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Article

Transcriptional Dynamics of Receptor-Based Genes Reveal Immunity Hubs in Rice Response to Magnaporthe oryzae Infection

by
Fatma Salem
1,2,
Ahmed ElGamal
3,
Xiaoya Tang
1,
Jianyuan Yang
4,* and
Weiwen Kong
1,*
1
College of Plant Protection, Yangzhou University, Yangzhou 225009, China
2
Faculty of Agriculture, Fayoum University, Fayoum 63514, Egypt
3
Virus and Phytoplasma Research Department, Plant Pathology Research Institute, Agricultural Research Center, Giza 12619, Egypt
4
Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(10), 4618; https://doi.org/10.3390/ijms26104618
Submission received: 27 March 2025 / Revised: 30 April 2025 / Accepted: 7 May 2025 / Published: 12 May 2025
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Versions Notes

Abstract

:
Rice blast caused by Magnaporthe oryzae (MOR) reigns as the top-most devastating disease affecting global rice production. Pattern-triggered immunity (PTI) is crucial for mitigating plant responses to pathogens. However, the transcriptional dynamics of PTI-related genes in rice response to MOR infection remain largely unexplored. In this study, we performed a meta-analysis of 201 RNA sequencing and 217 microarray datasets to investigate the transcriptional dynamics of rice under MOR infection at various infection stages. The transcriptional dynamics of extracellular/cytoplasmic receptor kinase genes (RLKs, RLCKs, WAKs) and downstream signaling intermediates, including mitogen-activated protein kinases (MAPKs) and Ca2+-related signaling genes, were identified as immunity hubs for PTI. Extracellular/cytoplasmic receptors were predominantly induced, in contrast to a marked decrease in the repression of these genes. Notably, a maximum of 141 and 154 receptor-based genes were frequently induced from the microarray and RNA-seq datasets, respectively. Moreover, 31 genes were consistently induced across all the transcriptomic profiles, highlighting their pivotal role in PTI-activating immunity regulation in rice under MOR stress. Furthermore, protein–protein interaction (PPI) analysis revealed that cytoplasmic receptor-based genes (RLCKs) and MAPK(K)s were highly interconnected. Among them, four core MAPKK genes, including SMG1, MKK1, MKK6, and MPKK10.2, were identified as the most frequently interconnected with receptor-based genes or other MAPKs under MOR infection, suggesting their critical role as intermediates during downstream signaling networks in response to MOR infection. Together, our comprehensive analysis provides insights into the transcriptional dynamics of receptor-based genes and downstream signaling intermediates as core PTI-related genes that can play crucial roles in modulating rice immune responses to MOR infection.

1. Introduction

Rice (Oryza sativa) is an important crop for global food security and serves as a model monocotyledonous plant for studying host defense mechanisms against pathogens. Magnaporthe oryzae (MOR), a fungal pathogen, is a major threat to rice cultivation and causes substantial yield losses in rice production worldwide [1,2]. MOR pathogen ranks among the top significant and destructive fungal pathogens affecting rice, simultaneously causing blast diseases on more than 50 plant species [3]. Plants have evolved a sophisticated immune system to oppose infection by various pathogens through a dual, two-tiered immune system [4]. The first layer, known as PAMP-triggered immunity (PTI), is initially activated upon the recognition of conserved microbial patterns known as pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) on the cell surface [5]. Additionally, plants have developed a second defense layer activated by the recognition of effectors through an intracellular nucleotide-binding domain and leucine-rich repeat (NB-LRR) immune receptors (NLRs) [6]. This immune system is called effector-triggered immunity (ETI). ETI often induces a hypersensitive response (HR) that is accompanied by cell death, which contributes to the restriction of pathogen proliferation. However, PTI machinery mostly contributes to the early signal downstream defense response. This process is driven by a rapid influx of cytosolic calcium (Ca2+), initiating signal transduction and triggering MAPK cascades [7,8,9]. Both PTI and ETI are involved in the downstream activation of robust plant defense mechanisms against pathogen infections, including the biosynthesis of salicylic acid (SA) and defense phytohormones that mediate systemic acquired resistance (SAR) [10,11]. However, recent studies revealed extensive synergy between PTI and ETI, leading to mutual potentiation and interdependency, which is critical for a full immune response [12]. Despite differences in their triggers and dynamics, they share several overlapping processes, including the production of reactive oxygen species (ROS), transcriptional reprogramming of pathogenesis-related (PR) proteins, and the accumulation of secondary metabolites [11].
At the forefront of the PTI defense mechanism are receptor-based genes, which encode proteins responsible for detecting PAMPs [13]. PTI is a mechanism mediated by PRRs, such as receptor-like kinases (RLKs), which are anchored in the plasma membrane [14,15,16,17]. This innate immune system includes receptors that actively recognize all pathogen classes and induce defense signaling responses that culminate in the expression of host resistance [17]. Surface-localized plant immune receptors encoding RLKs or receptor-like proteins (RLPs) play an important role in plant innate immunity. The RLK family and the associated receptor-like cytoplasmic kinases (RLCKs) have expanded in plants because of selective pressure from environmental stress and evolving pathogens. RLKs are mainly involved in recognizing a wide range of pathogen-associated molecules, such as cell wall fragments and extracellular alterations triggered by pathogen invasion [16,18]. Likewise, RLCKs are a cytoplasmic subclass of RLKs and can be crucial in downstream signal transduction, linking pathogen perception to the activation of coping mechanisms [19,20]. RLCKs lack an extracellular domain and tend to interact with RLKs and other membrane-localized proteins to participate in signal relay [20]. The plant cell wall acts as a structural defense against pathogen invasion and a hub for receptors that identify microbial effectors, conserved pathogen-associated molecular patterns, and damage from infections. One notable group of defense-related kinases is the wall-associated kinase (WAK) family. These kinases belong to the receptor-like kinase (RLK) class and are distinguished by their serine/threonine kinase domain and an epidermal growth factor-like (EGF-like) domain [18,21]. The extracellular domains of WAKs exhibit a strong ability to bind small pectic oligosaccharides, which are released during the pathogen-induced cell wall degradation [22]. Following receptor activation, downstream signaling pathways, including MAPK phosphorylation and calcium ion (Ca2+) sensors, are triggered to mediate transcriptional reprogramming [13,23,24]. These pathways involve calcium ion influx, activation of MAPKs, production of ROS in the extracellular space, transcriptional reprogramming, and the fortification of cell walls to enhance physical defenses [25].
Understanding the intricate relationships between hosts and pathogens relies heavily on deciphering transcriptional dynamics and genome reprogramming [26]. In addition, some studies have provided paradigms about RLK/RLCKs functions, but a lack of understanding of crop RLK/RLCKs regarding their role in host–pathogen interactions undermines their application.
Some resistance genes have been identified in response to MOR infection in rice, while little is known about the transcript dynamics of genes involved in PTI and signaling intermediates [27,28,29]. In addition, the role of several receptor-based genes, including extracellular and intracellular receptors in rice immunity against MOR, remains elusive. This study presents a comprehensive meta-analysis of transcriptomic datasets, leveraging a large collection of RNA-seq and microarray profiles. Our study aimed to elucidate the transcriptional features of receptor-based genes alongside other downstream signaling intermediates after MOR infection, uncovering key regulators of rice resistance to blast disease.

2. Results

2.1. Exploratory Data Analysis and Overview of the Analyzed Rice RNA-Seq Data

RNA-seq raw data from the retrieved sets were analyzed individually, followed by an overview of exploratory data analysis on all the datasets. Read counts from 201 RNA-seq samples were processed, yielding sequencing depths ranging from 2,342,996 to 26,054,301 reads, with an average sequencing coverage of 9,381,439 reads (Figure 1A; Supplementary Data S4). Initially, 37,840 aligned genes were identified across all experiments, with 27,752 mapped genes retained after applying a counts per million (CPM) cutoff filter of ≤0.5 (Figure 1G). Before conducting differential gene expression analysis, the RNA-seq data were evaluated for biological relevance, overall quality, and reliability. Principal component analysis (PCA) and Spearman’s correlation matrix revealed clear distinctions between MOR-infected and mock-treated groups (Figure 1B,C; Supplementary Figure S1). PCA further highlighted variations in eigenvalues for individual genes (Figure 1F) and samples (Figure 1B) across the top two principal components, PC1 (79.3%) and PC2 (6.5%), providing discrimination between the MOR-infected and mock groups (Supplementary Figure S1C,F,I,L). Pearson’s correlation analysis further highlighted variability between MOR-infected and mock groups (Figure 1C; Supplementary Figure S1A,D,G,J). R2 values between the MOR and mock groups predominantly ranged from 0.7 to 0.8, while almost all the biological replicates within the same group achieved R2 values exceeding 0.9 (Figure 1C; Supplementary Figure S1A,D,G,J; Supplementary Data S9 and S10). The density distribution of log-CPM values demonstrated a nearly unimodal distribution for each RNA-seq sample (Figure 1E and Supplementary Figure S1B,E,H,K), indicating that most samples exhibit a near normal distribution, thereby supporting the overall quality and uniformity of the data. Furthermore, the mean–variance relationship of CPM values exhibited a flat trend, with variance values plateauing at high expression levels (Figure 1D), suggesting that genes with lower expression levels showed greater variability across samples, while higher expression levels exhibited more consistent gene expression profiles.

2.2. Exploratory Data Analysis and Overview of the Rice Microarray Data

Exploratory data analysis was conducted for the expression profiles of 221 microarray samples from the Affymetrix GPL2025 and GPL6864 datasets (Figure 2; Supplementary Figures S2 and S3). The GPL2025 expression datasets generally exhibit a highly structured and homogeneous expression pattern, while the GPL6864 dataset shows more diverse expression patterns. PCA analysis revealed that PC1 accounted for 96.1% to 99.3% of the total variance in GPL2025 datasets, with PC2 capturing only 0.2% to 3.1% (Figure 2A,C,E,G,I). However, in the GPL6864 dataset, PC1 explained 84.3% of the variance, while PC2 showed 8.1%, indicating higher multidimensional variability within the GPL6864 profiles (Figure 2K). The density distribution of log-transformed values across 144 samples of the GPL6864 platform (GSE62893, GSE62894, and GSE6295) showed a bimodal distribution, which required forcing normalization before the differential gene expression analysis (Figure 2L). However, the expression profiles of the Affymetrix GPL2025 array sets (GSE30941, GSE41798, GSE95394, and GSE18361) generally exhibited predominantly unimodal distributions, except for GSE18361, which showed a bimodal pattern (Figure 2B,D,F,H,J). Additionally, the correlation matrix between samples indicated variability between both groups and among replicates within the same group (Supplementary Figure S2). In the GPL6864 expression profiles, the distance correlations between the MOR and mock groups varied, with R2 values ranging from 0.6 to 0.8 (Supplementary Figure S2E,F; Supplementary Data S11). In contrast, for the experimental profiles of the Affymetrix GPL2025 array sets, R2 values between the mock and MOR groups ranged from 0.8 to 0.9 (Supplementary Figure S2A–D; Supplementary Data S12). Additionally, all the microarray profiles exhibited a mean–variance relationship of CPM values with a flat trend, where variance values plateaued at high expression levels, suggesting consistent gene expression profiles for the highly expressed genes (Supplementary Figure S3A,C,G,I,K).

2.3. Transcriptional Landscape of Rice PTI-Related Genes upon MOR Infection

In this study, we identified the transcriptional dynamics of the receptor-like kinase genes and downstream factors involved in rice PTI in response to MOR infection. A comprehensive analysis of RNA-seq and microarray data revealed distinct expression patterns of the rice surface receptors and downstream signaling genes across various experimental conditions (Table 1; Figure 3; Supplementary Data S5 and S6). Notably, the rice extracellular receptors and downstream signaling genes, including MAPK and Ca2+ signaling genes, were consistently induced in response to MOR infection across all the transcriptomic profiles. Furthermore, most of these genes showed increased expression during pathogen infection rather than a decrease (Table 1; Figure 3).
In the microarray expression profiles, the transcriptional patterns of receptor-based genes and downstream signaling genes revealed dynamic regulatory changes in response to MOR infection (Table 1; Figure 3A–E). A total of 207 microarray datasets from two Affymetrix rice platforms (GPL6864 and GPL2025) were analyzed. The receptor-based genes showed a transcriptional trend, with induced genes ranging from 13 to 146 across different experimental conditions of the rice microarray profiles, while a pattern of one to ninety-eight repressed genes was observed (Table 1; Figure 3A–E). Similarly, MAPK/Ca2+ signaling genes exhibited transcriptional trends, with induced genes ranging from two to forty-six, while the number of repressed genes fluctuated from zero to eighteen across the experimental profiles. For instance, GSE62894 from the Affymetrix-GPL6864 dataset exhibited a higher transcriptional abundance, with 112 to 146 extracellular/cytoplasmic receptor genes being induced across different infection time points (Table 1; Figure 3C). However, the GLP2025 profiles (GSE30941, GSE95394, GSE41798, GSE18361, and GSE28308) showed a fluctuating transcription trend of 13 to 92 induced genes at different infection time points (Table 1; Figure 3A,B). Interestingly, rice root transcriptomic data from GSE18361 and GSE62895 profiles highlighted the transcriptional abundance of receptor- and MAPK/Ca2+ signaling-related genes in response to MOR infection (Table 1; Figure 3B,E). A total of 14 to 92 (GSE18361) and 14 to 17 (GSE62895) receptor genes were induced across infection time points. Remarkably, no repression was observed for MAPK/Ca2+ genes in either the rice root-transcriptomic dataset during MOR infection (Table 1; Figure 3B,E), suggesting a potential constitutive signaling response in rice roots during MOR infection.
Across all the microarray profiles, the transcriptional trends of receptor-based genes fluctuated at different infection time points (Figure 3). Generally, during the early stages of MOR infection (12 to 24 hpi), the transcription abundance of receptor genes ranged from 13 to 112 induced genes, with a pattern of 2 to 27 MAPK/Ca2+ signaling genes induced at the same time (Figure 3). The transcriptional activation of both receptor-based genes and MAPK/Ca2+ signaling almost peaked around the 48 and 72 hpi, with a maximum of 146 receptor genes and 16 signaling genes induced (Figure 3A,C,E). Notably, receptor genes exhibited a near peak of transcriptional repression as the infection progressed, particularly rice leaf transcriptomic profiles (Figure 3A,C,D). In contrast, rice root transcriptomic profiles showed gradual transcription repression of these genes as the infection advanced (Figure 3B,E), highlighting the complex regulatory mechanisms of these genes during MOR infection across different tissues.
Across the analyzed RNA-seq datasets, receptor and MAPK/Ca2+ signaling genes displayed varying transcriptional trends across infection stages. Analysis of 201 RNA-seq samples from eight distinct bio-projects (Table 1) revealed that the receptor-based genes generally exhibited higher activation levels than repression across all experimental conditions (Table 1; Figure 3F–K). The transcriptional patterns of receptor and MAPK/Ca2+ signaling genes across different infection stages exhibited dynamic fluctuations in both induced and repressed gene counts (Figure 3). During the early stages of MOR infection (8, 16, and 24 hpi), the number of induced receptor genes ranged from 24 to 76, while a total range of 10–30 genes was repressed. The transcriptional activation of MAPK/Ca2+ signaling genes during early infection ranged from five to eighteen induced genes, with repressed gene counts fluctuating between zero and eight (Table 1). In the mid-infection phases of MOR infection (36, 48, and 72 hpi), the receptor genes peaked with 145 induced genes at 72 hpi, while the number of repressed genes fluctuated, reaching a maximum of 45 (Figure 3F). Furthermore, the transcription level of receptor genes across 72 RNA-seq samples almost peaked at the late stages of MOR infection (96, 120, and 144 hpi), with up to 156 genes induced at 96 hpi (Figure 3F).
Overall, the complex regulatory patterns of receptor-based genes and signaling intermediates that vary across the tissues and the experimental conditions of both the microarray and RNA-seq profiles may underscore the intricate interplay between PTI mechanisms in rice response against MOR infection.

2.4. Transcript Dynamics of Robust Extracellular and Cytoplasmic Receptor-Based Genes

We further illustrated the transcript dynamics in response to MOR by monitoring the robust number of increased and reduced genes commonly expressed across the RNA-seq and microarray datasets. Data were organized into four time courses of each RNA-seq and microarray expression profile: T1 (8–24 hpi), T2 (36–48 hpi), T3 (72 hpi), and T4 (96–144 hpi). The robust rank aggregation (RRA) algorithm was employed to consolidate multiple expression profiles derived from individual experiments of the RNA-seq and microarray datasets. The robust induced and repressed genes were extracted from each dataset pattern (RNA-seq/microarray). The robust genes at each time point were algorithmically generated compared to a baseline distribution of log2fold changes across the entire DEG lists.
The results showed that the number of receptor-based genes with altered transcripts increased as the infection time progressed in both the RNA-seq and microarray profiles (Figure 4A–C,G; Supplementary Data S7). Receptor-like cytoplasmic kinases (RLCKs) exhibited high transcript dynamics in response to MOR infection. Initially, at T1 stage, 40 robust RLCK genes were induced in the RNA-seq profiles (derived from 60 samples), and 47 genes were induced in the microarray profiles (derived from 52 samples), increasing to 76/78 genes at T3/T4 based on a total of 47 RNA-seq and 35 microarray samples (Figure 4B,C). In contrast, two to fifty-four genes were repressed in all the microarray profiles (Figure 4A), whereas fourteen to thirty genes were repressed in all the RNA-seq profiles (Figure 4C). It seems that more RLCKs are needed to battle against the pathogen’s expansion in rice plants.
Notably, both wall-associated kinases (WAKs) and receptor-like kinases (RLKs) also exhibited remarkable responses to MOR infection, although the number of robustly induced and repressed genes was lower than in RLCK genes across all time points. The transcript abundance dynamics of WAKs and RLKs showed relative oscillations across the RNA-seq and microarray profiles (Figure 4A–C). Across the RNA-seq profiles (T1–T4), 20–32 RLK genes were robustly induced, compared to 5–10 genes in the microarray profiles (Figure 4A,B). However, 25–27 WAK genes were induced in the microarray profiles (Figure 4A), compared to 14–18 genes in RNA-seq profiles (Figure 4B). By combining all the robust genes for each transcriptomic data across all time points, we found that a maximum of 141–153 receptor genes were induced in the microarray and RNA-seq datasets, while 59 to 74 genes were repressed (Figure 4G).

2.5. Top Induced Receptors in Rice Response to MOR Infection

We identified the top consistently induced receptor-based genes across all the analyzed transcriptomes (Table 2). Notably, OsRLCK255 (Os08g0457400) was frequently most induced in 190 MOR-infected samples (72 microarrays/122 RNA-seq) across the four infection time courses. Among the wall-associated receptors, WAK20 and WAK24 were the most frequently top-induced genes, appearing in 126 (66%) and 158 (83%) MOR-infected samples, respectively (Table 2). Additionally, two Lysin-motif extracellular domain-containing RLK genes (OsLysM-RLK7 and OsLysM-RLK1) were top frequently upregulated in 54 and 18 samples, respectively (Table 2). All the top activated genes exhibited a notable increase in expression levels (log2-scale) in the MOR-infected samples compared to the mock (healthy) samples (Figure 5A). Furthermore, analysis of 144 microarray samples revealed that the significantly expressed extracellular and cytoplasmic genes tended to increase their transcription as the MOR infection time progressed (Figure 5B).

2.6. Common Robust Receptor-Based Genes in Rice Response to MOR Infection

We further provide an overview of the commonly induced and repressed genes across all the transcriptomic profiles at four different time courses (Figure 6A–D). The integrated responses from multiple receptors were identified including three RLKs (OsLysM-RLK7, OsLysM-RLK1, and OsCrRLK1L4); seven WAKs (OsWAK25, OsWAK1, OsWAK71, OsWAK98, OsWAK2, OsWAK32, and OsWAK4); as well as 17 RLCK genes, including OsRLCK345, OsRLCK162, OsRLCK245, OsRLCK185, OsRLCK83, and others (Figure 6A,B). The potential integration among the robust RLK/RLCK and RLCK may be synergized under MOR infection, demonstrating the intertwined genetic network in rice immune responses to MOR infection (Figure 6B). Conversely, among the robust downregulated receptor genes, only the OsRLCK 191 “Os05g0589700” gene was repressed across all the transcriptomic time points (Figure 6C,D). Overall, the consistent induction of these genes across all infection stages suggests their multifaceted roles as central components of the rice defense response, contributing to a dynamic and sustained resistance against MOR infection. These robust common genes may serve as genetic hubs for breeding strategies, providing informative clues that can be exploited to enhance signaling-dependent mechanisms contributing to durable and broad-spectrum resistance in rice against blast disease.

2.7. Stage-Specific Transcription of Receptor-Based Genes in Rice Response to MOR

The robust aggregation algorithm analysis across all RNA-seq and microarray datasets also highlighted distinct robust receptor genes uniquely induced or repressed at specific stages of MOR infection across all the DEG profiles (Figure 7A–C; Table 3). Inimitably, several unique genes were induced (Figure 7B) and repressed (Figure 7C) at specific time points, indicating their distinct response to MOR infection at different infection stages. At the T1 stage, a notable transcriptional activation of RLCK genes was observed, with 17 unique genes induced, including OsRLCK145, OsRLCK20, and other RLCK genes (Table 3; Figure 7A,B). Additionally, two unique WAK genes were induced (OsWAK37 and OsWAK123), while no RLK genes were specifically expressed at this stage (Figure 7). This suggests the early and dominant role of RLCKs in the initial stages of rice responses to MOR infection. At both the T2 and T3 stages of MOR infection, WAKs and RLCKs exhibited limited stage-based infection transcription compared to the T1 stage (Table 3; Figure 7). For instance, one WAK gene (OsWAK10) and three RLCK genes (OsRLCK188, OsRLCK212, and OsRLCK120) were specifically induced at the T2 stage profiles (Figure 7A,B). This specific transcription alteration of receptor genes across the early and mid-stages of MOR infection may indicate a kind of reconfiguration of receptor-mediated signaling pathways after the initial response. Furthermore, two WAKs (OsWAK128 and OsWAK102), two RLKs (OsRLK5 and OsCrRLK1L16), and five RLCKs were uniquely induced at all the T3 stage profiles (Table 3; Figure 7A,B). At the late infection stage (T4 profiles), three WAKs and seven RLCK genes were specifically induced, while one RLK gene (OsLysMRLK8) was induced, suggesting the specific role of these genes in sustaining the defense responses to MOR infection at late stages.
Conversely, a total of 20 RLCK and 6 WAK genes were specifically repressed at the late stage of the MOR infection stage (Figure 7; Table 3). Overall, our results underscore the dynamic and robust stage-specific transcriptional activation/suppression patterns of receptor-based kinase families in rice during the MOR infection. The complex regulatory patterns of these genes highlight an intricate interplay in rice responses, particularly within the PTI mechanism and its signaling intermediate.

2.8. Transcript Dynamic and Scaffolding Profile of Robust Downstream Signaling Intermediates in Response to MOR Infection

Downstream signaling factors, including mitogen-activated protein kinase (MAPK) and calcium (Ca2+)-mediated signaling pathway genes, exhibited notable responses to MOR infection (Figure 4D–F,H–K; Supplementary Data S8). The transcript abundance dynamics of MAPKs and Ca2+ sensors increased steadily as infection time progressed (Figure 4D–F). Across microarray data, five to nineteen MAPK robust genes were induced, compared to two to nine genes that underwent repression (Figure 4D). Simultaneously, genes involved in calcium-mediated signaling pathways were upregulated, with 13–54 genes induced and 1–23 genes repressed out of the 83 annotated Ca2+-related genes.
In the RNA-seq profiles, the numbers of induced and repressed MAPKs exhibited a relatively consistent pattern. Approximately six robust genes were upregulated at the T1 stage, reaching a peak of ten genes at the T3 stage (Figure 4E). Meanwhile, 4–6 MAPK genes were repressed across all time points (Figure 4F). Ca2+-related genes exhibited a prominent response to MOR infection (Figure 4F). A robust set of 16 genes was activated at the T1 stage, followed by 27, 33, and 30 upregulated genes in RNA-seq profiles at the subsequent time points. Conversely, four genes were consistently repressed at the T1 stage, reaching a maximum of 14 repressed genes at T4.
A total of 26 MAPK genes were induced across all the transcriptomic profiles, compared to 16 repressed genes (Figure 4H). Transcript patterns of Ca2+-related genes showed 71 induced genes, while 32 genes were repressed (Figure 4I). Among the 71 induced genes, 58 frequently activated genes belonged to different calcium sensor-binding proteins, including calcium-dependent protein kinase (CDPK), calmodulin-like proteins (CaMLs), vascular cation exchanger proteins (CAXs), calcium transporting ATPase (ACA), and N-terminal TM-C2 proteins (NTCMs). Additionally, 13 genes encoded EF-hand domain proteins (Supplementary Data S8).
We further illustrated the scaffolding profile of MAPKs and MAPK-like proteins for the most frequently regulated genes in rice response to MOR infection (Figure 4J; Supplementary Figure S4). Across the MAPK cascade, two MAPK-like genes, MAP3K6 and OsWNK5 (With no Lysine Kinase), were commonly observed in all the transcriptomics time points. Similarly, two MAPK genes (MPK6 and MSRMK2) and two MAPK(K) genes (OsMKK1 and SMG1-OsMAPKK4) showed consistent upregulation across all the transcriptomes under MOR infection (Figure 4J; Supplementary Figure S4).

2.9. Top-Induced Signal Intermediates in Rice Response to MOR Infection

We identified top activated signaling intermediates, including Ca2+ influx and MAPK scaffolding genes, which exhibited high frequency across all the transcriptomic data analyzed (Table 4). Among calcium-related genes, calmodulin-like protein-encoding genes (CaMLs) were the most frequently top-induced genes across all the time points. Specifically, CaML31 and CaML5 genes were frequently identified in 176 and 131 MOR-infected samples, respectively (Table 4). The Ca2+-dependent protein kinase (OsCDPk13) gene was frequently most activated at the T1-T3 profiles in 60 MOR samples. However, the OsNTMC2T2.1 gene with an N-terminal transmembrane was top-induced at the T1 stage in 18 microarray samples.
Regarding MAPK gene activation, MAP kinase-like protein genes, including MAP3K (OS03G0415200) and OsWNK5 (OS07G0584100), were observed among the top activated genes in 117 and 81 MOR-infected samples, respectively. OsMKK1 (OS06G0147800), a MAPK(K) gene, was frequently most induced in 120 samples. Among all MAPK genes, OsMSRMK2 (OS03G0285800) was consistently most activated across all the infection stages in 176 MOR-infected samples (Table 4).
Furthermore, the transcriptional profile of the top 10 upregulated genes across different infection stages was illustrated in a set of microarray samples to show the discrimination in the expression level of these genes under MOR-infected and mock-health conditions (Figure 5D). Likewise, scatter plots of robust MAPK and Ca2+-mediated downstream signaling genes derived from a total set of 144 microarray samples showed the transcriptional trend of significantly expressed genes across the infection stages (Figure 5C).

2.10. Key Interconnected Genes Among the Extracellular/Cytoplasmic Receptors and Signaling Intermediates in Rice Response to MOR Infection

To elucidate the complex interactions among key genes, we constructed a PPI map across all the time points, highlighting core genes among/between extracellular/cytoplasmic receptors and downstream signaling factors, including Ca2+ sensors and MAPKs (Figure 8A). Our analysis revealed diverse interactions between core genes from various protein kinases (MAPKs, MAPKKs), Ca2+ sensors, and receptor kinases (RLKs, RLCKs, WAKs).
Interestingly, our analysis revealed strong network interaction among four MAPKKs (Mitogen-Activated Protein Kinase Kinases) genes (SMG1, MKK1, MKK6, and MPKK10.2), along with forty-four receptors (thirty-two RLCKs, four RLKs, and eight WAKs) and other MAPK genes (Figure 8A). The connections between these MAPKK and receptor-based genes imply a complex role in the rice immune signaling response to MOR infection.
In response to MOR infection, the CPK13 gene emerged as a top Ca2+-related core gene, connecting to several MAPK genes and Ca2+ sensors. Approximately 11 protein kinase genes (MAPKs) were highly connected to CPK genes and other MAPKK genes. Additionally, two RLCKs (OsRLCK231, OsRLCK120) were interconnected with both MAPKs and MAPKKs, while another two (OsRLCK100, OsRLCK369) were mapped in connecting with CPK genes and MAPKK genes. Four EF-hand protein genes were mapped and connected to core Ca2+-related genes and core MAPK genes (Figure 8A). Taken together, these networks suggest that these signaling intermediates and receptor-based genes may play key roles in orchestrating immune responses to MOR infection in rice.
The expression profiles of six key genes (SMG, MKK1, MKK6, MPPK10.2, MPK6, and CPK1) were analyzed using the log2CPM values from 72 high-throughput RNA-seq samples to observe their transcriptional response between MOR-infected and mock-healthy conditions over four time points (Figure 8B–G). MKK1, MKK6, and CPK1 genes exhibited consistent transcription activation in the MOR-infected samples compared to mock samples (Figure 8). The highest levels of expression were observed at T3 stage for MKK1 (log2CPM = 16.25 vs. 1.62 (mock), log2FC = 3.4), at T1 stage for MKK6 (15.77 vs. 3.05 (mock), log2FC = 2.44), and at T4 stage for CPK1 (11.81 vs. 2.99 (mock), log2FC = 2.01), indicating a robust and sustained response to the MOR infection across different time points (Figure 8B,D,E). Similarly, the MKK6 gene demonstrated expression alteration, peaked at the T2 stage (log2CPM = 16.42 vs. 2.4; log2FC = 2.19), though expression slightly decreased as the infection progressed (Figure 8G). SMG1 gene shows a gradual increase in expression in the MOR-infected group compared to the control, with low alteration at the initial time point (log2CPM = 3.88 vs. 2.4 (mock), log2FC = 0.79) (Figure 8C). Overall, the MOR infection induces a varied response across the core genes, where MKK1 and CPK1 showed stable upregulation, while genes such as MPPK10.2 and MPK6 demonstrated more dynamic fluctuations.

3. Discussion

3.1. Overview and Exploratory Data Analysis of Rice RNA-Seq and Microarray Transcriptomic Data

Over the past two decades, RNA-seq and microarray-based studies have revolutionized our understanding of genome-wide transcriptomic changes, opening new avenues for investigating plant–pathogen interactions. Despite the wealth of data generated from these studies, a significant amount of information remains untapped, warranting further exploration and analysis. A central question guiding our integrative analysis is how to effectively combine and analyze the data from multiple existing RNA-seq and microarray studies conducted under Magnaporthe oryzae (MOR) infection conditions to assess the transcriptomic profiles of receptor-based genes and identify their responsiveness to pathogen infections.
Comparing gene lists across different studies can be challenging due to variations in datasets. Technical variations in RNA-seq/microarray methods, such as library preparation, sequencing method, as well as the bioinformatics data analysis pipeline, contribute to these challenges. To overcome these limitations, we employed linear modeling combined with empirical Bayes moderation (eBayes) for differential gene expression (DGE) analysis across both approaches, ensuring more accurate results and minimizing variability between RNA-seq and microarray datasets [30,31]. Using a consistent bioinformatics pipeline and analysis criteria for all the experimental conditions increases the robustness and reliability of output findings, notably in DEG selection.
Our exploratory data analysis dimensionality indicated an overview of the structure and relationships among the analyzed samples in all the experiments (Figure 1 and Figure 2; Supplementary Figures S1–S3; Supplementary Data S9–S12). Our results suggest the constitutive differences between experimental groups [32]. RNA-seq and array sets mostly showed a quite flat trend between the means and variances (Figure 1F and Figure 2A,C,E,G,I,K), reflecting high biological variation between experimental conditions [31].

3.2. Transcriptional Landscape of Receptor-Based Genes in Rice Response to MOR Infection

The study showed a significant alteration in transcriptional abundance upon MOR infection across different infection stages (Table 1; Figure 3; Supplementary Data S5 and S6). The differences among the set of expressed profiles are attributed to specific experimental conditions such as genotype, resistance/susceptibility, infected tissues (leaves, roots, or panicles), plant growth conditions, the methods of inoculation, and plant growth stage. Generally, transcriptomic profiles have been widely used to identify specific gene expressions, providing significant insights into the molecular mechanisms underlying plant resistance to pathogens such as MOR, and other significant pathogens that affect rice [33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51]. Despite extensive research and growing advances in understanding rice immunity to MOR infection, several key aspects of the interplay between rice and MOR remain poorly understood. While previous research has contributed valuable insights into the rice–MOR interaction, it often focuses on specific studies, including particular genes or specific signaling pathways [35,37,38,39,40], overlooking multifaceted pathways such as PTI, and the transcriptional profiles of receptor-based genes in the rice response to MOR infection. During our study, the landscape profile from individual studies provides an overview of our current understanding of receptor-based genes in rice genome reprogramming under MOR infection, justifies their important roles in rice–MOR interactions, and advocates for their incorporation into the plant immune system. This approach offers a comprehensive understanding of the variation in the rice transcriptional landscape of the receptor-based genes during the defense response to MOR.

3.3. PTI-Related Genes Were Prominently Induced in Rice Response to MOR Infection

Since individual transcriptional profiles may exhibit distinct patterns, our integrative analysis identified gene sets that were consistently regulated across conditions with a high degree of confidence, as well as those showing opposing expression patterns. Our findings shed light and provide a robust transcriptional pattern of the receptor-based genes and signaling downstream scaffolding that are mainly involved in PTI machinery in rice (Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8; Supplementary Data S7 and S8).
Our results suggest that the transcriptional induction of the extracellular and cytoplasmic receptor genes is prevalent in priming rice immune responses against MOR infection. RLCK receptors were highly responsive to MOR, where the number of expressed genes increased upon infection progression (Figure 4). RLCKs play important roles in triggering plant immunity by acting as intracellular signal transducers, while many of these receptors are still functionally ambiguous [20,52,53]. Likewise, some RLCKs integrate responses from multiple receptors recognizing distinct ligands. Although they lack extracellular domains that exist in other receptor kinases (RLKs and WAKs). However, a previous study showed that OsRLCK185 interacted with the chitin receptor complex of OsCERK1 and was phosphorylated by it, initiating downstream signaling in rice immune responses against the MOR infection [19,54].
Our study highlighted that the number of RLCK genes with altered expression was higher than other receptors across all the dynamic profiles (Figure 4A–C,G and Figure 6). Central to these genes, 17 RLCK genes were constantly activated across all the transcriptomic profiles (Figure 6B). Among them, eight genes (OsRLCK255, OsRLCK138, OsRLCK345, OsRLCK237, OsRLCK253, OsRLCK233, OsRLCK162, and OsRLCK106) were frequently found to be top upregulated across a considerable set of MOR–infected samples (Table 2), highlighting their potential significance in rice signal pathways in response to MOR infection. Furthermore, our results revealed that RLCK genes display stage-specific patterns of activation and repression, playing a critical role in the rice response to MOR infection (Figure 7; Table 3). RLCKs exhibited the most dynamic expression changes across all infection stages. During the early infection phase (T1), 17 RLCK genes were specifically activated, whereas during the late infection stages (T3–T4), RLCK genes were predominantly repressed, with 20 RLCKs specifically downregulated (Figure 7; Table 3). In this manner, it is plausible to suggest that the observed pattern of high transcriptional abundance of RLCKs during the early response to MOR infection, contrasted with their strong repression at later stages, may be balanced by the suppression of other receptor-related pathways at the late stages of infection. Overall, our results identified that some RLCK genes highly respond to MOR infection and appear to integrate signals from PAMP recognition, suggesting a level of functional specialization of these RLCK genes in rice immunity against MOR infection (Figure 6B).
RLKs and WAKs are extracellular proteins that perceive PAMPs, such as chitin and oligo-glucuronides from fungal cell walls [21,55,56]. Both receptors can coordinate with RLCKs in regulating hormone synthesis and responses, ROS production, Ca2+ signaling, activation of MAPK, and immune gene expression. Previous research revealed that 103 genes of these receptors were found to be induced in rice under MOR infection [33]. Our results identified robust transcript-altered genes among both subfamilies of receptors (Figure 4A–C). Additionally, we show that seven WAK and three RLK genes were commonly activated under MOR conditions across all the transcriptomic data (Figure 6B). Wall-associated kinases have been explored as positive and/or negative regulators of rice resistance to fungal blast disease. It has been shown that loss-of-function mutants of OsWAK14, OsWAK91, and OsWAK92 positively regulated the quantitative resistance to MOR infection, while OsWAK112d acts as a negative regulator of blast resistance [57]. Furthermore, multiple research studies have suggested that WAKs are key regulators of plant disease resistance, as they monitor the integrity of the cell walls and initiate intracellular signaling upon their disruption by pathogen attacks [58]. Genome-edited lines of the WAK1 gene were found to have impaired callose-induced cell wall reinforcement in response to bacterial flagellin detection [59]. In wheat, the TaWAK6 gene has been shown to effectively limit pathogen growth, contributing to adult plant resistance against leaf rust disease [60]. Likewise, several studies reported that the Stb6 gene, which encodes a WAK extracellular protein, was found to recognize a small secreted protein from the fungal pathogens of Septoria leaf blotch upon infection [61,62].
In the core of robust WAK and RLK genes, our findings show seven common activated WAK genes and three RLK genes that encode extracellular receptor proteins with lysin-motif domain (Figure 6B). Central to these genes, we show OsLysM-RLK7 and OsLysM-RLK1 as top upregulated genes across a set of 55 and 18 MOR-infected samples at the T1/T2/T3 stages (Table 2), while OsWAK1 gene was most activated across a set of 40 MOR-samples at the T1 and T4 stages (Table 2). The consistent activation of these common genes implies their potential role in initiating rice cellular signaling and immune responses upon MOR invasion. However, the interactions and networks of these receptors in response to both the early and late stages of MOR infection require further investigation to fully reveal their contribution to rice resistance against blast disease.
Based on our findings, the prominent transcriptional activation of these receptors underscores their importance in the downstream signaling of rice immunity against MOR infection. This suggests that PTI may play a crucial role in rice immunity against blast diseases by activating a robust set of extracellular receptors and cytoplasmic receptor-like kinases. However, the suppression of numerous receptor genes in the rice transcriptomes upon MOR infection is also evident (Figure 4G and Figure 6C). Given that receptor kinases are primarily involved in activating resistance mechanisms through PTI, this raises important questions. Specifically, it prompts consideration of whether these receptors might also play a contrasting role in promoting infection, potentially aligning with a phenomenon of PAMP-triggered susceptibility.

3.4. Interconnecting Genes of PTI and Downstream Signaling upon MOR Infection

Posttranslational modification of proteins is a crucial regulatory component of all cellular signaling, including plant immune signaling. The transcriptional regulation of plant immune receptors and signaling intermediates may enable rapid adjustment of defense response activation during pathogen infection. In this study, we identified the mode of gene association between extracellular/cytoplasmic receptors and downstream signaling genes (MAPKs and Ca2+-signaling genes). As interconnecting core genes, eight WAKs (e.g., WAK1, WAK2), four RLKs (e.g., OsCrRLK1L7), and thirty-two RLCKs show high connectivity with other MAPKK-mediated signaling genes, such as SMG1, MKK1, MKK6, and MPKK10.2 (Figure 8). MAPK cascades are crucial regulators of multiple biological processes in plants, including rice, such as immune responses; however, the actual mechanisms governing their functions are still not fully understood. Among the core genes, MKK6 and MPKK10.2 were identified in response to MOR infection (Figure 8). Previous studies revealed that the OsMKK6/OsMPK4 cascade is proposed to regulate rice resistance to MOR, and its disruption has been shown to activate OsMPK6 [63]. Furthermore, OsMPKK10.2/OsMPK6 signaling cascades are suppressed by enhanced disease resistance 1 gene (OsEDR1), which plays a role in pre-invasive nonhost resistance, and this suppression functions as a negative regulator of the immune response in rice [64]. OsMKK10.2–OsMPK6 cascade is required to activate the OsWRKY45 transcription factor, leading to salicylic acid-mediated rice disease resistance against pathogen infection [23,64,65]. Additionally, SMG1 (small grain 1), which encodes OsMKK4, was identified as a core interconnected gene along with MKK1 (Figure 8). To the best of our knowledge, no prior studies have investigated their roles in rice defense against MOR infection. Therefore, further functional characterization of SMG1 (OsMKK4) and MKK1 is essential to uncover their potential contributions to rice immunity against MOR.
Generally, kinase-dependent signaling, mediated through PAMPs, receptors, and downstream factors, mainly activates a series of defense responses such as cell death, ROS generation, and defense-related proteins [18,66]. Our findings consistently highlighted the rice RLCKs as key genes responsive to MOR infection, with a considerable set of genes strongly interconnected with multiple MAPK intermediates (Figure 8). RLCK subfamily members have been implicated in playing a crucial role in plant immunity, facilitating strong immune responses by interacting with both PRRs and NLR intracellular receptors. These kinases act as a bridge connecting surface-localized receptors to MAPK signaling cascades and are directly involved in triggering the generation of ROS [67,68]. However, the interactions between receptors and downstream cascades in signaling pathways leading to the activation of defense responses are still poorly understood. Overall, our results suggest that these interconnected signaling intermediates and receptor-based genes create a robust network to fine-tune the rice defense mechanisms in response to MOR infection.

4. Materials and Methods

4.1. Retrieving and Processing of High-Throughput Sequencing Data

4.1.1. Retrieving and Processing of RNA-Seq Datasets

RNA-seq raw data from rice infected by MOR across different time courses were retrieved from the NCBI sequence read archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra, accessed on 1 March 2024) using the NCBI SRA toolkit. A total of 201 RNA-seq samples were obtained at various infection stages, encompassing nine time points (8, 12, 24, 36, 48, 72, 96, 120, and 144 h post-infection, hpi) (Supplementary Data S1). SRA files were converted to Fastq format, followed by quality assessment with FastQC. Low-quality reads and adapter sequences were removed using the Trimmomatic v0.39 tool (Babraham Institute, UK, available at http://www.usadellab.org/cms/index.php?page=trimmomatic, accessed on 1 March 2024). Cleaned reads were mapped to the rice reference genome (IRGSP-1.0) using the HISAT2 tool (Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA), and BAM files were generated [69,70]. Gene counts were obtained using FeatureCounts and Samtools for downstream analysis [71].

4.1.2. Retrieving and Processing of Microarray Datasets

Gene expression profiles of rice microarray transcriptomic datasets were retrieved from the GEO database within the NCBI platform. Rice microarray samples (n = 221) of MOR-infected and uninfected controls were retrieved, covering various expression profiles (Supplementary Data S1). All the microarray samples (.CEL) are deposited under two Affymetrix platforms, GPL6864 and GPL2025. The GEOquery package was utilized to import GEO data into the R programming environment using the accession codes (GSE) associated with the expression profiles of each sample series [72]. The Bioconductor package SimpleAffy (Fred Hutchinson Cancer Research Center, Seattle, WA, USA, available at: https://www.bioconductor.org/packages//2.7/bioc/html/simpleaffy.html, accessed on 1 March 2024).was also utilized for quality assessment. Poor-quality samples were discarded, and all the rest were used to analyze the differentially expressed genes [73]. Prop IDs were annotated to gene IDs based on the Affymetrix Rice Genome Array (GPL2025 and GPL6864).

4.2. Exploratory Data Analysis and Differential Gene Expression Analysis

In this study, we applied linear modeling integrated with empirical Bayes moderation (eBayes) for differential gene expression (DGE) analysis across both approaches, enhancing accuracy and reducing variability between RNA-seq and microarray datasets. Microarray dataset profiles were normalized with log2 transformation when the 99th percentile (quantile 0.99) exceeds 100, or if the 25th percentile (quantile 0.25) is greater than 0 and the range between the maximum and minimum values (quantile 1–quantile 0) exceeds 50. For RNA-seq datasets, data were normalized to counts per million (CPM) using the edgeR package (v.3.64.0) (Fred Hutchinson Cancer Research Center, Seasttle, WA, USA) in R programming software [30,31,74]. The stats R package (V.4.6.0) (R Foundation for Statistical Computing, Vienna, Austria) was used to generate the density distributions of each experimental condition (samples), the correlation matrix, and principal component analysis (PCA). Empirical Bayes batch correction was applied to the samples exhibiting batch effects to achieve a normal distribution across experimental groups, using the “limma precision weights” function. The Limma R package (Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia, available at; https://bioconductor.org/packages/release/bioc/html/limma.html, accessed on 1 March 2024).was used to identify DEGs between MOR-infected and healthy groups based on the programming language workflow outlined by GEO2R [72]. For RNA-seq data, edgeR and Limma packages were used to identify the DEGs between samples in each experiment based on the edgeR–Limma bioinformatic pipeline for RNA-seq analysis [31,75]. Significantly differentially expressed genes were screened using an adj. p-value ≤ 0.05 and log2FC ≥ 1. Furthermore, the robust rank aggregation (RRA) algorithm package in R software was used to integrate multiple DEG lists of individual experiments at both the RNA-seq and microarray datasets [76]. DEG lists were separately integrated for RNA-seq and microarray data at each time point, and final lists were arranged by top-regulated genes. The RRA algorithm assessed the ranking of DEGs in each experiment and compared them to the baseline across all the DEG lists.

4.3. Analysis of Receptor-Based Genes and Signaling Intermediate Genes in Rice Response to MOR Infection

Genes related to host–pathogen interactions, including extracellular receptor proteins and downstream signaling intermediates, were monitored among MOR-infected and mock rice samples. Members of extracellular and cytoplasmic receptors (RLKs, RLCKs, and WAKs) and downstream factors (MAPK and Ca2+ signaling genes) were all retrieved based on the International Rice Genome Sequencing Project/Rice Annotation Project Database (RAP-DB). A total of 385 RLCKs, 151 RLKs, and 105 WAKs were retrieved (Supplementary Data S2). A total of 31 MAPKs and 83 Ca2+-sensing encoding genes were determined (Supplementary Data S3). The retrieved gene IDs were then overlapped with all the DGEs of our current study to screen the upregulated and downregulated genes upon MOR infection across all transcriptomes.

4.4. PPI Network Analyses

PPI network of the identified key modules was constructed based on the STRING-functional gene interacting networks database (http://string-db.org, accessed on 1 March 2024) [77]. Both known and predicted PPI networks were screened. PPI matrices were conducted for DEGs of extracellular/cytoplasmic receptors and signaling intermediate genes. Connected nodes (genes) were directly imported into the Cytoscape tool (version 3.7.2) for visual editing of the PPI networks [78,79].

4.5. Statistical Analysis

Statistical pairs analysis between MOR-infected and healthy groups was compared based on Student’s t-tests (p ≤ 0.05) during the differential gene expression analysis. The correlation between samples was evaluated based on Pearson correlation coefficients.

5. Conclusions

Our study revealed that pattern-triggered immunity (PTI) machinery, including several receptor-based genes and signaling intermediate genes, concomitantly responds to MOR infection in rice plants, indicating that PTI machinery plays a crucial role in regulating rice immunity against MOR-caused blast disease. This research provides a comprehensive overview of the transcript dynamics of receptor-based genes under MOR stress, in which a total of 31 reporter-based genes, including receptor-like cytoplasmic kinases (RLCKs), wall-associated kinases (WAKs), and receptor-like kinases (RLKs), were commonly found among all transcriptomic data. This research facilitates a deeper molecular understanding of rice immunity regulation in response to MOR infection. Surface receptor and cytoplasmic receptor genes have highly dynamic transcript expression patterns in response to MOR infection and steadily increase upon infection progression. The majority of RLCKs, WAKs, and RLKs are largely activated rather than repressed across all the transcriptomic profiles. However, molecular functions and dynamic interactions among these mechanisms still warrant various further research questions to investigate the interplay possibilities between receptor-based genes and signaling intermediates in rice innate immunity against blast disease.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijms26104618/s1.

Author Contributions

F.S.: conceptualized the study and database construction. F.S. and A.E.: data analysis, data visualization, results interpretation, and manuscript drafting. X.T.: data collection. J.Y.: manuscript reviewing. W.K.: provided supervision and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets analyzed in this meta-analysis during the current study are all available in the NCBI datasets. The microarray expression profiles are available in NCBI’s Gene Expression Omnibus (GEO) under GEO accessions: GSE95394, GSE30941, GSE41798, GSE28308, GSE18361, GSE62894, GSE62893, and GSE62895. For RNA seq datasets, all datasets are available under eight Bio-Projects accessions: PRJEB45007, PRJNA545418, PRJNA661210, PRJNA1062412, PRJNA590671, PRJNA563035, PRJNA310071, and PRJNA634330. Furthermore, the extra-related output findings of this research are deposited in 16 additional Supplementary Files, including 12 Supplementary Data Files and Figures S1–S4. Any further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MORMagnaporthe oryzae
RNA-seqRNA sequencing
GEOGene Expression Omnibus
GSEGene Expression Omnibus series
GPLGene expression platform
CPMCounts per million
PCAPrincipal component analysis
DEGsDifferentially expressed genes
PPIsProtein–protein interactions
PAMPspathogen-associated molecular patterns
DAMPshost damage-associated molecular patterns
PTIPathogen-triggered immunity
WAKWall-associated kinase
RLKReceptor-like kinases
RLCKReceptor-like cytoplasmic kinase
MAPK (MPKMitogen-activated protein kinases
MAPKK (MKK)MAPK kinases
MAPKKKMAPK kinases kinase
ROSReactive oxygen species
NLRnucleotide binding and leucine-rich-repeat

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Figure 1. Overview and exploratory analysis of 72 RNA sequencing samples of rice transcriptome profiles infected with MOR at different time courses. (A): Total read counts of all analyzed samples. (B,F); principal component analysis (PCA) revealed differences among samples (B) and in eigenvalues for individual (F) genes across the top two principal components. The separation of genes based on their first two principal components is illustrated (F), where red points indicate genes with highly significant separations, and grey points indicate genes with low significant separations. (C): Pearson’s correlation coefficients between samples indicated variability between groups and among replicates within the same group. All biological replicates for the same sample group exceeded 0.9 R2. (D): The mean–variance relation of CPM values illustrates a flat trend. (E): The density plot illustrating the distribution of log-CPM values exhibited a nearly unimodal distribution for each sample. (G): Number of genes mapped by type (CPM cutoff of ≥0.5). (H): A distribution plot of CPM values illustrates high variation between samples.
Figure 1. Overview and exploratory analysis of 72 RNA sequencing samples of rice transcriptome profiles infected with MOR at different time courses. (A): Total read counts of all analyzed samples. (B,F); principal component analysis (PCA) revealed differences among samples (B) and in eigenvalues for individual (F) genes across the top two principal components. The separation of genes based on their first two principal components is illustrated (F), where red points indicate genes with highly significant separations, and grey points indicate genes with low significant separations. (C): Pearson’s correlation coefficients between samples indicated variability between groups and among replicates within the same group. All biological replicates for the same sample group exceeded 0.9 R2. (D): The mean–variance relation of CPM values illustrates a flat trend. (E): The density plot illustrating the distribution of log-CPM values exhibited a nearly unimodal distribution for each sample. (G): Number of genes mapped by type (CPM cutoff of ≥0.5). (H): A distribution plot of CPM values illustrates high variation between samples.
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Figure 2. Overview and exploratory data analysis of the rice microarray samples. (A,C,E,G,I,K): Principal component analysis (PCA) variable plots. PCA revealed differences among experimental groups (MOR-infected/mock samples) via the top two principal components (PCA 1 and PCA 2). (B,D,H,F,J,L): Expression density plots illustrating the distribution of the expression values of genes in the dataset. Plot density curves appear to be nearly normally distributed in GSE30941, GSE41798, GSE95394, and GSE18361, while plot density curves appear not to be normally distributed and require further normalization before the screening for differentially expressed genes in GSE28308, GSE62893, GSE62894, and GSE62895 expression profiles.
Figure 2. Overview and exploratory data analysis of the rice microarray samples. (A,C,E,G,I,K): Principal component analysis (PCA) variable plots. PCA revealed differences among experimental groups (MOR-infected/mock samples) via the top two principal components (PCA 1 and PCA 2). (B,D,H,F,J,L): Expression density plots illustrating the distribution of the expression values of genes in the dataset. Plot density curves appear to be nearly normally distributed in GSE30941, GSE41798, GSE95394, and GSE18361, while plot density curves appear not to be normally distributed and require further normalization before the screening for differentially expressed genes in GSE28308, GSE62893, GSE62894, and GSE62895 expression profiles.
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Figure 3. Transcriptional trends of the induced and repressed receptor-based genes and MAPK/Ca2+ downstream signaling intermediates form the analyzed RNA-seq and microarray datasets infected by MOR. (A–E): Line graphs showing the number of induced and repressed genes in the rice microarray datasets. (F–K): Line graphs showing the number of induced and repressed genes in the rice microarray datasets. All the data are based on Table 1. The dashed lines indicate the induced receptor-based genes (dark purple color) and MAPK/Ca2+ genes (green color). The solid lines indicate the repressed receptor-based genes (dark blue color) and MAPK/Ca2+ genes (yellow color). “hpi” indicates hours post-infection (GSE18361 * and GSE62895 *: root samples, PRJNA563035 *: spike samples).
Figure 3. Transcriptional trends of the induced and repressed receptor-based genes and MAPK/Ca2+ downstream signaling intermediates form the analyzed RNA-seq and microarray datasets infected by MOR. (A–E): Line graphs showing the number of induced and repressed genes in the rice microarray datasets. (F–K): Line graphs showing the number of induced and repressed genes in the rice microarray datasets. All the data are based on Table 1. The dashed lines indicate the induced receptor-based genes (dark purple color) and MAPK/Ca2+ genes (green color). The solid lines indicate the repressed receptor-based genes (dark blue color) and MAPK/Ca2+ genes (yellow color). “hpi” indicates hours post-infection (GSE18361 * and GSE62895 *: root samples, PRJNA563035 *: spike samples).
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Figure 4. Transcript profile of robust receptor-based genes in the RNA-seq and microarray dataset in response to MOR infection at different stages. (A–C): Transcriptional level of extracellular and cytoplasmic receptors, including RLKs, WAKs, and RLCKs during infection. (A): The number of induced and repressed receptor genes in the microarray profiles. (B): The number of induced genes in RNA-seq profiles. (C): The number of repressed genes in the RNA-seq profiles. (D–F): Transcript dynamics of downstream gene groups, including Ca2+ and MAPKK. (D): The number of induced and repressed MAPKs and Ca2+ downstream signaling genes in microarray profiles. (E,F): The number of induced (blue line) and repressed (grey line) MAPK (E) and Ca2+ (F) signaling genes in the RNA-seq profiles. (G): Total number of activated and repressed receptor genes in both the RNA-seq and microarray profiles. (H,I): Total number of activated and repressed MAPK/Ca2+ downstream intermediate signaling genes in both RNA-seq and microarray profiles. (J): A simple diagram illustrating the most frequently activated key genes of the MAPK phosphorylation/activation cascade (MAPKs, MAPKKs, MAPKKKs, and MAP-like kinase genes), across MOR-infected profiles. (K): Frequently, key observed Ca2+ signaling genes (including CPKs, CMLs, CAX, ACA, and NTCM) were induced across profiles.
Figure 4. Transcript profile of robust receptor-based genes in the RNA-seq and microarray dataset in response to MOR infection at different stages. (A–C): Transcriptional level of extracellular and cytoplasmic receptors, including RLKs, WAKs, and RLCKs during infection. (A): The number of induced and repressed receptor genes in the microarray profiles. (B): The number of induced genes in RNA-seq profiles. (C): The number of repressed genes in the RNA-seq profiles. (D–F): Transcript dynamics of downstream gene groups, including Ca2+ and MAPKK. (D): The number of induced and repressed MAPKs and Ca2+ downstream signaling genes in microarray profiles. (E,F): The number of induced (blue line) and repressed (grey line) MAPK (E) and Ca2+ (F) signaling genes in the RNA-seq profiles. (G): Total number of activated and repressed receptor genes in both the RNA-seq and microarray profiles. (H,I): Total number of activated and repressed MAPK/Ca2+ downstream intermediate signaling genes in both RNA-seq and microarray profiles. (J): A simple diagram illustrating the most frequently activated key genes of the MAPK phosphorylation/activation cascade (MAPKs, MAPKKs, MAPKKKs, and MAP-like kinase genes), across MOR-infected profiles. (K): Frequently, key observed Ca2+ signaling genes (including CPKs, CMLs, CAX, ACA, and NTCM) were induced across profiles.
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Figure 5. Expression profiles of top activated genes of the extracellular/cytoplasmic and signaling intermediates across the different infection stages. (A): Heatmap plots illustrate the expression profiles of the top ten activated receptors across MOR-infected and healthy conditions across the four time points. Each time point was derived from 18 paired samples (MOR vs. Mock). Scatter plots illustrating the expression profiles of the extracellular/cytoplasmic receptors (B) and downstream factors (C), in a set of 144 microarray samples (Affymetrix GPL6864 platform). Each point represents a gene; significantly regulated genes (adj. p-val. ≤ 0.05) are marked in red, while non-significantly regulated genes (adj. p-val. ≥ 0.05) are marked in green. (D): Heatmap plots illustrate the expression profiles of the top ten activated signaling intermediates (Ca2+/MAPK) genes across the MOR-infected and healthy conditions across the four time points.
Figure 5. Expression profiles of top activated genes of the extracellular/cytoplasmic and signaling intermediates across the different infection stages. (A): Heatmap plots illustrate the expression profiles of the top ten activated receptors across MOR-infected and healthy conditions across the four time points. Each time point was derived from 18 paired samples (MOR vs. Mock). Scatter plots illustrating the expression profiles of the extracellular/cytoplasmic receptors (B) and downstream factors (C), in a set of 144 microarray samples (Affymetrix GPL6864 platform). Each point represents a gene; significantly regulated genes (adj. p-val. ≤ 0.05) are marked in red, while non-significantly regulated genes (adj. p-val. ≥ 0.05) are marked in green. (D): Heatmap plots illustrate the expression profiles of the top ten activated signaling intermediates (Ca2+/MAPK) genes across the MOR-infected and healthy conditions across the four time points.
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Figure 6. Transcript dynamics of extracellular and cytoplasmic receptors across all the rice transcriptome data. (A): Venn diagram illustrating the consistently activated receptors across the RNA-seq and microarray profiles during the four stages of MOR infection. (B): A common set of 31 activated receptors across all the infection stages. Among these, WAKs (wall-associated receptors) are cell wall-embedded and transmembrane receptors that may recognize any extracellular changes derived either from pathogen-associated molecular patterns (PAMPs) or host damage-associated molecular patterns (DAMPs). RLKs (receptor-like kinases) are central transmembrane proteins involved in the first lines of PTI machinery, initiating signaling cascades by PAMP recognition. RLCKs (receptor-like cytoplasmic kinases) are intracellular signaling molecules often associated with transmembrane proteins and lack extracellular domains. (C): Venn diagram showing the number of repressed receptor genes during different infection stages. (D): Commonly repressed receptors across all time points during infection. Only one receptor was repressed in common across all profiles (OsRLCKs191, Os05g589700). The number of RLCK genes with altered expression was higher than other receptors across all dynamic profiles.
Figure 6. Transcript dynamics of extracellular and cytoplasmic receptors across all the rice transcriptome data. (A): Venn diagram illustrating the consistently activated receptors across the RNA-seq and microarray profiles during the four stages of MOR infection. (B): A common set of 31 activated receptors across all the infection stages. Among these, WAKs (wall-associated receptors) are cell wall-embedded and transmembrane receptors that may recognize any extracellular changes derived either from pathogen-associated molecular patterns (PAMPs) or host damage-associated molecular patterns (DAMPs). RLKs (receptor-like kinases) are central transmembrane proteins involved in the first lines of PTI machinery, initiating signaling cascades by PAMP recognition. RLCKs (receptor-like cytoplasmic kinases) are intracellular signaling molecules often associated with transmembrane proteins and lack extracellular domains. (C): Venn diagram showing the number of repressed receptor genes during different infection stages. (D): Commonly repressed receptors across all time points during infection. Only one receptor was repressed in common across all profiles (OsRLCKs191, Os05g589700). The number of RLCK genes with altered expression was higher than other receptors across all dynamic profiles.
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Figure 7. Stage-specific activation and repression of receptor genes intimately respond to MOR infection at a specific time point. (A): Model diagram of the unique activated and repressed genes at each infection stage (T1, T2, T3, T4). The number of RLCK genes with altered expression was higher than other receptors across all dynamic profiles. At the early infection phase, only two extracellular WAK genes (OsWAK37 and OsWAK123) were specifically activated, followed by the activation of 17 cytoplasmic receptor genes (RLCKs), whereas no specific extracellular receptors of the RLK family were activated. At mid-infection phase, two specific extracellular genes of RLKs and two genes of WAKs were activated, and nine RLCKs were specifically activated. At the late infection stage, specific RLCK genes with altered repression were observed, with 20 repressed genes. (B,C): Line graphs showing the number of unique induced (B) and unique repressed (C) receptor-based genes at each time point. RLKs indicate receptor-like kinase genes, RLCKs indicate receptor-like cytoplasmic kinase genes, and WAKs indicate wall-associated kinase genes. All the unique induced and repressed are based on Table 3. The T1 stage indicates 8-, 12-, 16-, and 24-h post-infection (hpi), the T2 stage indicates 36–48 hpi, the T3 stage indicates 72 h, and the T4 stage indicates 96, 120, and 144 hpi.
Figure 7. Stage-specific activation and repression of receptor genes intimately respond to MOR infection at a specific time point. (A): Model diagram of the unique activated and repressed genes at each infection stage (T1, T2, T3, T4). The number of RLCK genes with altered expression was higher than other receptors across all dynamic profiles. At the early infection phase, only two extracellular WAK genes (OsWAK37 and OsWAK123) were specifically activated, followed by the activation of 17 cytoplasmic receptor genes (RLCKs), whereas no specific extracellular receptors of the RLK family were activated. At mid-infection phase, two specific extracellular genes of RLKs and two genes of WAKs were activated, and nine RLCKs were specifically activated. At the late infection stage, specific RLCK genes with altered repression were observed, with 20 repressed genes. (B,C): Line graphs showing the number of unique induced (B) and unique repressed (C) receptor-based genes at each time point. RLKs indicate receptor-like kinase genes, RLCKs indicate receptor-like cytoplasmic kinase genes, and WAKs indicate wall-associated kinase genes. All the unique induced and repressed are based on Table 3. The T1 stage indicates 8-, 12-, 16-, and 24-h post-infection (hpi), the T2 stage indicates 36–48 hpi, the T3 stage indicates 72 h, and the T4 stage indicates 96, 120, and 144 hpi.
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Figure 8. Key interconnected genes among the extracellular/cytoplasmic receptors and signaling intermediates in rice response to MOR infection. (A): Protein–protein interaction networks among key response gene groups. Extracellular receptors (RLKs, WAKs), cytoplasmic receptor kinase (RLCK), and downstream cascades of MAPKs and Ca2+ binding. Among Ca2+ sensors, eight CPK genes (calcium-dependent protein kinase) were highly connected to MAPKs, MAPKKs, and EF-hand proteins. Ten core MAPK genes were found to be connected with CPKs and MAPKKs. The four top-connected genes associated with MAPKKs were observed, including SMG1, MKK1, MKK6, and MPKK10.2. Four EF-hand protein genes were mapped and connected to core Ca2+ and MAPK signaling genes. (B–G): Expression profile of six core MAPK/Ca2+ intermediate genes across 72 RNA-seq samples.
Figure 8. Key interconnected genes among the extracellular/cytoplasmic receptors and signaling intermediates in rice response to MOR infection. (A): Protein–protein interaction networks among key response gene groups. Extracellular receptors (RLKs, WAKs), cytoplasmic receptor kinase (RLCK), and downstream cascades of MAPKs and Ca2+ binding. Among Ca2+ sensors, eight CPK genes (calcium-dependent protein kinase) were highly connected to MAPKs, MAPKKs, and EF-hand proteins. Ten core MAPK genes were found to be connected with CPKs and MAPKKs. The four top-connected genes associated with MAPKKs were observed, including SMG1, MKK1, MKK6, and MPKK10.2. Four EF-hand protein genes were mapped and connected to core Ca2+ and MAPK signaling genes. (B–G): Expression profile of six core MAPK/Ca2+ intermediate genes across 72 RNA-seq samples.
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Table 1. Differentially expressed gene (DEG) profiles of induced and repressed receptor-based genes and downstream signaling intermediate (Ca2+ and MAPKs) genes derived from the RNA-seq and microarray datasets.
Table 1. Differentially expressed gene (DEG) profiles of induced and repressed receptor-based genes and downstream signaling intermediate (Ca2+ and MAPKs) genes derived from the RNA-seq and microarray datasets.
Pathogen Infection Time Course
(HPI)		Total DEGsExtracellular Receptor
Genes		Signaling-MAPK/Ca2+
Signaling Genes		N—SamplesExpression Profile
(GEO-Code)		Data Type
UpDownUpDownUpDown
12 h (T1)785386193323 pairedGSE95394arrays
24 h (T1)2910251266131363 pairedGSE95394arrays
48 h (T2)31512059723224113 pairedGSE95394arrays
72 h (T3)44256352835024133 pairedGSE95394arrays
24 h (T1)51179113122512 MORGSE30941arrays
24 h (T1)19339756711139 pairedGSE41798arrays
72 h (T3)253528622823572 pairedGSE28308arrays
48 h (T2)2875191392112502 pairedGSE18361 *arrays
96 h (T4)150013247191202 pairedGSE18361 *arrays
144 h (T4)1182720503902 pairedGSE18361 *arrays
24 h (T1)331650911262706 pairedGSE62894arrays
48 h (T2)59051721108264416 pairedGSE62894arrays
72 h (T3)35681655146634696 pairedGSE62894arrays
120 h (T4)542778041309829186 pairedGSE62894arrays
24 h (T1)291117511162306 pairedGSE62893arrays
48 h (T2)4827196298254316 pairedGSE62893arrays
72 h (T3)3568165584312426 pairedGSE62893arrays
120 h (T4)23292372522616156 pairedGSE62893arrays
24 h (T1)169717481413206 pairedGSE62895 *arrays
48 h (T2)124718551723306 pairedGSE62895 *arrays
72 h (T3)141216801419406 pairedGSE62895 *arrays
120 h (T4)152313641613406 pairedGSE62895 *arrays
8 h (T1)1027176033251499 MORPRJEB45007RNA-seq
16 h (T1)1868248531231049 MORPRJEB45007RNA-seq
24 h (T1)2162185952301529 MORPRJEB45007RNA-seq
48 h (T2)2942148789212239 MORPRJEB45007RNA-seq
72 h (T3)43373294145453569 MORPRJEB45007RNA-seq
96 h (T4)49023807156473449 MORPRJEB45007RNA-seq
144 h (T4)43153604129452659 MORPRJEB45007RNA-seq
12 h (T1)224319133724573 pairedPRJNA545418RNA-seq
24 h (T1)2375195138271083 pairedPRJNA545418RNA-seq
12 h (T1)174919652722563 pairedPRJNA545418RNA-seq
24 h (T1)173421302418783 pairedPRJNA545418RNA-seq
12 h (T1)3098186966271806 pairedPRJNA661210RNA-seq
24 h (T1)1871116976101106 pairedPRJNA661210RNA-seq
36 h (T2)2497192195151646 pairedPRJNA661210RNA-seq
48 h (T2)2291201184111426 pairedPRJNA661210RNA-seq
24 h (T1)719668479623 pairedPRJNA1062412RNA-seq
48 h (T2)946803623623 pairedPRJNA1062412RNA-seq
72 h (T3)719298546503 pairedPRJNA1062412RNA-seq
24 h (T1)156094535151162 MORPRJNA590671RNA-seq
72 h (T3)2181105629231332 MORPRJNA590671RNA-seq
120 (T4)2046135841191582 MORPRJNA590671RNA-seq
24 h (T1)168314124811832 MORPRJNA310071RNA-seq
48 h (T2)1965168435131432 MORPRJNA310071RNA-seq
48 h (T2)8346102018542 pairedPRJNA563035 *RNA-seq
72 h (T3)11209181310252 pairedPRJNA563035 *RNA-seq
96 h (T4)10234573011352 pairedPRJNA563035 *RNA-seq
24 h (T1)29913014374010176 pairedPRJNA634330RNA-seq
* GSE18361 and * GSE62895: root samples * PRJNA563035: spike samples; N—samples: (251 MOR-infected samples/171 mock–healthy samples across all time points, including zero-time infection); Total DEGs: represent the transcriptional profiles of whole differentially expressed genes responsive to MOR infection in each experiment. Differentially expressed genes (DEGs), including total DEGs of expression profiles, up and downregulated genes of receptor-based genes, and downstream signaling genes, are all extracted from the analyzed dataset. The cutoff for DEG analysis is p ≤ 0.05, with log2FC = 1, (−1). Where “Up” represents “upregulated/induced genes” and “Down” represents “downregulated/repressed genes”. Extracellular receptor genes include all receptor-like kinase genes (RLKs, RLCKs, WAKs). HPI: hours post-infection. GEO: Gene Expression Omnibus-NCBI database. MOR: Magnaporthe oryzae. T1:8–24 h post-inoculation; T2: 36–48 h post-inoculation; T3: 72 h post-inoculation; T4: 96–144 h post-inoculation.
Table 2. Top extracellular and cytoplasmic receptors most frequently upregulated genes in response to MOR infection in rice transcriptomic datasets.
Table 2. Top extracellular and cytoplasmic receptors most frequently upregulated genes in response to MOR infection in rice transcriptomic datasets.
Gene IDGene NameReceptor TypeFrequency Across the Analyzed Datasets
Infection StageNo. of Samples
TotalArraysRNA-Seq
Os08G0457400OsRLCK255Cytoplasmic-like kinaseT1-T2-T3-T419072118
Os02G0807900OsWAK21WALL-associated kinaseT121183
Os02G0807200OsWAK18WALL-associated kinaseT1-T2-T3-T481729
Os09G0561500WAK90WALL-associated kinaseT1-T3-T4905436
Os04G0226600OsRLCK138Cytoplasmic-like kinaseT1-T3-T4451827
Os11G0666200OsRLCK345Cytoplasmic-like kinaseT1-T2-T3-T472720
Os02G0632800OsWAK14WALL-associated kinaseT1-T2-T354540
Os07G0541700OsRLCK237Cytoplasmic-like kinaseT1-T2-T3-T472720
Os02G0807800OsWAK20WALL-associated kinaseT1-T2-T3-T41267254
Os07G0686800OsRLCK241Cytoplasmic-like kinaseT1–T2633627
Os11G0557500OsLysM-RLK7Lysin-motif extracellular receptor proteinT1-T2-T354540
Os11G0514500--Leucine repeat with extracellular domainT2-T436360
Os08G0374600OsRLCK253Cytoplasmic-like kinaseT2-T3-T454540
Os12G0486900OsRLCK369Cytoplasmic-like kinaseT2-T3-T463549
Os07G0534500OsRLCK233Cytoplasmic-like kinaseT227189
Os03G0407900OsRLCK110Cytoplasmic-like kinaseT2–T3813645
Os04G0631800OsRLCK162Cytoplasmic-like kinaseT2–T336360
Os02G0193000OsLysM-RLK1Lysin-motif-extracellular receptorT318180
Os02G0811200OsWAK24WALL-associated kinaseT1-T2-T3-T415836122
Os04G0598900OsWAK50WALL-associated kinaseT3–T4451827
Os01G0136400OsWAK1WALL-associated kinaseT1–T4401822
Os04G0127500OsWAK29WALL-associated kinaseT2-T3-T457543
Os03G0264300OsRLCK106Cytoplasmic-like kinaseT4442618
OS02G0553000LRR-RLKLeucine-rich repeat receptor-like kinaseT1-T3-T460060
T1: 8–24 h post-inoculation; T2: 36–48 h post-inoculation; T3: 72 h post-inoculation; T4: 96–144 h post-inoculation.
Table 3. Unique extracellular and cytoplasmic receptor-based genes are frequently expressed under MOR infection at different specific infection stages in the rice transcriptomic datasets.
Table 3. Unique extracellular and cytoplasmic receptor-based genes are frequently expressed under MOR infection at different specific infection stages in the rice transcriptomic datasets.
Gene IDGene NameReceptor TypeStageRegulation
Os04G0369100OsRLCK145Cytoplasmic-receptor-like kinaseT1Up
Os01G0137200OsRLCK20Cytoplasmic-receptor-like kinaseT1Up
Os11G0225000OsRLCK319Cytoplasmic-receptor-like kinaseT1Up
Os06G0541600OsRLCK206Cytoplasmic-receptor-like kinaseT1Up
Os04G0369000OsRLCK144Cytoplasmic-receptor-like kinaseT1Up
Os03G0241600OsRLCK105Cytoplasmic-receptor-like kinaseT1Up
Os03G0179400OsRLCK103Cytoplasmic-receptor-like kinaseT1Up
Os02G0186500OsRLCK64Cytoplasmic-receptor-like kinaseT1Up
Os01G0929200OsRLCK53Cytoplasmic-receptor-like kinaseT1Up
Os01G0114900OsRLCK9Cytoplasmic-receptor-like kinaseT1Up
Os11G0609500OsRLCK339Cytoplasmic-receptor-like kinaseT1Up
Os01G0545500OsRLCK36Cytoplasmic-receptor-like kinaseT1Up
Os06G0727400OsRLCK220Cytoplasmic-receptor-like kinaseT1Up
Os01G0117300OsRLCK17Cytoplasmic-receptor-like kinaseT1Up
Os01G0784500OsRLCK44Cytoplasmic-receptor-like kinaseT1Up
Os01G0117200OsRLCK16Cytoplasmic-receptor-like kinaseT1Up
Os04G0365100OsWAK37WALL-associated kinaseT1Up
Os11G0694100OsWAK123WALL-associated kinaseT1Up
Os05G0463000OsRLCK188Cytoplasmic-receptor-like kinaseT2Up
Os06G0663900OsRLCK212Cytoplasmic-receptor-like kinaseT2Up
Os03G0825800OsRLCK120Cytoplasmic-receptor-like kinaseT2Up
Os01G0689900OsWAK10WALL-associated kinaseT2Up
Os02G0227700OsRLK5Extracellular receptorT3Up
Os06G0663200OsRLCK211Cytoplasmic-receptor-like kinaseT3Up
Os02G0787200OsRLCK87Cytoplasmic-receptor-like kinaseT3Up
Os01G0789200OsRLCK45Cytoplasmic-receptor-like kinaseT3Up
Os06G0202900OsRLCK203Cytoplasmic-receptor-like kinaseT3Up
Os03G0283900OsRLCK108Cytoplasmic-receptor-like kinaseT3Up
Os12G0615100OsWAK128WALL-associated kinaseT3Up
Os11G0549300OsLysM-RLK8Lysin-motif extracellular receptorT4Up
Os04G0655400OsRLCK169Cytoplasmic-receptor-like kinaseT4Up
Os09G0479200OsRLCK275Cytoplasmic-receptor-like kinaseT4Up
Os01G0267800OsRLCK29Cytoplasmic-receptor-like kinaseT4Up
Os02G0639100OsRLCK78Cytoplasmic-receptor-like kinaseT4Up
Os02G0565500OsRLCK74Cytoplasmic-receptor-like kinaseT4Up
Os09G0533600OsRLCK278Cytoplasmic-receptor-like kinaseT4Up
Os07G0537200OsRLCK234Cytoplasmic-receptor-like kinaseT4Up
Os04G0517700OsWAK51WALL-associated kinaseT4Up
Os03G0841100OsWAK28WALL-associated kinaseT4Up
Os01G0137500OsRLCK22Cytoplasmic-receptor-like kinaseT1Down
Os01G0546000OsLysM-RLK3Lysin-motif extracellular receptorT2Down
Os08G0506400OsRLCK257Cytoplasmic-receptor-like kinaseT2Down
Os04G0220300OsWAK30WALL-associated kinaseT2Down
Os10G0200000OsRLCK295Cytoplasmic-receptor-like kinaseT3Down
Os11G0300700OsRLCK325Cytoplasmic-receptor-like kinaseT3Down
Os04G0654600OsRLCK167Cytoplasmic-receptor-like kinaseT3Down
Os01G0929200OsRLCK53Cytoplasmic-receptor-like kinaseT4Down
Os10G0431900OsRLCK300Cytoplasmic-receptor-like kinaseT4Down
Os11G0445300OsRLCK327Cytoplasmic-receptor-like kinaseT4Down
Os01G0114600OsRLCK8Cytoplasmic-receptor-like kinaseT4Down
Os01G0117400OsRLCK18Cytoplasmic-receptor-like kinaseT4Down
Os05G0100700OsRLCK175Cytoplasmic-receptor-like kinaseT4Down
Os01G0296000OsRLCK30Cytoplasmic-receptor-like kinaseT4Down
Os01G0117000OsRLCK15Cytoplasmic-receptor-like kinaseT4Down
Os03G0844100OsRLCK123Cytoplasmic-receptor-like kinaseT4Down
Os02G0152300OsRLCK61Cytoplasmic-receptor-like kinaseT4Down
Os01G0117300OsRLCK17Cytoplasmic-receptor-like kinaseT4Down
Os04G0619600OsRLCK161Cytoplasmic-receptor-like kinaseT4Down
Os07G0134200OsRLCK222Cytoplasmic-receptor-like kinaseT4Down
Os03G0274800OsRLCK107Cytoplasmic-receptor-like kinaseT4Down
Os03G0130900OsRLCK96Cytoplasmic-receptor-like kinaseT4Down
Os06G0168800OsRLCK200Cytoplasmic-receptor-like kinaseT4Down
Os08G0200500OsRLCK247Cytoplasmic-receptor-like kinaseT4Down
Os11G0194900OsRLCK315Cytoplasmic-receptor-like kinaseT4Down
Os01G0115600OsRLCK11Cytoplasmic-receptor-like kinaseT4Down
Os01G0310500OsRLCK31Cytoplasmic-receptor-like kinaseT4Down
Os10G0180800WAK112WALL-associated kinaseT4Down
Os09G0471400OsWAK81WALL-associated kinaseT4Down
Os04G0365100OsWAK37WALL-associated kinaseT4Down
Os09G0471800OsWAK85WALL-associated kinaseT4Down
Os04G0286300OsWAK33WALL-associated kinaseT4Down
Os12G0615300OsWAK129WALL-associated kinaseT4Down
T1: 8–24 h post-inoculation; T2: 36–48 h post-inoculation; T3: 72 h post-inoculation; T4: 96–144 h post-inoculation; “Up” represents “upregulated/induced genes” while “Down” represents “downregulated/repressed genes”.
Table 4. Top downstream signaling genes (Ca2+/MAPK) that are most frequently upregulated in response to MOR infection in rice transcriptomic datasets.
Table 4. Top downstream signaling genes (Ca2+/MAPK) that are most frequently upregulated in response to MOR infection in rice transcriptomic datasets.
Gene IDGene NameProtein DomainFrequency Across the Analyzed Datasets
Infection StageNo. of Samples
TotalArraysRNA-Seq
Os01G0955100OsCML31Calmodulin-like proteinT1-T2-T3-T417654122
Os12G0603800OsCML5Calmodulin-like proteinT1-T2-T3-T413118113
Os05G0577500OsCML14Calmodulin-like proteinT1–T4633627
Os11G0105000OsCML25Calmodulin-like proteinT3–T4601842
Os12G0104900OsCML26Calmodulin-like proteinT427189
Os10G0418100ACA8Ca2+ P-type ATPaseT1-T2-T3-T445369
Os04G0644900OsNTMC2T2.1N-terminal trans-membraneT118180
Os03G0397400OsCAX2Vacuolar cation exchanger proteinT1–T3542133
Os12G0624200OsCCX4Ca2+ exchangerT3–T436360
Os04G0584600OsCDPK13Ca2+-dependent protein kinaseT1–T3601842
Os07G0631700EF-handEF-hand type domainT354540
Os07G0584100OsWNK5MAP kinase-like proteinT1-T2-T381729
Os12G0162100OsWNK9MAP kinase-like proteinT318180
Os03G0415200MAP3KMAP kinase-like proteinT1-T2-T31177245
Os01G0699500MAP3K6MAP kinase-like proteinT2-T3-T454540
Os06G0147800OsMKK1MAP (2) KT1-T2-T41207248
Os01G0510100OsMKK6MAP (2) KT2–T336360
Os03G0285800OsMSRMK2MAPKT1-T2-T3-T417654122
Os10G0533600OsMPK6MAPKT1-T2-T3723636
Os02G0135200OsMPK13MAPKT2–T4633627
T1: 8–24 h post-inoculation; T2: 36–48 h post-inoculation; T3: 72 h post-inoculation; T4: 96–144 h post-inoculation.
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Salem, F.; ElGamal, A.; Tang, X.; Yang, J.; Kong, W. Transcriptional Dynamics of Receptor-Based Genes Reveal Immunity Hubs in Rice Response to Magnaporthe oryzae Infection. Int. J. Mol. Sci. 2025, 26, 4618. https://doi.org/10.3390/ijms26104618

AMA Style

Salem F, ElGamal A, Tang X, Yang J, Kong W. Transcriptional Dynamics of Receptor-Based Genes Reveal Immunity Hubs in Rice Response to Magnaporthe oryzae Infection. International Journal of Molecular Sciences. 2025; 26(10):4618. https://doi.org/10.3390/ijms26104618

Chicago/Turabian Style

Salem, Fatma, Ahmed ElGamal, Xiaoya Tang, Jianyuan Yang, and Weiwen Kong. 2025. "Transcriptional Dynamics of Receptor-Based Genes Reveal Immunity Hubs in Rice Response to Magnaporthe oryzae Infection" International Journal of Molecular Sciences 26, no. 10: 4618. https://doi.org/10.3390/ijms26104618

APA Style

Salem, F., ElGamal, A., Tang, X., Yang, J., & Kong, W. (2025). Transcriptional Dynamics of Receptor-Based Genes Reveal Immunity Hubs in Rice Response to Magnaporthe oryzae Infection. International Journal of Molecular Sciences, 26(10), 4618. https://doi.org/10.3390/ijms26104618

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