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Review

Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants

by
Alpana Joshi
1,2,
Hyung-Geun Song
1,
Seo-Yeon Yang
3 and
Ji-Hoon Lee
1,3,*
1
Department of Bioenvironmental Chemistry, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju 54896, Republic of Korea
2
Department of Agriculture Technology & Agri-Informatics, Shobhit Institute of Engineering & Technology, Meerut 250110, India
3
Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea
*
Author to whom correspondence should be addressed.
Plants 2023, 12(13), 2454; https://doi.org/10.3390/plants12132454
Submission received: 22 May 2023 / Revised: 15 June 2023 / Accepted: 23 June 2023 / Published: 26 June 2023
(This article belongs to the Special Issue Plant-Microbe-Pest Interactions: Ecological Implications and Defense Mechanisms)
Versions Notes

Abstract

:
Modern plant pathology relies on bioinformatics approaches to create novel plant disease diagnostic tools. In recent years, a significant amount of biological data has been generated due to rapid developments in genomics and molecular biology techniques. The progress in the sequencing of agriculturally important crops has made it possible to develop a better understanding of plant–pathogen interactions and plant resistance. The availability of host–pathogen genome data offers effective assistance in retrieving, annotating, analyzing, and identifying the functional aspects for characterization at the gene and genome levels. Physical mapping facilitates the identification and isolation of several candidate resistance (R) genes from diverse plant species. A large number of genetic variations, such as disease-causing mutations in the genome, have been identified and characterized using bioinformatics tools, and these desirable mutations were exploited to develop disease resistance. Moreover, crop genome editing tools, namely the CRISPR (clustered regulatory interspaced short palindromic repeats)/Cas9 (CRISPR-associated) system, offer novel and efficient strategies for developing durable resistance. This review paper describes some aspects concerning the databases, tools, and techniques used to characterize resistance (R) genes for plant disease management.

1. Background

Phytopathogens have greatly threatened livelihoods and societal growth because they affect quality crop production. Plant diseases caused by pathogenic bacteria, fungi, and viruses account for nearly 20–40% of losses in agricultural crop yields worldwide [1]. The molecular basis of the host–pathogen interaction is better understood due to the advancements in molecular and bioinformatics technologies. Whole-genome sequencing technology facilitates the sequencing of a large number of pathogens and plant species. Scientists are now able to organize and analyze enormous amounts of biological data using bioinformatics tools. Additionally, they can be used to identify and characterize disease-related genes and develop new diagnostic tools [2]. Plants have developed a multi-layered defense system against microbial diseases during evolution. The first level of protection is provided by the physical barriers imposed by the plant surface. The second layer is related to the detection of pathogen-associated molecular patterns (PAMP) that are anchored to the plasma membrane and activate the PAMP-triggered immunity (PTI) [3]. The third layer involves receptors encoded by resistance genes (R genes) that recognize the presence of pathogen-effector proteins and activate effector-triggered immunity (ETI) [4]. Plant disease resistance can be classified into two categories, namely qualitative resistance and quantitative resistance. Qualitative resistance is controlled by single resistance (R) genes, while the latter is controlled by multiple genes or quantitative trait loci (QTLs) [5]. Disease resistance mediated by resistance (R) proteins is associated with nucleotide binding (NB) and leucine-rich repeat (LRR) domains that are collectively known as NB-LRRs. The R genes are broadly categorized into eight classes based on their conserved protein structures. Resistance genes contain the CC-NBS-LRR (CNL) proteins that are characterized by a coiled-coil domain (CC), e.g., RPM1 and RPS2 genes of Arabidopsis and the I2 resistance gene of Solanum lycopersicum (class I) [6,7]. The tobacco N gene and flax L6 gene belong to class II (TIR-NBS-LRR (TNL), characterized by mammalian toll interleukin-1 receptor (TIR) and an NBS-LRR domain [8,9]. Some resistance genes belong to the RLK and RLP categories, such as Cf-9, Cf-4, and Cf-2 for resistance to Cladosporum fulvum: (class III) [10,11,12]; Xa21 for resistance to Xanthomonas oryzae (class IV) [13]; and Ve1 and Ve2 genes for resistance to Verticillium wilt (class V) [14]. Genome-wide studies of different classes of R genes have been reported in various plant species, including Arabidopsis thaliana, Oryza sativa, Gossypium sp., Brassica napus, B. rapa, B. oleracea, Vitis vinifera, Triticum aestivum, Zea mays, and Hordeum vulgare [13,15,16,17,18,19,20,21,22,23,24,25].
Exploiting genetic variation in natural populations is the key to plant improvements, whereas during co-evolution, pathogens adapted to their host and developed resistance against plant defense mechanisms. Alternatively, there is a need for new and advanced gene editing technologies to improve plant health, such as mega nucleases (MNs), zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated nucleases) [26]. The CRISPR/Cas system is a widely used genome-editing technology due to its easiness, low cost, high efficiency, and reproducibility. The CRISPR/Cas system is based on different strategies, including gene knock-out, knock-in, targeted mutagenesis, and modification of the amino acid sequence. For example, resistance against powdery mildew has been successfully developed in T. aestivum, H. vulgare, and V. vinifera by creating a knock-out mutant at the MLO locus (mildew resistance locus o) [27]. This system also develops resistance against multiple RNA viruses in S. lycopersicum and Cucumis melo by introducing INDELs affecting eIF4Es (eukaryotic translation initiation factor 4E proteins) [28,29]. Similarly, the CRISPR/Cas method has been successfully applied in developing resistance against bacterial, fungal, and viral diseases in diverse plant species, such as A. thaliana, O. sativa, Glycine max, Malus domestica, Musa species, Nicotiana tabacum, Populus alba, S. lycopersicum, Solanum tuberosum, Sorghum bicolor, T. aestivum, and Z. mays [26]. In this review, we focus on the characterization of R genes and the application of the CRISPR-Cas system to the development of resistance against specific pathogens.

2. Genome Databases of Plant Pathogens

Genome databases integrated with specific bioinformatics tools have been developed to study the associations between genetic diversity and disease (Table 1). They also provide information related to host–pathogen interactions. PhytoPath is a bioinformatics resource for genomic and phenotypic data of important plant pathogen species. The PhytoPath project utilizes the Ensembl genome portals to provide genomic information, including genome sequences, structural and functional annotation of protein-coding and non-protein coding genes, DNA and protein-based alignments, and phylogeny for genes [30]. The National Institute of Agrobiological Sciences (NIAS) Genebank is implementing the NIAS Genebank Project to preserve and document plant, microorganism, and animal genetic resources related to agriculture in Japan; however, it lacks a classification of plant gene functions [31]. The PathoPlant database has been developed to explain the molecular processes involved in signal transduction during plant pathogenesis and the interactions between plants and pathogens at the organism level [32]. The Pathogen-Host Interactions database (PHI-base) was established in the year 2005, and PHI-base entries include experimentally verified pathogenicity, virulence, and effector genes from fungal and bacterial pathogens of animal, plant, fungal, and other hosts [33]. The identification and analysis of host–pathogen interactions (HPI) are crucial to study infectious diseases. HPIDB 3.0 is a resource that helps to annotate, predict, and display host–pathogen interactions [34]. Viral infections often cause diseases by disturbing several cellular processes in the infected host. VirusMentha is a new resource for studying virus–virus and virus–host interactions based on integration techniques created for mentha, as well as the detailed curation protocols of the IMEx consortium [35]. An extensive database for predicting Penicillium-crop protein–protein interactions is PCPPI [36]. Currently, data can be amplified by extracting the information from microorganism genomes databases, but there is still a need for more extensive plant pathogen genome databases to understand the mechanism of disease resistance [37].

3. Identification and Isolation of Resistance (R) genes and Plant NLRs

Gene cloning is improving our understanding of the molecular mechanisms underlying plant–pathogen interactions. Map-based cloning or Positional cloning utilizes the knowledge of genetic map positions. It is the standard method to isolate genes when the phenotype and genomic locations are known. The first cloned R gene was Hm1 from Z. mays against the HC toxin (the host-selective toxin pathogen) secreted by the fungus Cochliobolus carbonum [15]. Gene Hm1 encodes a reductase enzyme that detoxifies the HC toxin and develops resistance in plants against C. carbonum followed by Pto (encoding a serine-threonine kinase) from S. lycopersicum, which confers resistance against Pseudomonas syringae pv. tomato [38]. Most isolated R genes encode cytoplasmic proteins consisting of a central nucleotide-binding site (NBS) domain and a C-terminal domain containing leucine-rich repeats (LRRs), including Cf-9, a predicted membrane protein with an extracellular LRR domain [10]. The Cf-9 gene was isolated from S. lycopersicon through transposon tagging using the Maize Activator/Dissociation (Ac/Ds) system. Similarly, the N gene was isolated from tobacco (N. tabacum) via transposon tagging, and it conferred resistance to Tobacco mosaic virus (TMV) [8]. Furthermore, two genes (RPS2 and RPM1) were isolated from A. thaliana conferring resistance against P. syringae using a map-based cloning approach [39,40], in addition to the L6 gene in flax conferring against Melampsora lini using the Maize Activator/Dissociation (Ac/Ds) system [41]. Due to advancements in plant genomics and genetic engineering techniques, the positional cloning approach has made it easier to clone R genes from various crops or their wild relatives and transfer them into elite breeding lines or cultivars.

3.1. Plant NLRs

Nucleotide-binding site–leucine-rich repeats (NLRs) are encoded by hundreds of diverse genes per genome and can be divided into two major classes based on the presence of a distinct N-terminal domain: (i) CNL, containing a coiled-coil (CC) domain [6,7], and (ii) TNL, containing a Toll/interleukin-1 receptor (TIR) domain [8,9]. NLR proteins are abundant in plants, animals, fungi, and protists. Typically, several hundred NLRs are found in a plant genome [42], and the number, arrangement, and domain combinations of NLRs vary significantly in different plant species [43] (Table 2). For example, 3400 NLRs were identified in T. aestivum [44], 1000 NLRs in M. domestica [45], 535 NLRs in O. sativa, 245 NLRs in S. bicolor, 238 NLRs in Brachypodium dystachyon [46], 437 NLRs in Gossypium hirsutum [47], 459 NLRs in V. vinifera, 330 NLRs in Populus trichocarpa [48], 319 NLRs in G. max [49], 327 NLRs in Manihot esculenta [50], 571 NLRs in M. truncatula, 289 NLRs in Cajanus cajan, 337 NLRs in Phaseolus vulgaris [51], 151 in Z. mays [52], and 149 NLRs in A. thaliana [53]. Some plant species contain significantly low copy numbers of NLRs: for example, 54 NLRs in Carica papaya [52], 57 NLRs in Cucumis sativus [54], and 70, 55, and 55 NLRs in C. sativus, C. melo, and Citrullus lanatus, respectively [55]. Moreover, no correlation was observed between the total number of genes in the genome and genome size [46,56].

3.2. Resistance (R) Genes in Rice (O. sativa)

The rice crop is affected by several diseases, of which bacterial blight (BB) caused by Xanthomonas oryzae pv. oryzae is a serious disease that hinders the normal growth and production of rice. To date, 44 BB resistance genes have been discovered: 37 of which have been mapped and 15 have been cloned (viz., Xa1, Xa2/Xa31, Xa3/Xa26, Xa4, Xa5, Xa7, Xa10, Xa13, Xa14, Xa21, Xa23, Xa25, Xa27, Xa41, and Xa45) [13,25,58]. These isolated R genes can be classified into four groups based on their encoding proteins: (i) RLK (receptor-like kinase)—Xa21 [13], Xa3/Xa26 [59], and Xa4 [60]; (ii) SWEET (sugar will eventually be exported transporter)—Xa13 [61], Xa25 [58], and Xa41 [62]; (iii) executor genes—Xa10 [63], Xa23, and Xa27 [64]; and (iv) other types of genes—Xa1 [65] and Xa5 [66]. The other significant disease is rice blast, one of the most devastating diseases caused by the fungus Magnaporthe oryzae. More than 100 R genes have been identified, and 27 have been cloned viz., Pib, Pb1, Pita, Pi9, Pi2, Pizt, Pid2, Pi33, Pii, Pi36, Pi37, Pikm, Pit, Pi5, Pid3, Pid3–A4, Pikh, Pish, Pik, Pikp, Pia, PiCO39, Pi25, Pi1, Pi21, Pi50, and Pi65R [16,17,18,24,67,68,69]. Pia confers resistance to the blast fungus M. oryzae carrying the AVR-Pia, an avirulence gene, and a multifaceted genomics approach was employed to isolate the rice Pia gene [70,71,72,73]. Recently, new blast resistance genes were isolated, Pi25 (resistance allele of Pid3) from a resistant cultivar Gumei2, the Pi36 gene from the indica rice variety Kasalath, and Pi-64(t) and Pi66(t) from cultivar AS20-1. Moreover, the Pi65(t) gene was fine-mapped using a combination of bulk segregant analysis and next-generation sequencing, as well as Pi-jnw1 from the japonica rice landrace Jiangnanwan [5,19,74] (Table 3).

3.3. Resistance (R) Genes in Wheat (T. aestivum)

Powdery mildew leaf rust (Lr)-resistance genes have been used successfully in different breeding programs to develop disease-resistant wheat cultivars. The first resistance genes, namely Lr10, Lr21, and Lr1 against the fungal leaf rust disease caused by the pathogen Puccinia triticina were cloned in T. aestivum [75,76,77]. To date, more than 80 Lr genes have been characterized, and the majority of resistance genes (>50%) were derived from wild relatives of T. aestivum: (i) Lr21, Lr22a, and Lr39 from Aegilops tauschii, (ii) Lr24 from Thinopyrum ponticum, (iii) Lr57 from Ae. geniculate, (iv) Lr37/Yr17 from Aegilops ventricosa, (v) Lr9 from Aegilops umbellulata, (vi) Lr19 from Thinopyrum elongatum Zhuk., (vii) Lr24 from Agropyron elongatum, (viii) Lr26 from Secale cereale L, (ix) Lr59 from Aegilops peregrina, (x) Lr54 from Aegilops kotschyi, (xi) Lr56 from Aegilops sharonensis, (xii) Lr58 from Aegilops triuncialis, and (xiii) Lr62 from Aegilops neglecta [78,79,80]. Similarly, more than 60 genes conferring resistance against stem rust (Sr) resistance have been identified in wild relatives of T. aestivum viz., Sr5, Sr6, Sr7, Sr8, Sr9, Sr10, Sr13, Sr15, Sr16, Sr18, Sr19, Sr20, Sr21, Sr22, Sr23, Sr28, Sr29, Sr30, Sr33, Sr35, Sr41, Sr42, Sr45, Sr46, Sr48, Sr49, Sr50, and Sr60 [81,82,83,84,85,86,87,88,89]. Cereal cyst nematodes are serious pests affecting crop production. Resistance genes (Cre) were transferred into T. aestivum from its wild relatives to develop resistance against the root endoparasitic nematode Heterodera avenae, including Cre1 and Cre8 from T. aestivum; Cre3 and Cre4 from A. tauschii; Cre2, Cre5, and Cre6 from A. ventricosa; Cre7 from A. triuncialis; CreR from S. cereale; and CreV from Dasypium villosum [90].
Powdery mildew, caused by Blumeria graminis f. sp. Tritici, is a widespread disease in T. aestivum and responsible for severe yield loss. Resistance to powdery mildew has been associated with more than 140 genes in T. aestivum [91]. Map-based cloning and sequencing approaches have been employed to clone the resistance genes against powdery mildew, including Pm2 [21], Pm2a [22], Pm3 [92], Pm3b [93], Pm3c and Pm3b [94], Pm5e [24], Pm8 [20], Pm17 [95], Pm21 [23], Pm24 [96], Pm41 [97], Pm60 [98], PmR1 [99], and Pm2b [100]. Mutant chromosome sequencing (MutChromSeq) is a method in which mutated chromosomes are sequenced and compared to the wild-type chromosomes to identify the novel target gene; for example, Pm2a located on chromosome 5DS was cloned using the MutChromSeq method. The Pm3b genes are located on chromosome 1AS and cloned using chromosome walking using available genetic resources. Pm8 resistance genes have been introgressed from chromosome 1RS of S. cereal into T. aestivum using homology-based cloning. Similarly, Yr10, Yr18, Yr36, and Yr46 genes have been isolated using a map-based cloning approach to develop genetic resistance against the fungal pathogen Puccinia striiformis f. sp. tritici [101,102]. Target-sequence Enrichment Sequencing (TEnSeq) pipelines were used to clone Pm genes, including Pm1a [103], Pm2a [21], and Pm4b [104]. Most of the cloned Pm genes contain an NLR, whereas resistance genes Pm38 and Pm46 encode an ATP-Binding Cassette (ABC) transporter [79] and a hexose transporter [78], respectively, which confer dual resistance to wheat leaf rust and stripe rust, in addition to resistance to powdery mildew (Table 4).

3.4. Resistance (R) Genes in Maize (Z. mays)

Fungal diseases are a major threat to maize production worldwide. Hm-l was the first gene cloned against the northern leaf spot fungus Cochliobolus carbonum [15]. Northern corn leaf blight (NCLB) is also one of the most devastating fungal diseases for maize caused by the fungal pathogen Setosphaeria turcica. The four resistance genes Ht1, Ht2, Ht3, and Htn1 against the fungal pathogen S. turcica have been identified and cloned using a map-based cloning approach. The dominant and race-specific Htn1 gene is effective against the most prevalent NCLB races. Htn1 encodes the wall-associated receptor-like kinase ZmWAK-RLK1, and the strength of the Htn1 resistance depends on environmental conditions and the maize genotype [105,106]. To date, only sixteen resistance genes (Hm1, Htn1, Ht2, Ht3, Rp1-D21, RppC, RabGD1α, ZmABP1, ZmAuxRP1, ZmCCoAOMT2, ZmCCT, ZmFBL41, ZmMM1, ZmREM1.3, ZmTrxh, ZmWAK) have been cloned from maize [107,108,109,110,111,112,113,114,115,116,117,118,119]. Southern corn rust (SCR) is the predominant disease in the USA, Canada, Brazil, and China, caused by Puccinia polysora. Although eleven maize dominant resistance genes (Rpp1, RPP6, RPP7, RPP8, Rpp9, Rpp10, and Rpp11) and eight major resistance QTLs (RppC, RppCML470, RppD, RppM, RppP25, RppQ, RppS, and RppS313) have been identified against the fungal pathogen P. polysora, only RppC was cloned [108]. Moreover, the RppK gene, which belongs to the CC-NB-LRR class, was cloned, via map-based cloning, and is involved in resistance against the same pathogen [110]. A major resistance quantitative trait locus, qRfg1, significantly enhances maize resistance to Gibberella stalk rot caused by Fusarium graminearum. A CCT domain-containing gene, ZmCCT, is the causal gene at the qRfg1 locus and was cloned using a map-based cloning approach [113]. ZmFBL41 was identified through a genome-wide association study in maize and confers resistance to banded leaf and sheath blight caused by the fungus Rhizoctonia solani [114]. Multiple disease resistance (MDR) is a valuable tool for developing durable resistance, and only one MDR gene (ZmMM1) has been cloned in maize. ZmMM1 confers resistance to northern leaf blight (NLB), gray leaf spot (GLS), and southern corn rust (SCR) [115].
Virus infections are also prevalent in maize-growing regions around the world. Maize rough dwarf disease (MRDD) is caused by various species of viruses belonging to the genus Fijivirus. The Rab GDP dissociation inhibitor alpha (RabGDIα) is the host susceptibility factor for rice black-streaked dwarf virus [111]. These resistance alleles are valuable to improve resistance to rough dwarf disease in maize and potentially develop resistance against rice black-streaked dwarf virus in other crops. Sugarcane mosaic virus (SCMV) is one of the severe viral diseases in maize. Two resistance loci, namely Scmv1 and Scmv2, conferring complete resistance against SCMV have been identified. Scmv1 encodes ZmTrxh, a molecular chaperone suppressing viral RNA accumulation in the cytoplasm without stimulating a salicylic acid- or jasmonic acid-mediated defense response [118,119] (Table 5).

3.5. Resistance (R) Genes in Arabidopsis (A. thaliana)

The cloning of resistance genes facilitates the development of resistant cultivars and develops an understanding of the evolutionary history of R genes. Most of the R genes identified in Arabidopsis belong to either the TIR-NBS-LRR or LZ-NBS-LRR subclass. In addition, receptor-like kinases (RLKs) are also involved in plant development and defense. The most well-known RLKs in Arabidopsis are the leucine-rich repeat receptor kinases flagellin-sensitive 2 (FLS2) and BAK1, which initiate the MAP kinase cascade upon flg22 recognition, leading to plant innate immunity [120,121]. The TIR-NBS-LRR subclass is defined by an N-terminal region that resembles the cytoplasmic domain of the Toll and interleukin1 transmembrane receptors (TIRs), e.g., RPP1, RPP4, and RPP5 confer resistance to Peronospora parasitica [122,123,124]. In contrast, the LZ-NBS-LRR subclass contains a leucine zipper–like motif (LZ) in place of the TIR domain, e.g., RPS2, RPM1, RPP8, and RPP13 genes confer resistance to P. syringae [39,40,125,126]. Some R genes, RPW7 and RPW8, encode proteins with motifs for a nucleotide-binding site (NBS), and an LRR confers resistance to the powdery mildew pathogens Erysiphe cruciferarum [127].
RPP4-mediated resistance has been associated with multiple defense-signaling components, including EDS1 (enhanced disease susceptibility 1 [128], NDR1 (non-race-specific disease resistance 1) [129], and PBS1 [130], and the absence of functional alleles of either EDS1 or NDR1 leads to enhanced susceptibility to a diverse range of pathogens. In addition, EDS1 is required for RPS4-mediated disease resistance against P. syringae pv. tomato and does not specify resistance to P. parasitica, unlike other EDS1-dependent R genes [131]. The mapping and characterization of the RCH2 locus identified the pair of neighboring genes, namely RRS1 and RPS4, which confer dual resistance against fungal (Colletotrichum higginsianum) and bacterial (Ralstonia solanacearum) pathogens [132,133]. Similarly, map-based cloning has facilitated characterization of the RFO locus (RESISTANCE TO FUSARIUM OXYSPORUM (RFO), which is identical to WAKL22 (WALL-ASSOCIATED KINASE-LIKE KINASE 22) in Arabidopsis [134]. RPS5 belongs to the NBS-LRR subclass, and cloning RPS5 genes has facilitated the characterization of two rps5 mutations. The rps5-1 mutation causes a glutamate-to-lysine substitution within the LRR region and affects the function of several R genes and confers resistance to both pathogens (P. parasitica and P. syringae) [135]. In Arabidopsis, members of both subclasses (TIR-NBS-LRR and LZ-NBS-LRR) confer resistance to the fungus P. parasitica and the bacterium P. syringae, whereas RCY1, belonging to CC-NB-LRR subclass, confers viral resistance. Cucumber mosaic virus (CMV) has the widest host range and causes catastrophic crop loss in many areas. RCY1 is the dominant locus that confers resistance to the yellow strain of ecotype C24 in Arabidopsis [136] (Table 6).

3.6. Resistance (R) Genes in Tomato (S. lycopersicum)

The genome of tomato has been extensively explored to understand the structure and organization of resistance loci, and more than 100 loci have been identified [57]. The disease-resistance genes Pto [38], Ptil [137], and Fen [138] were discovered in S. lycopersicum, which confer resistance to bacterial speck caused by P. syringae pv. tomato (Pst). Another class of R genes, namely Cf-2 and Cf-9 from Solanum pimpinellifolium and Cf-4 and Cf-5 from Solanum peruvianum, have been identified and subsequently transferred into cultivated species to develop resistance against the leaf mold fungus Cladosporium fulvum [10,11,12,139]. Similarly, several other disease-resistance genes, including Cf9 [10], Cf5 [139], Prf ([140], Sw5 [141], I2 [142], Mi1-2 [143], Ve [14], Hero [144], Tm-2 [145], and Bs4 [146], were cloned using molecular markers, chromosome walking, and linkage analysis. The Sw-5 gene was introgressed from the wild species S. peruvianum to develop resistance against TSWV (tomato spotted wilt virus). Moreover, Sw-5 was also found to be resistant to Tomato chlorotic spot virus (TCSV) and Groundnut ring spot virus (GRSV) [126]. Late blight caused by Phytophthora infestans is one of the most destructive diseases, and more than 60 resistance genes against P. infestans (Rpi genes) have been identified in Solanum sp. The Ph-1 gene was the first reported Rpi gene (resistance against P. infestans), and after that, Ph-2 and Ph-2 genes have been identified in S. pimpinellifolium [132] and used to develop disease-resistant cultivars (Table 7).

4. NLR Annotation Tools

4.1. NLR-Parser

NLR-Parser is a tool to rapidly annotate the NLR (nucleotide-binding leucine-rich repeat) complement from sequenced plant genomes. It is a Java application used for the identification of NLR-like sequences. The pipeline was designed to use the MAST output from six-frame translated amino acid sequences and filters for predefined biologically curated motif compositions. Input reads can be derived from, for example, raw long-read sequencing data or contigs and scaffolds originating from plant genome projects. The output is a tab-separated file with information on the start and frame of the first NLR-specific motif, whether the identified sequence is a TNL or CNL, and whether it is potentially complete or fragmented. In addition, the output of the NB-ARC domain sequence can directly be used for phylogenetic analyses. NLR-parser can also discriminate pseudogenes by looking for the complete set of motifs defining an NLR protein. It uses motif alignment and a search tool (MAST) to search for 20 conserved motifs found in NLRs that use the highly-specific amino acid motif composition found in plant NLR gene products [148]. It can be downloaded from Git-Hub using the website (https://github.com/steuernb/NLR-Parser, accessed on 3 May 2023).

4.2. NLR-Annotator

NLR-Annotator is an extension of NLR-Parser to annotate NLR loci in genomic sequence data. Our pipeline dissects genomic sequences into overlapping fragments, and each fragment is translated in all six reading frames using NLR-Parser to preselect those fragments potentially harboring NLR loci. Using this approach, they could find putative candidate genes for NLR loci in stem rust, leaf rust, powdery mildew, and yellow rust resistance genes [44]. In 2018, NLR-Annotator, the improved version of NLR prediction, was released (https://github.com/steuernb/NLR-Annotator, accessed on 3 May 2023).

4.3. NLGenomeSweeper

Another pipeline to annotate functional NLR disease-resistance genes in genome assemblies is NLGenomeSweeper. It is a pipeline that searches a genome for NBS-LRR (NLR) disease-resistance genes based on the presence of the NB-ARC domain. This procedure can be used with a customized NB-ARC HMM consensus protein sequence(s) created for a species of interest for each type of NBS-LRR (TNLs, CNLs, and NLs) and merge them into a single fasta file for use. This pipeline shows high specificity for complete genes and structurally complete pseudogenes. This pipeline identified 152 potential NBS-LRR proteins; 140 of these matched the manually annotated Arabidopsis NLR set, which contains 146 genes (96% sensitivity) [149].

4.4. DRAGO2

Disease Resistance Analysis and Gene Orthology (DRAGO 2) is the second version of a pipeline to annotate resistance genes. It is an extensive, freely accessible, and user-friendly online platform for analyzing and predicting plant disease-resistance genes. The input of DRAGO 2 can be either DNA or protein sequences in FASTA format. DRAGO2 is available in PRGdb (http://prgdb.org, accessed on 3 May 2023). The core of the DRAGO2 pipeline is a Perl script that predicts putative pathogen receptor genes (PRGs) and LRR, kinase, NBS, and TIR domains. It can also detect CC and TM domains using COILS 2.2 and TMHMM 2.0c programs. More than 1700 possible PRGs were predicted using the DRAGO2 tools, which have the highest sensitivity compared to other tools [150].

4.5. NLRtracker

NLRtracker has been designed to overcome the limitation associated with the existing NLR tools. NLRtracker uses InterProScan and the predefined NLR motifs to annotate all sequences in a given proteome or transcriptome and then extracts and annotates NLRs based on the core NLR sequence features (late blight R1, TIR, RPW8, CC, NB-ARC, LRR, and integrated domains) found in the RefPlantNLR dataset. Additionally, NLRtracker extracts the NB-ARC domain for a comparative phylogenetic analysis [151].

5. CRISPR Gene Editing for the Generation of Disease Resistance

The CRISPR (clustered regulatory interspaced short palindromic repeats)/Cas9 (CRISPR-associated) system has surpassed alternative genome editing methods due to its simplicity, flexibility, better success rate, and cost-effectiveness. The CRISPR/Cas9 system can efficiently introduce mutations, including INDELs (insertion mutations and deletion) and base substitutions in the target site. One significant advantage of using the CRISPR/Cas9 system is the ability to edit multiple target genes simultaneously [152]. Several efficient plant genome editing web-based tools are available for designing sgRNAs and analyzing post-genome editing data [153] (Table 8). CRISPR/Cas systems have been divided into six types based on their signature Cas genes. Class 1 CRISPR/Cas systems (types I, III, and IV) employ multi-Cas protein complexes for interference, whereas class 2 systems (types II, V, and VI) accomplish interference with single effector proteins in complex with CRISPR RNAs (crRNAs) [154]. This system has been successfully applied to various plant species, such as A. thaliana, O. sativa, N. tabacum, S. bicolor, T. aestivum, Z. mays, G. max, S. lycopersicum, S. tuberosum, P. alba, M. domestica, and Musa species, to combat viral infection and fungal and bacterial diseases [26,155]. There are several strategies for developing plant disease resistance via the CRISPR/Cas system [156]: (i) knock-out of susceptibility genes of disease (e.g., MLO; a mildew resistance locus O) [27], (ii) deletion or modification of cis-elements in promoters [157], (iii) modification of the amino acid sequence of surface receptor proteins to suppress secreted pathogen effectors [153], (iv) knockdown of negative regulators of plant immunity [158], and (v) modification of central regulators of the defense response [159].
The CRISPR/Cas9 system has facilitated efficient and precise targeted mutagenesis in plants to enhance resistance to fungal diseases. Mildew resistance locus O (MLO) is the most widely studied gene for resistance to fungal diseases. Wild-type alleles of MILDEW RESISTANT LOCUS O (Mlo) are conserved throughout monocots and dicots, conferring susceptibility to the powdery mildew fungi Oidium neolycopersici. The generation of a resistant variety using CRISPR/Cas9 technology against the powdery mildew pathogen was reported in various crops: H. vulgare, A. thaliana, S. lycopersicum, Pisum sativum, Fragaria vesca, Capsicum annuum, T. aestivum, C. sativus, Rosa hybrid, N. tabacum, C. melo, V. vinifera, and M. domestica [27]. SlMlo1 is a major gene responsible for powdery mildew disease in S. lycopersicum, among 16 MLO genes studied so far. CRISPR/Cas9 technology has been employed to knock out SlMlo1 in developing resistance against the powdery mildew fungus O. neolycopersici without affecting the phenotype [27]. Similarly, a CRISPR/Cas9 system was used to mutate the susceptibility gene of Powdery Mildew Resistance 4 (PMR4), resulting in resistance to O. neolycopersici in S. lycopersicum. Additionally, both TALENs and CRISPR tools have been used to introduce mutations in one (TaMLO-A1) of the three MLO homoalleles, which resulted in improved resistance against B. graminis f. sp. tritici infection in T. aestivum [160,161]. In a similar study, a CRISPR-mediated MLO mutation resulted in the development of resistance to powdery mildew in H. vulgare (B. graminis f. sp. hordei), but at the same time, it increased susceptibility to the blast fungus M. grisea (M. oryzae) in O. sativa [162]. The CRISPR/Cas9-mediated editing of two susceptible genes, MLO-6 and DMR, resulted in increased resistance against the powdery mildew fungus Erysiphe necator and downy mildew fungus Plasmopara viticola in V. vinifera [163]. Another study in V. vinifera demonstrated that loss of the VvMLO7 gene increased resistance against E. necator through gene knock-down [164,165]. The CRISPR/Cas9-mediated knock-out of two genes, Solyc08g075770 and SlymiR482e-3p, in the different studies, resulted in resistance against the pathogen that causes Fusarium wilt in S. lycopersicon [166,167]. Similarly, a mutation in the Clpsk1 gene enhanced resistance against F. oxysporum in C. lanatus [168].
EDR1 (enhanced disease resistance) is highly conserved across plant species and negatively affects plant immunity. In Arabidopsis, EDR1 was reported to be a negative regulator of powdery mildew resistance, and this regulation was mediated by suppressing salicylic acid and enhancing abscisic acid signaling. Three homologs of the TaERD1 gene were mutated using CRISPR/Cas9, and the resultant Taedr1-mutant plants showed a significant reduction in blast lesions and resistance to powdery mildew in T. aestivum [169]. It was reported that the expression of EDR1 was induced by jasmonic acid (JA), salicylic acid, ethylene, and abscisic acid [170]. Moreover, both jasmonic and salicylic acid accumulation is associated with enhanced resistance against X. oryzae pv. oryzae (Xoo) in O. sativa. OsEDR1-knock-out plants demonstrated enhanced resistance against the bacterial blight-causing pathogen Xoo [171]. DMR6 (downy mildew resistance 6) has been identified as a susceptibility gene in S. tuberosum [172] and Arabidopsis [173]. Two DMR genes (StDMR6-1 and StDMR6-2) were edited simultaneously in S. tuberosum resulting in enhanced resistance against the late blight fungus P. infestans [174].
Rice blast is one of the most devastating diseases that affect rice production worldwide. Ethylene-responsive factors (ERFs) of the APETELA2/ERF (AP2/ERF) superfamily play crucial roles in adaptation to various biotic stress. Rice blast resistance to the fungus M. oryzae was enhanced mediated through the CRISPR/Cas9-mediated mutation of ERF922 gene [175]. Knock-down of the AP2/ERF transcription factor reduced abscisic acid accumulation and increased resistance against M. oryzae [176]. Similarly, the CRISPR/Cas9-mediated knock-out of AtERF019 in A. thaliana enhanced resistance to Phytophthora parasitica by suppressing PAMP-triggered immunity [177]. The overexpression of defense genes is one of the key biotechnological tools exploited to develop resistance against plant pathogens. In Theobroma cacao, overexpression of the TcNPR1 (Non-expressor of Pathogenesis-Related 1) gene reduced infection caused by Phytophthora spp. in leaf tissue [158].
Microrchidia (MORC) proteins are important nuclear regulators in prokaryotes and eukaryotes, involved in transcriptional gene silencing and the maintenance of genome stability [178]. In Arabidopsis, the role of MORC1 was discovered in plant immunity against turnip crinkle virus (TCV). Moreover, the role of AtMORC1, AtMORC2, and AtMORC6 are reported in multiple layers of defense responses against P. syringae and Hyaloperonospora arabidopsidis [49,179]. The CRISPR-Cas9 system from Streptococcus pyogenes (CRISPR/SpCas9) was used to introduce a mutation at HvMORC1 and HvMORC6a genes in H. vulgare. Similarly, MORCs have also been studied in S. tuberosum (StMORC1), S. lycopersicum (SlMORC1), and Nicotiana benthamiana (NbMORC1) [180,181]. WRKYs (WRKY transcription factors) have been identified in different plants in plant immune responses. Mutant analyses in Arabidopsis have revealed direct links between specific WRKY proteins (WRKY8, WRKY11, WRKY33, WRKY38, WRKY53, WRKY62, and WRKY70) and defense responses against P. syringae. Coronatine (COR) is the phytotoxic compound produced by the pathogen P. syringae pv tomato DC3000 (Pto3000), causing bacterial speck disease in S. lycopersicon. The CRISPR/Cas9-mediated mutation of the S1JAZ2 gene resulted in resistance to bacterial speck disease infestation in S. lycopersicum [182]. The role of the WRKY70 gene in the disease response to the fungus Sclerotinia sclerotiorum in B. napus was also documented in the literature [159]. In a similar study, the CRISPR/Cas9-mediated targeted mutagenesis of VvWRKY52 produced mutant lines in V. vinifera and the knock-out of WRKY52 enhanced resistance to Botrytis cinerea, causing gray mold disease [165].
Many viruses infecting economically important crops belong to the category of RNA viruses. CRISPR/Cas technology has been applied successfully to develop resistant plants against RNA viruses. Rice tungro disease is a severe problem caused by an interaction between rice tungro spherical virus and rice tungro bacilliform virus. In plants, eIF4E and eIF(iso)4E assist in recruiting ribosomes to the 5′ UTRs of mRNAs, which is eventually required to translate viral proteins. The copy numbers of the eIF4E and eIF(iso)4E genes vary from species to species [183]. A CRISPR/Cas9-mediated mutation in eIF4G provided resistance to rice tungro streak spherical virus in a susceptible variety (IR64) of O. sativa [184]. Mutation of the recessive eIF4E gene enhanced resistance against turnip mosaic virus in Arabidopsis and cucumber vein yellowing virus in cucumber [185,186]. Similarly, RNA virus resistance has been demonstrated by silencing the eIF4E gene in S. lycopersicum and C. melo [28,29]. A recent discovery of FnCas9 (Cas endonucleases) from Francisella novicida may be used as a new tool for attacking the genome of plant RNA viruses. FnCas9 was used to develop resistance against Cucumber mosaic virus (CMV) and Tobacco mosaic virus (TMV) in N. benthamiana and Arabidopsis plants, respectively [187]. Characterization of the functionality of Cas13a of Leptotrichia shahii (LshCas13a) demonstrated that the single effector Cas13a protein was a programmable RNA-guided single-stranded RNA (ssRNA) ribonuclease that provided immunity against bacteriophages of the bacteria Escherichia coli [188]. The LshCas13a system was used for developing resistance to Southern rice black-streaked dwarf virus (SRBSDV) and Rice stripe mosaic virus (RSMV) in O. sativa [189].
O. sativa is extensively used for genome editing studies against bacterial disease resistance. Rice bacterial blight is one of the invasive diseases caused by bacterial X. oryzae pv. oryzae (Xoo) [190]. X. oryzae secretes transcription-activator-like effectors (TALes) that bind specific promoter sequences and induce sucrose transporter genes (SWEET11, SWEET13, and SWEET14). The expression of sucrose transporter genes is required for disease susceptibility and mutations in effector binding element (EBE) regions in promoters of SWEET11, SWEET13, and SWEET14 genes [157]. The CRISPR/Cas9-mediated knockout of the Os8N3 gene resulted in enhanced resistance to most Xoo and bacterial blight [191]. Similarly, induced mutations in O. sativa into the coding regions of TMS5 (thermosensitive male sterile), Pi21 (proline-rich protein), and Xa13 (bacterial blight resistance) genes via CRISPR/Cas9 improved resistance against rice blast and bacterial blight [192]. The genus Xanthomonas is one of the significant genera affecting various horticultural crops. Citrus canker is one of the major diseases of citrus caused by the bacterium Xanthomonas citri ssp. citri (Xcc). Lateral Organ Boundaries 1 (CsLOB1) is a transcription factor that assists in the proliferation of X. citri spp. citri (Xcc). Effector binding element (EBE) regions present in the CsLOB1 promoter are recognized by the Xcc effector (PthA4), and expression of the CsLOB1 gene facilitates canker development in Citrus sp. CRISPR/Cas9-mediated editing of EBEs in the CsLOB1 promoter and coding region of the CsLOB1 gene provides resistance to citrus canker in C. sinensis and C. paradise [193]. Similarly, another transcription factor, WRKY22, was mutated through CRISPR/Cas9 technology and resulted in resistance to citrus canker in C. sinensis [194]. Fire blight is another devastating disease caused by Erwinia amylovora in M. domestica. The CRISPR/Cas9-mediated mutation of disease-specific interacting protein (DIPM-1, DIPM-2, and DIPM-4) genes provides resistance to the golden delicious variety of M. domestica against fire bight disease [195]. The application of the CRISPR/Cas system for disease resistance development by either targeting the pathogen genome or host genes to interfere with susceptibility has become more effective due to its simple operation, good knockout effect, low cytotoxicity, high specificity, and universal applicability. The CRISPR system has attracted more and more attention because CRISPR/Cas-induced mutations create pathogen-resistant genotypes when resistance resources in natural populations or wild relatives are limited. CRISPR/Cas also offers the opportunity to develop designer plants with multiple valuable attributes and resistance against biotic and abiotic stress. Thus, this technology should be explored and improved for creating novel disease-resistance genes/genotypes, which ultimately need reduced pesticide applications. These developments in genome editing will undoubtedly be advantageous for environmentally sustainable agriculture.
Intracellular nucleotide-binding leucine-rich repeat (NLR) receptors recognize pathogen effectors and initiate the immune response. The mechanisms of plant NLR activation remain unresolved, whereas animal NLRs undergo oligomerization upon binding to their effectors to activate downstream signaling. Our understanding of the plant NLR activation process has greatly increased due to the available structural data of CNL and TNL resistosomes. The composition and three-dimensional CNL structures of an Arabidopsis ZAR1 (HopZ-activated resistance) using cryo-EM microscopy structures illustrate differences between inactive and intermediate states of ZAR1 [196]. Similar studies uncovered the CNL structure of wheat Sr35 and found its resemblance to the ZAR1 resistosome structure of Arabidopsis [83,197]. In addition, the cryo-EM structures of TNL resistosomes from RPP1 (recognition of Peronospora parasitica 1) and ROQ1 (recognition of Xanthomonas outer protein Q 1) from A. thaliana and N. benthamiana, respectively, were determined using cryo-EM microscopy [198,199]. Recent advancements in computational methods, such as AlphaFold, have been used to predict the three-dimensional structure of the protein AVRamr1 (recognition of P. infestans effector) [200]. This structural framework moves us closer to developing novel immune receptors with modified recognition specificities and more effective plant disease-resistance proteins. Modern technology recognizes potential target regions of NLRs and the conserved resistosome structure, highlighting the future possibility of crop improvement through structure-guided NLR engineering. However, some questions are yet to be answered, such as whether all CNL and TNL immune receptors exhibit resistosome properties or if NLR activation requires the resistosome, as well as the possibility of monitoring resistosome formation using engineered NLR chimera.

6. Conclusions

NLRs play a crucial role in plant immunity by activating the strong resistance response leading to plant disease resistance. NLRs have a central nucleotide-binding (NB) domain which acts as an on/off activation switch, followed by a leucine-rich repeat (LRR) domain. The structure diversity, abundance, and chromosomal distribution of NLRs are fundamental for understanding disease resistance. The availability of high-throughput sequencing technology allows for the identification and cloning of several candidate resistance genes in different plant species. Gene editing technologies create a novel variation at the gene and genome levels. However, pathogens can eventually overcome disease resistance based on single-base editing due to their rapid evolution and genetic diversity of bacterial and fungal populations. The advanced variants of genome-editing tools, such as CRISPR/Cas, have brought many insights into the molecular mechanisms of site-specific mutagenesis. Moreover, durable resistance can be produced by pyramiding numerous genes and/or altering the plant and pathogen genomes using CRISPR/Cas9 technology. Protein engineering has redefined our ability to develop new or improved molecular recognition capabilities of NLRs, and engineered intracellular immune receptors can potentially improve disease resistance. The research on NLR proteins has been limited due to the unavailability of adequate three-dimensional structures of individual domains and homology models. However, in recent years, a significant advance in cryo–electron microscopy resolved the full-protein cryo-EM structure of NLR complexes, providing comprehensive insights into the complex biological mechanisms and functional complexity of NLRs. Moreover, modern computational technology, such as Alphafold, ca predict the three-dimensional structures of proteins with higher accuracy. These cutting-edge technologies may generate designer NLR receptors to confer broad-spectrum resistance in crop plants. Furthermore, more comprehensive tools are required for understanding accurate protein structures, ligand binding, and host–pathogen interactions. Overall, integrated computational and molecular biology tools provide a practical approach for efficiently breeding multiline cultivars and a strategy for generating designer crops with broader resistance and high yields.

Author Contributions

Conceptualization, A.J. and J.-H.L.; investigation, A.J.; resources, A.J., H.-G.S. and S.-Y.Y.; writing—original draft preparation, A.J.; writing—review and editing, A.J., H.-G.S., S.-Y.Y. and J.-H.L.; supervision, J.-H.L.; project administration, J.-H.L.; funding acquisition, J.-H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data sharing not applicable.

Acknowledgments

This study was funded by the Cooperative Research Programs for Agricultural Science and Technology Development (Project No. PJ015716032023), Rural Development Administration, Republic of Korea.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Databases related to important plant pathogen species.
Table 1. Databases related to important plant pathogen species.
DatabaseData SourcesMain PathogensAnalysis ToolURL
PhytoPath [30]Ensembl Genomes, PHI-baseBacteria, fungi, and protistsEnsembl data visualizationhttp://www.phytopathdb.org/ (accessed on 2 May 2023)
NIASGBdb [31]Experimental data and published literatureBacteria, fungi, and viruses--http://www.gene.affrc.go.jp/databases_en.php (accessed on 2 May 2023)
PathoPlant [32]GenBank, SWISS-PROT, TRANSFAC, PubMed and published literature Bacteria, fungi, viruses, and nematodesIn silico expression analysishttp://www.pathoplant.de/ (accessed on 2 May 2023)
PHI-base [33]NCBI, EMBL, and Web of ScienceBacteria, fungi, and protistsPHI-BLASThttp://www.phi-base.org (accessed on 2 May 2023)
HPIDB
[34]		IntAct, MINT, BioGRID, HPIDB, BIND, and VirHostNetBacteria, fungi, and virusesBLAST, visualization of interaction networkhttps://hpidb.igbb.msstate.edu/ (accessed on 2 May 2023)
VirusMentha [35]MatrixDB, BioGRID, MINT, IntAct, and DIPVirusVisualization of interaction networkhttp://virusmentha.uniroma2.it/ (accessed on 2 May 2023)
PCPPI [36,37]By predictingFungiBLAST, visualization of interaction networkhttp://pcppi.atcgn.com/blast.html (accessed on 2 May 2023)
BIND—The Biomolecular Interaction Network Database BioGRID (Biological General Repository for Interaction Datasets, DIP—Database of interacting proteins, EMBL—European Molecular Biology Laboratory, MINT—the Molecular INTeraction database, NCBI—National Center for Biotechnology Information, PHI-base—Pathogen-Host Interactions database, TRANSFAC—TRANScription FACtor database, VirHostNet—Virus–Host Network.
Table
Table 2. Distribution of NLR gene family in plant species.
Table 2. Distribution of NLR gene family in plant species.
SpeciesCC-NBSCC-NBS-LRRNBS-LRRTIR-NBSTIR-NBS-LRRReferences
Oryza sativa7715670--[46]
Hordeum vulgare6019884--[44]
Triticum urartu78275107--[44]
Aegilops tauschii70298113--[44]
Triticum aestivum4931181367--[44]
Zea mays93151---[51]
Brachypodium distachyon5320160--[46]
Vitis vinifera26200121490[48]
Populus trichocarpa14119-1073[48]
Manihot esculenta1111743529[50]
Medicago truncatula169413949121[51]
Cajanus cajan76368678[51]
Phaseolus vulgaris9128961376[51]
Glycine max810913724124[51]
Arabidopsis thaliana55132193[53]
Solanum lycopersicon3512348921[57]
Table
Table 3. Resistance genes, their donor parents, chromosomes location, and cloning techniques in O. sativa.
Table 3. Resistance genes, their donor parents, chromosomes location, and cloning techniques in O. sativa.
SourceR-GeneDiseasePathogenGene ProductChromosomeCloning
Technique		References
O. sativaXa1Bacterial blightX. oryzaeNBS-LRR4Map-based cloning[65]
O. sativaXa5Bacterial blightX. oryzaeNBS-LRR5Map-based cloning[66]
O. sativaXa10Bacterial blightX. oryzaeTranscription activator-like (TAL) effector11Map-based cloning[63]
O. sativaXa13Bacterial blightX. oryzae--8Map-based cloning[61]
O. sativaXa21Bacterial blightX. oryzaeReceptor kinase-like protein11Map-based cloning[13]
O. sativaXa25Bacterial blightX. oryzaeTransmembrane domain12Map based cloning[58]
O. sativaXa3/Xa26Bacterial blightX. oryzaeeLRR-TM-kinase or LRR receptor-kinase proteins11Map-based cloning[59]
O. minutaXa27Bacterial blightX. oryzaeReceptor kinase-like protein6Map-based cloning[64]
O. sativaPi36Bacterial blightM. oryzaeCC-NBS-LRR8Map-based cloning[19]
O. sativaPiaBlastM. oryzaeNBS-LRR11Map-based cloning[72]
O. sativaPi2BlastM. oryzaeNBS-LRR6Map-based cloning[70]
O. minutaPi9BlastM. oryzaeNBS-LRR6Map-based cloning[70]
O. sativaPi37BlastM. oryzaeNBS-LRR1Map-based cloning[69]
O. rhizomatisPi54BlastM. oryzaeCC-NBS-LRR-Map-based cloning[73]
O. sativaPibBlastM. oryzaeNBS-LRR2Map-based cloning[16]
O. sativaPi-taBlastM. oryzaeNBS-LRR12Map-based cloning[67]
O. sativaPi-KhBlastM. oryzaeNBS-LRR11Map-based cloning[68]
O. sativaPid3BlastM. oryzaeNBS-LRR6Map-based cloning[71]
O. minuta—Oryza minuta, O. rhizomatis—Oryza rhizomatis, O. sativa—Oryza sativa, M. oryzae—Magnaporthe oryzae, X. campestris—Xanthomonas campestris, X. oryzae—Xanthomonas oryzae pv. oryzae (Xoo), CC—coiled-coil domain, NBS-LRR—nucleotide-binding site leucine-rich repeat, TIR—Toll/interleukin-1 receptor-like domain.
Table
Table 4. Resistance genes, their donor parents, chromosomes location, and cloning techniques in T. aestivum.
Table 4. Resistance genes, their donor parents, chromosomes location, and cloning techniques in T. aestivum.
SourceR-GeneDiseasePathogenGene ProductChromosomeCloning
Technique		References
T. aestivumPm1aPowdery mildewB. graminisCC-NBS-LRR7ALMap-based cloning, MutChromSeq[103]
T. aestivumPm2aPowdery mildewB. graminisCC-NBS-LRR5DSMutChromSeq[21]
T. aestivumPm2bPowdery mildewB. graminisCC-NBS-LRR 5DSMap-based cloning[100]
T. aestivumPm3a and Pm3bPowdery mildewB. graminisCC-NBS-LRR 1ASMap-based cloning[93]
T. aestivumPm3c and Pm3fPowdery mildewB. graminisCC-NBS-LRR1ASMap-based cloning[94]
T. aestivumPm4bPowdery mildewB. graminisPutative chimeric protein of a serine/threonine kinase and multiple C2 domains 2ALMutChromSeq[104]
T. aestivumPm5ePowdery mildewB. graminisCC-NBS-LRR 7BLMap-based cloning[24]
S. cerealePm8Powdery mildewB. graminisCC-NBS-LRR 1RSHomology based cloning[20]
S. cerealePm17Powdery mildewB. graminisCC-NBS-LRR 1RSHomology based cloning[95]
D. villosumPm21Powdery mildewB. graminisCC-NBS-LRR 6VSMap-based cloning, MutRenSeq[23]
T. aestivumPm24Powdery mildewB. graminisA tandem kinase protein with putative kinase-pseudokinase domains1DSMap-based cloning[96]
T. turgidum spp. dicoccoidesPm41Powdery mildewB. graminisCC-NBS-LRR 3BLMap-based cloning[97]
T. urartuPm60a and Pm60bPowdery mildewB. graminisCC-NBS-LRR7ALMap-based cloning[98]
T. urartuPmR1Powdery mildewB. graminisCC-NBS-LRR7ALMap-based cloning[98]
T. urartuMlIW172Powdery mildewB. graminisCC-NBS-LRR7ALMap-based cloning[91]
T. aestivumPm38/Lr34Powdery mildewB. graminisATP-binding cassette transporter7DSMap-based cloning[79]
T. aestivumPm46/Lr67Powdery mildewB. graminisPredicted hexose transporter4DLMap-based cloning[78]
T. aestivumLr10Leaf rustP. triticinaCC-NBS-LRR1AMap-based cloning[75]
T. aestivumLr1Leaf rustP. triticinaCC-NBS-LRR5DMap-based cloning[77]
A. tauschiiLr21Leaf rustP. triticinaCC-NBS-LRR1DMap-based cloning[76]
A. tauschiiSr33Stem rustP. graminisCC-NBS-LRR1DMap-based cloning[82]
T. monococcumSr35Stem rustP. graminisCC-NBS-LRR3AMap-based cloning[83]
S. cerealeSr50Stem rustP. graminisCC-NB-LRR1RSMap-based cloning[84]
T. turgidum ssp. durumSr13Stem rustP. graminisCC-NB-LRR6ALMap-based cloning[86]
T. monococcumSr21Stem rustP. graminisCC-NB-LRR2AMap-based cloning[87]
T. monococcum ssp. boeoticumSr22Stem rustP. graminisCC-NB-LRR7ALMutRenSeq[85]
A. tauschiiSr45Stem rustP. graminisCC-NB-LRR1DSMutRenSeq[85]
A. tauschii var. meyeriSr46Stem rustP. graminisCC-NB-LRR2DSMap-based cloning[81]
T. monococcumSr60Stem rustP. graminisWheat Tandem Kinase 25AMap-based cloning[88]
T. aestivumCre3Cereal cystH. avenaeNBS-LRR2DMap-based cloning[90]
T. aestivumCre1Cereal cystH. avenaeNBS-LRR2BMap-based cloning[90]
T. aestivumYr10Stripe rustP. striiformisCC-NBS-LRR1BMap-based cloning[102]
T. aestivumYr36Stripe rustP. striiformisNBS-LRR6BMap-based cloning[101]
A. tauschii—Aegilops tauschii, A. thaliana—Arabidopsis thaliana, S. cereal—Secale cereal, T. aestivum—Triticum aestivum, T. monococcum—Triticum monococcum, T. turgidum—Triticum turgidum, T. urartu—Triticum Urart, B. graminis—Blumeria graminis, H. avenae—Heterodera avenae, P. graminis—Puccinia graminis, P. striiformis—Puccinia striiformis, P. triticina—Puccinia triticina, CC—coiled-coil domain, NBS-LRR—nucleotide-binding site leucine-rich repeat, TIR—Toll/interleukin-1 receptor-like domain.
Table
Table 5. Resistance genes, their donor parents, chromosomes location, and cloning techniques in Z. mays.
Table 5. Resistance genes, their donor parents, chromosomes location, and cloning techniques in Z. mays.
R-GeneDiseasePathogenGene ProductChromosomeCloning
Technique		References
Hm1Northern leaf spot C. carbonumNADPH HC toxin reductase1Transposon-induced mutagenesis[15]
Htn1Northern corn leaf blightS. turcicaReceptor-like kinase8Map-based cloning[105]
Ht2Northern corn leaf blightS. turcicaReceptor-like kinase2Map-based cloning[106]
Ht3Northern corn leaf blightS. turcicaReceptor-like kinase8Map-based cloning[106]
Rp1-D21Southern corn rustP. polysoraNBS-LRR10Transposon-induced mutagenesis[107]
RppCSouthern corn rustP. polysoraNBS-LRR10Map-based cloning[108]
ZmREM1.3Southern corn rustP. polysoraRemorin protein Map-based cloning[109]
RppKSouthern corn rustP. polysoraCC-NB-LRR 10Map-based cloning[110]
RabGD1αRough dwarf diseaseMRDD-8Map-based cloning[111]
ZmAuxRP1Gibberella stalk rotF. graminearumStroma-localized auxin-regulated protein1Map-based cloning[112]
ZmCCTGibberella stalk rotF. graminearumCCT-domain protein10Map-based cloning[113]
ZmFBL41Banded leaf and sheath blightR. solaniF-box protein4Map-based cloning[114]
ZmMM1Northern leaf blightGray leaf spot
Southern corn rust		S. turcica
C. zeae-maydis
P. polysora		MYB transcription factor7Map-based cloning[115]
ZmCCoAOMT2Gray leaf spotC. zeae-maydisCaffeoyl-CoA O-methyltransferase9Map-based cloning[116]
ZmWAKHead smutS. reilianumReceptor-like kinase Map-based cloning[117]
ZmTrxhMosaicSCMVh-type thioredoxin3Map-based cloning[118]
ZmABP1MosaicSCMVAuxin-binding protein3Map-based cloning[119]
MRDV—Maize rough dwarf disease, SCMV—Sugarcane mosaic virus, C. zeae-maydis—Cercospora zeae-maydis, C. carbonum—Cochliobolus carbonum, F. graminearum—Fusarium graminearum, P. polysora—Puccinia polysora, R. solani—Rhizoctonia solani, S. turcica—Setosphaeria turcica, S. reilianum—Sporisorium reilianum, CC—coiled-coil domain, NBS-LRR—nucleotide-binding site leucine-rich repeat.
Table
Table 6. Resistance genes, their donor parents, chromosomes location, and cloning techniques in Arabidopsis.
Table 6. Resistance genes, their donor parents, chromosomes location, and cloning techniques in Arabidopsis.
R-GeneDiseasePathogenGene ProductChromosomeCloning
Technique		References
RPS2Downy mildewP. syringaeCC-NBS-LRR 4Map-based cloning[39]
RPM1Downy mildewP. syringaeNBS-LRR 3Map-based cloning[40]
RPP8/HRTDowny mildewP. parasiticaNBS-LRR5Map-based cloning[125]
RPP13Downy mildewP. parasiticaLZ NBS-LRR3Map-based cloning[126]
RCY1MosaicCMV-YCC-NBS-LRR5Map-based cloning[136]
RPP1Downy mildewP. parasiticaTIR-NBS-LRR3Map-based cloning[122]
RPP4Downy mildewP. parasiticaTIR-NBS-LRR4Map-based cloning[124]
RPS4Powdery mildewP. syringaeTIR-NBS-LRR5Map-based cloning[131]
RPP5Downy mildewP. parasiticaTIR-NBS-LRR4Map-based cloning[123]
RPS5Downy mildewP. parasiticaNBS-LRR1Map-based cloning[135]
RRS1Bacterial wiltR. solanacearumTIR- NBS-LRR5Map-based cloning[132]
RFO1Fusarium wiltF. oxysporumReceptor-like kinase1Map-based cloning[134]
PBS1Powdery mildewP. syringaeSerine/threonine kinase5Map-based cloning[130]
FLS2Powdery mildewP. syringaeReceptor-like kinase5Map-based cloning[120]
BAK1Powdery mildewP. syringaeReceptor-like kinase4Map-based cloning[121]
NDR1Powdery mildew/Downey mildewP. syringae/P. parasiticaPlasma membrane-localized protein3Map-based cloning[129]
RPW8Powdery mildewE. cruciferarumNBS-LRR3Map-based cloning[127]
A. thaliana—Arabidopsis thaliana, E. cruciferarum—Erysiphe cruciferarum, F. oxysporum—Fusarium oxysporum, P. parasitica—Peronospora parasitica, P. syringae—Pseudomonas syringae, R. solanacearum—Ralstonia solanacearum, CMV—Cucumber mosaic virus, CC—coiled-coil domain, NBS-LRR—nucleotide-binding site leucine-rich repeat, TIR—Toll/interleukin-1 receptor-like domain.
Table
Table 7. Resistance genes, their donor parents, chromosomes location, and cloning techniques in S. lycopersicum.
Table 7. Resistance genes, their donor parents, chromosomes location, and cloning techniques in S. lycopersicum.
SourceR-GeneDiseasePathogenGene ProductChromosomeCloning
Technique		References
S. lycopersicumPtoBacterial speckP. syringaeSerine-threonine kinase5Map-based cloning[38]
S. pimpinellifoliumPrfBacterial speckP. syringaeLZ-NBS-LRR5Map-based cloning[140]
S. peruvianumMiRoot knotM. javanicaNBS-LRR6Map-based cloning[143]
S. lycopersicumI2Fusarium wiltF. oxysporumLZ-NBS-LRR11Map-based cloning[142]
S. pimpinellifoliumPh-1, 2 and 3Late blightP. infestansCC-NBS-LRR9Map-based cloning[147]
S. peruvianumSw-5Tomato spotted wiltTSWVNBS-LRR9Map-based cloning[141]
S. lycopersicumTm-2Tobacco mosaicTMVNBS-LRR9transposon tagging[145]
S. lycopersicumBs4Bacterial spotX. campestrisTIR-NBS-LRR5Map-based cloning[146]
S. pimpinellifoliumHeroPotato cystG. rostochiensisNBS-LRR4Map-based cloning[144]
S. pimpinellifoliumCf-2Leaf moldC. fulvumNBS-LRR6Map-based cloning[11]
S. peruvianumCf-4Leaf moldC. fulvumNBS-LRR1Map-based cloning[12]
S. peruvianumCf-5Leaf moldC. fulvumNBS-LRR6Map-based cloning[139]
S. pimpinellifoliumCf-9Leaf moldC. fulvumNBS-LRR1Transposon tagging (Ac-Ds system)[10]
S. lycopersicumVe1,2Verticillium wiltV. dahliaeReceptor-like kinase9Map-based cloning[14]
S. lycopersicumHcr9-4ELeaf moldC. fulvumReceptor-like kinase1Map-based cloning[12]
S. pimpinellifoliumFenBacterial speckP. syringaeSerine/threonine kinase5Map-based cloning[138]
S. lycopersicumPti1Bacterial speckP. syringaeSerine/threonine kinase12Map-based cloning[137]
S. lycopersicum—Solanum lycopersicum, S. pimpinellifolium—Solanum pimpinellifolium, S. peruvianum—Solanum peruvianum, C. fulvum—Cladosporium fulvum, F. oxysporum—Fusarium oxysporum, G. rostochiensis—Globodera rostochiensis, M. javanica—Meloidogyne javanica, P. syringae—Pseudomonas syringae, V. dahliae—Verticillium dahlia, TMV—Tobacco mosaic virus, TSWV—Tomato spotted wilt virus, Ac-Ds system—Activator and Dissociator system of Maize, CC—coiled-coil domain, NBS-LRR—nucleotide-binding site leucine-rich repeat, TIR—Toll/interleukin-1 receptor-like domain.
Table
Table 8. Commonly used sgRNA design tools and databases in plant genome editing.
Table 8. Commonly used sgRNA design tools and databases in plant genome editing.
NameCas Nuclease EnzymeMajor FeaturesWebsite
CRISPORCas9 orthologues and Cas variantsCloning, expressing, and validating sgRNA sequences for the CRISPR/Cas9 system, as well as providing primers needed for testing guide activity and target validationhttp://crispor.tefor.net/ (accessed on 3 May 2023)
CHOPCHOPCas9, Cas12, Cpf1, and TALENIt provides multi-targeting systems, such as knockout, knock-in, gene activation, and repression. It allows for the design of sgRNAs in a specific region, 5′ UTR, 3′ UTR, promoter, or the gene coding regionhttps://chopchop.cbu.uib.no/ (accessed on 3 May 2023)
CRISPR RGEN ToolsCas9 orthologues and Cas variantsIt provides multiple sgRNA design tools with high accuracyhttp://www.rgenome.net/cas-designer/ (accessed on 3 May 2023)
E-CRISPSpCas9It targets any nucleotide sequence of the genome. It also checks for target specificity of the putative designs and their genomic context (e.g., exons, transcripts, CpG islands)http://www.e-crisp.org/E-CRISP/index.html (accessed on 3 May 2023)
CRISPR-GESpCas9, FnCpf1, and AsCpf1It predicts the specificity of a target site and the design sgRNAs for different CRISPR/Cas systems. It also provides a primer design tool for vector construction and mutant detection http://skl.scau.edu.cn/ (accessed on 3 May 2023)
CRISPR-PCas9 and variantsIt provides on-target and off-target scoring and gRNA sequence analysis. It allows one to choose U3 or U6 sgRNA promoter-driven expression cassettes for designing sgRNAhttp://crispr.hzau.edu.cn/CRISPR2/ (accessed on 3 May 2023)
CRISPR-PLANT V2SpCas9It allows for the design and construction of sgRNAs for CRISPR-Cas9-mediated genome editinghttps://www.genome.arizona.edu/crispr2/ (accessed on 3 May 2023)
CRISPRlncSpCas9It provides a downloadable validated sgRNA databasehttp://www.crisprlnc.org/ (accessed on 3 May 2023)
SNP-CRISPRNGG, NAG, and PAMIt allows for the design of sgRNAs for targeting SNPs or Indelshttps://www.flyrnai.org/tools/snp_crispr/web/ (accessed on 3 May 2023)
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Joshi, A.; Song, H.-G.; Yang, S.-Y.; Lee, J.-H. Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants. Plants 2023, 12, 2454. https://doi.org/10.3390/plants12132454

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Joshi A, Song H-G, Yang S-Y, Lee J-H. Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants. Plants. 2023; 12(13):2454. https://doi.org/10.3390/plants12132454

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Joshi, Alpana, Hyung-Geun Song, Seo-Yeon Yang, and Ji-Hoon Lee. 2023. "Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants" Plants 12, no. 13: 2454. https://doi.org/10.3390/plants12132454

APA Style

Joshi, A., Song, H. -G., Yang, S. -Y., & Lee, J. -H. (2023). Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants. Plants, 12(13), 2454. https://doi.org/10.3390/plants12132454

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