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Review

Molecular Insights into Rice Immunity: Unveiling Mechanisms and Innovative Approaches to Combat Major Pathogens

1
Key Laboratory of Plant Functional Genomics of the Ministry of Education, Agricultural College of Yangzhou University, Yangzhou 225009, China
2
Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, Agricultural College of Yangzhou University, Yangzhou 225009, China
3
Department of Public Health, Medical College, Yangzhou University, Yangzhou 225009, China
4
Microelement Research Center, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
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Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
6
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur City 63100, Pakistan
7
Department of Life Sciences, Western Caspian University, Baku AZ1001, Azerbaijan
8
Department of Industrial Engineering, University of Applied Sciences Technikum Wien, Hoechstaedtplatz 6, 1200 Vienna, Austria
*
Authors to whom correspondence should be addressed.
Plants 2025, 14(11), 1694; https://doi.org/10.3390/plants14111694
Submission received: 24 April 2025 / Revised: 27 May 2025 / Accepted: 29 May 2025 / Published: 1 June 2025
(This article belongs to the Special Issue Rice-Pathogen Interaction and Rice Immunity)

Abstract

:
Rice (Oryza sativa) is a globally important crop that plays a central role in maintaining food security. This scientific review examines the critical role of genetic disease resistance in protecting rice yields, dissecting at the molecular level how rice plants detect and respond to pathogen attacks while evaluating modern approaches to developing improved resistant varieties. The analysis covers single-gene-mediated and multi-gene resistance systems, detailing how on one hand specific resistance proteins, defense signaling components, and clustered loci work together to provide comprehensive protection against a wide range of pathogens and yet their production is severely impacted by pathogens such as Xanthomonas oryzae (bacterial blight) and Magnaporthe oryzae (rice blast). The discussion extends to breakthrough breeding technologies currently revolutionizing rice improvement programs, including DNA marker-assisted selection for accelerating traditional breeding, gene conversion methods for introducing new resistance traits, and precision genome editing tools such as CRISPR/Cas9 for enabling targeted genetic modifications. By integrating advances in molecular biology and genomics, these approaches offer sustainable solutions to safeguard rice yields against evolving pathogens.

1. Introduction

Rice (Oryza sativa) is the primary food crop source for more than half of humanity and is particularly important in Asia, where approximately 92% of the world’s rice-growing area is cultivated [1,2]. As global population projections exceed 8 billion by 2025, agricultural systems face the dual challenge of increasing yields by 50% while transitioning to more sustainable practices [3]. This imperative makes controlling disease-related yield losses not just an agronomic concern, but a critical component of global food security and sustainable development goals. Rice crops continue to face threats from various destructive pathogens that have the potential to have severe economic impacts on agricultural communities around the world [4,5]. The most destructive rice diseases include the fungal rice blast, bacterial leaf blight, sheath blight, and bacterial panicle blight [5,6], each of which presents unique production challenges (Table 1). Rice blast is particularly destructive, with documented yield losses of up to 100% under epidemic conditions; these losses disproportionately affect resource-poor regions where rice constitutes up to 70% of daily caloric intake [7]. In developing countries where rice provides 50–80% of daily calories for over 3 billion people, disease outbreaks exacerbate food insecurity by reducing harvests and increasing market prices, pushing vulnerable populations toward malnutrition. Bacterial leaf blight impairs photosynthesis through leaf damage [8], while sheath blight, which thrives in warm, humid conditions, can reduce yields by 50% [8]. Bacterial panicle blight directly compromises grain development [9]. The insect-borne Tongro virus causes stunted growth and leaf discoloration in rice, significantly reducing yield [8]. Table 1 provides a comprehensive list of the characteristics and economic consequences of these pathogens, highlighting the need for improved control measurements.
Maintaining rice productivity requires an integrated disease management strategy, in which early detection is critical for timely intervention [13]. Traditional diagnostic methods rely on visual symptoms and laboratory testing, which are time-consuming and require technical expertise and therefore have limitations [14]. Understanding rice’s innate defense mechanisms provides a foundation for developing sustainable solutions. The plant immune system operates through a complex two-tiered defense strategy. The first layer, called PAMP-triggered immunity [15], is activated when cell surface receptors recognize conserved microbial patterns, triggering downstream defenses including kinase cascades, oxidative burst, and defense gene activation [16,17] (Figure 1). Pathogens counteract this by inhibiting the effector proteins of PTI, thereby establishing effector-triggered susceptibility [18]. Rice plants overcome this disruption by detecting resistance proteins of pathogen effectors, initiating a more robust effector-triggered immunity response characterized by local cell death and systemic resistance [18]. Modern breeding programs exploit these natural defense systems to develop resistant varieties to reduce reliance on pesticides while ensuring stable production [19].
Modern breeding programs exploit these natural defense mechanisms to develop varieties that reduce pesticide use by 30–50% while maintaining or improving yields [22,23]. Contemporary breeding combines three complementary approaches: conventional hybridization techniques [24], DNA marker-assisted selection, and genetic engineering methods [25,26] (Figure 2). Key to these efforts is the identification and characterization of resistance genes through molecular mapping, gene cloning, and transgenic line development [27]. While natural genetic variation provides the necessary resources, breeders complement this through mutagenesis and targeted genetic modification to overcome the limitations of available diversity [28].
This review systematically examines genetic resistance mechanisms in rice, focusing on molecular interactions between the host and pathogen, genetic determinants of immunity, signaling networks, and defense regulation. With a particular focus on resistance to major fungal, bacterial, and viral pathogens, Figure 2 illustrates modern breeding technologies, including conventional methods, marker-assisted selection, and transgenic approaches, which together have facilitated the development of the next generation of resistant varieties that can meet global food security challenges. By bridging fundamental research with practical breeding applications, we highlight pathways to develop rice varieties that can meet rising global demand while reducing agriculture’s environmental footprint, a crucial step toward achieving both food security and sustainability goals.

2. Rice–Pathogen Interactions at the Genetic Level

Strategic incorporation of resistance genes into rice varieties has become an essential approach for sustainable disease management, environmental protection, and reduced reliance on agrochemicals. Cutting-edge genome editing technologies, particularly the CRISPR-Cas platform, now allow precise modification of the rice genome to enhance defense responses to evolving pathogen populations [33]. Contemporary research efforts have successfully identified multiple genetic components that confer broad-spectrum resistance, including major R genes [34], defense regulatory elements [35], and quantitatively inherited chromosomal regions (QTLs) [36]. Notably, comprehensive genome-wide association analyses have identified key QTL clusters on chromosomes 5, 6, and 9 associated with durable resistance to the bacterial wilt pathogen, providing valuable genetic targets for breeding programs [37].
Recent studies have highlighted the crucial role of microRNAs (miRNAs) in regulating rice immune responses against bacterial and fungal pathogens. For instance, Osa-miR398 has been shown to negatively regulate rice blast resistance by targeting genes involved in reactive oxygen species (ROS) detoxification, including CSD1 and CSD2, thus modulating the oxidative burst during M. oryzae infection [38]. Similarly, Osa-miR164a has been implicated in enhancing resistance to Xanthomonas oryzae pv. oryzae by targeting NAC transcription factors involved in programmed cell death and pathogen defense [39]. These miRNA-target modules demonstrate the layered complexity of post-transcriptional regulation in rice–pathogen interactions and provide promising molecular targets for genetic improvement strategies focused on durable resistance. Deployment of natural resistance genes in commercial rice varieties is often challenging, as they are associated with reduced yield performance and rice quality parameters. This requires a thorough characterization of two fundamentally different resistance mechanisms: qualitative resistance, mediated by a single major gene with a dominant effect, and quantitative resistance, involving the cumulative effects of multiple minor-acting genes. A proper understanding of these complementary systems is essential for breeding rice varieties that achieve the optimal synergy between robust disease resistance and superior agronomic performance.

2.1. Qualitative Resistance Mechanisms in Rice

Qualitative resistance in rice is marked by clear phenotypic differences that follow predictable Mendelian inheritance patterns and are usually controlled by a small number of genes with major effects. These significant R genes provide strong defense against specific pathogen strains and can be efficiently identified and mapped through genetic screens [40]. A well-known example is the Sarawak landrace rice cultivar, where researchers isolated a resistance gene effective against the rice blast fungus, confirming the qualitative nature of this resistance [41]. This type of resistance operates on the gene-for-gene model, where the interaction between the plant’s R genes and the pathogen’s avirulence (Avr) genes triggers a hypersensitive response that blocks pathogen infection [37]. However, pathogens can evolve to overcome this resistance, as seen with rice blast strains that have defeated resistance conferred by genes like Pi2 and Pi9 through mutations in their Avr genes [24,26].
The genetic basis of qualitative resistance involves two key classes of proteins encoded by R genes: receptor-like kinases (RLKs) and nucleotide-binding leucine-rich repeat (NLR) proteins, both crucial for plant immunity [42]. RLKs recognize general pathogen-associated molecular patterns (PAMPs), while NLRs detect specific pathogen effectors. These recognition events activate two defense pathways, PAMP-triggered immunity and effector-triggered immunity [43], leading to the production of reactive oxygen species and antimicrobial compounds that restrict pathogen growth [44]. NLR proteins are particularly effective at inducing localized cell death to contain infections, a key mechanism in preventing pathogen spread [45]. Research on the OsSPK1-OsRac1-RAI1 signaling pathway has revealed a conserved defense mechanism among various NLR proteins in rice [46]. In the rice genome, genes encoding RLKs and NLRs are often clustered in disease resistance hotspots, which frequently overlap with quantitative trait loci (QTLs) linked to disease resistance [47]. For example, QTL-seq analysis identified key resistance regions on chromosomes 1, 9, and 10 against rice ear blight, with RLK and NLR genes as the primary candidates [48].
A major limitation of qualitative resistance is its race specificity, making it susceptible to evolving pathogens [49]. Xanthomonas oryzae pv. oryzae (Xoo), the bacterium causing bacterial blight, is a classic example, as it can adapt to overcome certain R gene defenses. Traditional farming has used multi-line breeding, growing different rice varieties with distinct resistance genes to reduce pathogen selection pressure. Modern breeding programs improve resistance durability by stacking multiple R genes into a single variety [50,51]. The Zhachanglong rice variety is an example, combining Xa3/Xa26, Xa22, and Xa31 genes for broad-spectrum resistance against multiple Xoo strains [52]. Advances in genetic engineering have also been promising. The N46(Xa23R) rice line, developed in Brazil, contains an effector-binding element in the xa23 gene promoter, providing resistance against multiple Xoo and Xoc strains without affecting yield [53].

2.2. Quantitative Resistance in Rice: Key Genetic Advances

Quantitative resistance in rice is governed by a complex network of multiple genetic loci, each contributing small but cumulative effects to overall disease resistance [54]. Unlike qualitative resistance that depends on a single major gene for complete protection against specific pathogens, quantitative resistance offers broader and more durable protection that is less susceptible to pathogen adaptation [55]. This form of resistance involves numerous genes participating in pathogen recognition, signal transmission, and hormonal regulation within the plant [54]. The foundation of quantitative resistance lies in quantitative trait loci (QTLs), which help mitigate diseases like rice streak necrosis virus (RSNV) and false smut [56]. These QTLs are distributed across various chromosomal regions and influence different defensive mechanisms to reduce disease impact. Significant progress has been made in identifying and characterizing these QTLs, providing insights into the genetic architecture of disease resistance. For instance, the qHBV4.1 locus has been established as a major contributor to resistance against white heads disease [57]. Research on false smut has also uncovered genomic regions rich in resistance genes, with the QTL qRFSr9.1 on chromosome 9 showing particularly strong phenotypic effects, making it a prime target for breeding programs. These QTLs correlate with critical resistance indicators such as infection rates per plant and smut ball formation per panicle [58].
Additional studies have deepened our understanding of resistance mechanisms. Research by Inoue and Hayashi demonstrated that the qPbm11 QTL, which provides blast resistance in Miyazaki Mochi varieties, functions independently of the known Pb1 gene [59]. This finding suggests that combining multiple QTLs through gene pyramiding could enhance blast resistance. Similarly, genome-wide association studies by Zhang et al. [60] highlighted the significance of jasmonic acid and salicylic acid pathways in regulating resistance to sheath blight, suggesting that these hormonal pathways may be potential targets for breeding strategies.
Further discoveries include the identification of qRFS12.01, a novel QTL associated with false smut resistance, emphasizing the value of quantitative resistance given the absence of completely resistant rice varieties [61,62]. Through QTL analysis, researchers mapped a new resistance gene, OsDRq12, to chromosome 12. This gene belongs to the NLR family and significantly boosts disease resistance in rice cultivars [63]. Large-scale genome-wide association studies have identified 74 QTLs linked to resistance against panicle blight and leaf blight, with the qPBR1 locus showing particularly strong, development-stage-independent resistance [64]. Research by Okello et al. [37], using the MAGIC indica panel, pinpointed three QTLs on chromosomes 5, 6, and 9 that confer broad resistance against African bacterial blight strains, underscoring the need for novel resistance genes against evolving pathogens. An important development has been the strategic combination of multiple QTLs, particularly those conferring resistance to major diseases like blast, sheath blight, and bacterial blight, into clusters within specific chromosomal regions (Table 2) [43,65]. This clustering not only refines the genetic targeting of QTLs but also facilitates the identification of candidate genes for breeding programs. These advances enable scientists to substantially enhance rice resistance and develop more sustainable disease management approaches in rice cultivation [66,67].

3. Gene-for-Gene Concept in Rice Disease Resistance

The gene-for-gene concept forms a fundamental framework for understanding rice–pathogen interactions and plays a crucial role in developing disease-resistant rice varieties. Originally proposed by Harold Flor in the 1950s [75], this model establishes that specific R genes in the host plant interact with corresponding Avr genes in the pathogen. Extensive research in rice has validated this principle through studies of its interactions with major pathogens, including Xoo and M. oryzae, revealing the intricate molecular interplay between host defenses and pathogen virulence mechanisms [76].
This concept has been particularly well documented in rice’s defense against bacterial blight and blast disease, where the recognition of pathogen Avr proteins by plant R proteins triggers a strong immune response (Figure 3). The interaction follows a precise molecular recognition system, where the presence of both matching R and Avr genes leads to resistance, while the absence or mutation in either component can result in susceptibility. These findings have not only confirmed Flor’s original hypothesis but have also provided critical insights for breeding programs aiming to develop durable resistance in rice cultivars through the strategic deployment of R genes. The elucidation of genetic mechanisms such as gene-for-gene interactions and the identification of specific resistance loci (e.g., Pi and Xa genes) have provided a strong molecular foundation for modern rice breeding. These insights not only clarify how plants mount defense responses but also guide the strategic use of breeding technologies, such as marker-assisted selection and CRISPR-based genome editing to introduce, pyramid, or fine-tune resistance traits in elite cultivars. The following sections build upon these genetic principles by examining how breeders translate them into practical strategies to develop resilient, high-yielding rice varieties capable of withstanding evolving pathogen threats.

3.1. Gene-for-Gene Resistance Mechanisms in Rice Against Xanthomonas oryzae

The interaction between rice and Xanthomonas oryzae (Xoo) operates through a precise gene-for-gene relationship, where specific R genes in rice recognize corresponding Avr genes in the pathogen [79]. This molecular recognition system serves as a cornerstone of rice immunity against bacterial blight. A well-characterized example is the Xa23 gene in rice, which confers resistance to Xoo strains carrying the matching avrXa23 gene [53]. Similarly, other R genes, including Xa3, Xa2, xa5, and xa8, recognize their respective Avr counterparts (avrXa3, avrXa2, avrxa5, avrxa8) and provide resistance in compatible rice varieties [24]. When an R protein detects its cognate Avr effector, it triggers a robust immune response. For instance, Xa3 detects pathogen-associated molecular patterns (PAMPs) on bacterial membranes, activating localized defense reactions that restrict pathogen spread [80]. This often leads to hypersensitive cell death at infection sites, creating a physical barrier against further invasion.
Central to this process is Xoo’s type III secretion system (T3SS), which delivers Avr effectors directly into rice cells [81]. Recent studies have elucidated key aspects of these interactions. For example, the Avr effector Xa7 binds to the promoter region of the rice Xa7 resistance gene, inducing a hypersensitive response that suppresses bacterial growth [82]. Similarly, research by Zou et al. [83] demonstrated how Avr recognition activates rice defense pathways, effectively halting disease progression. These insights highlight the potential for leveraging R-Avr interactions to engineer broad-spectrum resistance in rice breeding programs. A critical feature of rice–Xoo interactions involves transcription activator-like effectors (TALEs), which Xoo secretes via T3SS to manipulate host gene expression [84]. TALEs function as virulence factors by activating susceptibility genes or suppressing plant immunity [85]. However, rice has evolved countermeasure R genes like Xa1, Xa10, and Xa23 that detect specific TALEs and mount a hypersensitive response to block infection [53].
This defense is sometimes circumvented by Xoo strains producing interfering TALEs (iTALEs), which disrupt R gene recognition and enable immune evasion [84]. Such adaptations underscore the ongoing evolutionary arms race between rice and Xoo [86]. The avrBs3/pthA gene family in Xoo plays a particularly significant role in modulating resistance. These genes, which may exist singly or in clusters within the pathogen genome, influence resistance patterns in rice [87]. The gene-for-gene model explains why specific cultivars are resistant to bacterial blight while others remain susceptible. Notably, some R genes (e.g., Xa3 and Xa21) share signaling pathways, suggesting partially overlapping yet distinct defense mechanisms [88]. Many R genes, including Xa3, Xa26, and Xa4, encode receptor-like kinases (RLKs) that recognize PAMPs and initiate immune responses such as cell wall reinforcement and defense pathway activation [51]. These RLKs are pivotal components of rice immunity, and deciphering their interactions could inform strategies for developing disease-resistant rice varieties with durable immunity. By harnessing this knowledge, breeders can design rice cultivars with stacked R genes or edited promoter regions to outpace pathogen evolution and sustain crop protection.

3.2. Gene-for-Gene Resistance Mechanisms in Rice Against Magnaporthe oryzae

The genetic interactions between rice and the rice blast fungus Magnaporthe oryzae (formerly M. grisea) exemplify a sophisticated coevolutionary arms race. The rice blast fungus spreads through spores carried by the wind, germinating on rice seedlings and forming adhesive organs to penetrate the tissue. Inside the host, it causes damage, produces new spores, and completes this cycle in 5 to 7 days. The pathogen can survive in infected residues and seeds during the season, leading to recurrent outbreaks (Figure 4). At the core of this battle are specific R genes in rice, particularly the Pi genes (Pi-ta, Pia, Pii) that recognize the corresponding Avr genes in the pathogen [5]. When a rice plant carrying a Pi gene encounters a blast strain with the matching Avr effector, it triggers a hypersensitive response that halts fungal invasion. The Pi-ta/AVR-Pita interaction serves as a paradigm: the cytoplasmic NLR protein encoded by Pi-ta directly binds the AVR-Pita effector, initiating defense responses, including localized cell death, to contain the infection [89].
This recognition system drives continuous adaptation on both sides. Pathogen populations evolve through Avr gene mutations and haplotype diversification to evade detection, as seen in variants like AvrPi54 and AvrPii [92]. The emergence of novel effectors (e.g., AVR-Pi9, AVR-Mgk1) demonstrates the pathogen’s ability to circumvent existing resistance, necessitating ongoing surveillance and adaptive breeding. To date, researchers have documented over 30 rice R genes and 12 M. oryzae Avr genes, revealing diverse recognition mechanisms [44]. While some NLR receptors like Pi-ta detect effectors through direct binding, others (e.g., Pik) employ integrated decoy domains for indirect recognition, as shown in the Pik/AVR-Pik and Pia/AVR-Pia systems [93,94]. The evolutionary dynamics vary across rice subspecies, with indica and japonica cultivars often exhibiting distinct resistance spectra due to differential pathogen adaptation. Breeding strategies now emphasize pyramiding multiple Pi genes (e.g., Pi2, Pi9, Pi54) to create durable, broad-spectrum resistance [95]. The application of Pi genes in breeding programs has led to the development of several successful rice cultivars with enhanced resistance to blast. For instance, the cultivar IRBL9-W incorporates the Pi9 gene and has shown durable resistance to a wide range of M. oryzae strains. Similarly, Putta Basmati 1509, which combines Pi2 and Pi54, has been widely adopted in India due to its broad-spectrum blast resistance. These examples demonstrate how knowledge of specific R-Avr interactions can be harnessed to develop and deploy resistant varieties in real-world agriculture. This approach leverages the observation that combined R genes can collectively block diverse fungal strains. The continued identification of novel Avr genes and their interactions with host NLR proteins remains critical for developing next-generation blast-resistant rice, particularly as climate change accelerates pathogen evolution. These efforts are further supported by advances in effectoromics, which enable systematic screening of Avr gene diversity in field populations to predict and counteract emerging virulence trends [96].

4. MAPK Signaling in Rice Immunity: Key Roles in Defense Against Xoo

Mitogen-activated protein kinase (MAPK) cascades serve as central regulators of rice immune responses against Xoo infection. These signaling pathways are rapidly activated upon pathogen recognition, initiating phosphorylation cascades that amplify defense mechanisms [97]. Transcriptomic analyses reveal that MAPK-mediated signaling drives critical defensive processes, including cell wall fortification and biosynthesis of antimicrobial compounds [98]. Key components like OsMKK6 and OsMPK4 form an interconnected network that enhances resistance to bacterial blight [97]. Within hours of Xoo infection, MAPKs such as OsMPK3, OsMPK4, and OsMPK6 are activated, implicating their role in early defense responses [99]. These kinases phosphorylate transcription factors, including WRKY13 and WRKY45, which subsequently orchestrate the expression of defense-related genes [100]. This coordinated action bridges local and systemic immunity, enabling comprehensive pathogen resistance [101,102].
Concurrently, MAPK signaling induces structural defenses, such as callose deposition and lignin biosynthesis, thereby reinforcing physical barriers against bacterial invasion [91]. However, Xoo employs counterstrategies to subvert these defenses, beyond its well-characterized TAL effectors; the pathogen secretes additional virulence factors that actively suppress MAPK activation [103]. This highlights the dynamic interplay between rice immune signaling and bacterial evasion tactics. Deciphering the architecture of the MAPK cascade and its manipulation by Xoo provides critical insights for developing novel resistance strategies [104]. By targeting specific nodes within this pathway, either through genetic engineering or precision breeding, researchers can potentially engineer rice varieties with enhanced, durable resistance to bacterial blight. These approaches could focus on stabilizing MAPK activation or blocking effector-mediated suppression to maintain robust immune responses.

5. Conventional Breeding for Disease-Resistant Rice: Challenges and Advances

Conventional breeding has long served as the foundation for developing disease-resistant rice varieties, helping to safeguard yields and ensure global food security. Through methods such as phenotypic selection, controlled crossing, and backcrossing, breeders have successfully introduced resistance to major diseases like rice blast, bacterial blight, and sheath blight [105,106]. A key strategy involves transferring resistance genes from wild relatives or naturally resistant landraces into high-yielding but susceptible elite cultivars. Notable examples include the introgression of the Pi2 and Pi9 blast resistance genes into commercial rice varieties, significantly enhancing protection against this devastating fungal pathogen [107]. Despite its successes, conventional breeding faces several limitations. A major challenge is linkage drag, where undesirable traits from donor plants are inadvertently transferred alongside resistance genes. This can negatively impact critical agronomic qualities such as yield potential, grain quality, or stress tolerance, reducing farmer adoption of new varieties [108]. Additionally, traditional breeding is inherently slow, often requiring 8 to 12 generations of meticulous crossing and backcrossing to achieve the ideal combination of disease resistance and superior agronomic performance [109]. Another critical issue is the durability of resistance: pathogens can rapidly evolve to overcome single-gene resistance introduced through conventional methods, leading to breakdowns in field efficacy [110]. Furthermore, balancing resistance with essential traits like high productivity remains an ongoing challenge, as some resistance mechanisms may incur fitness costs or alter plant physiology in ways that compromise yield [111].
To address these constraints, modern breeding has adopted marker-assisted selection as a complementary tool. By using DNA markers linked to resistance genes, breeders can precisely track and select desired traits while minimizing linkage drag [112]. This approach accelerates the development of resilient varieties that maintain yield and quality, bridging the gap between traditional breeding and advanced biotechnological solutions. While conventional methods remain indispensable, integrating MAS and other precision breeding techniques offers a pathway to more efficient and sustainable disease management in rice cultivation.

5.1. Marker-Assisted Selection in Rice Breeding: Successes, Challenges, and Future Directions

Marker-assisted selection (MAS) has revolutionized rice breeding by enabling precise introgression of resistance genes into elite varieties, significantly enhancing their ability to combat major pathogens [113]. This approach has proven particularly effective against devastating diseases like rice blast and bacterial blight, allowing breeders to develop cultivars with durable, broad-spectrum resistance [28]. The technique’s success is evident in several landmark achievements: in China, resistant lines such as Huahui 7713 and Huahui 3006 were developed by incorporating the Pigm, Bph6, and Bph9 genes, leading to high-yielding hybrids like Weiliangyou 7713 that maintain both disease resistance and superior grain quality [28]. Similar success was seen in India, where MAS introduced Xa21, xa13, and xa5 into aromatic rice varieties, creating lines with robust bacterial blight resistance without compromising desirable traits [15].
However, MAS faces significant challenges that limit its effectiveness. The rapid evolution of pathogens can render resistance genes ineffective over time, as seen with some Xanthomonas oryzae strains that have overcome Xa23-mediated resistance [53]. Additionally, the process of stacking multiple resistance genes remains technically demanding and time-consuming, complicated by genetic interactions and environmental influences [114]. Perhaps most critically, MAS typically targets specific pathogens or strains, leaving crops susceptible to emerging diseases or new pathogen variants [115]. These limitations highlight the need for complementary approaches to ensure durable resistance. Looking ahead, the integration of MAS with emerging technologies offers promising solutions [116]. CRISPR/Cas9 genome editing enables precise modification of resistance genes or their regulatory elements, potentially broadening and stabilizing resistance [117]. High-throughput phenotyping accelerates the identification and validation of resistance traits, while combining MAS with integrated pest management strategies could provide more sustainable disease control [118]. Despite its challenges, MAS remains an indispensable tool in rice breeding, though its long-term success will depend on strategic integration with these advanced approaches and careful consideration of region-specific agricultural challenges. While MAS has significantly enhanced breeding precision, several limitations persist. Stacking multiple resistance genes remains technically complex due to epistatic interactions and linkage drag, where undesirable traits may co-segregate with beneficial alleles. Environmental interactions may also affect the expression of QTLs or resistance genes, leading to genotype-by-environment variability in disease response. To overcome these hurdles, breeders are now integrating MAS with genomic selection (GS) and high-throughput phenotyping platforms, which allow for the simultaneous selection of multiple traits with greater predictive power. Furthermore, the use of tightly linked or gene-specific markers, such as SNPs derived from resistance gene sequences, has improved selection accuracy and reduced linkage drag. These advances make MAS more robust, particularly in combination with other precision breeding tools.

5.2. CRISPR/Cas9: A Revolutionary Tool for Enhancing Disease Resistance in Rice

CRISPR/Cas9 technology has revolutionized rice breeding by enabling precise genome editing to enhance disease resistance, particularly against bacterial blight [53]. The system works by using a designed single-guide RNA (sgRNA) to direct the Cas9 nuclease to specific DNA sequences, creating double-strand breaks that are subsequently repaired through either error-prone non-homologous end joining (NHEJ) or precise homology-directed repair (HDR) (Figure 5) [119]. This approach has successfully generated rice plants with improved resistance to both bacterial blight and rice blast diseases [120]. A groundbreaking application involves editing the OsSWEET14 susceptibility gene, which Xoo exploits through its transcription activator-like effectors (TALEs) [121]. Researchers used CRISPR/Cas9 to disrupt effector-binding elements (EBEs) in the OsSWEET14 promoter of Super Basmati rice, creating edited lines (SB-E1 to SB-E4) that showed significantly reduced lesion lengths and enhanced resistance compared to wild-type plants [122]. This strategy demonstrates how targeted editing of host susceptibility factors can confer resistance without introducing foreign DNA, offering a sustainable solution for disease management [123]. In fungal disease control, deletion of the Bsr-d1 susceptibility gene enhanced blast resistance in Japonica rice, with protective effects evident from the seedling stage. Multiplex editing has proven particularly powerful, as shown by simultaneous modification of Pi21 and OsSULTR3;6 genes, which conferred dual resistance to blast and bacterial leaf spot while preserving yield potential [124]. Researchers have also successfully targeted systemic defense pathways, such as creating OsS5H mutants that exhibit broad-spectrum resistance through salicylic acid-mediated defense activation [125].
CRISPR/Cas9’s precision allows for sophisticated modifications like promoter engineering, exemplified by editing the xa23 gene promoter to incorporate multiple EBEs, resulting in durable resistance to bacterial blight and streak [53]. Importantly, these genetic improvements can be achieved without compromising plant growth or grain quality. The integration of CRISPR technology with conventional breeding and other biotechnological tools presents a comprehensive strategy for developing next-generation rice varieties that combine high productivity with robust, durable disease resistance and a critical advancement for global food security in an era of evolving pathogen threats. Despite the promise of CRISPR/Cas9 in rice disease resistance breeding, the technique is not without limitations. Off-target mutations, unintended edits in non-target regions, pose a risk of unwanted phenotypic changes or compromised plant fitness. Breeders are actively mitigating these concerns by employing high-fidelity Cas9 variants (e.g., SpCas9-HF1, eSpCas9) and guide RNA design tools that increase target specificity. Additionally, delivery methods such as ribonucleoprotein complexes (RNPs) reduce the risk of stable integration and transiently expose the genome to editing components, further minimizing off-target effects. Beyond technical issues, regulatory hurdles, especially in regions where genome-edited crops are treated similarly to GMOs, remain a significant challenge. To address this, researchers are focusing on non-transgenic genome editing approaches, such as using CRISPR to generate edits without integrating foreign DNA, which may ease regulatory acceptance in some jurisdictions.

6. Development and Impact of Disease-Resistant Rice Varieties

Disease-resistant rice varieties represent a strategic breakthrough in combating major rice pathogens through the targeted incorporation of resistance genes that strengthen the plant’s innate defense mechanisms. These genetically enhanced cultivars have substantially decreased dependence on chemical pesticides while promoting sustainable crop production and boosting yield stability [128]. By integrating specific resistance traits, these varieties maintain consistent productivity even under significant disease pressure, establishing themselves as indispensable components of modern rice cultivation systems.
Several high-performing varieties exemplify this approach through their effective management of devastating diseases like bacterial blight and rice blast [129]. These cultivars, often developed through meticulous breeding programs, showcase how genetic resistance can be practically deployed to prevent disease outbreaks and minimize yield losses. Their success stems from incorporating well-characterized resistance genes that trigger robust immune responses upon pathogen recognition. The effectiveness of these varieties is evident in their widespread adoption across different rice-growing regions, where they have demonstrated reliable performance against evolving pathogen populations. Table 3 summarizes key disease-resistant rice varieties along with their incorporated resistance genes, highlighting their specific roles in controlling major rice diseases. These examples underscore the critical need for ongoing research and breeding innovation to develop new resistant varieties capable of countering emerging pathogen strains while maintaining optimal agronomic performance. The continued development and deployment of such varieties remain essential for ensuring global rice security in the face of persistent and evolving disease threats.

7. Environmental Impact of Disease-Resistant Rice Varieties

The development of disease-resistant rice varieties represents a critical advancement in sustainable agriculture, offering a powerful solution to reduce pesticide dependence while addressing major threats to global rice production like bacterial leaf blight and rice blast [104]. These genetically enhanced cultivars provide multiple environmental benefits, primarily through dramatically decreased pesticide application. This reduction lowers farming costs while preventing chemical runoff that contaminates waterways and soils, thereby protecting aquatic ecosystems and maintaining soil health [28,137]. Beyond pollution control, disease-resistant varieties actively promote biodiversity conservation in rice-growing regions. By minimizing broad-spectrum pesticide use, they safeguard beneficial insects, soil microbes, and aquatic organisms that form the foundation of healthy agroecosystems [138]. This preserved biodiversity enhances natural pest control, improves soil fertility, and increases ecosystem resilience to climate variability, all crucial factors for sustainable rice production [139,140].
However, these benefits must be balanced against potential ecological risks, particularly concerning gene flow to wild rice populations. Uncontrolled transfer of resistance genes through cross-pollination could alter the genetic diversity of wild relatives, potentially compromising their natural adaptive capacity to environmental stresses [141]. Such genetic contamination might disrupt ecological balances and reduce the genetic reservoirs needed for future crop improvement [142]. To maximize benefits while minimizing risks, strategic implementation is essential. This includes maintaining buffer zones around resistant varieties, continuous monitoring of wild populations, and developing containment strategies for engineered genes. When properly managed, disease-resistant rice varieties serve as a cornerstone of sustainable intensification, simultaneously boosting food security and environmental protection [143]. Their responsible deployment demonstrates how agricultural innovation can align with ecological preservation to meet the dual challenges of productivity and sustainability in rice farming systems.

8. The Future of Disease-Resistant Rice: Challenges and Opportunities

As global rice demand rises, developing disease-resistant varieties is essential to safeguarding food security. The field faces both transformative opportunities and complex challenges that demand innovation and cross-disciplinary collaboration. Cutting-edge gene editing tools like CRISPR/Cas9 have revolutionized rice breeding, enabling precise modifications to disrupt susceptibility genes or introduce robust resistance traits. Yet, hurdles remain; improving editing efficiency, minimizing off-target effects, and navigating regulatory landscapes must be addressed to fully realize this technology’s potential. Climate change adds urgency to these efforts, as shifting temperatures and weather patterns alter pathogen dynamics and geographic ranges. Future rice varieties must combine disease resistance with resilience to abiotic stresses like drought, salinity, and extreme heat. This requires integrated breeding strategies that simultaneously target biotic and abiotic pressures, ensuring adaptability in a changing environment. Pathogen evolution remains a persistent threat, necessitating durable solutions. Approaches like gene pyramiding (stacking multiple R genes), harnessing QTLs for stable partial resistance, and mining wild rice germplasm for novel resistance sources will be critical. These strategies can extend the longevity of resistance traits while reducing reliance on chemical controls. Breakthroughs in multiomics technologies (genomics, transcriptomics, proteomics, and metabolomics) promise to deepen our understanding of rice–pathogen interactions. By uncovering new resistance mechanisms and precise molecular targets, these tools can accelerate the development of precision-bred varieties with enhanced defenses.
However, technological advances must align with sustainable agricultural practices. Reducing pesticide dependence through resistant varieties should complement integrated pest management (IPM) systems, ensuring ecological balance. Equally important is addressing societal and regulatory concerns, fostering transparency, engaging stakeholders, and establishing science-based policies will be key to deploying these innovations globally. The path forward hinges on balancing innovation with responsibility. By uniting advanced breeding tools, climate-smart strategies, and ecological stewardship, next-generation rice varieties can deliver both high yields and long-term sustainability, securing food systems for future generations.

9. Conclusions

The field of disease-resistant rice development has undergone transformative progress through genetic and molecular breakthroughs. The gene-for-gene model has served as a cornerstone for deciphering plant–pathogen interactions, guiding the identification and utilization of critical resistance genes like the Xa series against bacterial blight and Pi genes against rice blast. These discoveries have revolutionized breeding methodologies, enabling precision strategies such as marker-assisted selection and gene pyramiding to create robust, high-performing rice varieties. Emerging technologies, particularly CRISPR-based genome editing, have further expanded the toolkit for enhancing disease resistance. By enabling targeted modifications of susceptibility genes or regulatory elements, these approaches allow for the development of resistant cultivars without compromising yield or quality. Coupled with growing insights into plant immune mechanisms, from pathogen recognition to defense signaling cascades, these innovations are making resistance breeding more efficient and effective. However, the rapid evolution of pathogens threatens to overcome single-gene resistance, while the polygenic nature of quantitative resistance complicates breeding efforts. Additionally, integrating disease resistance with other vital traits, such as drought tolerance or grain quality, remains a delicate balancing act. Climate change exacerbates these challenges by altering pathogen distributions and infection dynamics, demanding more adaptable varieties. Moving forward, a multi-disciplinary, integrated approach will be essential. Combining traditional breeding with cutting-edge molecular tools, genomic selection, and high-throughput phenotyping can accelerate the development of durable, broad-spectrum resistance. Equally critical is the adoption of sustainable agricultural practices, such as diversified cropping systems and integrated pest management, to prolong resistance efficacy. Success will hinge on strengthened collaboration among breeders, pathologists, molecular biologists, and agronomists to address these complex, interconnected challenges. By leveraging advances in science while maintaining ecological and agronomic balance, the global community can ensure rice remains a resilient, productive staple crop in the face of evolving threats to food security.

Author Contributions

M.U.Y. participated in the writing, reviewing, and editing of the original manuscript. S.Z. provided funding and contributed to the reviewing and editing of the manuscript. B.R., I.A., M.Q., Z.F., G.W., Q.S. and X.X. helped with the literature review and participated in writing the original manuscript. R.I. and M.L. revised the manuscript and eliminated grammatical mistakes. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially supported by the Jiangsu Provincial Agricultural Science and Technology Innovation Fund (CX(23)3001), the Jiangsu Province Government (JBGS [2021]001), and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Younas, M.U.; Wang, G.; Du, H.; Zhang, Y.; Ahmad, I.; Rajput, N.; Li, M.; Feng, Z.; Hu, K.; Khan, N.U. Approaches to reduce rice blast disease using knowledge from host resistance and pathogen pathogenicity. Int. J. Mol. Sci. 2023, 24, 4985. [Google Scholar] [CrossRef] [PubMed]
  2. Bag, M.K.; Raghu, S.; Banerjee, A.; Prabhukarthikeyan, S.R.; Baite, M.S.; Yadav, M. Durable resistance of rice to major and emerging diseases: Current status. Open Agric. J. 2023, 17. [Google Scholar] [CrossRef]
  3. Younas, M.U.; Qasim, M.; Ahmad, I.; Feng, Z.; Iqbal, R.; Jiang, X.; Zuo, S. Exploring the molecular mechanisms of rice blast resistance and advances in breeding for disease tolerance. Mol. Biol. Rep. 2024, 51, 1093. [Google Scholar] [CrossRef]
  4. Rajput, N.; Younas, M.U.; Qasim, M.; Memon, S.P.; Memon, S.; El-Rahman, M.A.; Aghayeva, S.; Ercisli, S.; Iqbal, R.; Zuo, S. Understanding rice blast: Investigating biotechnological methods to speed up the development of robust rice cultivars. Genet. Resour. Crop Evol. 2024, 72, 1333–1352. [Google Scholar] [CrossRef]
  5. Younas, M.U.; Ahmad, I.; Qasim, M.; Ijaz, Z.; Rajput, N.; Parveen Memon, S.; UL Zaman, W.; Jiang, X.; Zhang, Y.; Zuo, S. Progress in the management of Rice Blast Disease: The role of Avirulence and Resistance genes through gene-for-gene interactions. Agronomy 2024, 14, 163. [Google Scholar] [CrossRef]
  6. Gülmez, B. Advancements in rice disease detection through convolutional neural networks: A comprehensive review. Heliyon 2024, 10, e33328. [Google Scholar] [CrossRef]
  7. Karam, N.; Kumar, P.; Anand, Y.R.; Choudhury, D. Management of Rice Blast—A Review of the Strategies Available. Biopestic. Int. 2023, 19. [Google Scholar] [CrossRef]
  8. Sabar, M.; Sana-e-Mustafa, M.I.; Khan, R.A.R.; Fatima, R.; Saher, H.; Shahzadi, F.; Javed, H.M.; Zafar, S.A.; Siddique, S.; Saleem, M.U. Sheath Blight and Bacterial Blight Resistance in Rice: Mechanisms, Progress and Future Perspectives for Sustainable Rice Production. Plant Bull. 2024, 3, 102–112. [Google Scholar] [CrossRef]
  9. Islam, M.R.; Jannat, R.; Protic, I.A.; Happy, M.N.A.; Samin, S.I.; Mita, M.M.; Bashar, S.; Masud, M.M.; Islam, M.H.; Uddin, M.N. First report of bacterial panicle blight in rice caused by Burkholderia gladioli in Bangladesh. Plant Dis. 2023, 107, 2837. [Google Scholar] [CrossRef]
  10. Nalley, L.L.; Tsiboe, F.; Durand-Morat, A.; Shew, A.; Thoma, G. Economic and Environmental Impact of Rice Blast Pathogen (Magnaporthe oryzae) Alleviation in the United States. PLoS ONE 2016, 11, e0167295. [Google Scholar] [CrossRef]
  11. Mew, T.W.; Vera Cruz, C.M.; Medalla, E.S. Changes in Race Frequency of Xanthomonas oryzae pv. oryzae in Response to Rice Cultivars Planted in the Philippines. Plant Dis. 1992, 76, 1029–1032. [Google Scholar] [CrossRef]
  12. Sahebi, M.; Hanafi, M.M.; Rafii, M.Y.; Mahmud, T.M.M.; Azizi, P.; Osman, M.; Taheri, S. Molecular Breeding Strategies and Challenges Towards Improvement of Blast Disease Resistance in Rice Crop. Front. Plant Sci. 2015, 6, 886. [Google Scholar] [CrossRef]
  13. Shrivastava, V.K.; Pradhan, M.K.; Minz, S.; Thakur, M.P. Rice plant disease classification using transfer learning of deep convolution neural network. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2019, 42, 631–635. [Google Scholar] [CrossRef]
  14. Khakimov, A.; Salakhutdinov, I.; Omolikov, A.; Utaganov, S. Traditional and current-prospective methods of agricultural plant diseases detection: A review. IOP Conf. Ser. Earth Environ. Sci. 2022, 1043, 012002. [Google Scholar] [CrossRef]
  15. Kumar, M.; Singh, R.P.; Jena, D.; Singh, V.; Rout, D.; Arsode, P.B.; Choudhary, M.; Singh, P.; Chahar, S.; Samantaray, S. Marker-Assisted improvement for durable bacterial blight resistance in aromatic rice cultivar HUR 917 popular in Eastern parts of India. Plants 2023, 12, 1363. [Google Scholar] [CrossRef] [PubMed]
  16. Zhang, C.; Xie, Y.; He, P.; Shan, L. Unlocking nature’s defense: Plant pattern recognition receptors as guardians against pathogenic threats. Mol. Plant-Microbe Interact. 2024, 37, 73–83. [Google Scholar] [CrossRef]
  17. Giulietti, S.; Bigini, V.; Savatin, D.V. ROS and RNS production, subcellular localization, and signaling triggered by immunogenic danger signals. J. Exp. Bot. 2024, 75, 4512–4534. [Google Scholar] [CrossRef]
  18. Dodds, P.N.; Rathjen, J.P. Plant immunity: Towards an integrated view of plant–pathogen interactions. Nat. Rev. Genet. 2010, 11, 539–548. [Google Scholar] [CrossRef]
  19. Ijaz, U.; Zhao, C.; Shabala, S.; Zhou, M. Molecular Basis of Plant–Pathogen Interactions in the Agricultural Context. Biology 2024, 13, 421. [Google Scholar] [CrossRef]
  20. Jones, J.D.G.; Dangl, J.L. The plant immune system. Nature 2006, 444, 323–329. [Google Scholar] [CrossRef]
  21. Bigeard, J.; Colcombet, J.; Hirt, H. Signaling mechanisms in pattern-triggered immunity (PTI). Mol. Plant 2015, 8, 521–539. [Google Scholar] [CrossRef] [PubMed]
  22. Chen, L.; Ma, Y.; He, T.; Chen, T.; Pan, Y.; Zhou, D.; Li, X.; Lu, Y.; Wu, Q.; Wang, L. Integrated transcriptome and metabolome analysis unveil the response mechanism in wild rice (Zizania latifolia griseb.) against sheath rot infection. Front. Genet. 2023, 14, 1163464. [Google Scholar] [CrossRef]
  23. Leung, H.; Zhu, Y.; Revilla-Molina, I.; Fan, J.X.; Chen, H.; Pangga, I.; Cruz, C.V.; Mew, T.W. Using genetic diversity to achieve sustainable rice disease management. Plant Dis. 2003, 87, 1156–1169. [Google Scholar] [CrossRef] [PubMed]
  24. Tian, D.; Deng, Y.; Yang, X.; Li, G.; Li, Q.; Zhou, H.; Chen, Z.; Guo, X.; Su, Y.; Luo, Y. Association analysis of rice resistance genes and blast fungal avirulence genes for effective breeding resistance cultivars. Front. Microbiol. 2022, 13, 1007492. [Google Scholar] [CrossRef]
  25. Gilbert, G.; Parker, I. The Evolutionary Ecology of Plant Disease; Oxford University Press: Oxford, UK, 2023. [Google Scholar]
  26. Younas, M.U.; Qasim, M.; Ahmad, I.; Feng, Z.; Iqbal, R.; Abro, T.F.; Ahmad, S.; Jiang, X.; Rajput, N.; Zuo, S. Integrated Approaches for Enhancing Magnaporthe oryzae Resistance: Mechanisms and Breeding Strategies. Plant Mol. Biol. Rep. 2025. [Google Scholar] [CrossRef]
  27. Naidu, B.N.; Madhavilatha, L.; Maraskole, S.K.; Panotra, N.; Singh, O.B.; Kumar, A.; Devi, O.R.; Laishram, B.; Anbarasan, S. Enhancing the Genetic Understanding of Rice for Strategic Breeding of High Yielding and Superior Quality Varieties. J. Adv. Biol. Biotechnol. 2024, 27, 1252–1261. [Google Scholar] [CrossRef]
  28. Deng, Z.; Qin, P.; Liu, K.; Jiang, N.; Yan, T.; Zhang, X.; Fu, C.; He, G.; Wang, K.; Yang, Y. The development of multi-resistant rice restorer lines and hybrid varieties by pyramiding resistance genes against blast and brown planthopper. Agronomy 2024, 14, 878. [Google Scholar] [CrossRef]
  29. Qu, S.; Liu, G.; Zhou, B.; Bellizzi, M.; Zeng, L.; Dai, L.; Han, B.; Wang, G.-L. The Broad-Spectrum Blast Resistance Gene Pi9 Encodes a Nucleotide-Binding Site–Leucine-Rich Repeat Protein and Is a Member of a Multigene Family in Rice. Genetics 2006, 172, 1901–1914. [Google Scholar] [CrossRef]
  30. Ramalingam, J.; Raveendra, C.; Savitha, P.; Vidya, V.; Chaithra, T.L.; Velprabakaran, S.; Saraswathi, R.; Chandrababu, R. Gene Pyramiding for Achieving Enhanced Resistance to Bacterial Blight, Blast, and Sheath Blight Diseases in Rice. Front. Plant Sci. 2020, 11, 591457. [Google Scholar] [CrossRef]
  31. Oliva, R.; Ji, C.; Atienza-Grande, G.; Huguet-Tapia, J.C.; Perez-Quintero, A.; Li, T.; Eom, J.-S.; Li, C.; Nguyen, H.; Liu, B.; et al. Broad-Spectrum Resistance to Bacterial Blight in Rice Using Genome Editing. Nat. Biotechnol. 2019, 37, 1344–1350. [Google Scholar] [CrossRef]
  32. Koch, A.; Biedenkopf, D.; Furch, A.; Weber, L.; Rossbach, O.; Abdellatef, E.; Linicus, L.; Johannsmeier, J.; Jelonek, L.; Goesmann, A.; et al. An RNAi-Based Control of Fusarium graminearum Infections Through Spraying of Long dsRNAs Involves a Plant Passage and Is Controlled by the Fungal Silencing Machinery. PLoS Pathog. 2016, 12, e1005901. [Google Scholar] [CrossRef] [PubMed]
  33. Sang, S.; Wang, Y.; Yao, G.; Ma, T.; Sun, X.; Zhang, Y.; Su, N.; Tan, X.; Abbas, H.M.K.; Ji, S. A Critical Review of Conventional and Modern Approaches to Develop Herbicide-Resistance in Rice. Physiol. Plant. 2024, 176, e14254. [Google Scholar] [CrossRef] [PubMed]
  34. Maeda, S.; Goto, S.; Inoue, H.; Suwazono, H.; Takatsuji, H.; Mori, M. Improvement of Broad-Spectrum Disease-Resistant Rice by the Overexpression of BSR1 via a Moderate-Strength Constitutive Promoter and a Pathogen-Inducible Promoter. Plants 2024, 13, 1138. [Google Scholar] [CrossRef]
  35. Gong, Q.; Sha, G.; Han, X.; Guo, Z.; Yang, L.; Yang, W.; Tan, R.; Chen, G.; Li, Y.; Shen, X. Mutation of phosphatidate phosphohydrolase genes confers broad-spectrum disease resistance in plants. bioRxiv 2024. [Google Scholar] [CrossRef]
  36. Devanna, B.N.; Sucharita, S.; Sunitha, N.; Anilkumar, C.; Singh, P.K.; Pramesh, D.; Samantaray, S.; Behera, L.; Katara, J.L.; Parameswaran, C. Refinement of rice blast disease resistance QTLs and gene networks through meta-QTL analysis. Sci. Rep. 2024, 14, 16458. [Google Scholar] [CrossRef]
  37. Okello, M.; Ssemakula, M.O.; Lamo, J.; Onaga, G.; Odong, T.L.; Geoffrey, T.; Tukamuhabwa, P.; Mukasa, S.B.; Wasswa, P.; Ogwal, J. Genome-wide association mapping in rice MAGIC indica panel detects QTLs and genes for broad-spectrum resistance breeding against African bacterial blight. Oryza 2024, 61, 1–10. [Google Scholar] [CrossRef]
  38. Li, Y.; Zhang, Q.; Zhang, J.; Wu, L.; Qi, Y.; Zhou, J.-M. Identification of MicroRNAs Involved in Pathogen-Associated Molecular Pattern-Triggered Plant Innate Immunity. Plant Physiol. 2010, 152, 2222–2231. [Google Scholar] [CrossRef]
  39. Wang, H.; Wang, H.; Shao, H.; Tang, X. Recent Advances in Utilizing Transcription Factors to Improve Plant Abiotic Stress Tolerance by Transgenic Technology. Front. Plant Sci. 2016, 7, 67. [Google Scholar] [CrossRef]
  40. Lai, K.Y.; Hussin, N.A.; Mohamad, N.K.; Ten, H.Y.; San Lai, L.; San Yeo, F.K. Qualitative resistance of Sarawak rice landraces against Pyricularia oryzae. Borneo J. Resour. Sci. Technol. 2019, 9, 115–118. [Google Scholar]
  41. Zuanetti, D.A.; Soler, J.M.P.; Krieger, J.E.; Milan, L.A. Bayesian diagnostic analysis for quantitative trait loci mapping. Stat. Methods Med. Res. 2020, 29, 2238–2249. [Google Scholar] [CrossRef]
  42. Wisser, R.J.; Sun, Q.; Hulbert, S.H.; Kresovich, S.; Nelson, R.J. Identification and characterization of regions of the rice genome associated with broad-spectrum, quantitative disease resistance. Genetics 2005, 169, 2277–2293. [Google Scholar] [CrossRef] [PubMed]
  43. Gao, P.; Li, M.; Wang, X.; Xu, Z.; Wu, K.; Sun, Q.; Du, H.; Younas, M.U.; Zhang, Y.; Feng, Z. Identification of Elite R-gene combinations against blast disease in Geng rice varieties. Int. J. Mol. Sci. 2023, 24, 3984. [Google Scholar] [CrossRef] [PubMed]
  44. Sun, L.; Zhang, J. Regulatory role of receptor-like cytoplasmic kinases in early immune signaling events in plants. FEMS Microbiol. Rev. 2020, 44, 845–856. [Google Scholar] [CrossRef]
  45. Dalio, R.J.; Paschoal, D.; Arena, G.D.; Magalhães, D.M.; Oliveira, T.S.; Merfa, M.V.; Maximo, H.J.; Machado, M.A. Hypersensitive response: From NLR pathogen recognition to cell death response. Ann. Appl. Biol. 2021, 178, 268–280. [Google Scholar] [CrossRef]
  46. Yu, M.; Zhou, Z.; Liu, X.; Yin, D.; Li, D.; Zhao, X.; Li, X.; Li, S.; Chen, R.; Lu, L. The OsSPK1–OsRac1–RAI1 defense signaling pathway is shared by two distantly related NLR proteins in rice blast resistance. Plant Physiol. 2021, 187, 2852–2864. [Google Scholar] [CrossRef] [PubMed]
  47. Mizuno, H.; Katagiri, S.; Kanamori, H.; Mukai, Y.; Sasaki, T.; Matsumoto, T.; Wu, J. Evolutionary dynamics and impacts of chromosome regions carrying R-gene clusters in rice. Sci. Rep. 2020, 10, 872. [Google Scholar] [CrossRef]
  48. Riangwong, K.; Aesomnuk, W.; Sonsom, Y.; Siangliw, M.; Unartngam, J.; Toojinda, T.; Wanchana, S.; Arikit, S. QTL-seq identifies genomic regions associated with resistance to dirty panicle disease in rice. Agronomy 2023, 13, 1905. [Google Scholar] [CrossRef]
  49. Michel, S.; Löschenberger, F.; Ametz, C.; Bürstmayr, H. Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection. Theor. Appl. Genet. 2023, 136, 79. [Google Scholar] [CrossRef]
  50. Song, Z.; Zheng, J.; Zhao, Y.; Yin, J.; Zheng, D.; Hu, H.; Liu, H.; Sun, M.; Ruan, L.; Liu, F. Population genomics and pathotypic evaluation of the bacterial leaf blight pathogen of rice reveals rapid evolutionary dynamics of a plant pathogen. Front. Cell. Infect. Microbiol. 2023, 13, 1183416. [Google Scholar] [CrossRef]
  51. Mondal, K.K.; Kalaivanan, N. T3SS mediated transcriptional reprogramming of rice by the virulent Indian race 4 of Xanthomonas oryzae pv. oryzae. ORYZA-Int. J. Rice 2023, 60, 249–259. [Google Scholar]
  52. Wongsa, T.; Chankaew, S.; Monkham, T.; Sanitchon, J. Broad-Spectrum Resistance and Monogenic Inheritance of Bacterial Blight Resistance in an Indigenous Upland Rice Germplasm ULR207. Agronomy 2024, 14, 898. [Google Scholar] [CrossRef]
  53. Wang, M.; Li, S.; Li, H.; Song, C.; Xie, W.; Zuo, S.; Zhou, X.; Zhou, C.; Ji, Z.; Zhou, H. Genome editing of a dominant resistance gene for broad-spectrum resistance to bacterial diseases in rice without growth penalty. Plant Biotechnol. J. 2023, 22, 529. [Google Scholar] [CrossRef]
  54. Gou, M.; Balint-Kurti, P.; Xu, M.; Yang, Q. Quantitative disease resistance: Multifaceted players in plant defense. J. Integr. Plant Biol. 2023, 65, 594–610. [Google Scholar] [CrossRef]
  55. Langlands-Perry, C.; Pitarch, A.; Lapalu, N.; Cuenin, M.; Bergez, C.; Noly, A.; Amezrou, R.; Gélisse, S.; Barrachina, C.; Parrinello, H. Quantitative and qualitative plant-pathogen interactions call upon similar pathogenicity genes with a spectrum of effects. Front. Plant Sci. 2023, 14, 1128546. [Google Scholar] [CrossRef] [PubMed]
  56. Liu, Z.; Zhu, Y.; Shi, H.; Qiu, J.; Ding, X.; Kou, Y. Recent progress in rice broad-spectrum disease resistance. Int. J. Mol. Sci. 2021, 22, 11658. [Google Scholar] [CrossRef]
  57. Silva, A.; Montoya, M.E.; Quintero, C.; Cuasquer, J.; Tohme, J.; Graterol, E.; Cruz, M.; Lorieux, M. Genetic bases of resistance to the rice hoja blanca disease deciphered by a quantitative trait locus approach. G3 Genes Genomes Genet. 2023, 13, jkad223. [Google Scholar] [CrossRef]
  58. Neelam, K.; Kumar, K.; Kaur, A.; Kishore, A.; Kaur, P.; Babbar, A.; Kaur, G.; Kamboj, I.; Lore, J.S.; Vikal, Y. High-resolution mapping of the quantitative trait locus (QTLs) conferring resistance to false smut disease in rice. J. Appl. Genet. 2022, 63, 35–45. [Google Scholar] [CrossRef]
  59. Inoue, H.; Hayashi, N. The panicle blast resistance mechanism of qPbm11 in the rice cultivar Miyazaki-mochi is independent from that of Pb1. Jpn. Agric. Res. Q. JARQ 2019, 53, 289–293. [Google Scholar] [CrossRef]
  60. Zhang, F.; Zeng, D.; Zhang, C.-S.; Lu, J.-L.; Chen, T.-J.; Xie, J.-P.; Zhou, Y.-L. Genome-wide association analysis of the genetic basis for sheath blight resistance in rice. Rice 2019, 12, 1–13. [Google Scholar] [CrossRef]
  61. Fu, R.; Zhao, L.; Chen, C.; Wang, J.; Lu, D. Conjunctive analysis of bsa-seq and ssr markers unveil the candidate genes for resistance to rice false smut. Biomolecules 2024, 14, 79. [Google Scholar] [CrossRef]
  62. Hiremath, S.S.; Bhatia, D.; Jain, J.; Hunjan, M.S.; Kaur, R.; Zaidi, N.W.; Singh, U.S.; Zhou, B.; Lore, J.S. Identification of potential donors and QTLs for resistance to false smut in a subset of rice diversity panel. Eur. J. Plant Pathol. 2021, 159, 461–470. [Google Scholar] [CrossRef]
  63. Zhao, D.-D.; Chung, H.; Jang, Y.-H.; Farooq, M.; Choi, S.Y.; Du, X.-X.; Kim, K.-M. Analysis of rice blast fungus genetic diversity and identification of a novel blast resistance OsDRq12 gene. Phytopathology 2024, 114, 1917–1925. [Google Scholar] [CrossRef]
  64. Wang, J.; Hu, H.; Jiang, X.; Zhang, S.; Yang, W.; Dong, J.; Yang, T.; Ma, Y.; Zhou, L.; Chen, J. Pangenome-wide association study and transcriptome analysis reveal a novel QTL and candidate genes controlling both panicle and leaf blast resistance in rice. Rice 2024, 17, 27. [Google Scholar] [CrossRef] [PubMed]
  65. Kumar, I.S.; Nadarajah, K. A meta-analysis of quantitative trait loci associated with multiple disease resistance in rice (Oryza sativa L.). Plants 2020, 9, 1491. [Google Scholar] [CrossRef] [PubMed]
  66. Hu, K.-M.; Qiu, D.-Y.; Shen, X.-L.; Li, X.-H.; Wang, S.-P. Isolation and manipulation of quantitative trait loci for disease resistance in rice using a candidate gene approach. Mol. Plant 2008, 1, 786–793. [Google Scholar] [CrossRef]
  67. Kou, Y.; Wang, S. Toward an understanding of the molecular basis of quantitative disease resistance in rice. J. Biotechnol. 2012, 159, 283–290. [Google Scholar] [CrossRef] [PubMed]
  68. Zuo, S.; Zhang, Y.; Yin, Y.; Li, G.; Zhang, G.; Wang, H.; Chen, Z.; Pan, X. Fine-mapping of qSB-9 TQ, a gene conferring major quantitative resistance to rice sheath blight. Mol. Breed. 2014, 34, 2191–2203. [Google Scholar] [CrossRef]
  69. Hossain, M.K.; Islam, M.R.; Sundaram, R.M.; Bhuiyan, M.A.R.; Wickneswari, R. Introgression of the QTL qSB11-1TT conferring sheath blight resistance in rice (Oryza sativa) into an elite variety, UKMRC 2, and evaluation of its backcross-derived plants. Front. Plant Sci. 2023, 13, 981345. [Google Scholar] [CrossRef]
  70. Oh, Y.; Lee, S.; Rioux, R.; Singh, P.; Jia, M.H.; Jia, Y.; Mysore, K.S. Analysis of Differentially Expressed Rice Genes Reveals the ATP-Binding Cassette Transporters as Candidate Genes Against the Sheath Blight Pathogen, Rhizoctonia solani. PhytoFrontiers™ 2022, 2, 105–115. [Google Scholar] [CrossRef]
  71. Xie, X.; Chen, Z.; Zhang, B.; Guan, H.; Zheng, Y.; Lan, T.; Zhang, J.; Qin, M.; Wu, W. Transcriptome analysis of xa5-mediated resistance to bacterial leaf streak in rice (Oryza sativa L.). Sci. Rep. 2020, 10, 19439. [Google Scholar] [CrossRef]
  72. Park, J.-R.; Lee, C.-M.; Ji, H.; Baek, M.-K.; Seo, J.; Jeong, O.-Y.; Park, H.-S. Characterization and QTL Mapping of a Major Field Resistance Locus for Bacterial Blight in Rice. Plants 2022, 11, 1404. [Google Scholar] [CrossRef] [PubMed]
  73. Saito, H.; Tomita, A.; Yoshida, T.; Nakamura, M.; Suzuki, T.; Ikeda, A.; Kato, T.; Nakajima, Y.; Tanimoto, R.; Tani, T. Characterization of Six Partial Resistance Genes and One Quantitative Trait Locus to Blast Disease Using Near Isogenic Lines with a Susceptible Genetic Background of Indica Group Rice (Oryza sativa). PhytoFrontiers™ 2022, 2, 230–241. [Google Scholar] [CrossRef]
  74. Yasuda, N.; Mitsunaga, T.; Hayashi, K.; Koizumi, S.; Fujita, Y. Effects of pyramiding quantitative resistance genes pi21, Pi34, and Pi35 on rice leaf blast disease. Plant Dis. 2015, 99, 904–909. [Google Scholar] [CrossRef] [PubMed]
  75. Dodds, P.N. From gene-for-gene to resistosomes: Flor’s enduring legacy. Mol. Plant-Microbe Interact. 2023, 36, 461–467. [Google Scholar] [CrossRef]
  76. Terauchi, R.; Fujisaki, K.; Shimizu, M.; Oikawa, K.; Takeda, T.; Takagi, H.; Abe, A.; Okuyama, Y.; Yoshida, K.; Saitoh, H. Using genomics tools to understand plant resistance against pathogens: A case study of Magnaporthe-rice interactions. Appl. Plant Biotechnol. Improv. Resist. Biot. Stress 2020, 8, 181–188. [Google Scholar] [CrossRef]
  77. Jia, Y.; McAdams, S.A.; Bryan, G.T.; Hershey, H.P.; Valent, B. Direct Interaction of Resistance Gene and Avirulence Gene Products Confers Rice Blast Resistance. EMBO J. 2000, 19, 4004–4014. [Google Scholar] [CrossRef]
  78. Li, W.; Wang, B.; Wu, J.; Lu, G.; Hu, Y.; Zhang, X.; Zhang, Z.; Wang, H.; Wang, S. The Magnaporthe oryzae Avirulence Gene AvrPiz-t Encodes a Predicted Secreted Protein that Triggers Piz-t–Mediated Resistance in Rice. Mol. Plant-Microbe Interact. 2009, 22, 411–420. [Google Scholar] [CrossRef]
  79. Wang, C.; Chen, S.; Feng, A.; Su, J.; Wang, W.; Feng, J.; Chen, B.; Zhang, M.; Yang, J.; Zeng, L. Xa7, a small orphan gene harboring promoter trap for AvrXa7, leads to the durable resistance to Xanthomonas oryzae pv. oryzae. Rice 2021, 14, 48. [Google Scholar] [CrossRef]
  80. Rathore, J.S.; Ghosh, C. Pathogen-associated molecular patterns and their perception in plants. Mol. Asp. Plant-Pathog. Interact. 2018, 79–113. [Google Scholar] [CrossRef]
  81. Thakur, A.; Verma, S.; Reddy, V.P.; Sharma, D. Hypersensitive responses in plants. Agric. Rev. 2019, 40, 113–120. [Google Scholar] [CrossRef]
  82. He, L.; Liu, P.; Mei, L.; Luo, H.; Ban, T.; Chen, X.; Ma, B. Disease resistance features of the executor R gene Xa7 reveal novel insights into the interaction between rice and Xanthomonas oryzae pv. oryzae. Front. Plant Sci. 2024, 15, 1365989. [Google Scholar] [CrossRef] [PubMed]
  83. Zou, H.; Zhao, W.; Zhang, X.; Han, Y.; Zou, L.; Chen, G. Identification of an avirulence gene, a vrxa5, from the rice pathogen Xanthomonas oryzae pv. oryzae. Sci. China Life Sci. 2010, 53, 1440–1449. [Google Scholar] [CrossRef]
  84. Ji, C.; Ji, Z.; Liu, B.; Cheng, H.; Liu, H.; Liu, S.; Yang, B.; Chen, G. Xa1 allelic R genes activate rice blight resistance suppressed by interfering TAL effectors. Plant Commun. 2020, 1, 100087. [Google Scholar] [CrossRef]
  85. Kaur, A.; Rana, R.; Bansal, K.; Patel, H.K.; Sonti, R.V.; Patil, P.B. Insights into the diversity of transcription activator-like effectors in Indian pathotype strains of Xanthomonas oryzae pv. oryzae. Phytopathology 2023, 113, 953–959. [Google Scholar] [CrossRef] [PubMed]
  86. Char, S.N.; Park, S.; Yang, B. Interaction of rice and Xanthomonas TAL effectors. Rice Genom. Genet. Breed. 2018, 375–391. [Google Scholar]
  87. Wu, X.; Li, Y.; Zou, L.; Chen, G. Gene-for-gene relationships between rice and diverse avrBs3/pthA avirulence genes in Xanthomonas oryzae pv. oryzae. Plant Pathol. 2007, 56, 26–34. [Google Scholar] [CrossRef]
  88. Vikal, Y.; Bhatia, D. Genetics and genomics of Bacterial Blight Resistance. Chapter 2017, 10, 175–213. [Google Scholar]
  89. Wilson, R.A.; Talbot, N.J. Under Pressure: Investigating the Biology of Plant Infection by Magnaporthe oryzae. Nat. Rev. Microbiol. 2009, 7, 185–195. [Google Scholar] [CrossRef]
  90. Talbot, N.J. On the Trail of a Cereal Killer: Investigating the Biology of Magnaporthe grisea. Annu. Rev. Microbiol. 2003, 57, 177–202. [Google Scholar] [CrossRef]
  91. Han, J.; Wang, X.; Wang, F.; Zhao, Z.; Li, G.; Zhu, X.; Su, J.; Chen, L. The fungal effector Avr-Pita suppresses innate immunity by increasing COX activity in rice mitochondria. Rice 2021, 14, 12. [Google Scholar] [CrossRef]
  92. Li, J.; He, C.; Dong, C.; Lu, L.; He, C.; Bi, Y.; Shi, Z.; Fan, H.; Shi, J.; Wang, K. Diversity and Evolution of the Avirulence Gene AvrPi54 in Yunnan Rice Fields. Agronomy 2024, 14, 454. [Google Scholar] [CrossRef]
  93. Ma, L.; van den Burg, H.A.; Cornelissen, B.J.; Takken, F.L. Molecular basis of effector recognition by plant NB-LRR proteins. Mol. Plant Immun. 2012, 23–40. [Google Scholar] [CrossRef]
  94. Wang, L.; Jia, Y.; Osakina, A.; Olsen, K.M.; Huang, Y.; Jia, M.H.; Ponniah, S.; Pedrozo, R.; Nicolli, C.; Edwards, J.D. Receptor-ligand interactions in plant inmate immunity revealed by AlphaFold protein structure prediction. bioRxiv 2024. [Google Scholar] [CrossRef]
  95. Sugihara, Y.; Abe, Y.; Takagi, H.; Abe, A.; Shimizu, M.; Ito, K.; Kanzaki, E.; Oikawa, K.; Kourelis, J.; Langner, T. Disentangling the complex gene interaction networks between rice and the blast fungus identifies a new pathogen effector. PLoS Biol. 2023, 21, e3001945. [Google Scholar] [CrossRef] [PubMed]
  96. Duan, G.; Bao, J.; Chen, X.; Xie, J.; Liu, Y.; Chen, H.; Zheng, H.; Tang, W.; Wang, Z. Large-scale genome scanning within exonic regions revealed the contributions of selective sweep prone genes to host divergence and adaptation in Magnaporthe oryzae species complex. Microorganisms 2021, 9, 562. [Google Scholar] [CrossRef] [PubMed]
  97. Wang, S.; Han, S.; Zhou, X.; Zhao, C.; Guo, L.; Zhang, J.; Liu, F.; Huo, Q.; Zhao, W.; Guo, Z. Phosphorylation and ubiquitination of OsWRKY31 are integral to OsMKK10-2-mediated defense responses in rice. Plant Cell 2023, 35, 2391–2412. [Google Scholar] [CrossRef]
  98. Taj, G.; Giri, P.; Tasleem, M.; Kumar, A. MAPK signaling cascades and transcriptional reprogramming in plant–pathogen interactions. In Approaches to Plant Stress and Their Management; Springer: Berlin/Heidelberg, Germany, 2014; pp. 297–316. [Google Scholar]
  99. Jiang, R.; Zhou, S.; Da, X.; Yan, P.; Wang, K.; Xu, J.; Mo, X. OsMKK6 regulates disease resistance in rice. Int. J. Mol. Sci. 2023, 24, 12678. [Google Scholar] [CrossRef] [PubMed]
  100. Goto, S.; Sasakura-Shimoda, F.; Suetsugu, M.; Selvaraj, M.G.; Hayashi, N.; Yamazaki, M.; Ishitani, M.; Shimono, M.; Sugano, S.; Matsushita, A. Development of disease-resistant rice by optimized expression of WRKY45. Plant Biotechnol. J. 2015, 13, 753–765. [Google Scholar] [CrossRef]
  101. Li, S.; Khoso, M.A.; Xu, H.; Zhang, C.; Liu, Z.; Wagan, S.; Dinislam, K.; Liu, L. WRKY Transcription Factors (TFs) as Key Regulators of Plant Resilience to Environmental Stresses: Current Perspective. Agronomy 2024, 14, 2421. [Google Scholar] [CrossRef]
  102. Zhou, X.; Lei, Z.; An, P. Post-Translational Modification of WRKY Transcription Factors. Plants 2024, 13, 2040. [Google Scholar] [CrossRef]
  103. Balakrishnan, D.; Bateman, N.; Kariyat, R.R. Rice physical defenses and their role against insect herbivores. Planta 2024, 259, 110. [Google Scholar] [CrossRef] [PubMed]
  104. Zhang, F.; Wang, J.; Chen, Y.; Huang, J.; Liang, W. Genome-Wide Identification of MKK Gene Family and Response to Hormone and Abiotic Stress in Rice. Plants 2024, 13, 2922. [Google Scholar] [CrossRef]
  105. Nadim, M.; Islam, M.; Hoque, M.; Hasan, M.; Uddin, M. Development of blast-resistant rice varieties through marker-assisted selection: Development of blast-resistant rice varieties. Bangladesh J. Agric. 2024, 49, 41–51. [Google Scholar] [CrossRef]
  106. Shanmugam, A.; Suresh, R.; Ramanathan, A.; Anandhi, P.; Pushpa, R.; Sassikumar, D. Characterization of Traditional Rice Varieties for Leaf Blast Resistant Genes Pi5, Pi54, Pi9 and Pi2 using Gene Specific Markers. Res. Biot. 2023, 5, 158–161. [Google Scholar] [CrossRef]
  107. Tang, L.; Song, J.; Cui, Y.; Fan, H.; Wang, J. Detection and Evaluation of Blast Resistance Genes in Backbone Indica Rice Varieties from South China. Plants 2024, 13, 2134. [Google Scholar] [CrossRef]
  108. Bian, Z.; Cao, D.-P.; Zhuang, W.-S.; Zhang, S.-W.; Liu, Q.-Q.; Zhang, L. Revelation of rice molecular design breeding: The blend of tradition and modernity. Yi Chuan Hered. 2023, 45, 718–740. [Google Scholar]
  109. Xu, Y.; Li, P.; Zou, C.; Lu, Y.; Xie, C.; Zhang, X.; Prasanna, B.M.; Olsen, M.; Prasanna, B. Enhancing genetic gain in the era of molecular breeding. J. Exp. Bot. 2017, 68, 2641–2666. [Google Scholar] [CrossRef] [PubMed]
  110. Ofori, A.D.; Zheng, T.; Titriku, J.K.; Appiah, C.; Xiang, X.; Kandhro, A.G.; Ahmed, M.I.; Zheng, A. The Role of Genetic Resistance in Rice Disease Management. Int. J. Mol. Sci. 2025, 26, 956. [Google Scholar] [CrossRef]
  111. Mondal, S.; Rutkoski, J.E.; Velu, G.; Singh, P.K.; Crespo-Herrera, L.A.; Guzman, C.; Bhavani, S.; Lan, C.; He, X.; Singh, R.P. Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches. Front. Plant Sci. 2016, 7, 991. [Google Scholar] [CrossRef]
  112. Sabar, M.; Mustafa, S.E.; Ijaz, M.; Khan, R.A.R.; Shahzadi, F.; Saher, H.; Javed, H.M.; Zafar, S.A.; Saleem, M.U.; Siddique, S. Rice Breeding for Yield Improvement through Traditional and Modern Genetic Tools. Eur. J. Ecol. Biol. Agric. 2024, 1, 14–19. [Google Scholar] [CrossRef]
  113. Thulasinathan, T.; Ayyenar, B.; Kambale, R.; Manickam, S.; Chellappan, G.; Shanmugavel, P.; Narayanan, M.B.; Swaminathan, M.; Muthurajan, R. Marker assisted introgression of resistance genes and phenotypic evaluation enabled identification of durable and broad-spectrum blast resistance in elite rice cultivar, CO 51. Genes 2023, 14, 719. [Google Scholar] [CrossRef] [PubMed]
  114. Haque, M.A.; Rafii, M.Y.; Yusoff, M.M.; Ali, N.S.; Yusuff, O.; Datta, D.R.; Anisuzzaman, M.; Ikbal, M.F. Recent advances in rice varietal development for durable resistance to biotic and abiotic stresses through marker-assisted gene pyramiding. Sustainability 2021, 13, 10806. [Google Scholar] [CrossRef]
  115. Punniakotti, E.; Kousik, M.; Chaitra, K.; Harika, G.; Kumar, T.D.; Mastanbee, S.; Vivek, G.; Rekha, G.; Aleena, D.; Sinha, P. International Journal of Current Microbiology and Applied Sciences. Int. J. Curr. Microbiol. Appl. Sci. 2023, 12, 275–282. [Google Scholar]
  116. Mapari, A.R.; Mehandi, S. Enhancing Crop Resilience: Advances and Challenges in Marker-Assisted Selection for Disease Resistance. J. Adv. Biol. Biotechnol. 2024, 27, 569–580. [Google Scholar] [CrossRef]
  117. Mishra, R.; Joshi, R.K.; Zhao, K. Genome editing in rice: Recent advances, challenges, and future implications. Front. Plant Sci. 2018, 9, 1361. [Google Scholar] [CrossRef]
  118. Mthiyane, P.; Aycan, M.; Mitsui, T. Strategic advancements in rice cultivation: Combating heat stress through genetic innovation and sustainable practices—A review. Stresses 2024, 4, 452–480. [Google Scholar] [CrossRef]
  119. Acosta-Soto, A.F.; López-Díaz, D.; Esquivel-Ramírez, J.; Mora-Soriano, J.; Lazalde-Medina, B. Fundamentals of CRISPR-Cas9: Gene-editing technology and basic. GSC Adv. Res. Rev 2024, 20, 42–49. [Google Scholar] [CrossRef]
  120. Bhuyan, S.J.; Kumar, M.; Ramrao Devde, P.; Rai, A.C.; Mishra, A.K.; Singh, P.K.; Siddique, K.H. Progress in gene editing tools, implications and success in plants: A review. Front. Genome Ed. 2023, 5, 1272678. [Google Scholar] [CrossRef]
  121. Rifhani, N.F.; Apriana, A.; Sisharmini, A.; Santoso, T.J.; Trijatmiko, K.R.; Slamet-Loedin, I.H.; Yunus, A. Construction of the CRISPR/Cas9 module and genetic transformation of aromatic rice cv. Mentik Wangi for developing bacterial leaf blight resistance. Biodiversitas J. Biol. Divers. 2023, 24. [Google Scholar] [CrossRef]
  122. Schepler-Luu, V.; Sciallano, C.; Stiebner, M.; Ji, C.; Boulard, G.; Diallo, A.; Auguy, F.; Char, S.N.; Arra, Y.; Schenstnyi, K. Genome editing of an African elite rice variety confers resistance against endemic and emerging Xanthomonas oryzae pv. oryzae strains. Elife 2023, 12, e84864. [Google Scholar] [CrossRef]
  123. Zafar, K.; Khan, M.Z.; Amin, I.; Mukhtar, Z.; Yasmin, S.; Arif, M.; Ejaz, K.; Mansoor, S. Precise CRISPR-Cas9 mediated genome editing in super basmati rice for resistance against bacterial blight by targeting the major susceptibility gene. Front. Plant Sci. 2020, 11, 575. [Google Scholar] [CrossRef] [PubMed]
  124. Zhang, Y.; Lin, X.-F.; Li, L.; Piao, R.-H.; Wu, S.; Song, A.; Gao, M.; Jin, Y.-M. CRISPR/Cas9-mediated knockout of Bsr-d1 enhances the blast resistance of rice in Northeast China. Plant Cell Rep. 2024, 43, 100. [Google Scholar] [CrossRef] [PubMed]
  125. Liu, X.; Yu, Y.; Yao, W.; Yin, Z.; Wang, Y.; Huang, Z.; Zhou, J.Q.; Liu, J.; Lu, X.; Wang, F. CRISPR/Cas9-mediated simultaneous mutation of three salicylic acid 5-hydroxylase (OsS5H) genes confers broad-spectrum disease resistance in rice. Plant Biotechnol. J. 2023, 21, 1873–1886. [Google Scholar] [CrossRef]
  126. Li, T.; Liu, B.; Spalding, M.H.; Weeks, D.P.; Yang, B. High-Efficiency TALEN-Based Gene Editing Produces Disease-Resistant Rice. Nat. Biotechnol. 2012, 30, 390–392. [Google Scholar] [CrossRef] [PubMed]
  127. Dong, N.Q.; Lin, H.X. Contribution of Genome Editing to Rice Genetic Improvement. J. Integr. Plant Biol. 2021, 63, 312–328. [Google Scholar] [CrossRef]
  128. Kozub, N.; Sozinova, O.; Sozinov, I.; Karelov, A.; Janse, L.; Mishchenko, L.; Borzykh, O.; Blume, Y. Advances in durable resistance to diseases in staple food crops: A review. Open Agric. J. 2022, 17. [Google Scholar] [CrossRef]
  129. Mishra, S.; Srivastava, A.; Singh, A.; Pandey, G.C.; Srivastava, G. An overview of symbiotic and pathogenic interactions at the fungi-plant interface under environmental constraints. Front. Fungal Biol. 2024, 5, 1363460. [Google Scholar] [CrossRef]
  130. Pandian, B.A.; Joel, J.; Nachimuthu, V.V.; Swaminathan, M.; Govintharaj, P.; Tannidi, S.; Sabariappan, R. Marker-aided selection and validation of various Pi Pi gene combinations for rice blast resistance in elite rice variety ADT 43. J. Genet. 2018, 97, 945–952. [Google Scholar] [CrossRef]
  131. Sagar, V.; Dhawan, G.; Gopala Krishnan, S.; Vinod, K.; Ellur, R.K.; Mondal, K.K.; Rathour, R.; Prakash, G.; Nagarajan, M.; Bhowmick, P.K. Marker assisted introgression of genes governing resistance to bacterial blight and blast diseases into an elite Basmati rice variety, ‘Pusa Basmati 1509’. Euphytica 2020, 216, 16. [Google Scholar] [CrossRef]
  132. He, Z.; Xin, Y.; Wang, C.; Yang, H.; Xu, Z.; Cheng, J.; Li, Z.; Ye, C.; Yin, H.; Xie, Z. Genomics-Assisted improvement of super high-yield hybrid rice variety “super 1000” for resistance to bacterial blight and blast diseases. Front. Plant Sci. 2022, 13, 881244. [Google Scholar] [CrossRef]
  133. Xu, J.; Jiang, J.; Dong, X.; Ali, J.; Mou, T. Introgression of bacterial blight (BB) resistance genes Xa7 and Xa21 into popular restorer line and their hybrids by molecular marker-assisted backcross (MABC) selection scheme. Afr. J. Biotechnol. 2012, 11, 8225–8233. [Google Scholar]
  134. Jiang, G.; Xu, C.; Tu, J.; Li, X.; He, Y.; Zhang, Q. Pyramiding of insect-and disease-resistance genes into an elite indica, cytoplasm male sterile restorer line of rice,‘Minghui 63’. Plant Breed. 2004, 123, 112–116. [Google Scholar] [CrossRef]
  135. Sundaram, R.M.; Vishnupriya, M.R.; Biradar, S.K.; Laha, G.S.; Reddy, G.A.; Rani, N.S.; Sarma, N.P.; Sonti, R.V. Marker assisted introgression of bacterial blight resistance in Samba Mahsuri, an elite indica rice variety. Euphytica 2008, 160, 411–422. [Google Scholar] [CrossRef]
  136. Arshad, H.M.I.; Sahi, S.T.; Atiq, M.; Wakil, W. Appraisal of resistant genes and gene pyramid lines of rice against indigenous pathotypes of Xanthomonas oryzae pv. oryzae in Punjab, Pakistan. Pak. J. Agric. Sci. 2016, 53, 365–370. [Google Scholar]
  137. Oliveira-Garcia, E.; Budot, B.O.; Manangkil, J.; Lana, F.D.; Angira, B.; Famoso, A.; Jia, Y. An efficient method for screening rice breeding lines against races of Magnaporthe oryzae. Plant Dis. 2024, 108, 1179–1187. [Google Scholar] [CrossRef]
  138. Tahir, R.; Afzal, F.; Jamil, H.; Razzaq, M.; Khan, M. Physiological Impacts of Pesticidal Contamination: Challenge to Sustainable Agriculture and Biodegradation Methods. Pak. J. Agric. Agric. Eng. Vet. Sci. 2024, 40, 24–37. [Google Scholar] [CrossRef]
  139. Onorati, F.; Tornambé, A.; Paina, A.; Maggi, C.; Sesta, G.; Berducci, M.T.; Bellucci, M.; Rivella, E.; D’Antoni, S. Ecotoxicological and chemical approach to assessing environmental effects from pesticide use in organic and conventional rice paddies. Water 2022, 14, 4136. [Google Scholar] [CrossRef]
  140. Boeraeve, F.; Hatt, S. Integrating agroecological practices to manage pests while combining organic and conservation agriculture. Concept Ecostacking: Tech. Appl. 2024, 163–190. [Google Scholar] [CrossRef]
  141. Saba, N.; Balwan, W.K. Genetic Pollution: A Safe or Risky Bet. Sch. Acad. J. Biosci 2023, 4, 159–162. [Google Scholar] [CrossRef]
  142. Mangosongo, H.M.; Mneney, E.E.; Wanjala, B. Gene Flow Between the Wild Rice Species (Oryza longistaminata) and Two Varieties of Cultivated Rice (Oryza sativa) in Kilombero District, Tanzania. Tanzan. J. Sci. 2023, 49, 943–953. [Google Scholar] [CrossRef]
  143. Wang, Z.; Wang, L.; Wang, Z.; Lu, B.-R. Non-random transmission of parental alleles into crop-wild and crop-weed hybrid lineages separated by a transgene and neutral identifiers in rice. Sci. Rep. 2017, 7, 10436. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Mechanisms of plant immune responses to fungal, bacterial, and viral pathogens, including PAMP-triggered immunity (PTI), effector-triggered immunity (ETI), activation of defense genes, and systemic immunity, leading to resistance. Rice plants recognize pathogen-associated molecular patterns (PAMPs) via cell surface receptors, initiating PTI through the mitogen-activated protein kinase (MAPK) cascade and reactive oxygen species (ROS) burst, leading to defense gene activation [20,21]. Additionally, intracellular nucleotide-binding leucine-rich repeat receptor (NLR) proteins recognize specific pathogen effectors, triggering ETI characterized by hypersensitive response and systemic immunity [18,20]. Both pathways contribute synergistically to establishing resistance in rice, as represented by the outcome of a resistant plant.
Figure 1. Mechanisms of plant immune responses to fungal, bacterial, and viral pathogens, including PAMP-triggered immunity (PTI), effector-triggered immunity (ETI), activation of defense genes, and systemic immunity, leading to resistance. Rice plants recognize pathogen-associated molecular patterns (PAMPs) via cell surface receptors, initiating PTI through the mitogen-activated protein kinase (MAPK) cascade and reactive oxygen species (ROS) burst, leading to defense gene activation [20,21]. Additionally, intracellular nucleotide-binding leucine-rich repeat receptor (NLR) proteins recognize specific pathogen effectors, triggering ETI characterized by hypersensitive response and systemic immunity [18,20]. Both pathways contribute synergistically to establishing resistance in rice, as represented by the outcome of a resistant plant.
Plants 14 01694 g001
Figure 2. The figure outlines four key approaches: (1) conventional breeding (selection, mutation, hybridization, and back-crossing) [29]; (2) marker-assisted selection (MAS, including marker-assisted backcrossing (MABS), genome-wide association studies (GWAS), and genomic selection (GS)) [30]; (3) transgenic technologies (RNA interference (RNAi) and virus-induced gene silencing (VIGS)) [31]; and (4) DNA editing (identification of resistant/susceptible genes or mutated variants, genetic modification via transgenes, and foreground/background selection) [32]. These methods collectively target resistance mechanisms against bacterial, viral, and fungal pathogens in rice.
Figure 2. The figure outlines four key approaches: (1) conventional breeding (selection, mutation, hybridization, and back-crossing) [29]; (2) marker-assisted selection (MAS, including marker-assisted backcrossing (MABS), genome-wide association studies (GWAS), and genomic selection (GS)) [30]; (3) transgenic technologies (RNA interference (RNAi) and virus-induced gene silencing (VIGS)) [31]; and (4) DNA editing (identification of resistant/susceptible genes or mutated variants, genetic modification via transgenes, and foreground/background selection) [32]. These methods collectively target resistance mechanisms against bacterial, viral, and fungal pathogens in rice.
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Figure 3. Schematic representation of effector-triggered immunity (ETI) in rice, illustrating the interaction between pathogen-derived avirulence (Avr) proteins and host resistance (R) proteins. Recognition of effectors such as AvrXa7, AvrPita, and AvrPiz-t by corresponding R proteins (Xa7, Pi-ta, and Piz-t, respectively) activates a robust immune response in the host plant. These gene-for-gene interactions are well characterized in Xanthomonas oryzae pv. oryzae and Magnaporthe oryzae systems [77,78].
Figure 3. Schematic representation of effector-triggered immunity (ETI) in rice, illustrating the interaction between pathogen-derived avirulence (Avr) proteins and host resistance (R) proteins. Recognition of effectors such as AvrXa7, AvrPita, and AvrPiz-t by corresponding R proteins (Xa7, Pi-ta, and Piz-t, respectively) activates a robust immune response in the host plant. These gene-for-gene interactions are well characterized in Xanthomonas oryzae pv. oryzae and Magnaporthe oryzae systems [77,78].
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Figure 4. Life cycle of M. oryzae, showing key stages including spore germination, hyphal growth, lesion development, sporulation, and sexual reproduction via perithecium formation [90,91].
Figure 4. Life cycle of M. oryzae, showing key stages including spore germination, hyphal growth, lesion development, sporulation, and sexual reproduction via perithecium formation [90,91].
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Figure 5. Genome editing strategies for developing disease-resistant rice. The figure illustrates key approaches, including (1) disruption of susceptibility genes (e.g., NHEJ, OsSWEET14) [32], (2) homology-directed repair (HDR) for introducing resistance alleles (e.g., xa23) [126], (3) multiplex editing of multiple targets (e.g., Pi21, OsSULTR3,6), and (4) generation of broad-spectrum resistance through edited genes (e.g., OsS5H mutant), compared to wild-type plants. Edited lines show enhanced resistance to pathogens [127].
Figure 5. Genome editing strategies for developing disease-resistant rice. The figure illustrates key approaches, including (1) disruption of susceptibility genes (e.g., NHEJ, OsSWEET14) [32], (2) homology-directed repair (HDR) for introducing resistance alleles (e.g., xa23) [126], (3) multiplex editing of multiple targets (e.g., Pi21, OsSULTR3,6), and (4) generation of broad-spectrum resistance through edited genes (e.g., OsS5H mutant), compared to wild-type plants. Edited lines show enhanced resistance to pathogens [127].
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Table 1. Common rice diseases: etiology, symptomatology, and financial impact.
Table 1. Common rice diseases: etiology, symptomatology, and financial impact.
DiseasePathogenSymptomsRegion and YearEconomic ImpactReferences
Rice BlastMagnaporthe oryzaeLeaf lesions, neck rot, panicle blastMid-South USA, 2016Annual producer gains of USD 69.34 million with blast-resistant rice adoption.[10]
Bacterial BlightXanthomonas oryzae pv. oryzaeWater-soaked lesions, wilting, yellowing of leavesIndia, 1980sYield losses up to 30% in the Punjab region.[11]
Sheath BlightRhizoctonia solaniLesions on leaf sheaths, lodging, reduced grain qualityIndia (Uttar Pradesh), 2015Yield losses ranged between 14.3% and 39.7% across surveyed districts.[12]
Table 2. Major QTLs and genes associated with resistance to important fungal and bacterial pathogens in rice.
Table 2. Major QTLs and genes associated with resistance to important fungal and bacterial pathogens in rice.
Gene/QTLsPathogenRoleReferences
qSB-9R. solaniDecreases the severity of sheath blight infection[68]
qSBR11R. solaniPromotes sheath blight resistance[69]
hb9-2R. solaniImparts partial resistance to sheath blight[70]
qBlsr5aXooIncreases host resistance to bacterial leaf streak[71]
qSBR11-1XooProvides durable resistance across bacterial blight races[72]
Pi21M. oryzaeOffers partial resistance to M. oryzae[73]
Pi35M. oryzaeProvides partial resistance to M. oryzae[74]
Table 3. Prominent disease-resistant rice cultivars and their role in controlling plant diseases.
Table 3. Prominent disease-resistant rice cultivars and their role in controlling plant diseases.
Resistant GenesVarietyDiseaseReferences
Pi-1, Pi-2, Pi-33C101A51Rice blast[130]
Pi-2, Pi-54Puta Basmati 1509Rice blast[131]
Pi9IRBL9-WRice blast[30]
Xa23, Pi9Super 1000Bacterial blight, rice blast[132]
Xa21IR72Bacterial blight[133]
Xa21, Xa23Minghui 63Bacterial blight[134]
X4, X5, X13, X21IR36Bacterial blight[89]
Xa21, xa13, Xa5Samba MahsuriBacterial blight[135]
Xa21IRBB21Bacterial blight[136]
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Younas, M.U.; Rao, B.; Qasim, M.; Ahmad, I.; Wang, G.; Sun, Q.; Xuan, X.; Iqbal, R.; Feng, Z.; Zuo, S.; et al. Molecular Insights into Rice Immunity: Unveiling Mechanisms and Innovative Approaches to Combat Major Pathogens. Plants 2025, 14, 1694. https://doi.org/10.3390/plants14111694

AMA Style

Younas MU, Rao B, Qasim M, Ahmad I, Wang G, Sun Q, Xuan X, Iqbal R, Feng Z, Zuo S, et al. Molecular Insights into Rice Immunity: Unveiling Mechanisms and Innovative Approaches to Combat Major Pathogens. Plants. 2025; 14(11):1694. https://doi.org/10.3390/plants14111694

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Younas, Muhammad Usama, Bisma Rao, Muhammad Qasim, Irshad Ahmad, Guangda Wang, Quanyi Sun, Xiongyi Xuan, Rashid Iqbal, Zhiming Feng, Shimin Zuo, and et al. 2025. "Molecular Insights into Rice Immunity: Unveiling Mechanisms and Innovative Approaches to Combat Major Pathogens" Plants 14, no. 11: 1694. https://doi.org/10.3390/plants14111694

APA Style

Younas, M. U., Rao, B., Qasim, M., Ahmad, I., Wang, G., Sun, Q., Xuan, X., Iqbal, R., Feng, Z., Zuo, S., & Lackner, M. (2025). Molecular Insights into Rice Immunity: Unveiling Mechanisms and Innovative Approaches to Combat Major Pathogens. Plants, 14(11), 1694. https://doi.org/10.3390/plants14111694

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