1 Introduction

Rice (Oryza sativa L.) is a staple food crop that feeds more than half of the world's population. However, the stability and growth of rice yield are facing threats from a variety of biotic and abiotic factors (Yang, 2024). Among them, various pathogens, including bacteria, fungi, and viruses, which lead to significant yield losses. Bacterial blight (BB) and bacterial leaf streak (BLS), caused by Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc) respectively, are particularly detrimental (Jiang et al., 2020). Additionally, fungal diseases such as rice blast, caused by Magnaporthe oryzae, and other fungal pathogens like Pyricularia oryzae, Ustilaginoidea virens, and Rhizoctonia solani, pose severe threats to rice crops globally (Liu et al., 2014; He et al., 2022). Understanding the interactions between rice and these pathogens is crucial for developing effective disease management strategies.

The study of transcriptomic responses in rice during pathogen interactions has provided significant insights into the molecular mechanisms underlying plant defense and pathogen attack. Transcriptomics, which involves the comprehensive analysis of RNA transcripts, allows researchers to identify infection-responsive genes and understand their roles in disease resistance and susceptibility (Sarki et al., 2020). For instance, transcriptomic analyses have revealed the upregulation of specific genes in rice that are involved in defense responses, such as pathogenesis-related proteins and phytoalexin biosynthetic genes, during interactions with pathogens (Kawahara et al., 2012). Moreover, the identification of pathogen-associated molecular patterns (PAMPs) and effector-triggered immunity (ETI) has advanced our understanding of the complex signaling networks that govern plant immunity (Liu et al., 2014; Liu and Wang, 2016). These insights are essential for developing new strategies to enhance disease resistance in rice through genetic and genomic approaches.

This study utilizes transcriptomic approaches to investigate the interactions between rice and its pathogens. By analyzing the gene expression profiles of both rice and its pathogens during infection, this study identifies key genes and pathways involved in the defense response and pathogen virulence; focuses on the characterize of the transcriptomic changes in rice during interactions with bacterial and fungal pathogens; identifies and analyzes the expression of infection-responsive genes in both rice and pathogens, and explores the potential roles of identified genes in disease resistance and susceptibility. This study aims to provide insights into the molecular mechanisms underlying rice-pathogen interactions that can inform the development of disease-resistant rice varieties.

2 Overview of Rice Pathogens

2.1 Major rice pathogens and their economic impact

Rice (Oryza sativa L.) is a staple food crop for more than half of the world's population, making it a critical component of global food security. However, rice production is severely threatened by various pathogens, including bacteria, fungi, viruses, and nematodes, which can lead to significant yield losses. Among the bacterial pathogens, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc) are responsible for bacterial blight (BB) and bacterial leaf streak (BLS), respectively, both of which are major diseases affecting rice production worldwide (Khojasteh et al., 2017; Jiang et al., 2020). Fungal pathogens such as Magnaporthe oryzae, which causes rice blast disease, also pose a significant threat, leading to substantial yield reductions (Liu and Wang, 2016). Additionally, the root-knot nematode (RKN), Meloidogyne graminicola, is known to cause extensive yield decline in rice crops (Kumari et al., 2016). The economic impact of these pathogens is profound, as they not only reduce grain yield but also affect the quality of the produce, thereby threatening the livelihood of millions of farmers and the food supply for billions of people (Kazan and Gardiner, 2018; Wei, et al., 2023).

2.2 Host-pathogen interaction mechanisms in rice

Understanding the mechanisms of host-pathogen interactions in rice is essential for developing effective disease management strategies. Rice has evolved a multi-layered immune system to combat pathogen invasion. This includes pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI). PTI is initiated upon recognition of conserved microbial molecules, while ETI is activated by specific pathogen effectors recognized by resistance (R) genes in the host (Costa, 2012; Liu and Wang, 2016). For instance, the interaction between rice and Xoo involves a series of R genes and their corresponding avirulence (Avr) genes, which play a crucial role in the plant's defense response (Khojasteh et al., 2017; Jiang et al., 2020). Similarly, proteomic studies have identified several proteins involved in rice's defense against Magnaporthe oryzae, including receptor-like kinases (RLKs) and mitogen-activated protein kinases (MAPKs), which are pivotal in signal transduction and defense response. Hormonal pathways, such as those regulated by salicylate, jasmonate, and ethylene, also play significant roles in modulating the plant's defense mechanisms against various pathogens (Kumari et al., 2016).

2.3 Traditional methods for studying rice pathogen interactions

Traditional methods for studying rice-pathogen interactions have primarily focused on genetic and genomic approaches. These include the identification and characterization of R genes and their corresponding Avr genes, as well as the use of quantitative trait loci (QTL) mapping to understand the genetic basis of disease resistance (Jiang et al., 2020; Roychoudhury, 2020). Microarray and gene expression studies have been employed to identify differentially expressed genes (DEGs) in response to pathogen infection, providing insights into the molecular mechanisms underlying host-pathogen interactions (Khojasteh et al., 2017). Proteomic analyses have also been instrumental in identifying key proteins involved in the plant's defense response, thereby enhancing our understanding of the complex interactions between rice and its pathogens (Meng et al., 2019; Wei, et al., 2023). These traditional methods have laid the foundation for more advanced transcriptomic and proteomic studies, which continue to unravel the intricate details of rice-pathogen interactions and aid in the development of disease-resistant rice varieties.

3 Transcriptomic Approaches

3.1 Introduction to transcriptomics and its relevance in plant-pathogen studies

Transcriptomics, the study of the complete set of RNA transcripts produced by the genome under specific circumstances, has become a pivotal tool in understanding plant-pathogen interactions. This field allows researchers to capture a snapshot of gene expression at a given time, providing insights into the molecular mechanisms underlying these interactions. By analyzing the transcriptome, scientists can identify which genes are activated or suppressed in response to pathogen attack, thereby elucidating the pathways involved in plant defense and pathogen virulence (Wise et al., 2007; Lowe et al., 2017). The advent of high-throughput sequencing technologies has significantly advanced our ability to perform comprehensive transcriptomic analyses, making it possible to study complex interactions at a genome-wide scale (Lee et al., 2018; Tyagi et al., 2022).

3.2 High-throughput sequencing technologies

High-throughput sequencing technologies, particularly RNA sequencing (RNA-Seq), have revolutionized transcriptomic studies. RNA-Seq provides a high-resolution, sensitive, and quantitative method for analyzing the transcriptome, enabling the detection of both known and novel transcripts, including those expressed at low levels (Zhang et al., 2010; Tyagi et al., 2022). This technology has been instrumental in uncovering the complexity of the rice transcriptome, revealing extensive alternative splicing events and novel transcripts that were previously undetected (Zhang et al., 2010). RNA-Seq has also been applied to study the transcriptomes of both the rice plant and its pathogens, such as the rice blast fungus Magnaporthe oryzae, providing valuable insights into the dynamic interactions during infection (Jeon et al., 2020). The ability to simultaneously capture the transcriptomes of both host and pathogen through dual RNA-Seq further enhances our understanding of these interactions (Westermann et al., 2017).

3.3 Bioinformatics tools for transcriptomic data analysis

The analysis of transcriptomic data requires sophisticated bioinformatics tools to process and interpret the vast amounts of data generated by high-throughput sequencing. These tools are essential for tasks such as read alignment, transcript assembly, differential expression analysis, and functional annotation (Lowe et al., 2017). Advances in bioinformatics have enabled more accurate and comprehensive analyses, facilitating the identification of key regulatory genes and pathways involved in plant-pathogen interactions (Lee et al., 2018). For instance, bioinformatics tools have been used to analyze the transcriptomes of rice and its pathogens, leading to the discovery of novel genes and regulatory mechanisms that play crucial roles in disease resistance and susceptibility (Wise et al., 2007; Jeon et al., 2020).

3.4 Functional annotation and gene ontology (GO) analysis in rice-pathogen studies

Functional annotation and Gene Ontology (GO) analysis are critical steps in interpreting transcriptomic data. These approaches help to categorize genes into functional groups based on their biological processes, molecular functions, and cellular components, providing a clearer understanding of the roles they play in plant-pathogen interactions (Lowe et al., 2017; Tyagi et al., 2022). In rice-pathogen studies, GO analysis has been used to identify key functional categories and pathways that are differentially regulated during infection, shedding light on the molecular mechanisms of plant defense and pathogen virulence (Jeon et al., 2020). For example, transcriptomic studies have revealed that genes involved in defense response, signal transduction, and secondary metabolism are often upregulated in rice in response to pathogen attack, highlighting their importance in the plant's immune response (Zhang et al., 2010).

4 Case Study

4.1 Selection criteria for the case study

The selection of case studies for this research on transcriptomic approaches to studying rice pathogen interactions was based on several key criteria. These criteria were designed to ensure that the selected studies provide comprehensive and high-quality data on the interactions between rice and its pathogens, particularly focusing on transcriptomic analyses. The primary criterion was the relevance of the study to the interactions between rice (Oryza sativa) and its pathogens. Studies that provided insights into the molecular and genetic mechanisms underlying these interactions were prioritized. For instance, the study by (Jeon et al., 2020) focused on the transcriptome profiling of the rice blast fungus Magnaporthe oryzae and its host during infection, providing valuable data on the expression profiles of both the pathogen and the host.

Studies employing advanced transcriptomic techniques such as RNA sequencing (RNA-seq) were selected to ensure high-quality and comprehensive data. The use of microdissection-based RNA sequencing in (Jeon et al., 2020) exemplifies the application of advanced techniques to obtain high-quality transcriptomes of both the pathogen and the host during infection. To cover a broad spectrum of rice-pathogen interactions, studies involving different pathogens and various resistance mechanisms were included. For example, the study by (Zhang et al., 2020) on the response of rice to Rice black-streaked dwarf virus (RBSDV) infection provided insights into the regulatory networks involving long non-coding RNAs (lncRNAs) and mRNAs, highlighting the diversity of pathogen interactions and defense responses.

Studies that integrated genomic and genetic analyses to identify resistance loci and gene interactions were also considered. The research by (Wisser et al., 2005) on the identification and characterization of regions of the rice genome associated with broad-spectrum, quantitative disease resistance is a prime example of such an approach, providing a framework for understanding the genetic basis of disease resistance. Preference was given to studies that reported novel findings or made significant contributions to the field of plant-pathogen interactions. The discovery of a new pathogen effector and its interaction with rice immune receptors in Sugihara et al., (2022) illustrates the type of groundbreaking research that was prioritized.

4.2 Detailed analysis of a specific rice-pathogen interaction using transcriptomics

In the study of rice-pathogen interactions, transcriptomic approaches have provided significant insights into the molecular dynamics between rice and its pathogens. One notable example is the interaction between rice and the bacterial pathogen Xanthomonas oryzae pv. oryzae (Xoo), which causes bacterial leaf blight, a devastating disease affecting rice crops globally. A detailed transcriptomic analysis was conducted to understand the interaction between rice and Xoo, focusing on the roles of host-induced carbohydrate metabolism enzymes in Xoo virulence and the rice defense response. Using comparative proteomics and transcriptomics, researchers identified two novel host-induced virulence factors, XanA and Imp, in Xoo. These factors were shown to significantly affect the global gene expression profiles in susceptible rice varieties (Wu et al., 2021).

The study revealed that mutants of the carbohydrate metabolism enzyme-encoding genes, ΔxanA and Δimp, elicited enhanced defense responses in rice and nearly abolished Xoo virulence. Transcriptomic analysis of rice treated with these mutants identified a total of 1 521 and 227 differentially expressed genes (DEGs) for PXO99A vs Δimp at 1 and 3 days post-inoculation (dpi), respectively, and 131 and 106 DEGs for PXO99A vs ΔxanA at the same time points. These DEGs were involved in various biological processes, including photosynthesis, signal transduction, oxidation-reduction, and the metabolism of carbohydrates, lipids, amino acids, secondary metabolites, and hormones (Wu et al., 2021). Further analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and MapMan revealed that while many pathways were associated with both Δimp and ΔxanA treatments, the underlying genes were not the same. This indicates that although the overall defense mechanisms might be similar, the specific genes involved in these processes differ depending on the virulence factor (Figure 1) (Wu et al., 2021).

Figure 1 MapMan analysis and comparison of the metabolic changes in IR24 rice at 1 and 3 d after infection with the mutants ΔxanA and Δimp relative to those with PXO99A infection (Adopted from Wu et al., 2021) Image caption: MapMan analysis and comparison of the metabolic changes in IR24 rice at 1 and 3 d after infection with the mutants ΔxanA and Δimp relative to those with PXO99A infection. In each comparison group, the corresponding DEGs with |log2 (fold change)| ≥ 1 were imported into MapMan software. The gray circles indicate that no differentially expressed genes were matched in this process. The red and blue squares attached to each metabolic pathway represent up- and downregulated genes, respectively. The color intensity represents the gene expression level (log2 ratio mutant/PXO99A), as indicated by the color scale (Adopted from Wu et al., 2021)

Another study employing dual RNA-seq provided additional insights into the rice-Xoo interaction. This approach allowed simultaneous examination of the transcriptomes of both rice and Xoo during infection. The study found that the type three secretion system (T3SS) of Xoo plays a crucial role in its pathogenic lifestyle. In rice inoculated with a T3SS-defective strain, significant changes were observed in the expression of plant defense-related genes, including those involved in plant signaling pathways and the biosynthesis of phenylalanine, flavonoids, and momilactones. These changes suggest a repression of plant defense responses and a reduction in callose deposition and phytoalexin accumulation, which are critical for plant immunity (Liao et al., 2019).

4.3 Interpretation of key findings from the case study

The transcriptomic approaches to studying rice-pathogen interactions have yielded significant insights into the molecular mechanisms underlying these complex biological processes. One of the key findings from the case study on the rice blast fungus Magnaporthe oryzae and its host Oryza sativa is the high-quality transcriptome data obtained using a microdissection-based RNA sequencing approach. This method allowed for a comprehensive expression profiling of both the fungal pathogen and the rice host during infection, providing a valuable resource for understanding plant-microbe interactions at the genomic level (Jeon et al., 2020). Another important discovery was made using a subtractive hybridization-assisted screening method, which identified 32 distinct genes involved in the rice-Magnaporthe oryzae interaction. This approach efficiently isolated candidate genes that play roles in the disease process, demonstrating the utility of functional screening in elucidating the roles of differentially expressed genes in plant-pathogen interactions (Chang et al., 2019).

Furthermore, a comparative transcriptome analysis between Rice black-streaked dwarf virus (RBSDV)-infected and non-infected rice plants revealed a network of differentially expressed long non-coding RNAs (lncRNAs) and mRNAs. This network suggests that these lncRNAs and mRNAs are crucial in rice's innate immunity against RBSDV, highlighting the regulatory roles of lncRNAs in plant defense mechanisms (Zhang et al., 2020). Simultaneous RNA-Seq analysis of the mixed transcriptome of rice and blast fungus during infection provided insights into the gene expression profiles of both organisms. This study identified upregulated fungal transcripts encoding putative secreted proteins, glycosyl hydrolases, cutinases, and LysM domain-containing proteins, which are likely involved in the initial infection processes. Concurrently, rice pathogenesis-related and phytoalexin biosynthetic genes were also upregulated, indicating a coordinated defense response (Kawahara et al., 2012).

4.4 Implications for Future Research and Crop Improvement

The advancements in transcriptomic approaches to studying rice-pathogen interactions have profound implications for future research and crop improvement. The integration of genomics, transcriptomics, and proteomics has significantly enhanced our understanding of the molecular mechanisms underlying host-pathogen interactions in rice. This comprehensive understanding is crucial for developing innovative strategies to improve crop resilience and productivity. One of the key areas for future research is the application of high-throughput sequencing technologies to generate detailed transcriptomic profiles of both rice and its pathogens during infection. For instance, the use of microdissection-based RNA sequencing has provided high-quality transcriptomes of the rice blast fungus Magnaporthe oryzae and its host Oryza sativa, offering valuable insights into plant-microbe interactions at the genomic level (Jeon et al., 2020). Such detailed expression profiling can help identify critical genes and pathways involved in disease resistance, which can be targeted for genetic improvement.

Moreover, the development of single-cell genomics approaches has opened new avenues for studying the rice rhizosphere microbiome. By isolating microbial cells from paddy soil and determining their genomic sequences, researchers can gain novel insights into the roles of plant growth-promoting microbes (PGPMs) in rice cultivation (Aoki et al., 2022). This knowledge can be leveraged to develop microbial technologies that enhance crop yield in a sustainable manner. Genome-wide association studies (GWAS) have also proven to be powerful tools for identifying genes associated with agronomic traits in rice. By using whole-genome sequencing, researchers have rapidly identified new genes influencing important traits, which can accelerate efforts aimed at crop improvement (Yano et al., 2016). Additionally, the identification and characterization of regions of the rice genome associated with broad-spectrum, quantitative disease resistance (BS-QDR) provide a framework for future investigations into disease resistance in rice and related crop species (Wisser et al., 2005).

Proteomics has further contributed to our understanding of rice-microbe interactions by identifying proteins involved in disease resistance and elucidating the host's innate immune response (Chulang et al., 2010; Wei et al., 2023). Targeting these proteins and pathways can lead to the development of stress-resistant rice varieties. The advent of genome editing technologies, particularly the CRISPR/Cas9 system, has revolutionized the field of plant science and agriculture. These technologies offer precise and efficient tools for genetic manipulation, enabling the development of rice varieties with improved resistance to biotic and abiotic stresses (Mishra et al., 2018). The continued refinement and application of these genome editing tools will be essential for meeting the challenges of global food security and ensuring sustainable rice production.

5 Key Findings from Recent Transcriptomic Studies

5.1 Differential gene expression in rice during pathogen attack

Recent transcriptomic studies have revealed significant differential gene expression in rice when attacked by various pathogens. For instance, in rice infected by Xanthomonas oryzae pv. oryzae (Xoo), 1,680 genes were differentially expressed, with 1,159 up-regulated and 521 down-regulated. These genes are involved in multiple biological processes, including defense response and photosynthesis (Yu et al., 2014). Similarly, during infection with the rice blast fungus Magnaporthe grisea, a cDNA library analysis identified 359 novel expressed sequence tags (ESTs), with a significant portion related to stress or defense responses (Rauyaree et al., 2001). Another study using suppression subtractive hybridization (SSH) identified 25 unique cDNA clones differentially expressed in rice when inoculated with different races of the blast fungus, indicating race-specific resistance mechanisms (Haiyan et al., 2007).

5.2 Identification of resistance (R) genes and susceptibility (S) genes

The identification of resistance (R) and susceptibility (S) genes is crucial for understanding rice-pathogen interactions. A study on rice infected with Pseudomonas avenae identified genes expressed in both compatible and incompatible interactions. For example, the IAI1 and IAI2 genes were expressed in incompatible interactions, while the CAI1 gene was expressed in compatible interactions, suggesting their roles in resistance and susceptibility, respectively (Che et al., 2002). Additionally, the WRKY gene superfamily in rice has been shown to play a significant role in defense responses, with several WRKY genes being differentially regulated upon pathogen attack (Ryu et al., 2006).

5.3 Role of non-coding RNAs in rice-pathogen interactions

Non-coding RNAs, particularly long non-coding RNAs (lncRNAs), have emerged as important regulators in rice-pathogen interactions. A transcriptome analysis of rice infected with Rice black-streaked dwarf virus (RBSDV) identified 22 differentially expressed lncRNAs. These lncRNAs were found to co-express with 56 differentially expressed mRNAs involved in the plant-pathogen interaction pathway, suggesting their essential roles in rice innate immunity (Zhang et al., 2020). Furthermore, microRNAs (miRNAs) have also been implicated in these interactions, with specific miRNAs being up-regulated during infection, indicating their regulatory roles in defense mechanisms (Mahesh et al., 2020).

5.4 Comparative transcriptomics in different rice varieties

Comparative transcriptomic studies across different rice varieties have provided insights into the genetic basis of resistance and susceptibility. For example, a meta-analysis of microarray data for rice infected by viruses from the Reoviridae and Sequiviridae families identified shared and divergent gene co-expression profiles. This study revealed four highly preserved gene modules and 83 common transcription factors targeting hub genes, highlighting the conserved and unique aspects of gene expression in response to different viral infections (Sahu et al., 2019). Additionally, transcriptome analysis of rice infected with different races of the blast fungus has shown that the timing and levels of gene expression can determine race-specific resistance (Haiyan et al., 2007).

6 Challenges and Limitations

6.1 Technical challenges in transcriptomic studies

Transcriptomic studies in rice-pathogen interactions face several technical challenges. One significant issue is the difficulty in obtaining high-quality in planta transcriptome data during infection. For instance, previous studies on the rice blast fungus Magnaporthe oryzae and its host Oryza sativa have struggled with technical difficulties, resulting in suboptimal fungal transcriptome data (Jeon et al., 2020). Additionally, the complexity of the rice transcriptome, which includes a high number of novel transcripts, exons, and untranslated regions, further complicates the analysis (Zhang et al., 2010). The need for high-throughput and precise techniques, such as microdissection-based RNA sequencing, is essential to overcome these challenges and achieve comprehensive expression profiling (Jeon et al., 2020).

6.2 Limitations of current bioinformatics tools

The current bioinformatics tools used for transcriptomic analysis have limitations that hinder the full understanding of rice-pathogen interactions. For example, while RNA sequencing (RNA-seq) has advanced our ability to map and quantify the transcriptome, the functional complexity of the rice transcriptome is still not fully elucidated (Lu et al., 2010). Many novel transcriptional active regions (nTARs) identified through RNA-seq lack homologs in public protein data, making functional annotation challenging (Lu et al., 2010). Moreover, the high percentage of alternative splicing events in rice genes adds another layer of complexity that current bioinformatics tools are not fully equipped to handle (Zhang et al., 2010).

6.3 Data interpretation and biological relevance

Interpreting the vast amount of data generated from transcriptomic studies and determining its biological relevance is a significant challenge. The complexity of transcriptional regulation in rice, including alternative splicing and trans-splicing events, makes it difficult to draw clear conclusions about gene function and expression (Zhang et al., 2010). Additionally, the differential expression patterns observed in various studies need to be carefully analyzed to understand their implications in the context of host-pathogen interactions (Lu et al., 2010). The integration of transcriptomic data with other genomic and proteomic data is crucial for a more comprehensive understanding but remains a challenging task (Wise et al., 2007).

6.4 Integration with other omics approaches

Integrating transcriptomic data with other 'omics' approaches, such as proteomics and metabolomics, is essential for a holistic understanding of rice-pathogen interactions but presents several challenges. Proteomics, for instance, provides insights into protein-level changes during rice-microbe interactions, which are crucial for understanding disease resistance mechanisms (Figure 2) (Wei et al., 2023). However, combining these data sets requires sophisticated analytical tools and methodologies that can handle the complexity and volume of multi-omics data (Chulang et al., 2010). The integration of epigenomic data, such as miRNA and siRNA regulation, further complicates the analysis but is necessary for a complete picture of host-pathogen interactions (Sarki et al., 2020).

Figure 2 Proteomics-based schematic diagram of rice–microbe interactions (Adopted from Wei et al., 2023) Image caption: Sensing of bacterial and fungal pathogens by membrane-localized pattern recognition receptors leads to the phosphorylation of MAPK cascade and CDPKs in rice, which subsequently activates the downstream transcription factors, especially WRKKYs. The abundance of glycoside hydrolase family proteins (GHs), reactive-oxygen-species-related proteins (ROSs), pathogenesis-related proteins (PRs), cell-wall-modification-related proteins, and protein-degradation-related proteins are significantly increased and highly accumulated in the apoplastic region through protein secretion. Secondary metabolite biosynthesis-related proteins are also highly accumulated upon bacterial (left panel) and fungal pathogen (right panel) infection. Accumulation of SA and ET biosynthesis regulating proteins prohibitin, ICS1, and HSM were increased upon bacterial and pathogen infection, respectively. Phosphorylation of PP2Cs, a negative regulator of ABA signaling, is increased upon bacterial infection (Adopted from Wei et al., 2023)

7 Future Directions

7.1 Advancements in transcriptomics and emerging technologies

The field of transcriptomics has seen significant advancements with the advent of next-generation sequencing technologies, particularly RNA sequencing (RNA-seq). These technologies have enabled comprehensive profiling of gene expression, providing deeper insights into the molecular mechanisms underlying host-pathogen interactions in rice. Emerging technologies such as dual RNA-seq, CRISPR/Cas9 screening, and organ-on-chip models are poised to further revolutionize the study of these interactions. Dual RNA-seq, for instance, allows simultaneous analysis of gene expression changes in both the pathogen and the host, offering a more holistic view of the interaction dynamics (Westermann et al., 2012; Baddal, 2019). Additionally, the development of high-quality transcriptomes using microdissection-based RNA sequencing approaches has improved our understanding of specific pathogen-host interactions, such as those between Magnaporthe oryzae and Oryza sativa (Jeon et al., 2010).

7.2 Integrating transcriptomics with systems biology approaches

Integrating transcriptomics with systems biology approaches can provide a more comprehensive understanding of the complex networks involved in rice-pathogen interactions. Systems biology approaches, which include genomics, proteomics, and metabolomics, can complement transcriptomic data to elucidate the regulatory networks and pathways that govern host responses to pathogen attacks. For example, combining transcriptomic data with epigenomic studies can reveal how gene expression is reprogrammed during biotic stress (Sarki et al., 2020). Furthermore, the integration of bioinformatics tools and computational models can help in the identification of key regulatory genes and potential targets for genetic manipulation (Wise et al., 2007; McGettigan, 2013).

7.3 Potential for developing disease-resistant rice varieties

The insights gained from transcriptomic studies can be leveraged to develop disease-resistant rice varieties. By identifying genes and pathways that confer resistance to specific pathogens, researchers can employ genome editing technologies such as CRISPR/Cas9 to introduce or enhance these traits in rice cultivars. Recent advancements in genome editing, including the CRISPR/Cpf1 system and base editors, offer more precise and efficient tools for genetic improvement. These technologies, combined with the wealth of genomic resources available for rice, hold great promise for accelerating the development of rice varieties with enhanced resistance to a broad spectrum of pathogens (Mishra et al., 2018; Sarki et al., 2020).

7.4 Opportunities for translational research and agricultural applications

The application of transcriptomic data extends beyond basic research to translational research and agricultural practices. Understanding the molecular basis of host-pathogen interactions can inform the development of novel antimicrobial strategies and disease management practices. For instance, transcriptomic studies have identified unique immunosignatures and key transcriptional factors that can be targeted to enhance disease resistance (Rao et al., 2019). Additionally, the integration of transcriptomic data with field studies can help in the development of predictive models for disease outbreaks, enabling more effective and timely interventions (Wise et al., 2007; Zanardo et al., 2019). The ultimate goal is to translate these findings into practical solutions that can improve crop productivity and sustainability in the face of biotic stressors.

8 Concluding Remarks

This study has provided significant insights into the complex interactions between rice and its pathogens. By leveraging advanced transcriptomic techniques, the research has elucidated the molecular and genetic mechanisms underlying host-pathogen interactions. Key findings include the identification of specific genes and pathways that are activated in response to pathogen attacks, as well as the role of post-transcriptional regulators such as miRNA and siRNA in modulating these interactions. These insights are crucial for developing targeted strategies to enhance rice resistance to various biotic stress factors, thereby improving crop productivity and sustainability.

Transcriptomics has emerged as a powerful tool in the field of plant pathology, offering a comprehensive view of gene expression changes during pathogen infection. This study has demonstrated the utility of transcriptomic approaches in identifying key regulatory genes and pathways involved in rice's defense mechanisms. By understanding these molecular responses, researchers can develop more effective disease management strategies, such as breeding rice varieties with enhanced resistance or designing targeted interventions to disrupt pathogen virulence. The integration of transcriptomic data with other omics approaches, such as genomics and epigenomics, further enhances our ability to combat rice diseases in a holistic manner.

The future of rice pathogen research looks promising, with transcriptomics playing a central role in advancing our understanding of host-pathogen interactions. As transcriptomic technologies continue to evolve, they will provide even deeper insights into the dynamic and complex nature of these interactions. Future research should focus on integrating multi-omics data to build comprehensive models of rice defense mechanisms, which can be used to predict and mitigate the impact of emerging pathogens. Additionally, the development of novel bioinformatics tools and techniques will be essential for analyzing the vast amounts of data generated by transcriptomic studies, ultimately leading to more resilient rice crops and sustainable agricultural practices.

Acknowledgments

Author extends sincere thanks to two anonymous peer reviewers for their invaluable feedback on the manuscript.

Conflict of Interest Disclosure

Authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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