Identification of Fusarium solani f. sp. pisi (Fsp) Responsive Genes in Pisum sativum

Pisum sativum (pea) is rapidly emerging as an inexpensive and significant contributor to the plant-derived protein market. Due to its nitrogen-fixation capability, short life cycle, and low water usage, pea is a useful cover-and-break crop that requires minimal external inputs. It is critical for sustainable agriculture and indispensable for future food security. Root rot in pea, caused by the fungal pathogen Fusarium solani f. sp. pisi (Fsp), can result in a 15–60% reduction in yield. It is urgent to understand the molecular basis of Fsp interaction in pea to develop root rot tolerant cultivars. A complementary genetics and gene expression approach was undertaken in this study to identify Fsp-responsive genes in four tolerant and four susceptible pea genotypes. Time course RNAseq was performed on both sets of genotypes after the Fsp challenge. Analysis of the transcriptome data resulted in the identification of 42,905 differentially expressed contigs (DECs). Interestingly, the vast majority of DECs were overexpressed in the susceptible genotypes at all sampling time points, rather than in the tolerant genotypes. Gene expression and GO enrichment analyses revealed genes coding for receptor-mediated endocytosis, sugar transporters, salicylic acid synthesis, and signaling, and cell death were overexpressed in the susceptible genotypes. In the tolerant genotypes, genes involved in exocytosis, and secretion by cell, the anthocyanin synthesis pathway, as well as the DRR230 gene, a pathogenesis-related (PR) gene, were overexpressed. The complementary genetic and RNAseq approach has yielded a set of potential genes that could be targeted for improved tolerance against root rot in P. sativum. Fsp challenge produced a futile transcriptomic response in the susceptible genotypes. This type of response is hypothesized to be related to the speed at which the pathogen infestation advances in the susceptible genotypes and the preexisting level of disease-preparedness in the tolerant genotypes.

Introduction

The food industry contributes significantly to the world’s total greenhouse gas emissions (Poore and Nemecek, 2018). About 18% of the global greenhouse gas emissions are caused by livestock production, which supplies the majority of the dietary protein (Stehfest et al., 2009). Proposed mitigation efforts include a shift to plant-based protein as it is an environmentally sustainable option. The demand for plant-based protein is on the rise due to its health benefits (World Health Organization, 2015), as well as due to the ethical concerns related to exploiting animals as a source of protein (Johansson, 2019). The global plant-based protein market is expected to keep growing at a compound annual growth rate of 8.1% from 2019 to 2025 (Research and Markets, 2019). Popular plant-based meats from Impossible Foods and Beyond Meat have already reached some of the biggest food and retail brands in the United States.

Pea (Pisum sativum L.; Family Fabaceae) is a major contributor to this plant-derived protein market and has gained particular interest lately due to its high protein content (20–30%), especially lysine; overall high nutritional value; and relatively low cost (do Carmo et al., 2016; Peng et al., 2016; Xiong et al., 2018). The market for pea protein is expected to be $34.8 million in 2020 due to growing consumer interest in plant-based proteins as an alternative to animal-derived protein (Grand View Research, 2015; Pietrysiak et al., 2018). Pea also plays a critical role in sustainable agriculture due to its nitrogen-fixing capability, short life cycle, and low water usage; all of which make it a useful cover-and-break crop requiring minimal external inputs.

The United States is one of the world’s major pea producers. In the United States, harvested area of peas has increased by over 300% during the last 25 years; however, the yields have decreased an average of 7.5% throughout this timespan. This is likely due to the fact that the cultivars that were originally developed in the Pacific Northwest are being increasingly grown in less productive areas such as Montana and the Dakotas (Vandemark et al., 2014). Sustainable production of pea has been negatively affected by several diseases, predominantly root rots (Akhtar and Azam, 2014; Bodah et al., 2016). Root rots are the diseases of greatest impact to crop production worldwide (Kumari and Katoch, 2020). Frequently, root rot diseases involve more than one pathogen; therefore, the disease is known as root rot complex. Pathogens such as oomycetes and fungi are commonly involved in this root rot complex (Xu et al., 2012; Chittem et al., 2015; Gossen et al., 2016; Bodah, 2017).

One of the predominant causal agent of root rots in P. sativum is the soil fungus, Fusarium solani f. sp. pisi (Fsp). F. solani is a fungal soil-borne facultative parasite that is present worldwide (Zhang et al., 2006). The yields of P. sativum cultivars can be reduced by 15–60% by Fsp (Seaman, 1976; Grünwald et al., 2003; Porter et al., 2014). Over the years, hundreds of pea cultivars and germplasm core collections have been screened for Fsp resistance, and lines have been developed that demonstrate partial resistance to selected Fsp races (Coyne et al., 2008). An effort to identify tolerance to root rot in wild pea germplasm resulted in the identification of eight accessions with high levels of partial resistance (Porter, 2010). These accessions have been utilized for developing new cultivars. However, in tests replicated in the greenhouse and/or the field with derived selections, complete tolerance to Fsp has not been obtained (Grünwald et al., 2003; Porter et al., 2014; Bodah et al., 2016).

Understanding the genetic basis of tolerance to Fsp in a wide array of different pea breeding lines and cultivars has been pursued in several studies. The first QTL for Fsp tolerance was reported from a field study utilizing various parental lines that showed resistance to multiple root rots (Kraft, 1992; Feng et al., 2011). Recent studies conducted under controlled conditions have reported three QTLs; QTL Fsp-Ps 2.1 explains 44.4–53.4% of the phenotypic variance within a 1.2 cM confidence interval. The other two QTLs, Fsp-Ps 3.2, and Fsp-Ps 3.3 explain 3.6–4.6% of the phenotypic variance related to Fsp root rot tolerance (Coyne et al., 2015, 2019). While the genes underlying these QTLs have not yet been identified, there is a reason for optimism given the recent release of the pea reference genome (Kreplak et al., 2019). It is expected to facilitate the characterization of potential transcription factors, stress-associated phytohormone genes, Pathogenesis-related (PR) proteins, or pea phytoalexin Pisatin (Kendra and Hadwiger, 1984) in the interaction between pea and Fsp.

While genetic approaches for identifying disease tolerance or resistance genes are common, gene expression approaches to identify key genes in response to pathogen challenge remain scarce. A report of Aphanomyces euteiches-mediated root rot of pea was investigated using a gene expression approach, and novel genes responsive during the pathogenic interaction with Medicago truncatula were reported (Nyamsuren et al., 2003). Besides the expected induction of PR and defense genes, several novel genes were also reported to be overexpressed during the plant-pathogen interaction.

To the best of our knowledge, a gene expression approach to identify genes involved in Fsp tolerance in pea is yet to be reported. For gaining a comprehensive insight into the transcriptomic responses during the Fsp challenge, a comparative time-course RNAseq expression analysis was performed on four tolerant and four susceptible P. sativum genotypes that were selected from a preceding study (Bodah et al., 2016). Data analysis reaffirmed the role of Disease-Resistance Response 230 (DRR230) and sugar transporters, as well as expression patterns of genes associated with receptor-mediated endocytosis and exocytosis, cell death, and anthocyanin synthesis. Interestingly, several previously uncharacterized genes were also identified to be differentially expressed in both tolerant and susceptible genotypes, which may help illuminate the novel mechanism of pea-Fsp interaction.

Materials and Methods

Plant Material and Fsp Isolates

A total of eight, white-flowered pea genotypes were selected for pathogen challenge (Table 1). Four tolerant genotypes – 00-5001, 00-5003, 00-5004, and 00-5007 – were selected from the Fsp tolerant 5000 series (Porter et al., 2014). The 5000 series pea breeding lines were previously developed via single-seed descent from crosses initiated in a USDA–ARS greenhouse in Prosser, WA, in 1998. The parentage of 00-5001 is PH14-119/M7477// Coquette/3/86-2197/74-410-2 (Kraft, 1989; USDA–ARS NGRP, 2020). The parentage of 00-5003 is 69PH42-691004/Recette//Popet/3/PH14-119/ DL-1/3/B563-429-2/PI257593//DSP TAC (USDA–ARS NGRP, 2020). The parentage of 00-5004 is 79-2022/ICI 1203-1//Menlo/3/PI189171/ DL-2//75-786 (Kraft and Tuck, 1986; USDA–ARS NGRP, 2020). The parentage of 00-5007 is 00-5005/00-5006. 00-5005 parentage is B669-87-0/M7477//Blixt B5119/3/00-5001/74SN5/3/PH14-119/DL-1//74SN3/Recette/5/ FR-725 (Kraft and Giles, 1976; USDA–ARS NGRP, 2020). 00-5006 parentage is 00-5003/00-5004.

TABLE 1

Table 1. Selected white-flowered pea genotypes for time-course transcriptome analysis in response to Fusarium solani f. sp. pisi (Fsp) challenge.

Four susceptible genotypes – “Aragorn,” “Banner,” “Bolero,” and “DSP” – were identified among frequently used commercial pea varieties. These eight genotypes were selected based on their contrasting root disease severity index reported in a preceding study (Bodah et al., 2016). The eight genotypes were classified as either tolerant or susceptible post-Fsp challenge by phenotyping of the root disease severity index, plant height, shoot dry weight, and root dry weight (Bodah et al., 2016). The 5000 series pea breeding lines were found to be the most tolerant lines among the white-flowered pea lines.

The Fsp isolates Fs 02, Fs 07, and Fs 09, were obtained from infected pea roots collected in the Palouse Region of Washington and Idaho, United States soils by Dr. Lyndon Porter, USDA-ARS Vegetable and Forage Crops Research Unit, Prosser, WA. The three isolates were single-spored, identified as Fsp, based on partial translation elongation factor 1-a sequences using Fusarium-ID (Geiser et al., 2004), and their pathogenicity to pea was confirmed (Bodah et al., 2016). The three isolates were grown on pentachloronitrobenzene (PCNB) selective media for 6 days (Nash and Snyder, 1962). Cultures were transferred to KERR’s media (Kerr, 1963), and incubated on a shaker at 120 rpm under continuous light for 6 days at 23–25°C. The spore concentration of each isolate was determined using a hemocytometer and diluted to 1 × 10⁶ spores/ml of water. A spore suspension inoculum containing equal parts by volume of each of the three isolates was created.

Fsp Disease Challenge

Fusarium solani f. sp. pisi disease challenge was performed as reported previously (Bodah et al., 2016). Briefly, seeds of each pea genotype were sterilized in a 0.6% sodium hypochlorite solution and rinsed in sterile distilled H₂O. Seeds were then soaked for 16 h in either the Fsp spore suspension (inoculated set) or in sterile H₂O (control set). At the end of 16 h, seeds from the control and inoculated set were separated from the respective solutions and maintained on perlite under ambient light and controlled greenhouse conditions with temperature ranging between 21 and 25°C for either 6- or 12-h duration. The 0-h time point was represented by the endpoint of the 16-h soaking period. The embryo containing region of the seed was excised and harvested from 600 seeds per genotype per time point (0, 6, and 12 h), immediately frozen under liquid nitrogen and transferred to storage at −80°C for subsequent RNA extraction. The experiment was repeated three times in tandem under identical conditions.

RNA Isolation, cDNA Library Construction and Sequencing

The frozen seed material from two out of the three pathogen challenge experiments was randomly selected and pulverized in a SPEX SamplePrep 6870 FreezerMill (SPEX SamplePrep, Metuchen, NJ, United States) for five cycles. Each cycle consisted of cooling for 2 min and grinding at 15 counts per second for 4 min. Total RNA was isolated from the pulverized tissue using the RNeasy Plant RNA Extraction Kit (Qiagen, Hilden, Germany). A Nanodrop ND-8000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States) and a Qubit Fluorometer (Life Technologies, Camarillo, CA, United States) were used to quantify the extracted RNA. Contaminating DNA was removed using the TURBO DNA-free Kit (Life Technologies, Camarillo, CA, United States) using the manufacturer’s instructions. RNA quality was verified via electrophoresis on a 1% agarose gel.

Equimolar amounts of RNA samples from tolerant and susceptible genotypes were bulked for each time point before the construction of RNAseq libraries. RNAseq libraries were constructed using 1 μg of RNA, and the Illumina TruSeq kits (Illumina Inc., San Diego, CA, United States). RNA was purified with an Oligo(dT) cellulose affinity matrix and subsequently fragmented into short pieces of an average size of 450 base pairs with Ampure XP beads (Beckman Coulter, Brea, CA, United States). All libraries were quantified on a Qubit Fluorometer (Life Technologies, Camarillo, CA, United States) and analyzed on an Agilent BioAnalyzer (Agilent Technologies, Santa Clara, CA, United States) to determine the concentration, final size, and purity of the library. A total of 24 libraries were sequenced using the HiSeq2000 configuration 100 PE (Illumina Inc., San Diego, CA, United States) at the Michigan State University Genomics core laboratory.

RNAseq Data Processing and Statistical Analysis

The generated fastq files were analyzed for quality with CLC Bio Genomics Workbench 6.0.1 (CLC Bio, Aarhus, Denmark) and trimmed with trimmomatic (Bolger et al., 2014). De novo RNAseq assembly was performed using data from all 24 samples to obtain a master assembly with the software Trinity v2.8.4 (Grabherr et al., 2011). Dependencies for Trinity, Bowtie2 v1.2.3 (Langmead and Salzberg, 2012), Salmon v0.12.0 (Patro et al., 2017), and JELLYFISH v2.2.3 (Marçais and Kingsford, 2011), were used during assembly. Bowtie2 and Salmon were used for abundance estimation, and JELLYFISH was used as a k-mer counting software.

The software Kalisto was used for transcript quantification (Bray et al., 2016). The reads were quantified for each of the two biological replicates of the tolerant or susceptible genotypes at three time points, 0, 6, 12 h after inoculation, and for each control or treatment. This analysis resulted in 24 separate quantification groups that were used for comparison. Differentially expressed contigs (DECs) with p-value of <0.001 and a greater than two-fold change in expression were identified using the Baggerley’s test for data from each of the two biological replicates generated for each genotype group, time point, and inoculation treatment. The RPKM (Reads Per Kilobase of transcript per Million mapped reads) expression values were also ascertained for each contig. Heat maps showing fold-change of RPKM values between control (C) and inoculated (I) sets and among genotypes were created in Microsoft Excel 365 ProPlus (Microsoft Corporation, Redmond, WA, United States).

Functional Annotation, Statistical Gene Ontology (GO) Enrichment, and Pathway Analysis

Functional annotation of the master assembly and DECs was conducted via BLAST in BLAST2GO v. 3.3. (Conesa and Götz, 2008). Default parameters were used for the functional annotation, as well as for Gene ontology (GO) mapping, and InterProScan. The two-tailed Fisher’s exact test (FDR < 0.05) was used to ascertain over- and under-represented functions during the Fsp challenge. A heat map representing “biological process” GO terms over-represented in the Fsp inoculated treatment was created in Microsoft Excel 365 ProPlus (Microsoft Corporation, Redmond, WA, United States). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to identify pathways represented by the set of DECs for each time point and genotype.

Real-Time Quantitative PCR

RNA was extracted utilizing the RNeasy Plant DNA Extraction Kit (Qiagen, Mainz, Germany) from the same sampled tissues utilized for the RNAseq analysis. After DNase treatment, equimolar amounts of RNA from the tolerant and susceptible genotypes were bulked for each time point. First-strand cDNA synthesis was performed using 1,500 ng of each bulked RNA sample with the SuperScript Vilo kit (Thermo Fisher Scientific, Waltham, MA, United States). Nine genes were randomly selected from the list of DECs for RT-qPCR analysis (Supplementary Table S1). Primers for RT-qPCR were designed with the Primer3 software (Rozen and Skaletsky, 2000) with the corresponding transcriptome contig as the query sequence for each primer set. The P. sativum root border cell-specific protein (GenBank accession AF1139187.1) was used as an internal reference control as it showed invariant expression across genotypes and treatments in the RNAseq data.

The QUBIT 3.0 fluorometer (Invitrogen, Carlsbad, CA, United States) was used to quantify cDNA library concentration. For each reaction, 16 ng of cDNA was used with the iTaq Universal SYBR Green Supermix (Bio-Rad, Berkeley, CA, United States). Each RT-qPCR reaction was performed in triplicate for each of the three biological replicates using the Stratagene M×3005P (Thermo Fisher Scientific, Waltham, MA, United States). The amplification profile consisted of an initial denaturation at 95°C for 150 s, 40 cycles of 20 s at 95°C for denaturation, 20 s at 60°C for annealing, and 20 s at 72°C for extension. A melting curve analysis was performed post amplification to ensure the presence of a unique amplicon and performed with an initial denaturation at 95°C for 1 min and a decrease of temperature to 50°C for annealing. The temperature was then increased in 0.5°C increments at 5 s/step from 50 to 95°C for fluorescence readings. Raw fluorescence data was used as input for crossover threshold (Ct) calculations and reaction efficiencies adjusted with LinRegPCR 2012.0 software (Ruijter et al., 2009). The ΔΔCt method offered by PE Applied Biosystems (Perkin Elmer, Forster City, CA, United States) was used to obtain relative differential expression values after reaction efficiencies were adjusted with the LinRegPCR 2012.0 software (Pfaffl, 2001).

Functional Annotation of QTL Associated With Fsp Tolerance in Pea

Fsp-Ps 2.1, the major QTL found to be associated with Fsp tolerance in pea (Coyne et al., 2019), was annotated using the transcriptome data generated in this study to determine if there are any differentially expressed genes located in the selected genomic region. Fsp-Ps 2.1 explains 44.4–53.4% of the phenotypic variance, and it is located on chromosome II within a 1.2 cM confidence interval of marker Ps900203 (Duarte et al., 2014; Coyne et al., 2019). The genomic sequence of the Fsp-Ps2.1 ± 1.2 cM (±201,800 nt) was obtained from the pea genome (Kreplak et al., 2019). The length of the QTL sequence was calculated based on the distance between the Ps900203 and Ps000075 markers. Marker Ps000075 is 0.6 cM = 168,166 nt away from Ps900203. The transcriptome data from this study was aligned via BLAST against the Fsp-Ps2.1 ± 1.2 cM sequence in CLC Bio Genomics Workbench 6.0.1 (CLC Bio, Aarhus, Denmark).

Results

Assembly of Transcriptome Data and Identification of Differentially Expressed Contigs (DECs)

A total of 850 million reads were generated after the sequencing of the 24 libraries (Supplementary Table S2). For these 24 libraries, the mean Q score ranged from 34.00 to 34.88; these Q scores validate the quality of the assay (Supplementary Table S2). After QC and trimming of low-quality reads, 69.49% of reads were used for assembly of the master transcriptome. The master transcriptome, generated in this study, was composed of 185,721 contigs (Supplementary Table S3), which had a mean contig length of 1503.15 nucleotides (nt) with a length range of 184–18,990 nt.

Mapping of reads to the master assembly showed a different number of contigs with zero mapped reads for each time point (0, 6, 12 h) and genotype (tolerant and susceptible). After expunging contigs with zero mapped reads, the total number of contigs ranged from 102,382 to 141,530, or 55.13 to 76.21% of the total 185,721 contigs, respectively (Supplementary Table S2). The total number of reads mapped to each contig for each time point and genotype and RPKM values are summarized in Supplementary Table S3.

For each contig, twelve pairwise comparisons were performed (Tables S3, S4). In order to identify which genes were differentially expressed in response to the Fsp challenge, six comparisons were performed that evaluated the expression values of genes in non-inoculated versus Fsp-inoculated treatments, as listed in Supplementary Table S4-comparisons 1 to 6 (Figure 1A). In order to identify which genes responded differentially to Fsp between the tolerant (bulked-tolerant) and susceptible (bulked-susceptible) genotypes, six additional pairwise comparisons were performed and are summarized in Supplementary Table S4-comparisons 7 to 12 (Figure 1B). The twelve pairwise comparisons resulted in the identification of 42,905 DECs out of the 185,721 contigs.

FIGURE 1

Figure 1. Venn diagrams representing the number of DECs (Differentially Expressed Contigs) for the 12 pairwise comparisons. (A) Number of DECs for pairwise comparisons between control and inoculated samples collected at 0, 6, and 12-h time points for the tolerant and the susceptible genotypes. (B) Number of DECs for pairwise comparisons between the tolerant and susceptible genotypes for each time point (0, 6, and 12 h) for control and inoculated conditions.

Pairwise comparisons 1 to 6 that compare non-inoculated versus Fsp-inoculated treatments, yielded the number of upregulated DECs obtained for each time point (Figure 2). For the Fsp inoculated tolerant genotypes, the number of upregulated DECs varied between 1,200 and 1,460 DECs across 0, 6-, and 12-h time points. The number of suppressed (under-expressed) DECs in the inoculated tolerant genotypes was larger (2795-4453 DECs). For the Fsp inoculated susceptible genotypes, the total number of upregulated DECs was 5–7 times larger than the total number for the Fsp inoculated tolerant genotypes (Figure 2). The number of suppressed genes in both sets of genotypes was similar at 0 h. However, for the susceptible genotypes, these numbers were 1.34 and 2.21 times larger than the tolerant genotypes at 6 and 12 h, respectively. A total of 5.7 and 5.4% of the DECs were shared across the three time points for the tolerant and susceptible genotypes, respectively (Supplementary Table S3). From the total upregulated DECs in the Fsp inoculated sets, 10 (0.10%), 48 (0.52%), and 12 (0.12%) DECs are shared among the tolerant and susceptible genotypes at the 0, 6, and 12-h time points, respectively (Supplementary Table S3).

FIGURE 2

Figure 2. The total number of overexpressed and underexpressed DECs in the inoculated treatments for tolerant and susceptible genotypes at each time point in response to the Fsp challenge.

Functional Annotation of DECs and GO-Term Enrichment

From the 185,721 contigs of the master assembly, 120,132 returned positive BLAST hits when queried to the NCBI database (National Center for Biotechnology Information, 2020). A total of 4,734 contigs or 2.55% of the total contigs were annotated as proteins of unknown function or hypothetical proteins. The top BLAST hits showed similarity to M. truncatula, Trifolium pratense, Cicer arietinum, and Trifolium subterraneum with a distribution of 34.0, 16.1, 16.0, and 14.6% respectively. P. sativum, with a 3.6% match, was fifth in the rankings. The low percentage of hits was most likely due to the relatively small number of P. sativum RNAseq data represented in the NCBI database. Of the 42,905 DECs, 36,923 (86.1%) returned positive BLAST hits when aligned to the NCBI database. Interestingly, 33 contigs (0.09%) of the 36,923 hits were classified as proteins of unknown function or hypothetical proteins, which could be useful candidates for understanding the pea-Fsp interaction.

The GO enrichment analysis identified significant over- and under-represented GO terms for each of the three Fsp inoculated time points (Supplementary Table S5). GO terms related to routine DNA processes, such as DNA metabolic process, DNA biosynthetic process, and DNA integration were significantly underrepresented at different time points in the tolerant and susceptible genotypes. DNA integration and DNA metabolic process terms were underrepresented across the three times in the susceptible genotypes, but only at 0 and 6 h for the tolerant genotypes. Nucleic acid phosphodiester bond hydrolysis was also underrepresented only during certain times points in the susceptible genotypes but not in the tolerant genotypes.

Figure 3 represents a heat map with the overrepresented biological process GO terms for each time point in the inoculated treatment for the tolerant and susceptible genotypes. These terms provide a comparative perspective of biological processes that responded to Fsp in the two subsets of genotypes.

FIGURE 3

Figure 3. Biological process-GO terms over-represented in the Fsp inoculated treatment for tolerant and susceptible genotypes at 0, 6, and 12-h time points. The tone of colors in the heatmap denotes the p-value calculated using two-tailed Fisher’s exact test. Significantly over-represented GO terms showed a p > 0.05. p-values.

Quantitative RT-PCR Verification

To verify the expression results obtained from RNAseq data, RT-qPCR analysis was performed on nine randomly selected genes at all time points for the tolerant and susceptible genotypes. The expression trends of eight out of nine genes (89%) correlated with the RPKM values indicating the robustness of RNAseq results (Figure 4). The internal reference control (GenBank accession AF1139187.1) showed invariant expression across genotypes and treatments in this RT-qPCR analysis.

FIGURE 4

Figure 4. RT-qPCR validation of select genes in control (C) and Fsp inoculated (I) plants. The gray and black bars represent relative gene expression for the tolerant and susceptible genotypes, respectively. First column: RT-qPCR data show the average relative expression of three biological samples with three technical replicates each. Second column: RPKM values calculated for each gene. The error bars represent the standard error between replicates in RT-qPCR analysis. Gene 1: TRINITY_DN2419_c0_g1_i5 (BLAST accession: XM_003592027.3), Gene 2: TRINITY_DN2754_c0_g1_i11 (BLAST accession: XM_004502933.3), Gene 3: TRINITY_DN5727_c0_g1_i1 (BLAST accession: MK618561.1), Gene 4: TRINITY_DN6240_c0_g1_i9 (BLAST accession: XM_003592048.3), Gene 5: TRINITY_DN2169_c1_g1_i2 (BLAST accession: XM_004506541.3), Gene 6: TRINITY_DN1232_c0_g1_i11 (BLAST accession: XM_004504351.3), Gene 7: TRINITY_DN5529_c0_g1_i9 (BLAST accession: XM_024782286.1), Gene 8: TRINITY_DN8631_c0_g1_i1 (BLAST accession: XM_013611166.2), Gene 9: TRINITY_DN1795_c0_g1_i2 (BLAST accession: XM_004514502.3).

Fsp-Induced Differential Gene Expression

A closer look at the 42,905 DECs showed that most of these genes (86.1%) returned positive BLAST hits. The genes were placed into seven broad categories for understanding their potential role in responding to Fsp challenge: (1) Expression of signaling-related genes (Supplementary Table S3), (2) Genes involved in transcriptional regulation (Figure 5 and Supplementary Table S3), (3) PR genes (Figure 6 and Supplementary Table S3), (4) Anthocyanin and lignin biosynthetic pathway genes (Figure 7 and Tables S3, S6), (5) Sugar metabolism (Supplementary Table S3), (6) Phytohormones (Figure 8 and Supplementary Table S3), (7) Cell wall and membrane metabolism and toxin metabolism (Figure 9 and Supplementary Table S3).

FIGURE 5

Figure 5. Differentially expressed transcription factors in tolerant and susceptible pea genotypes in response to Fsp challenge. The color key denotes fold-change. Pairwise comparisons that displayed greater than two-fold difference (p < 0.001) in expression were identified with a color that ranges from light yellow (fold change >2) to dark red (fold change >10).

FIGURE 6

Figure 6. Key differentially expressed Pathogenesis-related contigs in tolerant and susceptible pea genotypes in response to Fsp challenge. The color key denotes fold-change. Pairwise comparisons that displayed greater than two-fold difference (p < 0.001) in expression were identified with a color that ranges from light yellow (fold change >2) to dark red (fold change >10).

FIGURE 7

Figure 7. Heatmap representation of changes in the expression of genes associated with the anthocyanin biosynthetic pathway in tolerant and susceptible pea genotypes after Fsp challenge. (A) A subset of differentially expressed genes involved in the anthocyanin biosynthetic pathway in tolerant and susceptible pea genotypes after the Fsp challenge. The color key denotes fold-change. Pairwise comparisons that displayed greater than two-fold difference (p < 0.001) in expression were identified with a color that ranges from light yellow (fold change >2) to dark red (fold change >10). (B) Anthocyanin biosynthesis pathway (Adapted from Solfanelli et al., 2006). (C) KEGG pathway analysis of metabolic processes related to the anthocyanin biosynthetic pathway. Abbreviations (Abbr.) PAL, phenylalanine ammonia lyase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone-3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; DFR, dihydroflavonol 4-reductase; LDOX, leucoanthocyanidin dioxygenase; UF3GT, UDP glucose-flavonoid 3-o-glucosyltransferase; FLS, Flavonol synthase; F3′5′H, Flavonoid 3′,5′-hydroxylase.

FIGURE 8

Figure 8. Number of DECs associated with the salicylic (SA) and jasmonate/ethylene (JA/ET) biosynthetic and signaling pathway in a pea tolerant and susceptible genotype after Fsp challenge. (A) DECs associated with the salicylic (SA) biosynthetic and signaling pathway. (B) DECs associated with the jasmonate/ethylene (JA/ET) biosynthetic and signaling pathway.

FIGURE 9

Figure 9. Heatmap representation of differentially expressed genes associated with cell wall metabolism, toxin metabolism and transport in tolerant and susceptible pea genotypes in response to Fsp challenge. The color key denotes fold-change. Pairwise comparisons that displayed greater than two-fold difference (p < 0.001) in expression were identified with a color that ranges from light yellow (fold change >2) to dark red (fold change >10).

Discussion

Assembly of Transcriptome Data and Identification of Differentially Expressed Contigs (DECs)

The susceptible genotypes showed a larger number of upregulated DECs that represented a more diverse and higher number of transcriptionally regulated genes, in contrast to the tolerant genotypes. This higher number of upregulated DECs in the susceptible genotypes may be due to involvement of various biological processes associated with successful Fsp infection in pea embryonic tissue. This observation is consistent with previous studies, which showed that pathogen attack engages a broader range of pathways and a larger proportion of genes in the susceptible genotypes compared to the resistant ones (Bagnaresi et al., 2012; Zheng et al., 2013; Matic̃ et al., 2016).

Only 10 (0.10%), 48 (0.52%), and 12 (0.12%) DECs are shared among the tolerant and susceptible genotypes at the 0, 6, and 12-h time points, respectively. Therefore, the response mechanisms involved in the Fsp challenge were divergent between the tolerant and susceptible genotypes. The large difference in overexpressed DECs could explain the difference in tolerance between these sets of genotypes. The genes that were overexpressed in susceptible genotypes were numerically different from the ones in the tolerant genotypes.

Functional Annotation of DECs and GO-Term Enrichment

The GO-term enrichment analysis showed that common terms related to basic plant metabolism, such as gene expression, regulation of primary metabolism, transcription, and protein synthesis were over-represented at the 0-h time point in the tolerant genotypes. Conversely, these terms were upregulated in at least two time points in the susceptible genotypes. Therefore, these results showed the tolerant genotypes may have moved toward a basal metabolic state after the 0-h time point, while the susceptible genotypes were responding to Fsp throughout the entire time course presented in this study.

Gene ontology terms such as ribosome biogenesis, protein export from nucleus, exocytosis, and secretion by cell were over-represented at 0 h in the tolerant genotypes. Plants are known to transport antimicrobial molecules, such as peptides and/or secondary metabolites, outside the cell to function in plant immunity (Kwon and Yun, 2014). In contrast, the susceptible genotypes seemed to be importing substances inside the cell since terms such as receptor-mediated endocytosis and import to the cell were over-represented. Endocytosis seems not only to play a role in pathogen-associated molecular patterns (PAMP)-triggered immunity and effector-triggered immunity but also in susceptibility. Vesicle endocytosis can be manipulated by pathogens and can import pathogen-derived effectors into the plant cell (Driouich et al., 1997; Kwon and Yun, 2014). In this study, while the tolerant genotypes seemed to export antimicrobial molecules potentially to counter Fsp, the susceptible genotypes seemed to import substances. Thus, it is hypothesized that the suppression of exocytosis mechanisms in the susceptible genotypes might block or delay the transport and release of antimicrobial substances against pathogens.

Gene ontology terms in the cell signaling, and response to biotic stress categories also showed an early and unique response at 0 h in the tolerant genotypes, while this response was present throughout the entire experiment (0, 6, and 12 h) or at later stages (6 or 12 h) in susceptible genotypes. Several studies have analyzed how pea responds to Fsp and the non-host pathogen F. solani f. sp. phaseoli (Fsph). These studies concluded that the major difference is the speed at which the pea plants react. The type of response exhibited by pea varies with the rate of induction of PR genes and other associated biochemical pathways. In the case of either Fsph or Fsp infection, the fungus releases DNAses, which localize to the host nuclei and digest the nuclear DNA (Hadwiger and Adams, 1978; Hadwiger, 2008, 2015). Fungal DNAses can also impact the nuclei in the fungal mycelia and trigger their deterioration (Hadwiger, 2008). In the case of a compatible interaction (successful infection leading to disease) between Fsp and pea, the slower reaction rate of the pea host allows Fsp to protect a small number of its nuclei from fungal DNAses. The slower reaction allows the growth of Fsp to resume after 12 h post-inoculation (Klosterman et al., 2001; Hadwiger, 2015). In contrast, the relatively rapid response generated in the host against Fsph terminates the growth of the fungi at 6 h. post-inoculation (Hadwiger, 2008, 2015). Given this information, it is hypothesized the speed of reaction to the pathogen may be one of the mechanisms of tolerance in the tolerant genotypes.

The GO terms associated with salicylic acid and cell death category were, in most cases, overrepresented only in the susceptible genotypes. Therefore, the susceptible genotypes were expected to have a more intense response to Fsp through induced systemic resistance, host programmed cell death, and plant-type hypersensitive response.

The GO terms associated with the production of flavanones, flavones, flavonols, proanthocyanidins, and anthocyanins showed over-representation in both genotypes. In the tolerant genotypes, these terms were only over-represented at 6 h. In the susceptible genotypes, GO terms from this category were overrepresented at the 0, 6, and 12-h time points, indicating a more intense response. GO terms associated with the jasmonate pathway, phytoalexin synthesis, toxin metabolism, and lipid metabolism were overrepresented only in the susceptible genotypes at all time points (Figure 3 and Supplementary Table S5).

Fsp-Induced Differential Gene Expression

Expression of Signaling-Related Genes

Plants detect pathogens via host sensors known as pattern-recognition receptors (PRR), which act by detecting PAMPs and triggering PAMP-triggered immunity (PTI). Beck et al. (2012) identified three PRRs that have been proven to be specific to fungi: the Chitin Elicitor Binding Protein (CEBiP), the chitin elicitor receptor kinase I (CERK1), and the ethylene-inducing xylanase (Eix2). CEBiP and CERK1 cooperatively regulate chitin elicitor signaling to activate plant defense system (Hayafune et al., 2014). Interestingly, CEBiP was not identified in this study. Contigs corresponding to CERK1 and Eix2 genes were identified, however they were not differentially expressed in any of the genotypes.

In this study, several LRR-RLK receptors were found to be differentially expressed in response to Fsp challenge (Supplementary Table S3). Most of these genes were upregulated in the susceptible rather than in the tolerant genotypes. The L-type lectin-domain containing receptor kinase, proline-rich receptor-like protein kinase, cysteine-rich receptor-like protein kinase and the wall-associated receptor kinases are also upregulated in the susceptible genotypes, following the same trend as above. The activity of Mitogen-activated protein kinases (MAPK), MAPK kinases (MAPKK), and MAPKK kinases (MAPKKK) were, in most cases, upregulated in the tolerant and susceptible genotypes after the challenge with Fsp when compared to the control. However, the expression was, in most cases, significantly higher in the susceptible genotypes throughout the time-course when compared to the tolerant genotypes.

Two contigs identified as receptors were found to change significantly in expression after the Fsp challenge in the susceptible genotypes only. Contig DN1290_c0_g1_i9 and Contig DN7023_c0_g2_i5 were identified as a receptor-like cytoplasmic kinase 176 and CC-NBS-LRR resistance protein (Supplementary Table S3), respectively. The receptor-like cytoplasmic kinase 176 acts downstream of the CERK1 gene in the fungal chitin signaling pathways that mediates innate immunity responses such as reactive oxygen species generation, defense gene expression, and callose deposition (Ao et al., 2014). The CC–NBS–LRR proteins initiate a resistance response that often includes a type of cell death known as the hypersensitive response (HR) (Moffett et al., 2002). In the susceptible genotypes, contig DN1290_c0_g1_i9 was significantly overexpressed at 6 h (FC = 3.19) after the Fsp challenge. Expression of Contig DN7023_c0_g2_i5 was found to be lowered at 12 h (FC = −3.59) after the Fsp challenge. Interestingly, no change was observed in the expression of the two contigs in the tolerant genotypes. These two contigs also showed higher expression in the susceptible genotypes when compared to their expression in the tolerant genotypes. Contigs DN1290_c0_g1_i9 and DN7023_c0_g2_i5 were significantly upregulated at 0 (FC = 3.49), 6 (FC = 5.08), and 12 h (FC = 6.77), and 6 h (FC = 71.69), respectively, in the inoculated treatments of the susceptible genotypes when their expression values were compared to the tolerant genotypes. The data on the observed induction of genes coding for receptor-like cytoplasmic kinase 176 and CC-NBS-LRR resistance protein in the susceptible genotypes suggest that the pathogen likely recruits oxygen species generation, hypersensitive response, defense gene expression, and callose deposition to establish infection. It raises a question if loss of function mutation in these genes in the susceptible genotypes could confer tolerance to Fsp. A loss-of-function mutation in receptor-like cytoplasmic kinases and CC-NBS-LRRs genes has proven to confer tolerance to different pathogens (Lorang et al., 2007; Sweat and Wolpert, 2007; Zhang et al., 2019).

Genes Involved in Transcriptional Regulation

The following TFs were found to be differentially expressed between the tolerant and susceptible genotypes and/or were influenced by Fsp challenge: bZIP, ERF, MYB, GATA, MADS-box, NAC, PLATZ, KAN2, PosF21, WRKY, C2H2, bHLH, DIVARICATA, E2F, GLABRA, ICE, IIIB, Jumonji, PIF, RF2a, SRM1, TCP19, TGA, UNE12, and HMG. From this list of TFs, bZIP, ERF, MYB, MADS-box, NAC, WRKY, C2H2, bHLH, E2F, Jumonji, PIF, RF2a, TCP19, TGA, and HMG have been reported to be master regulators of defense responses against pathogens (Pontier et al., 2001; Vailleau et al., 2002; Dong et al., 2003; Pré et al., 2008; Isaac et al., 2009; Wang et al., 2009; Kiełbowicz-Matuk, 2012; Alves et al., 2013; Li et al., 2013; Song et al., 2013; Chandran et al., 2014; Khong et al., 2015; Li, 2015; Paik et al., 2017; Im et al., 2019). However, the involvement of following TFs in response to pathogen challenge has not been reported previously – GATA, PLATZ, KAN2, PosF21, DIVARICATA, GLABRA, ICE, IIIB, SRM1, UNE1.

In the tolerant genotypes, TFs were either not differentially expressed when the control and inoculated samples were compared, or their expression was significantly suppressed after Fsp challenge. In the susceptible genotypes, however, the expression of TFs remained the same or increased after Fsp challenge. When the inoculated treatments in the tolerant and the susceptible genotypes were compared at either time point, TFs were overexpressed in the susceptible genotypes (Figure 5). It is well documented that overexpression of certain TFs causes susceptibility to certain pathogens (Kim et al., 2006; Lai et al., 2008; Thatcher et al., 2012). Some examples include the enhanced susceptibility of the WRKY7-overexpressing Arabidopsis plants to Pseudomonas syringae infection. Overexpression of the AtWRKY4 gene in Arabidopsis enhances susceptibility toward the biotrophic bacterium P. syringae (Lai et al., 2008). In a disease screen with Fusarium oxysporum, it was found that disruption of the LATERAL ORGAN BOUNDARIES (LOB) DOMAIN (LBD) TF led to increased resistance to F. oxysporum root-rot disease in Arabidopsis thaliana.

From the extensive list of differentially expressed TFs identified in this study, only the HMG A has been previously shown to participate during pea-Fsp interaction (Hadwiger, 2008). HMG A has been shown to complex with promoter areas of Disease-resistance response (DRR) genes in pea after Fsp infection (Klosterman et al., 2003). Transient expression experiments implicate the HMG-I/Y abundance in the down-regulation of DRR206 gene expression in pea (Klosterman et al., 2003). In this study, contigs DN4895 and DN12030 were identified as HMG-I/Y genes. Interestingly, the expression of the DN4895 contig decreases significantly at 0 h (FC = −3.59) and 6 h (FC = −2.65) after Fsp challenge in the tolerant genotypes. On the other hand, the expression of this contig increased significantly at 6 h (FC = 3.51) after Fsp challenge in the susceptible genotypes. Furthermore, the expression of contig DN4895 was significantly higher under inoculated conditions at 0 h (FC = 2.56) and 12 h (FC = 7.91) for the susceptible genotypes compared to these expression values in the tolerant genotypes. Contig DN12030 is also significantly overexpressed under inoculated conditions in the susceptible genotypes at 0 h (FC = 3.23) and 12 h (FC = 5.70). It is plausible that HMG-I/Y mediates the reduction of expression of defense-related genes in the susceptible genotypes; however, this assumption will need to be further evaluated.

Pathogenesis-Related (PR) Genes

The inoculation of the tolerant and susceptible genotypes with Fsp generated changes in the expression of PR genes. All the PR protein encoding genes that were identified in this experiment were overexpressed in the susceptible genotypes over the tolerant genotypes except one (Figure 6). The contig DN5959_c0_g1_i3 was identified as a defensin named P. sativum pI230 mRNA (e-value: 0.0, percentage identity: 97.23%). The expression of the DN5959_c0_g1_i3 contig was significantly higher in the tolerant genotypes over the susceptible genotypes under control conditions at 0 (FC = −69.21) and 6 h (FC = −151.78), and under inoculated conditions at 0 (FC = −7.93) and 12 h (FC = −43.5). The pI230 mRNA is the precursor for the DRR230 protein, which is a disease resistance response protein identified previously in P. sativum. DRR230 defensin was first identified in pea pods in response to infection by Fsp (Chiang and Hadwiger, 1991). This defensin was also found to co-localize with a major QTL (mpIII-4) involved in resistance to Mycosphaerella pinodes in pea (Prioul-Gervais et al., 2007). DRR230 gene does not co-localize with any of the major QTLs identified by Coyne et al. (2019).

Disease-resistance response 230 was isolated by Almeida et al. (2000) and characterized as a small cysteine-rich polypeptide. Almeida et al. (2000) also determined that DRR230 is very effective as a fungal growth inhibitor against Aspergillus niger, Aspergillus versicolor, Fsph, and Neurospora crassa. The specific function of DRR230 is not yet known, however plant defensins form a characteristic structure known as the cysteine-stabilized α/β motif, a feature that is also shared by several toxins from insects, scorpions, honeybees, and spider venoms (Hadwiger, 2008). The pea DRR230 was overexpressed in canola, and these plants inhibited were significantly more resistant to Leptosphaeria maculans, a hemibiotrophic fungus (Wang et al., 1999). The transcriptome analysis presented in this study reinforces recent and preceding studies that suggest that DRR230 may play a key role in resistance or tolerance to Fsp induced root rot.

Anthocyanin and Lignin Biosynthetic Pathway Genes

The germplasm utilized in this study consisted of white-flowered lines that present partial tolerance to Fsp. RNAseq analysis showed that the white-flowered lines contain a large set of DECs that participate in the anthocyanin biosynthetic pathway (Figure 7). Genes coding for enzymes in the phenylalanine ammonia lyase (PAL) to chalcone isomerase (CHI) biochemical pathway were upregulated in the inoculated treatments in both the tolerant and susceptible genotypes. Furthermore, some of these enzymes were either overexpressed in the susceptible genotypes or were expressed at a similar level between the tolerant and susceptible genotypes (Figure 7).

The expression patterns were more variable for genes coding for enzymes from the flavanone-3-hydroxylase (F3H) to leucoanthocyanidin dioxygenase (LDOX), as well as for the flavonoid 3′,5′-hydroxylase (F3′5′H), UDP glucose-flavonoid 3-o-glucosyltransferase (UF3GT), and flavonol synthase (FLS) enzymes (Figures 7A,B). Some isoforms of F3H were overexpressed in the susceptible genotypes, but also some other isoforms were overexpressed in the tolerant genotypes. F3H, UF3GT, and FLS were upregulated in the susceptible genotypes, but most or all isoforms of LDOX, and F3′5′ were upregulated in the tolerant genotypes. In the susceptible genotypes, expression of some isoforms of LDOX was suppressed after challenge with Fsp (Figures 7A,B).

Figure 7C also shows the differential expression of genes at each time point, treatment, and genotype in this study. Early steps in the phenylpropanoid pathway such as phenylalanine biosynthesis, phenylalanine metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis, flavone and flavonol biosynthesis are active in the control replicates of the tolerant genotypes at the three times points. In the susceptible controls, fewer, or none of the enzymes were overexpressed. This observation suggests that the tolerant genotypes had a higher level of expression of genes in the phenylpropanoid pathway under the basal conditions and, therefore, it was potentially better prepared to defend against Fsp.

Coyne et al. (2019) reported a significant QTL (Fsp-Ps2.1) that accounts for 44.4 to 53.4% of the phenotypic variance for resistance to Fsp and this QTL shows a confidence interval of 1.2 cM. Fsp-Ps2.1 was mapped within the interval of the pigmented flower/anthocyanin pigmentation gene called as gene A in that study. However, the gene was mapped in a white flower cross. One hypothesis is that the resistance gene(s) may have been linked in the genome with gene A in the pigmented lines. The white-flowered, resistant parent may have been obtained through a linkage break between Fsp-Ps2.1 and A. Alternatively, a metabolite, possibly a colorless one, in the anthocyanin pathway might be the one that provides this resistance. Fine mapping or gene knockouts are necessary to test this hypothesis.

The transcriptome data, generated in this study, was aligned via BLAST against the Fsp-Ps2.1 ± 1.2 cM sequence (Supplementary Table S6). BLAST analysis returned 500 positive blast hits when queried to the transcriptome data. A total of 156 contigs showed differential expression after Fsp challenge or when the tolerant and susceptible genotypes expression values are compared (Supplementary Table S6). A total of 22 of the 156 contigs were annotated as proteins of unknown function or hypothetical proteins. Only the contig TRINITY_DN4823 was identified as a disease-related gene, soyasaponin III rhamnosyltransferase. However, this contig was overexpressed in the susceptible genotypes when compared to the tolerant genotypes. No genes associated with pigmentation were identified in the Fsp-Ps2.1 region during this analysis.

Legumes contain the isoflavone synthase enzyme, which redirects phenylpropanoid pathway intermediates, such as naringenin, to the synthesis of isoflavonoid phytoalexins (Sreevidya et al., 2006). The isoflavonoid phytoalexins are low molecular weight antimicrobial compounds (Smith and Banks, 1986; Jeandet et al., 2014). Pisatin is an extensively studied phytoalexin from pea. In pea, the presence of Fsp increases the production of pisatin (Hadwiger and Beckman, 1980). The 6a-hydroxymaackiain-3-O-methyltransferase, enzyme directly upstream from the synthesis of pisatin, was expressed in both the tolerant and susceptible genotypes but overexpressed only in the susceptible genotypes at 0 h. Degradation of pisatin is an important mechanism by which Fsp resists pisatin and a crucial factor in the pathogenicity of Fsp in pea (Mackintosh et al., 1989). However, based on the results in this and previous studies, pisatin does not seem to play a role in the tolerance to Fsp (Mackintosh et al., 1989; Hadwiger, 2008).

The lignin biosynthetic pathway involves the central phenylpropanoid biosynthetic pathway. Genes involved in the lignin biosynthetic pathway, such as PAL, 4CL, trans-cinnamate 4-monooxygenase (C4M), and caffeoyl-o-methyltransferase (COMT), were overexpressed upon Fsp inoculation in both genotypes but at a significantly higher level and more consistently in the susceptible genotypes. It is well documented that the lignin biosynthetic pathway produces lignin rapidly in response to cell wall structure perturbations (Caño-Delgado et al., 2003; Tronchet et al., 2010; Sattler and Funnell-Harris, 2013; Miedes et al., 2014). Therefore, it seems the susceptible genotypes are responding to the aggressive Fsp invasion with a late and futile effort that involves a higher level of lignin synthesis and deposition.

Sugar Metabolism

This study identified DECs involved in sugar transport such as sugar transporter ERD6-like 6, sugar carrier protein C-like, sucrose transport protein SUC3, sugar transport protein 13, probable alkaline/neutral invertase D, bidirectional sugar transporter SWEET2-like, and invertase inhibitor-like protein. These sugars transporters are upregulated in the susceptible genotypes (Supplementary Table S3).

Certain pathogens are known to modulate the expression and activity of sugar transporters during their interaction with the plant host. Bacterial and fungal pathogens induce the overexpression of different sugar efflux transporters, such as the SWEET genes; this overexpression results in sucrose accumulating in the apoplast for use in pathogen nutritional gain and growth (Chen et al., 2010; Lanubile et al., 2015). The results of this study are in concordance with the literature, suggesting that the active mobilization of sucrose in the Fsp-inoculated susceptible genotypes supported successful infection by Fsp. Of all the DECs identified in the susceptible genotypes, 78% (25 genes) were overexpressed, and only 22% (7 genes) were suppressed after the Fsp challenge. In the tolerant genotypes, 15% (3 genes) were overexpressed, and 85% (17 genes) were suppressed after the Fsp challenge. These data would support the scenario explained previously; Fsp is either manipulating sugar metabolism or taking advantage of the active mobilization of sucrose in the susceptible genotypes.

Phytohormones

A large group of DECs were identified that were involved in the synthesis and signaling of salicylic acid (SA), jasmonic acid (JA), and ethylene (ET). The GO enrichment analysis also showed overrepresented GO terms related to the synthesis and signaling of these three hormones. From this set of DECs, a few were overexpressed in the tolerant genotypes, but the vast majority were overexpressed in the susceptible genotypes after the Fsp challenge.

It is generally assumed that the trophic nature of the pathogen determines which signal transduction pathway (SA or JA/Et) becomes activated in the plant host (Halim et al., 2006). Biotrophic pathogens generally elicit the defense responses via the salicylic acid signaling pathway, while necrotrophs activate a JA-dependent defense response (McDowell and Dangl, 2000; Dangl and Jones, 2001; Thomma et al., 2001; Van Wees et al., 2003; Grant and Lamb, 2006; Halim et al., 2006; Trusov et al., 2009; El Rahman et al., 2012). SA production promotes cell death, and that in turn promotes additional SA production. SA-signaling travels through the plant activating systemic acquired resistance (SAR) (Glazebrook, 2005). Necrotrophic pathogens have been shown to hijack plant defense responses to produce SA to further promote disease development. The necrotrophic fungi, Botrytis cinerea, and Alternaria solani use the SA-signaling pathway to exacerbate the disease in tomato (El Rahman et al., 2012). Both pathogens use the SA-signaling pathway through NPR1, a master regulator of SA signaling, and TGA1a TF to promote disease development in tomato. NPR1 and TGA1a suppress the expression of proteinase inhibitors, which in turn suppress the expression of two JA-dependent defense genes (El Rahman et al., 2012).

In this study, differential expression of genes associated with SA synthesis and signaling, cell death and HR in both the tolerant and susceptible genotypes was observed. However, when comparisons were made between the tolerant and susceptible genotypes, these DECs were observed to be overexpressed in the susceptible genotypes. The contig DN5429 in this study was identified as TGA1.a transcription factor (involved in SA signaling) and its isoform DN5429_c0_g1_i1 was overexpressed 6.82-fold in the susceptible genotypes over the tolerant genotypes. These are important observations, at least at the gene expression level, since the current understanding portends that overexpression of SA-related genes should not be observed in interactions between Fsp and pea, as cell death in plant hosts does not limit pathogen growth (Glazebrook, 2005). SA-dependent responses and SAR are not predicted to play a role in tolerance against Fsp, whereas responses mediated by JA and ET are expected to do so.

The overexpression of genes associated with JA/ET synthesis and signaling also happens in both the tolerant and susceptible genotypes. When comparisons were made between the tolerant and susceptible genotypes, 22 DECs (80% of JA/ET-associated genes) were observed to be overexpressed in the susceptible genotypes. Based on these data, it is difficult to draw conclusions on the effect of SA and/or JA/ET in the response of the tolerant and susceptible genotypes to Fsp. However, a comparison was made using the number of DECs associated with the SA and JA/ET biosynthetic and signaling pathway in the tolerant and susceptible genotypes after the Fsp challenge (Figure 8). The susceptible genotypes showed an upsurge in the overexpression of genes related to SA biosynthesis and signaling (Figure 8). Alternatively, in the tolerant genotypes, the majority of genes related to SA biosynthesis and signaling were suppressed after the Fsp challenge. These changes related to the SA-pathway genes in the susceptible genotypes might have (1) deteriorated the action of the JA-signaling pathway, (2) increased the cell death, and therefore, (3) facilitated successful infection by Fsp. Genetic mapping or gene knockouts are needed to evaluate the involvement of the SA-signaling pathway in the development of root rot disease caused by Fsp in the susceptible genotypes. Some potential targets for gene knockout experiments would be NPR1 and TGA1.a.

Others: Cell Wall and Membrane Metabolism and Toxin Metabolism

Cell wall and membranes play important roles in plant defense as they act as a barrier that prevents pathogen invasion (Vorwerk et al., 2004). In this study, genes related to the cell wall and membrane modification, and callose deposition, were mostly upregulated in the susceptible genotypes (Figure 9). The genes for cell membrane transporters, proteins that work on the detoxification (antiporter activity) of substances, and proteins that break down toxins accumulated in the plant host were also overexpressed in the susceptible genotypes (Figure 9). Interestingly, after the Fsp challenge, the majority of genes associated with cell wall metabolism, toxin metabolism and transport were suppressed in the tolerant genotypes, while they were overexpressed in the susceptible genotypes (Figure 9). These responses, at least at the gene expression level, indicate that the response of the susceptible genotypes was delayed as the pathogen had already infested the tissues, and therefore, the host made a futile effort in response to the pathogen attack. Most likely, the tolerant genotypes already possessed physical and biochemical barriers, and the expression of the genes related to these pathways were actually being suppressed.

Conclusion

The time course RNAseq results presented in this study provided a comprehensive insight into the transcriptomic changes that accompany Fsp infection in tolerant and susceptible P. sativum genotypes. Eight different pea genotypes used in this study represent four most tolerant and four most susceptible genotypes identified in a preceding study based on root disease severity (RDS) index (Bodah et al., 2016). While phenotypically they were either tolerant or susceptible, four genotypes in each category represent different parentage. It is hypothesized that each of the genotypes possesses different tolerance or susceptible alleles. The identification of several key disease-related genes including, DRR230 (Chiang and Hadwiger, 1991), a known Fsp-tolerance gene, is indicative that a complementary approach used in this study could be utilized for other self-pollinating species for the identification of stimulus-responsive genes.

The observed changes in the expression of genes are associated with various physiological and biochemical processes that are known to be involved in plant disease response against pathogens. Fsp challenge produced a more intense and diverse overexpression of genes, across the entire time-course, in the susceptible genotypes compared to the tolerant genotypes. This type of response is hypothesized to be related to the speed at which the pathogen infestations advances in the susceptible genotypes and the preexisting level of disease-preparedness in the tolerant genotypes. The transcriptomic effort demonstrated by the susceptible genotypes seems futile and lacked key specific responses that were present in the tolerant genotypes. In contrast, the tolerant genotypes showed a fine-tuned response: fewer changes in the expression of defense-related genes that helps preserve energy and a faster reset to a basal metabolic state.

This RNAseq analysis helped identify alternate strategies and potential genes that could be evaluated to confer improved tolerance against root rot in P. sativum. Specific genes or pathways that might have a key role in tolerance or susceptibility to Fsp are as follows: receptor-like cytoplasmic kinase 176, CC-NBS-LRR resistance protein, WRKY7 TF, WRKY4 TF, LBD TF, HMG A TF, anthocyanin biosynthetic pathway, SWEET genes, JA/ET-signaling pathway, cell death, NPR1, and TGA1.a. SA-signaling genes, and most importantly, the DRR230 protein. Functional characterization of these genes is expected to provide mechanistic information regarding pea-Fsp interaction, as well as gene targets for breeding. Since Fsp tolerance is a polygenic trait, pyramiding Fsp tolerance into pea cultivars is laborious but remains the most sustainable option, and it can be pursued rapidly via molecular breeding (Bodah et al., 2016) or gene editing (Ghogare et al., 2019). The rapid development of Fsp tolerant pea cultivars is critical for reducing yield losses and address the increasing demand for pea-derived protein for human nutrition.

Data Availability Statement

The raw sequencing data used for RNAseq analysis is publicly available from the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA630497.

Author Contributions

AD, RS, and BW-B designed the study. AD supervised the study. BW-B and EB performed the experiments and generated the data. BW-B and GN analyzed the data. LP provided the tolerant pea genotypes and Fsp isolates. All authors read and approved the final manuscript.

Funding

BW-B acknowledges graduate research assistantship support from Washington State University Graduate School. Work in the Dhingra lab was supported in part by Washington State University Agriculture Research Center Hatch Grant WNP00011 and USA Dry Pea & Lentil Council. The authors acknowledge that this study was in part supported by funding from ProGene Plant Research. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors are grateful to Kurt Braunwart, CEO, ProGene Plant Research for critical discussions and his support for the project. This manuscript has been released as a pre-print at bioRxiv (Willamson-Benavides et al., 2020).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2020.00950/full#supplementary-material

References

Akhtar, M. S., and Azam, T. (2014). Effects of PGPR and antagonistic fungi on the growth, enzyme activity and fusarium root-rot of pea. Arch. Phytopathol. Plant Prot. 47, 138–148. doi: 10.1080/03235408.2013.805487

CrossRef Full Text | Google Scholar

Almeida, M. S., Cabral, K. M. S., Zingali, R. B., and Kurtenbach, E. (2000). Characterization of two novel defense peptides from pea (Pisum sativum) seeds. Arch. Biochem. Biophys. 378, 278–286. doi: 10.1006/abbi.2000.1824

PubMed Abstract | CrossRef Full Text | Google Scholar

Alves, M. S., Dadalto, S. P., Gonçalves, A. B., De Souza, G. B., Barros, V. A., and Fietto, L. G. (2013). Plant bZIP transcription factors responsive to pathogens: a review. Int. J. Mol. Sci. 14, 7815–7828. doi: 10.3390/ijms14047815

PubMed Abstract | CrossRef Full Text | Google Scholar

Ao, Y., Li, Z., Feng, D., Xiong, F., Liu, J., Li, J., et al. (2014). Os CERK 1 and Os RLCK 176 play important roles in peptidoglycan and chitin signaling in rice innate immunity. Plant J. 80, 1072–1084. doi: 10.1111/tpj.12710

PubMed Abstract | CrossRef Full Text | Google Scholar

Bagnaresi, P., Biselli, C., Orrù, L., Urso, S., Crispino, L., Abbruscato, P., et al. (2012). Comparative transcriptome profiling of the early response to Magnaporthe oryzae in durable resistant vs susceptible rice (Oryza sativa L.) genotypes. PLoS One 7:e51609. doi: 10.1371/journal.pone.0051609

PubMed Abstract | CrossRef Full Text | Google Scholar

Beck, M., Heard, W., Mbengue, M., and Robatzek, S. (2012). The INs and OUTs of pattern recognition receptors at the cell surface. Curr. Opin. Plant Biol. 15, 367–374. doi: 10.1016/j.pbi.2012.05.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Bodah, E. T. (2017). Root rot diseases in plants: a review of common causal agents and management strategies. Agric. Res. Tech. Open Access J. 5:555661.

Google Scholar

Bodah, E. T., Porter, L. D., Chaves, B., and Dhingra, A. (2016). Evaluation of pea accessions and commercial cultivars for fusarium root rot resistance. Euphytica 208, 63–72. doi: 10.1007/s10681-015-1545-6

CrossRef Full Text | Google Scholar

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. doi: 10.1093/bioinformatics/btu170

PubMed Abstract | CrossRef Full Text | Google Scholar

Bray, N. L., Pimentel, H., Melsted, P., and Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527. doi: 10.1038/nbt.3519

PubMed Abstract | CrossRef Full Text | Google Scholar

Caño-Delgado, A., Penfield, S., Smith, C., Catley, M., and Bevan, M. (2003). Reduced cellulose synthesis invokes lignification and defense responses in Arabidopsis thaliana. Plant J. 34, 351–362. doi: 10.1046/j.1365-313x.2003.01729.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Chandran, D., Rickert, J., Huang, Y., Steinwand, M. A., Marr, S. K., and Wildermuth, M. C. (2014). Atypical E2F transcriptional repressor DEL1 acts at the intersection of plant growth and immunity by controlling the hormone salicylic acid. Cell Host Microbe 15, 506–513. doi: 10.1016/j.chom.2014.03.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, L.-Q., Hou, B.-H., Lalonde, S., Takanaga, H., Hartung, M. L., Qu, X.-Q., et al. (2010). Sugar transporters for intercellular exchange and nutrition of pathogens. Nature 468, 527–532.

Google Scholar

Chiang, C. C., and Hadwiger, L. A. (1991). The Fusarium solani-induced expression of a pea gene family encoding high cysteine content proteins. Mol. Plant Microbe Interact. 4, 324–331.

Google Scholar

Chittem, K., Mathew, F. M., Gregoire, M., Lamppa, R. S., Chang, Y. W., Markell, S. G., et al. (2015). Identification and characterization of Fusarium spp. associated with root rots of field pea in North Dakota. Eur. J. Plant Pathol. 143, 641–649. doi: 10.1007/s10658-015-0714-8

CrossRef Full Text | Google Scholar

Conesa, A., and Götz, S. (2008). Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int. J. Plant Genomics 2008:619832. doi: 10.1155/2008/619832

PubMed Abstract | CrossRef Full Text | Google Scholar

Coyne, C. J., Pilet−Nayel, M., McGee, R. J., Porter, L. D., Smıkal, P., and Grünwald, N. J. (2015). Identification of QTL controlling high levels of partial resistance to Fusarium solani f. sp. pisi in pea. Plant Breed. 134, 446–453.

Google Scholar

Coyne, C. J., Porter, L. D., Boutet, G., Ma, Y., McGee, R. J., Lesné, A., et al. (2019). Confirmation of Fusarium root rot resistance QTL Fsp-Ps 2.1 of pea under controlled conditions. BMC Plant Biol. 19:98. doi: 10.1186/s12870-019-1699-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Coyne, C. J., Porter, L. D., Inglis, D. A., Grünwald, N. J., McPhee, K. E., and Muehlbauer, F. J. (2008). Registration of W6 26740, W6 26743, and W6 26745 green pea germplasm resistant to fusarium root rot. J. Plant Regist. 2, 137–139. doi: 10.3198/jpr2007.12.0674crg

CrossRef Full Text | Google Scholar

Dangl, J. L., and Jones, J. D. G. (2001). Plant pathogens and integrated defence responses to infection. Nature 411, 826–833. doi: 10.1038/35081161

PubMed Abstract | CrossRef Full Text | Google Scholar

do Carmo, C. S., Nunes, A. N., Silva, I., Maia, C., Poejo, J., Ferreira-Dias, S., et al. (2016). Formulation of pea protein for increased satiety and improved foaming properties. RSC Adv. 6, 6048–6057. doi: 10.1039/c5ra22452g

CrossRef Full Text | Google Scholar

Dong, J., Chen, C., and Chen, Z. (2003). Expression profiles of the Arabidopsis WRKY gene superfamily during plant defense response. Plant Mol. Biol. 51, 21–37.

Google Scholar

Driouich, A., Jauneau, A., and Staehelin, L. A. (1997). 7-Dehydrobrefeldin A, a naturally occurring brefeldin A derivative, inhibits secretion and causes a cis-to-trans breakdown of Golgi stacks in plant cells. Plant Physiol. 113, 487–492. doi: 10.1104/pp.113.2.487

PubMed Abstract | CrossRef Full Text | Google Scholar

Duarte, J., Rivière, N., Baranger, A., Aubert, G., Burstin, J., Cornet, L., et al. (2014). Transcriptome sequencing for high throughput SNP development and genetic mapping in Pea. BMC Genomics 15:126. doi: 10.1186/1471-2164-15-126

PubMed Abstract | CrossRef Full Text | Google Scholar

El Rahman, T. A., El Oirdi, M., Gonzalez-Lamothe, R., and Bouarab, K. (2012). Necrotrophic pathogens use the salicylic acid signaling pathway to promote disease development in tomato. Mol. Plant Microbe Interact. 25, 1584–1593. doi: 10.1094/mpmi-07-12-0187-r

PubMed Abstract | CrossRef Full Text | Google Scholar

Feng, J., Hwang, R., Chang, K. F., Conner, R. L., Hwang, S. F., Strelkov, S. E., et al. (2011). Identification of microsatellite markers linked to quantitative trait loci controlling resistance to Fusarium root rot in field pea. Can. J. Plant Sci. 91, 199–204. doi: 10.4141/CJPS09176

CrossRef Full Text | Google Scholar

Geiser, D. M., del Mar Jiménez-Gasco, M., Kang, S., Makalowska, I., Veeraraghavan, N., Ward, T. J., et al. (2004). FUSARIUM-ID v. 1.0: a DNA sequence database for identifying Fusarium. Eur. J. plant Pathol. 110, 473–479. doi: 10.1007/978-1-4020-2285-2_2

CrossRef Full Text | Google Scholar

Ghogare, R., Williamson-Benavides, B., Ramírez-Torres, F., and Dhingra, A. (2019). CRISPR-associated nucleases: the Dawn of a new age of efficient crop improvement. Transgenic Res. 29, 1–35. doi: 10.1007/s11248-019-00181-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Glazebrook, J. (2005). Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu. Rev. Phytopathol. 43, 205–227. doi: 10.1146/annurev.phyto.43.040204.135923

PubMed Abstract | CrossRef Full Text | Google Scholar

Gossen, B. D., Conner, R. L., Chang, K. F., Pasche, J. S., McLaren, D. L., Henriquez, M. A., et al. (2016). Identifying and managing root rot of pulses on the northern great plains. Plant Dis. 100, 1965–1978. doi: 10.1094/PDIS-02-16-0184-FE

PubMed Abstract | CrossRef Full Text | Google Scholar

Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., et al. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29:644. doi: 10.1038/nbt.1883

PubMed Abstract | CrossRef Full Text | Google Scholar

Grand View Research (2015). Pea Protein Market by Type (Isolates, Concentrates, Textured), Application, Textured Pea Protein by Form (Dry, Wet), & by Region – Global Trends & Forecast to 2020. Pune: Grand View Research.

Google Scholar

Grant, M., and Lamb, C. (2006). Systemic immunity. Curr. Opin. Plant Biol. 9, 414–420.

Google Scholar

Grünwald, N. J., Coffman, V. A., and Kraft, J. M. (2003). Sources of partial resistance to Fusarium root rot in the Pisum core collection. Plant Dis. 87, 1197–1200. doi: 10.1094/pdis.2003.87.10.1197

PubMed Abstract | CrossRef Full Text | Google Scholar

Hadwiger, L. A. (2008). Pea–Fusarium solani interactions contributions of a system toward understanding disease resistance. Phytopathology 98, 372–379. doi: 10.1094/phyto-98-4-0372

PubMed Abstract | CrossRef Full Text | Google Scholar

Hadwiger, L. A. (2015). Anatomy of a nonhost disease resistance response of pea to Fusarium solani: PR gene elicitation via DNase, chitosan and chromatin alterations. Front. Plant Sci. 6:373. doi: 10.3389/fpls.2015.00373

PubMed Abstract | CrossRef Full Text | Google Scholar

Hadwiger, L. A., and Adams, M. J. (1978). Nuclear changes associated with the host-parasite interaction between Fusarium solani and peas. Physiol. Plant Pathol. 12, 63–72. doi: 10.1016/0048-4059(78)90019-x

CrossRef Full Text | Google Scholar

Hadwiger, L. A., and Beckman, J. M. (1980). Chitosan as a component of pea-Fusarium solani interactions. Plant Physiol. 66, 205–211. doi: 10.1104/pp.66.2.205

PubMed Abstract | CrossRef Full Text | Google Scholar

Halim, V. A., Vess, A., Scheel, D., and Rosahl, S. (2006). The role of salicylic acid and jasmonic acid in pathogen defence. Plant Biol. 8, 307–313. doi: 10.1055/s-2006-924025

PubMed Abstract | CrossRef Full Text | Google Scholar

Hayafune, M., Berisio, R., Marchetti, R., Silipo, A., Kayama, M., Desaki, Y., et al. (2014). Chitin-induced activation of immune signaling by the rice receptor CEBiP relies on a unique sandwich-type dimerization. Proc. Natl. Acad. Sci. U.S.A. 111, E404–E413.

Google Scholar

Im, J. H., Lee, S. G., Lee, E., Park, S. R., Ahn, I., and Hwang, D.-J. (2019). OsbZIP75 positively regulates plant defense against the bacterial leaf blight pathogen Xanthomonas oryzae pv. oryzae. Plant Biotechnol. Rep. 13, 645–651. doi: 10.1007/s11816-019-00561-0

CrossRef Full Text | Google Scholar

Isaac, J., Hartney, S. L., Druffel, K., and Hadwiger, L. A. (2009). The non-host disease resistance response in peas; alterations in phosphorylation and ubiquitination of HMG A and histones H2A/H2B. Plant Sci. 177, 439–449. doi: 10.1016/j.plantsci.2009.07.007

CrossRef Full Text | Google Scholar

Jeandet, P., Hébrard, C., Deville, M.-A., Cordelier, S., Dorey, S., Aziz, A., et al. (2014). Deciphering the role of phytoalexins in plant-microorganism interactions and human health. Molecules 19, 18033–18056. doi: 10.3390/molecules191118033

PubMed Abstract | CrossRef Full Text | Google Scholar

Johansson, M. (2019). Pea protein-Systems for Plant Based Protein Products, The Effect of Insoluble and Soluble Lentil Fractions on Rheology, Microstructure and Gelation Properties of Heat Induced Pea Protein Gels. Göteborg: Chalmers tekniska högskola.

Google Scholar

Kendra, D. F., and Hadwiger, L. A. (1984). Characterization of the smallest chitosan oligomer that is maximally antifungal to Fusarium solani and elicits pisatin formation in Pisum sativum. Exp. Mycol. 8, 276–281. doi: 10.1016/0147-5975(84)90013-6

CrossRef Full Text | Google Scholar

Kerr, A. (1963). The root rot-Fusarium wilt complex of peas. Aust. J. Biol. Sci. 16, 55–69.

Google Scholar

Khong, G. N., Pati, P. K., Richaud, F., Parizot, B., Bidzinski, P., Mai, C. D., et al. (2015). OsMADS26 negatively regulates resistance to pathogens and drought tolerance in rice. Plant Physiol. 169, 2935–2949.

Google Scholar

Kiełbowicz-Matuk, A. (2012). Involvement of plant C2H2-type zinc finger transcription factors in stress responses. Plant Sci. 185, 78–85. doi: 10.1016/j.plantsci.2011.11.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, K.-C., Fan, B., and Chen, Z. (2006). Pathogen-induced Arabidopsis WRKY7 is a transcriptional repressor and enhances plant susceptibility to Pseudomonas syringae. Plant Physiol. 142, 1180–1192. doi: 10.1104/pp.106.082487

PubMed Abstract | CrossRef Full Text | Google Scholar

Klosterman, S. J., Chen, J., Choi, J. J., Chinn, E. E., and Hadwiger, L. A. (2001). Characterization of a 20 kDa DNase elicitor from Fusarium solani f. sp. phaseoli and its expression at the onset of induced resistance in Pisum sativum. Mol. Plant Pathol. 2, 147–158. doi: 10.1046/j.1364-3703.2001.00062.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Klosterman, S. J., Choi, J. J., and Hadwiger, L. A. (2003). Analysis of pea HMG−I/Y expression suggests a role in defence gene regulation. Mol. Plant Pathol. 4, 249–258. doi: 10.1046/j.1364-3703.2003.00171.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kraft, J. M. (1989). Registration of 86-638, 86-2197, 86-2231, and 86-2236 PEA Germplasms. Crop Sci. 29, 494–495.

Google Scholar

Kraft, J. M. (1992). Registration of 90−2079−2131, and 90−2322 PEA Germplasms. Crop Sci. 32:1076. doi: 10.2135/cropsci1992.0011183x003200040063x

CrossRef Full Text | Google Scholar

Kraft, J. M., and Giles, R. A. (1976). Registration of 74SN3, 74SN4, and 74SN5 PEA Germplasm1 (Reg. No. GP 15 to GP 17). Crop Sci. 16:126. doi: 10.2135/cropsci1976.0011183x001600010042x

CrossRef Full Text | Google Scholar

Kraft, J. M., and Tuck, J. A. (1986). Registration of 75-786, 84-1638, and 84-1930 Pea Germplasms. Crop Sci. 26, 1262–1263. doi: 10.2135/cropsci1986.0011183x002600060053x

CrossRef Full Text | Google Scholar

Kreplak, J., Madoui, M. A., Cápal, P., Novák, P., Labadie, K., Aubert, G., et al. (2019). A reference genome for pea provides insight into legume genome evolution. Nat. Genet. 51, 1411–1422. doi: 10.1038/s41588-019-0480-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumari, N., and Katoch, S. (2020). “Wilt and root rot complex of important pulse crops: their detection and integrated management,” in Management of Fungal Pathogens in Pulses, eds B. P. Singh, G. Singh, K. Kumar, S. Nayak, and N. Srinivasa (Berlin: Springer), 93–119. doi: 10.1007/978-3-030-35947-8_6

CrossRef Full Text | Google Scholar

Kwon, C., and Yun, H. S. (2014). Plant exocytic secretion of toxic compounds for defense. Toxicol. Res. 30, 77–81. doi: 10.5487/tr.2014.30.2.077

PubMed Abstract | CrossRef Full Text | Google Scholar

Lai, Z., Vinod, K. M., Zheng, Z., Fan, B., and Chen, Z. (2008). Roles of ArabidopsisWRKY3 and WRKY4 transcription factors in plant responses to pathogens. BMC Plant Biol. 8:68. doi: 10.1186/1471-2229-8-68

PubMed Abstract | CrossRef Full Text | Google Scholar

Langmead, B., and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9:357. doi: 10.1038/nmeth.1923

PubMed Abstract | CrossRef Full Text | Google Scholar

Lanubile, A., Muppirala, U. K., Severin, A. J., Marocco, A., and Munkvold, G. P. (2015). Transcriptome profiling of soybean (Glycine max) roots challenged with pathogenic and non-pathogenic isolates of Fusarium oxysporum. BMC Genomics 16:1089. doi: 10.1186/s12864-015-2318-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, S. (2015). The Arabidopsis thaliana TCP transcription factors: a broadening horizon beyond development. Plant Signal. Behav. 10:e1044192. doi: 10.1080/15592324.2015.1044192

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, T., Chen, X., Zhong, X., Zhao, Y., Liu, X., Zhou, S., et al. (2013). Jumonji C domain protein JMJ705-mediated removal of histone H3 lysine 27 trimethylation is involved in defense-related gene activation in rice. Plant Cell 25, 4725–4736. doi: 10.1105/tpc.113.118802

PubMed Abstract | CrossRef Full Text | Google Scholar

Lorang, J. M., Sweat, T. A., and Wolpert, T. J. (2007). Plant disease susceptibility conferred by a “resistance” gene. Proc. Natl. Acad. Sci. U.S.A. 104, 14861–14866. doi: 10.1073/pnas.0702572104

PubMed Abstract | CrossRef Full Text | Google Scholar

Mackintosh, S. F., Matthews, D. E., and VanEtten, H. D. (1989). Two additional genes for pisatin demethylation and their relationship to the pathogenicity of Nectria haematococca on pea. Mol. Plant-Microbe Interact 2, 354–362.

Google Scholar

Marçais, G., and Kingsford, C. (2011). A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770. doi: 10.1093/bioinformatics/btr011

PubMed Abstract | CrossRef Full Text | Google Scholar

Matic̃, S., Bagnaresi, P., Biselli, C., Carneiro, G. A., Siciliano, I., Valé, G., et al. (2016). Comparative transcriptome profiling of resistant and susceptible rice genotypes in response to the seedborne pathogen Fusarium fujikuroi. BMC Genomics 17:608. doi: 10.1186/s12864-016-2925-6

PubMed Abstract | CrossRef Full Text | Google Scholar

McDowell, J. M., and Dangl, J. L. (2000). Signal transduction in the plant immune response. Trends Biochem. Sci. 25, 79–82. doi: 10.1016/s0968-0004(99)01532-7

CrossRef Full Text | Google Scholar

Miedes, E., Vanholme, R., Boerjan, W., and Molina, A. (2014). The role of the secondary cell wall in plant resistance to pathogens. Front. Plant Sci. 5:358. doi: 10.3389/fpls.2014.00358

PubMed Abstract | CrossRef Full Text | Google Scholar

Moffett, P., Farnham, G., Peart, J., and Baulcombe, D. C. (2002). Interaction between domains of a plant NBS–LRR protein in disease resistance−related cell death. EMBO J. 21, 4511–4519. doi: 10.1093/emboj/cdf453

PubMed Abstract | CrossRef Full Text | Google Scholar

Nash, S. M., and Snyder, W. C. (1962). Quantitative estimations by plate counts of propagules of the bean root rot Fusarium in field soils. Phytopathology 52, 567–572.

Google Scholar

National Center for Biotechnology Information (2020). National Library of Medicine (US). Bethesda, MD: National Center for Biotechnology Information.

Google Scholar

Nyamsuren, O., Colditz, F., Rosendahl, S., Tamasloukht, M., Bekel, T., Meyer, F., et al. (2003). Transcriptional profiling of Medicago truncatula roots after infection with Aphanomyces euteiches (oomycota) identifies novel genes upregulated during this pathogenic interaction. Physiol. Mol. Plant Pathol. 63, 17–26. doi: 10.1016/j.pmpp.2003.09.001

CrossRef Full Text | Google Scholar

Paik, I., Kathare, P. K., Kim, J.-I., and Huq, E. (2017). Expanding roles of PIFs in signal integration from multiple processes. Mol. Plant 10, 1035–1046. doi: 10.1016/j.molp.2017.07.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., and Kingsford, C. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14:417. doi: 10.1038/nmeth.4197

PubMed Abstract | CrossRef Full Text | Google Scholar

Peng, W., Kong, X., Chen, Y., Zhang, C., Yang, Y., and Hua, Y. (2016). Effects of heat treatment on the emulsifying properties of pea proteins. Food Hydrocoll. 52, 301–310. doi: 10.1016/j.foodhyd.2015.06.025

CrossRef Full Text | Google Scholar

Pfaffl, M. W. (2001). A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 29:e45.

Google Scholar

Pietrysiak, E., Smith, D. M., Smith, B. M., and Ganjyal, G. M. (2018). Enhanced functionality of pea-rice protein isolate blends through direct steam injection processing. Food Chem. 243, 338–344. doi: 10.1016/j.foodchem.2017.09.132

PubMed Abstract | CrossRef Full Text | Google Scholar

Pontier, D., Miao, Z., and Lam, E. (2001). Trans−dominant suppression of plant TGA factors reveals their negative and positive roles in plant defense responses. Plant J. 27, 529–538. doi: 10.1046/j.1365-313x.2001.01086.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Poore, J., and Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992. doi: 10.1126/science.aaq0216

PubMed Abstract | CrossRef Full Text | Google Scholar

Porter, L. (2010). Identification of tolerance to Fusarium root rot in wild pea germplasm with high levels of partial resistance. Pisum Genet. 42, 1–6.

Google Scholar

Porter, L. D., Kraft, J. M., and Grünwald, N. J. (2014). Release of pea germplasm with Fusarium resistance combined with desirable yield and anti-lodging traits. J. Plant Regist. 8, 191–194. doi: 10.3198/jpr2013.07.0041crg

CrossRef Full Text | Google Scholar

Pré, M., Atallah, M., Champion, A., De Vos, M., Pieterse, C. M. J., and Memelink, J. (2008). The AP2/ERF domain transcription factor ORA59 integrates jasmonic acid and ethylene signals in plant defense. Plant Physiol. 147, 1347–1357. doi: 10.1104/pp.108.117523

PubMed Abstract | CrossRef Full Text | Google Scholar

Prioul-Gervais, S., Deniot, G., Receveur, E. M., Frankewitz, A., Fourmann, M., Rameau, C., et al. (2007). Candidate genes for quantitative resistance to Mycosphaerella pinodes in pea (Pisum sativum L.). Theor. Appl. Genet. 114, 971–984. doi: 10.1007/s00122-006-0492-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Research and Markets (2019). Plant Based Protein Market by Type (Soy Protein, Wheat Protein, Pea Protein, Potato Protein, Rice Protein, Corn Protein) and Application (Foods and Beverage, Animal Feed, Nutrition and Health Supplements, Pharmaceuticals) - Global Forecast to 2025. Dublin: Research and Markets.

Google Scholar

Rozen, S., and Skaletsky, H. (2000). “Primer3 on the WWW for general users and for biologist programmers,” in Bioinformatics Methods and Protocols, eds S. Misener and S. A. Krawetz (Berlin: Springer), 365–386. doi: 10.1385/1-59259-192-2:365

CrossRef Full Text | Google Scholar

Ruijter, J. M., Van der Velden, S., and Ilgun, A. (2009). LinReg PCR (11.0). Analysis of Quantitative RT-PCR Data. Amsterdam: Heart Failure Research Center.

Google Scholar

Sattler, S., and Funnell-Harris, D. (2013). Modifying lignin to improve bioenergy feedstocks: strengthening the barrier against pathogens? Front. Plant Sci. 4:70. doi: 10.3389/fpls.2013.00070

PubMed Abstract | CrossRef Full Text | Google Scholar

Seaman, W. L. (1976). Yield loss conversion factors for fusarium root rot of peal. Can. Plant Dis. Surv. 56, 25–32.

Google Scholar

Smith, D. A., and Banks, S. W. (1986). Biosynthesis, elicitation and biological activity of isoflavonoid phytoalexins. Phytochemistry 25, 979–995. doi: 10.1016/s0031-9422(00)81542-3

CrossRef Full Text | Google Scholar

Solfanelli, C., Poggi, A., Loreti, E., Alpi, A., and Perata, P. (2006). Sucrose-specific induction of the anthocyanin biosynthetic pathway in Arabidopsis. Plant Physiol. 140, 637–646. doi: 10.1104/pp.105.072579

PubMed Abstract | CrossRef Full Text | Google Scholar

Song, S., Qi, T., Fan, M., Zhang, X., Gao, H., Huang, H., et al. (2013). The bHLH subgroup IIId factors negatively regulate jasmonate-mediated plant defense and development. PLoS Genet. 9:e1003653. doi: 10.1371/journal.pgen.1003653

PubMed Abstract | CrossRef Full Text | Google Scholar

Sreevidya, V. S., Srinivasa Rao, C., Sullia, S. B., Ladha, J. K., and Reddy, P. M. (2006). Metabolic engineering of rice with soybean isoflavone synthase for promoting nodulation gene expression in rhizobia. J. Exp. Bot. 57, 1957–1969. doi: 10.1093/jxb/erj143

PubMed Abstract | CrossRef Full Text | Google Scholar

Stehfest, E., Bouwman, L., Van Vuuren, D. P., Den Elzen, M. G. J., Eickhout, B., and Kabat, P. (2009). Climate benefits of changing diet. Clim. Change 95, 83–102. doi: 10.1007/s10584-008-9534-6

CrossRef Full Text | Google Scholar

Sweat, T. A., and Wolpert, T. J. (2007). Thioredoxin h5 is required for victorin sensitivity mediated by a CC-NBS-LRR gene in Arabidopsis. Plant Cell 19, 673–687. doi: 10.1105/tpc.106.047563

PubMed Abstract | CrossRef Full Text | Google Scholar

Thatcher, L. F., Powell, J. J., Aitken, E. A. B., Kazan, K., and Manners, J. M. (2012). The lateral organ boundaries domain transcription factor LBD20 functions in Fusarium wilt susceptibility and jasmonate signaling in Arabidopsis. Plant Physiol. 160, 407–418. doi: 10.1104/pp.112.199067

PubMed Abstract | CrossRef Full Text | Google Scholar

Thomma, B. P. H. J., Penninckx, I. A. M. A., Cammue, B. P. A., and Broekaert, W. F. (2001). The complexity of disease signaling in Arabidopsis. Curr. Opin. Immunol. 13, 63–68. doi: 10.1016/s0952-7915(00)00183-7

CrossRef Full Text | Google Scholar

Tronchet, M., Balague, C., Kroj, T., Jouanin, L., and Roby, D. (2010). Cinnamyl alcohol dehydrogenases−C and D, key enzymes in lignin biosynthesis, play an essential role in disease resistance in Arabidopsis. Mol. Plant Pathol. 11, 83–92. doi: 10.1111/j.1364-3703.2009.00578.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Trusov, Y., Sewelam, N., Rookes, J. E., Kunkel, M., Nowak, E., Schenk, P. M., et al. (2009). Heterotrimeric G proteins−mediated resistance to necrotrophic pathogens includes mechanisms independent of salicylic acid−, jasmonic acid/ethylene−and abscisic acid−mediated defense signaling. Plant J. 58, 69–81. doi: 10.1111/j.1365-313x.2008.03755.x

PubMed Abstract | CrossRef Full Text | Google Scholar

USDA–ARS NGRP (2020). Germplasm Resources Information Network- (GRIN). Washington, DC: USDA–ARS NGRP.

Google Scholar

Vailleau, F., Daniel, X., Tronchet, M., Montillet, J.-L., Triantaphylides, C., and Roby, D. (2002). A R2R3-MYB gene, AtMYB30, acts as a positive regulator of the hypersensitive cell death program in plants in response to pathogen attack. Proc. Natl. Acad. Sci. U.S.A. 99, 10179–10184. doi: 10.1073/pnas.152047199

PubMed Abstract | CrossRef Full Text | Google Scholar

Van Wees, S. C. M., Chang, H.-S., Zhu, T., and Glazebrook, J. (2003). Characterization of the early response of Arabidopsis to Alternaria brassicicola infection using expression profiling. Plant Physiol. 132, 606–617. doi: 10.1104/pp.103.022186

PubMed Abstract | CrossRef Full Text | Google Scholar

Vandemark, G. J., Brick, M. A., Osorno, J. M., Kelly, J. D., and Urrea, C. A. (2014). Edible Grain Legumes. Yield Gains Major US F. Crop. Scottsbluff, NE: University of Nebraska Panhandle Research & Extension Center, 87–124.

Google Scholar

Vorwerk, S., Somerville, S., and Somerville, C. (2004). The role of plant cell wall polysaccharide composition in disease resistance. Trends Plant Sci. 9, 203–209. doi: 10.1016/j.tplants.2004.02.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, X., Basnayake, B. M. V. S., Zhang, H., Li, G., Li, W., Virk, N., et al. (2009). The Arabidopsis ATAF1, a NAC transcription factor, is a negative regulator of defense responses against necrotrophic fungal and bacterial pathogens. Mol. Plant-Microbe Interact. 22, 1227–1238. doi: 10.1094/mpmi-22-10-1227

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., Nowak, G., Culley, D., Hadwiger, L. A., and Fristensky, B. (1999). Constitutive expression of pea defense gene DRR206 confers resistance to blackleg (Leptosphaeria maculans) disease in transgenic canola (Brassica napus). Mol. Plant Microbe Interact. 12, 410–418. doi: 10.1094/mpmi.1999.12.5.410

CrossRef Full Text | Google Scholar

Willamson-Benavides, B. A., Sharpe, R. M., Nelson, G. M., Bodah, E. T., Porter, L. D., and Dhingra, A. (2020). Identification of Fusarium solani f. sp. pisi (Fsp) responsive genes in Pisum sativum. bioRxiv[Preprint] doi: 10.1101/2020.05.12.091892

CrossRef Full Text | Google Scholar

World Health Organization (2015). Obesity and overweight. Geneva: WHO.

Google Scholar

Xiong, T., Xiong, W., Ge, M., Xia, J., Li, B., and Chen, Y. (2018). Effect of high intensity ultrasound on structure and foaming properties of pea protein isolate. Food Res. Int. 109, 260–267. doi: 10.1016/j.foodres.2018.04.044

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, L., Ravnskov, S., Larsen, J., and Nicolaisen, M. (2012). Linking fungal communities in roots, rhizosphere, and soil to the health status of Pisum sativum. FEMS Microbiol. Ecol. 82, 736–745. doi: 10.1111/j.1574-6941.2012.01445.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, N., O’Donnell, K., Sutton, D. A., Nalim, F. A., Summerbell, R. C., Padhye, A. A., et al. (2006). Members of the Fusarium solani species complex that cause infections in both humans and plants are common in the environment. J. Clin. Microbiol. 44, 2186–2190. doi: 10.1128/jcm.00120-06

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Y., Liu, Q., Zhang, Y., Chen, Y., Yu, N., Cao, Y., et al. (2019). LMM24 Encodes Receptor-Like Cytoplasmic Kinase 109, Which Regulates Cell Death and Defense Responses in Rice. Int. J. Mol. Sci. 20, 3243. doi: 10.3390/ijms20133243

PubMed Abstract | CrossRef Full Text | Google Scholar

Zheng, W., Ma, L., Zhao, J., Li, Z., Sun, F., and Lu, X. (2013). Comparative transcriptome analysis of two rice varieties in response to rice stripe virus and small brown planthoppers during early interaction. PLoS One 8:e82126. doi: 10.1371/journal.pone.0082126

PubMed Abstract | CrossRef Full Text | Google Scholar