Introduction

In response to different disease pressures, plants have evolved intricate recognition and signal transduction systems to ward off pathogens. On the leaf surface, plants have different layers of waxes, hairs or trichomes, and a cell wall that act as physical barriers against non-adapted pathogens. At the cell surface, the presence of the pathogen is first recognized by receptor like kinases (RLKs) and receptor like proteins (RLPs) which function as pattern recognition receptors (PRRs) in interactions called pathogen/microbe-associated molecular patterns (PAMP/MAMP) to activate a pattern-triggered immunity (PTI) response [1]. Non-adaptive pathogens are usually stopped from entering plant cells at this point. Adapted pathogens can penetrate the cells to release pathogenic effector proteins and activate resistance (R) proteins of the host in a second line of defense, called effector triggered immunity (ETI) response [2]. In both PTI and ETI, plant activate an array of immune responses such as Ca2+ spike, reactive oxygen species (ROS) burst, MAP kinase (MAPK) activation, production of phytohormones, and modulation in transcriptional regulation [3].

Resistance (R) or effector–receptor gene candidates have been associated with plant disease resistance and have been identified in important crop plants based on conserved DNA motifs through genome sequencing and homologous gene cloning [4,5,6]. There are seven conserved motifs or domains: Toll/interleukin-1 receptor (TIR), leucine zipper (LZ), coiled–coiled (CC), nucleotide-binding site (NBS), leucine-rich repeat (LRR), transmembrane (TM), and serine-threonine kinase (STK) which can be broadly categorized into five main classes: TIR–NBS–LRR (TNL), CC–NBS–LRR (CNL), RLK, RLP, and other variations [2].

Peanut (Arachis hypogaea L.) is an important source of proteins, vitamins, and oil. It is grown in many parts of the world, with China and India as leading producers followed by Nigeria and USA [7]. Peanut is challenged by diseases, especially foliar diseases that have worldwide impact on yield and quality. Early leaf spot (ELS) caused by Cercospora arachidicola (Hori) and late leaf spot (LLS) caused by Phaeoisariopsis personata (Berk. & M.A. Curtis) are important foliar fungal diseases that can cause complete defoliation and significantly reduce plant productivity. A combination of cultural practices such as crop rotation, proper management of residue by tillage practices [8], weather predictive models for disease outbreak [9, 10], and proper irrigation can minimize plant diseases. Application of fungicide can effectively control these diseases [11] but can be costly and maybe prohibited to subsistence peanut growing areas. Development of resistant peanut cultivars would be a sustainable solution for many parts of the world.

Because of the polyploidy nature of the cultivated peanut and the low DNA marker polymorphisms, progress in the application of marker-assisted plant breeding has been difficult. A large number (> 10,000) of simple sequence repeat (SSR) potential markers are available, but < 7% are polymorphic among cultivated peanuts [12]. Validated marker-trait associations for nematode resistance and high oleic chemistry have been applicable in breeding programs [13, 14]. Recent research utilizing a recombinant inbred line (RIL) population that segregated for quantitative field resistance to LLS identified several quantitative trait loci (QTLs) [15]. Even with the discovery of a few candidate gene markers, application of marker-trait association continues to be a challenge since field performance evaluation, or phenotyping, can be significantly variable based on year, location, or environmental differences. Furthermore, defense responses and disease resistance (R)-gene activation have a fitness cost which can reduce plant growth and production [16]. In nature, plants select the ‘perfect’ combination of genes and coordinate gene-regulatory patterns necessary to ensure survival and productivity [17].

Recently, significant progress has been made in peanut genomics research which culminated in the sequencing of the two closest diploid peanut progenitors, Arachis duranensis and Arachis ipaensis [18], and the cultivated allotetraploid peanut (A. hypogaea) is now available through PeanutBase.org. Through a concerted genomics effort, there are currently 281,451 ESTs and a composite of 50,777 transcriptome shotgun assemblies (TSAs) archived on NCBI database. A first generation (58K) and a second generation (48K) single nucleotide polymorphism (SNP) chips have been utilized in different gene-expression experiments [19, 20]. These resources, as well as other bioinformatic projects in peanuts, provide a tremendous platform to identify functional genes that can lead to the development of disease resistant peanut varieties and was utilized to identify resistance gene analogs (RGAs), as resistance (R)-genes, associated with ELS and LLS. In peanuts, 78 RGAs were identified from peanut cultivar ‘Tahu’ and four diploid species [21] and later Yuksel et al. [22] identified 234 RGAs from cultivars ‘Florunner’ and ‘UF-439-16-1003-2’ from genomic DNA. Liu et al. [23] integrated previously identified RGAs with new EST sequences available at the time and derived 385 putative RGAs (156 contigs and 229 singletons).

The goals of this research are to identify and clone expressed R-gene candidates in peanut plants challenged with ELS and LLS pathogens and to associate these sequences with molecular pathways that may be used as disease resistant gene markers for peanut variety development. Gene-expression profiling of transcribed R-gene candidates in peanuts challenged with diseases provide a more comprehensive picture of disease resistance gene-regulation network and facilitate future peanut breeding.

Materials and methods

Identification of R-genes through database search

RGA sequences were utilized from different groups [21,22,23]. All subsequent sequence nomenclatures are loosely assigned RGAs, or R-genes, to include all five major classes [2]. Sequence analyses were performed using Sequencher DNA analysis software (Gene Codes, Ann Arbor, MI, USA). Unique sequences with potential open reading frame (ORF) and with low E-value in BLASTx search (NCBI) results were selected for analysis. Sequences were searched against all Arachis EST and TSA NCBI databases (Online Resource 1). Sequences of each EST and TSA were downloaded and re-assembled to verify uniformity of each alignment and to obtain longer ORFs. Newly assembled sequences were evaluated to ensure the presence of an ORF and returned a significant BLASTx and HMMER (EMBL-EBI) matches to proteins with R-gene motifs [2]. Sequences that did not have ORFs and did not match to potential R-genes were not evaluated further.

Peanut genotypes and plant treatment

Two peanut varieties (Flavorrunner (FR) 458 and Georgia (Ga) 12Y), a breeding line (Exp27-1516), and a PI 268868 were evaluated. FR458, released in 1996, is a runner-type peanut that is highly susceptible to most peanut diseases and is utilized as a susceptible check to tomato spotted wilt (TSW), caused by Tomato spotted wilt virus [24]. Ga12Y, released in 2012, is also a runner-type peanut with resistance to TSWV and white mold or stem rot (caused by Sclerotium rolfsii Sacc.) [25]. Exp27-1516, a runner-type with medium resistance to ELS and LLS resistance and highly resistant to TSWV, was provided by Dr. Charles Chen (Auburn University) through a USDA/Auburn joint breeding program. PI 268868, a Virginia-type peanut with observed field resistance to ELS and LLS, was kindly provided by the USDA peanut germplasm repository in Griffin, GA. Seeds were planted at the rate of 6 seeds per 1 m row, with 6 row replicates randomly distributed in a 5.5 × 12 m plastic house with screens on the sides for open air. Best agricultural plant treatment was utilized and no fungicides were applied throughout the growing season.

Leaf spot disease evaluation and sample collection

Visual assessment of leaf spot disease severity was based on a Florida 1–10 scale where 1 represents no disease or visual symptoms and 10 is complete leaf defoliation [26]. Leaf spot symptoms were assessed at three dates near the end of the growing season (107, 114 and 121 days after planting, DAP) (Fig. 1). Leaf samples were collected at 121 DAP (3rd assessment), following the disease rating, for RNA analysis. This developmental stage represents late-season leaf spot infection, culminating to severe plant disease response and correlates to significant yield losses without fungicide applications. Fully expanded leaves were collected from a prominent stem from four randomly selected plants in 1 m linear row. Round punches (2 cm) of each leaf from four plants were pooled, placed into a 2 mL tube, frozen and stored at − 80 °C until processed.

Fig. 1
figure 1

Leaf spot progressive disease ratings, comparing four peanut genotypes near the end of the peanut growing season (top). These evaluations were based on a Florida scale from 1 to 10 with 1 = no symptom and 10 = complete defoliation. Picture of FR458 (susceptible) and Ga12Y (tolerant) to late-season leaf spot disease at 107 DAP (bottom)

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RNA extraction, cDNA synthesis and PCR product sequencing

Total RNAs from fresh-frozen peanut leaves were extracted utilizing TRIzol Reagent (Ambion, Austin, TX, USA) according to manufacturer’s instruction. RNA was quantified using Nanodrop 2000 spectrophotometer (ThermoFisher Sci. Waltham, MA, USA) and quality was determined based on agarose gel electrophoresis analysis. RNA was DNase-treated with Turbo DNA-free (Ambion) prior to cDNA synthesis. 1 μg total RNA was used as template and cDNAs were produced according to Dang et al. [27]. cDNAs were diluted 1:10 with sterile water and used as template in standard PCR reaction. Primers were designed using Clone Manager (Sci-Ed Software, Denver, CO, USA) to obtain the largest ORF sequence possible for each predicted RGA (Online Resource 2). The 20 μL PCR reaction consisted of 3 μL of diluted cDNAs, 10 μL GoTaq Green Master mix (Promega, Madison, WI, USA) and 0.4 μM of each primer, with cycling conditions of 2 min at 94 °C to completely denature cDNAs, followed by 40 cycles of 20 s at 94 °C, 20 s at 55 °C and 50 s at 72 °C, and a final cycle 10 min at 72 °C to produce complete PCR products. PCR products were resolved on 1% TAE gel-electrophoresis, single bands at the predicted molecular weight were isolated and purified utilizing QIAquick Gel Extraction Kit (Qiagen, Valencia, CA, USA), and 80 ng of purified-PCR products were sent for dideoxy-chain termination method sequencing (Eurofins MWG Operon, Louisville, KY, USA) with the forward or reverse specific primer.

Cloning of PCR products, plasmid isolation and sequencing

Gel purified PCR products (50 ng) were cloned using StrataClone PCR Cloning Kit (Stratagene, San Diego, CA, USA). Single bacteria-colonies were selected and grown overnight at 37 °C with shaking with ampicillin antibiotic selection. Plasmids were extracted using QIAprep Spin Miniprep kit (Qiagen) and purified plasmids (300 ng) were sequenced (Eurofins) with T3 or T7 promoter sequencing primers.

Quantitative (q) RT-PCR

Diluted cDNAs were used as template in real-time fluorescence qRT-PCR with specific gene primers (Online Resource 3). Data was generated on QuantStudio7 Flex real-time PCR system (ThermoFisher Sci. Waltham, MA, USA) utilizing relative quantitation method as described by manufacturer. The 20 μL reaction consisted of 3 μL of diluted cDNAs, 10 μL PowerUp SYBR green master mix (ThermoFisher Sci.) and 0.4 mM of each primer, with PCR cycling conditions of 2 min at 50 °C, 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C and 1 min at 58 °C, and a dissociation curve analysis cycle of 15 s at 95 °C, 20 s at 58 °C and 15 s at 95 °C. The threshold cycle (Ct) was automatically calculated by QuantStudio Real-Time PCR software (ThermoFisher Sci.) and relative expression was calculated based on \({2^ - }^{{\Delta \Delta {\text{Ct}}}}\) described by Livak and Schmittgen [28]. All samples were first normalized to Actin (EZ723877) as an internal control then transformed data were normalized with FR458 \({2^ - }^{{\Delta \Delta {\text{Ct}}}}\) values and compared with the other three peanut genotypes to determine relative fold changes in gene-expression.

Results

Identification of potential R-genes

From over 400 initial R-gene candidate sequence targets, 381 were observed to have ORFs and matched BLASTx to proteins with R-gene motifs. These sequence sizes ranged from 404 to 3582 bp. Primers were designed to cover a large segment of predicted ORFs. Reverse-transcribed PCR analysis, utilizing RNA from leaf spot infected leaves, identified 241 primer-pairs that resulted in PCR products on agarose gel-electrophoresis. PCR products were purified and sequenced, resulting in a total of 214 RGA transcripts that produced ORFs and matched to an R-gene motif in BLASTx and HMMER searches (Table 1).

Table 1 Identification of conserved R-gene motifs by HMMER and BLASTX searches and the associated protein functions
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Conservation of R-genes in coding region

SNPs were observed and reported for each R-gene (Table 2). From the 214 candidate RGAs, 172 produced observable PCR bands in four peanut genotypes and these products were cloned and sequenced. When sequencing results were compared, 86 RGAs had 0 SNP and 86 had between 1 and 16 SNPs in their respected DNA sizes (232–1776 bp). From the same set, 107 were identified to be single copy genes when electronically mapped to A. duranensis or A. ipaensis genomes, 64 had 2–5 allelic variants, and 2 had 7–10 variants.

Table 2 RGAs associated with the numbers of SNPs and the predicted allelic variants observed in A. duranensis (D) and A. ipaensis (I) diploid genomes
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Discovery of insertions/deletions (indels)

From the 214 RGAs identified, four indels were discovered through PCR product cloning. RGA 14a has a 1074 bp length with a 6 bp indel. Blastx search matched to a serine/threonine kinase HT1-like protein. RGA108, 348 bp in length containing a 9 bp indel, codes for a TMV resistance N-like protein. RGA188, 378 bp in length with a 3 bp indel, codes for a receptor-like protein 12. RGA322a, 1369 bp in length with a 3 bp indel, codes for a receptor-like protein kinase 5. These RGAs are all inframe indels that has potential add function to native transcripts.

Mapping R-genes to peanut diploid genomes

These sequences were searched using Blastn algorithm in NCBI database selecting Arachis as search organism. Verified sequencing transcripts were electronically mapped to A. duranensis or A. ipaensis genomes using Blastn algorithm utilizing NCBI nucleotide database (Table 3). Eighteen RGAs were mapped to A. duranensis (chromosome A01) and only 14 of the same RGAs mapped to A. ipaensis (B01), with 4 RGAs mapped to different A. ipaensis chromosomes. Thirteen RGAs mapped to A. duranensis (A02) and same RGAs were also mapped to A. ipaensis (B02), with an additional RGA108 mapped only to A. ipaensis. On chromosome 3, 34 RGAs were mapped to A. duranensis (A03) while only 32 mapped to A. ipaensis (B03). Twelve RGAs mapped to both A. duranensis (A04) and A. ipaensis (B04), with RGA 34 mapped only to A. duranensis (A04). RGAs 123, 265 and 293 mapped only to A. ipaensis (B04). On A. duranensis (A05) and A. ipaensis genomes (B05), 32 RGAs were mapped to both diploid chromosomes with an additional RGA 99 only present on A. duranensis genome (A05). Ten RGAs were mapped to both A. duranensis (A06) and A. ipaensis (B06) chromosomes, with an additional two RGAs 202 and 216 present on A. ipaensis genome (B06). Out of the 25 RGAs mapped to either A. duranensis (A07) or A. ipaensis (B07) chromosomes, only 11 were present on both diploid chromosomes. RGAs 91b, 170, 198, 202, 341 and 374 were mapped only to A. duranensis (A07), while RGAs 27, 31, 36, 146, 148, 215, 314 and 369 mapped to A. ipaensis (B07) but were present on different A. duranensis chromosomes. Out of the 26 mapped to both A. duranensis (A08) and A. ipaensis (B08) chromosomes, 14 RGAs were represented in both. RGAs 3, 31, 36, 215, 314, 315, 366 and 369 were present on A. duranensis genome (A08) while same RGAs were on different A. ipaensis chromosomes. Fourteen RGAs were present on both A. duranensis (A09) and A. ipaensis (B09) chromosomes, while RGAs 91b and 170 were on A. ipaensis (B09) but on different A. duranensis chromosomes. RGA208 was only mapped to A. duranensis (A09) and not present on any A. ipaensis chromosomes. Out of the 25 RGAs represented on A. duranensis (A10) and A. ipaensis (B10) chromosomes, 16 RGAs were mapped on both diploid chromosomes. RGAs 27 and 148 were mapped to A. duranensis (A10) but on different A. ipaensis chromosomes. RGAs 3, 28, 54, 141, 366 and 374 were present on A. ipaensis (B10) but on different A. duranensis chromosomes. RGA165 was present on A. ipaensis genome (B10) but absent from any A. duranensis chromosomes.

Table 3 Electronic mapping and placement of RGAs on A. duranensis and A. ipaensis chromosomes
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Relative gene-expression and correlation to leaf spot resistance

Real time qPCR primers were designed and tested for efficiency (Online Resource 3). From the 89 RGAs that were evaluated for qRT-PCR, 39 were up-regulated and 38 were down-regulated (Fig. 2a, b). The remaining 12 were both up- and down-regulated (mix) among the 4 peanut genotypes tested. From the 39 up-regulated genes, 28 were identified as RLKs, 4 were RLPs, and 7 were TNLs. From the 38 down-regulated genes, 28 were RLKs, 5 were RLPs, and 5 were TNLs. From the remaining 12 genes, 10 were RLKs, 1 was an RLP, and 1 was a TNL. From the all 13 TNLs, 12 were associated with TMV resistance protein N-like and 1 code for a disease resistance RPPP13-like protein. When leaf spot susceptible peanut variety (FR458) was compared to the other 3 (more tolerant) peanut genotypes, 32 R-genes were positively correlated (labeled in italic) and 32 R-genes were negatively correlated (labeled in bold) with gene-expression levels (Table 1). These 64 candidate genes are potential gene-expression markers that can be utilized to select leaf spot resistance in peanut breeding programs.

Fig. 2
figure 2

a, b Relative gene-expression levels of RGAs. All samples were first normalized to Actin (EZ723877) as an internal control then transformed data were normalized with FR458 and compared to the other peanut genotypes to obtain relative gene-expression levels

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Discussions

Plants are challenged with adverse biotic and abiotic pressures which require constant monitoring and modulating protective mechanisms, yet maintaining high productivity. For example, plants grown in high disease environments would invest more energy to maintain a “ready” state or be on a constant induction of disease responsive genes. RGAs, as R-genes, are essential in the plant immune system and are not well characterized in peanuts. From that aspect, a systematic approach was utilized to identify and sequence expressed R-genes in response to ELS and LLS pathogens.

Lately, there has been progress on the introgression and the identification of QTLs associated with ELS and LLS resistance [15, 29,30,31,32]. Because of a high number of QTLs and strong G × E interactions, predicting consistent disease resistance traits across different peanut genotypes is difficult. Identification of expressed R-genes in cultivated peanuts may help to further ascertain gene-expression patterns that may better correlate genetic backgrounds as well incorporating environmental (biotic and abiotic) responses that will result in leaf spot resistance. Because of the conserved sequence domains, homologous sequence cloning as well as bioinformatics approach have identified a high number of potential peanut RGAs [21,22,23]. The resulting number of potential R-genes is a representative of the search databases and included 205,442 ESTs from A. hypogaea (allotetraploid) and other diploids such as 35,291 from A. duranensis, 32,787 from A. ipaensis, 6264 from A. stenosperma, 750 from A. magna, 400 from A. appressipila, and 280 from A. Arabica, 75 from A. diogoi (NCBI EST database, August, 2018). R-gene conversion is correlated with sequence identity, close physical proximity on the chromosome, gene orientation, and recombination rate [33]. From the point of view that cultivated peanut (A. hypogaea) came from two closest diploid progenitors (A. duranensis and A. ipaensis), the number of R-genes from cultivated peanuts may be similar to the diploids. Indeed from the peanut diploid sequencing projects, 345 and 397 R-gene candidates were identified in A. duranensis and A. ipaensis genotypes, respectively [18], which closely approximate the number of identified candidate R-genes (381) in cultivated peanuts across different taxon. Mapping R-genes from cultivated peanuts onto diploid genomes (A and B) revealed that chromosomes 1–6 and 9 have similar set of genes. Chromosomes 7, 8, and 10 showed significant divergent between A and B genomes in this study, verifying what was observed by Bertioli et al. [18] that chromosomes 7 and 8 have undergone complex rearrangements in DNA segment exchange. The importance of identification of disease resistance gene through evaluation of R-genes will complement molecular mapping of different peanut genomes.

Out of the 381 potential R-gene candidates, 214 were identified and sequenced. From these, 72 (34%) were identified as RLKs and 25 (12%) as RLPs. RLKs and RLPs are PRRs that can interact in PAMP/MAMP to initiate signal transduction to elevate plant immunity response. The two molecules are structurally similar with a signal peptide at the N-terminus, extracellular domains to perceive the pathogen/microbial pattern as LRRs, and transmembrane to anchor RLK and RLP in the plasma membrane [2]. In contrast to RLKs, RLPs lack an intracellular kinase domain and do not independently transduce perceived signal downstream. Notable examples of are Flg22 and EFR, bacterial PAMPs for flagellin and elongation factor Tu (EF-Tu) sensing, that recruit FLS2 and BAK1 to activate kinase signaling cascade to initiate plant immune response [34, 35]. Receptor recognition of DNA, lipoproteins, peptidoglycans, and fungal chitin are also involved [36]. Plant resistance utilizing PRR activation has been thought to provide broad-spectrum resistance, but has not had much attention in breeding for disease resistance. In this study, 9 lectin-binding RLKs were identified (3 G-type and 6 L-type). In Arabidopsis, one of the largest class of RLKs are the L-type lectin receptor kinases (LecRKs) [37], and transgenic tobacco plants over-expressing Arabidopsis lectin receptor kinase gene (LecRK-1.9 or LecRK-1X.1) show enhanced resistance to Phytophthora pathogens [38].

Another class designated as “other” variations included 85 (40%) with STK domain. These included RGAs 016a and 222 that code for a pto-interacting protein 1-like; and RGAs 091b, 140, 327a which code for the corresponding pti1-like tyrosine-protein kinase important in disease resistance signaling mechanism in tomato [39]. In the mitogen-activated protein kinase (MAPK) signal transduction cascade, RGAs 014a, 092, 147b, 172, 237, and 377 code for mitogen-activated protein kinase kinase kinase; and RGA078 codes for a mitogen-activated protein kinase kinase. Plant MAPK cascades regulate a wide range of responses including stress, hormone regulation, innate immunity, and development [40]. RGA105 and 116 code for polygalacturonase-inhibiting protein gene 2. Expression of a polygalacturonase-inhibiting protein gene 2 (MsPGIP2) in alfalfa confer resistance to common leaf spot [41]. However in this study, RGA 116 was observed to be down-regulated compared to susceptible control, FR458. This may be a target for overexpression in plants to confer disease resistance.

Only 30 (14%) are represented by 1 CNL and 29 TNL combined. A large number (22 RGAs) codes for TMV resistance protein N-like and gene-expression may confer virus resistance [42]. Sequence variations for N-like proteins in peanut may recognize common Avr proteins in different viruses [43]. CNL and TNL are receptors that are regulated in ETI response, which can be stronger and longer than PTI response. In terms of R-gene evolution and transmission, it is theorized that R-genes are duplicated, rearranged, and/or mutated with synonomous or non-synonomous insertions/deletions (indels) or single nucleotide variations (SNPs) [44]. In this study, the majority of identified R-genes were observed to have low DNA polymorphism. Out of the 172 RGAs cloned and sequenced, 86 (50%) showed no SNPs and another 86 (50%) showed fewer than 16 SNPs in expressed transcripts ranging from 232 to 1776 bp. Only four RGAs with different size indels (3–9 bp), representing synonomous and inframe indels, were discovered. In peanuts, expressed genes with indels can be associated with disease resistance [12] and peanut agronomic traits [45]. In rice, a major rice blast resistance gene Pi54 (Pikh), is associated with an NBS–LRR containing protein with a 144-bp insertion/deletion (indel) [46]. The rest the R-genes showed high SNP polymorphism which may represent more than one allele for each transcript. Sequences with multiple variants, such as SNPs or indels, can code for different proteins and perhaps add new complementary function. For cultivated peanuts, these 214 RGAs represent transcript expression levels sufficient to be observed on agarose gel-electrophoresis analysis. Differences in the levels of gene-expression of RGAs can be associated to different levels of disease resistance, identifying potential disease resistance genes. Since the majority of the R-genes in study belong to PRRs and others with STK domains (~ 80%), it is difficult to draw a significant conclusion about all R-gene evolution in peanuts. In a study analyzing molecular phylogeny and evolution in legumes, R-genes were observed to undergo purifying selection instead of positive selection [47]. Initial low-frequency of genes introduced by random recombination may be lost (perhaps due lack of disease pressures, artificial selection by domestication, or fitness costs). The introgression wild peanut species may provide a novel source of disease resistance genes since some diploid peanuts have been observed to be more disease resistant than cultivated peanuts [48].

Disease resistance in plants is complex and involves a balance between disease responses and plant productivity. Comparison of disease susceptible versus more tolerant genotypes identified a group of up-regulated and down-regulated R-genes that are potential targets for molecular breeding or applications in biotechnology. Research provides valuable information to understand disease resistance mechanism(s) relating to the gene-expression of R-genes in cultivated peanut and provides gene targets to develop disease resistant peanut varieties.