a crucial role in a eukaryotic metabolic and signalling process such as the glycerolipid metabolic process, intracellular signal transduction source, lipid phosphorylation, or the protein kinase C-activating G protein-coupled receptor signalling pathway. Phosphatidic acid is highly required for plant development, abiotic stress and pathogen attack. The presence of diacylglycerol kinase 5 (DAG5) among highly significant DEGs may be an indicator of high resistance among resistant lines [43].

Plant disease resistance in genes encodes two main classes of nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins; TIR-domain-containing (TNL) and CC-domain-containing (CNL). TNLs and CNLs regulate plant resistance through different downstream pathways by inducing a series of defence responses, such as the activation of an oxidative burst, mitogen-associated protein kinase cascade, the induction of pathogenesis-related genes, and the hypersensitive response. In our study, four RGA isoforms; one for RGA2 and three for RGA4, were found among significant DEGs. All four RGAs were found at different genomic locations (Table 3), but all isoforms were having a relatively low but consistent gene expression. RGA isoforms are having an RX-like_CC motif (IPR038005), NB-ARC, LRR domains and belong to the CNL family. The consistent expression of the CNL type of RGAs indicates the “non-race specific” disease resistance and also the activated alternative non-race specific resistance induced by disease resistance proteins RPP8 and RPP13 which indicate a high presence of non-host-specific pathogens in the growing field [44]. LRR receptor-like serine/threonine-protein kinases such as FLS2, Y3475, leaf rust and rust resistance kinases determine the specific perception of pathogen-associated molecular patterns and initiate the innate immune MAP kinase signalling for enhanced resistance against pathogens. The expression of receptor-like cytoplasmic kinase (RLCK185) is clear evidence of the innate immunity triggered by fungal chitin signalling pathways through MAP kinases. Chitin recognition by CERK1 receptor triggers the MAP kinase (MPK3 and MPK6) cascade to search for a host protein that can interact with effector proteins and participates in the activation of defence genes during response to microbial peptidoglycans and chitin [45]. A plasma membrane-associated G-type lectin S-receptor-like serine/threonine-protein kinase (CES101) was also expressed among the significantly expressed DEGs and its involvement was suggested in innate immune response, protein phosphorylation and response to fungus which might have role in the recognition of the lectin-associated molecular pattern of fungus [46]. All RGAs were expressed in the resistant genotypes. Perhaps, the functionality of these RGAs was suppressed by pathogens or lost molecular function due to several genomic modifications in the susceptible genotype. The presence of the RGA4 isoform under an unclassified genome needs to be re-investigated in order to be used as a marker for plant breeding purposes [47]. The expression of protein-like TIFY 6b, known as a repressor of jasmonate responses, is suggesting that plants may be reducing the developmental and metabolic processes for better resource utilization under a biotic stress such as salt tolerance, dehydration and wounding [48]. In general, susceptible cultivars were shown to have a higher gene expression level than the resistant cultivars at the global transcriptome level, which might be due to the higher production and resources allocation against infection and diseases (Figure 6).

The mechanism of cell wall communication to the cytoplasm is not very well known for plant resistance. Wall-associated kinases (WAKs) have the potential to provide clues for the cell wall and the cytoplasm crosstalk. It has been found that WAKs were expressed at organ junctions of shoot, root and leaf in response to wall disturbances, and the expression of an antisense WAK gene in leaves reduced the WAK protein levels and exhibited a loss of cell expansion. The presence of WAKs on the cell wall is providing the evidence that the receptor-like kinase may have a significant role in the control of cell expansion, morphogenesis and development [49]. In our study, five transcripts were found to be related to wall-associated receptor kinases viz. WAK1, WAK3, WAK5 and WAK9. WAK1 and WAK3 were found on chromosomes 5A and 2B on reverse strands, respectively. The higher expression of WAK1 might show the reduced cell expansion during infection and disease. WAK5 and WAK9 were found on chromosomes 6B and 6D in the forward strand, respectively, which might indicate their involvement in the control of cell expansion, morphogenesis and development under normal circumstances.

The differential gene expression data analysis of wheat lines has increased our understanding of plant defence mechanism in the field and shown evidence for a pathogen-specific and broad-spectrum disease resistance mechanism. Metagenomics analysis of the RNA libraries of samples has also provided evidence for a pathogen-specific disease resistance mechanism along with broad-spectrum disease resistance (Figure 7). In metagenomics analysis, P. striiformis was found as the most dominant pathogen among susceptible wheat lines which indicates the susceptibility of wheat lines for this pathogen. The significant differential expression of genes involved in non-race-specific disease resistance among resistant wheat lines provides the evidence for the activation of PAMP-triggered innate immunity (PTI). The presence of other fungal pathogens such as B. sorokiniana, F. culmorum and Z. tritici were supporting the cause of the high activation of innate immunity in the field.

5. Conclusions

In the present study, transcriptome analysis was conducted between a resistant and susceptible wheat genotype with a different level of resistance in the field, and several differentially expressed genes were identified through reference and de novo transcriptome assembly. In the transcriptome analysis, the key challenge was the handling of cross-species eukaryotic molecular content of field samples, especially during field infection. In our comparative transcriptome study, the de novo approach was found to be more explorative than the reference assembly process due to high dependency on the reference genome, and the gene expression of similar genes from cross eukaryotic species like fungus has a high chance of influencing the gene expression quantification process due to short reads. In this study, many genes related to plant defence were upregulated in resistant wheat lines in the field. A significant number of genes involved in non-race specific resistance were overrepresented in resistant lines, which might be a reason for the good resistance ability of resistant wheat lines. Expression ofcysteine-rich receptor-like protein kinases, CNL type kinases and wall-associated kinases (WAKs) are suggesting their role in determining broad-spectrum plant defence in the field. GO enrichment and pathway analysis further confirmed that PTI triggered the innate immunity-related genes which were overrepresented in the wheat resistant lines. Potential candidate genes found in this study might provide a basis for future functional host–pathogen genomics research for field wheat. Molecular techniques such as RNA interference can be further used to understand the role of these genes in the field plant resistance.