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To cause the devastating rice blast disease, the hemibiotrophic fungus Magnaporthe oryzae produces invasive hyphae (IH) that are enclosed in a plant-derived interfacial membrane, known as the extra-invasive hyphal membrane (EIHM), in living rice cells. Little is known about when the EIHM is disrupted and how the disruption contributes to blast disease. Here we show that the disruption of the EIHM correlates with the hyphal growth stage in first-invaded susceptible rice cells. Our approach utilized GFP that was secreted from IH as an EIHM integrity reporter. Secreted GFP (sec-GFP) accumulated in the EIHM compartment but appeared in the host cytoplasm when the integrity of the EIHM was compromised. Live-cell imaging coupled with sec-GFP and various fluorescent reporters revealed that the loss of EIHM integrity preceded shrinkage and eventual rupture of the rice vacuole. The vacuole rupture coincided with host cell death, which was limited to the invaded cell with presumed closure of plasmodesmata. We report that EIHM disruption and host cell death are landmarks that delineate three distinct infection phases (early biotrophic, late biotrophic, and transient necrotrophic phases) within the first-invaded cell before reestablishment of biotrophy in second-invaded cells. M. oryzae effectors exhibited infection phase-specific localizations, including entry of the apoplastic effector Bas4 into the host cytoplasm through the disrupted EIHM during the late biotrophic phase. Understanding how infection phase-specific cellular dynamics are regulated and linked to host susceptibility will offer potential targets that can be exploited to control blast disease.

The necessity to understand plant adaptations to environmental stressors is underscored by the role of polyploidy in species evolution. This study focuses on the superior stress resistance exhibited by autotetraploid rice, which arises from chromosome doubling, in comparison to its diploid donor. We provide a quantitative analysis that highlights the differing susceptibilities of diploid (GFD-2X) and autotetraploid (GFD-4X) rice to rice blast disease, with GFD-2X being significantly more susceptible. Our investigation centers on transcription factors (TFs), which are crucial in regulating biological stress responses, by analyzing their expression in the face of a pathogen attack. This study uncovers variations in the number and expression timing of differentially expressed TF genes, providing a quantitative view of GFD-4X's resistance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses confirm the role of specific pathways, including "response to stimulus" and the "MAPK signaling pathway," in resistance mechanisms. An extensive analysis of protein–protein interaction networks further clarifies the complex role of TFs during stress responses. The rationale for our experimental approach is rooted in the imperative to decipher the molecular basis of disease resistance across different ploidies, which has implications for crop enhancement. The conclusion from our research is that autotetraploid rice has a unique and more effective defense response regulation system, facilitated by transcription factors, when faced with rice blast disease. This finding provides a foundation for future genetic strategies aimed at improving crop resistance. [ABSTRACT FROM AUTHOR]

Rice production needs to be sustained in the coming decades, as the changeable climatic conditions are becoming more conducive to disease outbreaks. The majority of rice diseases cause enormous economic damage and yield instability. Among them, rice blast caused by Magnaportheoryzae is a serious fungal disease and is considered one of the major threats to world rice production. This pathogen can infect the above-ground tissues of rice plants at any growth stage and causes complete crop failure under favorable conditions. Therefore, management of blast disease is essentially required to sustain global food production. When looking at the drawback of chemical management strategy, the development of durable, resistant varieties is one of the most sustainable, economic, and environment-friendly approaches to counter the outbreaks of rice blasts. Interestingly, several blast-resistant rice cultivars have been developed with the help of breeding and biotechnological methods. In addition, 146 R genes have been identified, and 37 among them have been molecularly characterized to date. Further, more than 500 loci have been identified for blast resistance which enhances the resources for developing blast resistance through marker-assisted selection (MAS), marker-assisted backcross breeding (MABB), and genome editing tools. Apart from these, a better understanding of rice blast pathogens, the infection process of the pathogen, and the genetics of the immune response of the host plant are very important for the effective management of the blast disease. Further, high throughput phenotyping and disease screening protocols have played significant roles in easy comprehension of the mechanism of disease spread. The present review critically emphasizes the pathogenesis, pathogenomics, screening techniques, traditional and molecular breeding approaches, and transgenic and genome editing tools to develop a broad spectrum and durable resistance against blast disease in rice. The updated and comprehensive information presented in this review would be definitely helpful for the researchers, breeders, and students in the planning and execution of a resistance breeding program in rice against this pathogen.

Nowadays, rice farming is affected by various diseases that are economically significant and worthy of attention. One of these diseases is blast. Rice blast is one of the most important limiting factors in rice yield. The purpose of this study is the timely and rapid diagnosis of rice blast based on the image processing technique in field conditions. To do so, color images were prepared using image processing technique and improved KNN algorithm by K‐means was used to classify the images in Lab color space to detect disease spots on rice leaves. Squared classification was based on Euclidean distance, and the Otsu method was used to perform an automatic threshold histogram of images based on shape or to reduce the gray level in binary images. Finally, to determine the efficiency of the designed algorithm, sensitivity, specificity, and overall accuracy were examined. The classification results showed that the sensitivity and specificity of the designed algorithm were 92% and 91.7%, respectively, in the determination of the number of disease spots, and 96% and 95.65% in determining the quality of disease spots. The overall accuracy of the designed algorithm was 94%. Generally, the results obtained showed that the above method has a great potential for timely diagnosis of rice blast. [ABSTRACT FROM AUTHOR]

Abstract Background In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant “signals” during the “early warning” period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Conclusions Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and – in cases when training datasets are available – offer a potentially greater adaptability to new contexts.

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Rice blast is one of the most serious diseases of rice and a major threat to rice production. Breeding disease-resistant rice is one of the most economical, safe, and effective measures for the control of rice blast. As a complement to traditional crop breeding, the transgenic method can avoid the time-consuming process of crosses and multi-generation selection. In this study, maize (Zea mays) Activator (Ac)/Dissociation (Ds) transposon vectors carrying green fluorescent protein (GFP) and red fluorescent protein (mCherry) genetic markers were used for generating marker-free transgenic rice. Double fluorescent protein-aided counterselection against the presence of T-DNA was performed together with polymerase chain reaction (PCR)-based positive selection for the gene of interest (GOI) to screen marker-free progeny. We cloned an RNAi expression cassette of the rice Pi21 gene that negatively regulates resistance to rice blast as a GOI into the Ds element in the Ac/Ds vector and obtained marker-free T1 rice plants from 13 independent transgenic lines. Marker-free and Ds/GOI-homozygous rice lines were verified by PCR and Southern hybridization analysis to be completely free of transgenic markers and T-DNA sequences. qRT-PCR analysis and rice blast disease inoculation confirmed that the marker-free transgenic rice lines exhibited decreased Pi21 expression levels and increased resistance to rice blast. TAIL-PCR results showed that the Ds (Pi21-RNAi) transgenes in two rice lines were reintegrated in intergenic regions in the rice genome. The Ac/Ds vector with dual fluorescent protein markers offers more reliable screening of marker-free transgenic progeny and can be utilized in the transgenic breeding of rice disease resistance and other agronomic traits.

Understanding how plants and pathogens interact, and whether that interaction culminates in defense or disease, is required to develop stronger and more sustainable strategies for plant health. Advances in methods that more effectively image plant-pathogen samples during infection and colonization have yielded tools such as the rice leaf sheath assay, which has been useful in monitoring infection and early colonization events between rice and the fungal pathogen, Magnaporthe oryzae. This hemi-biotrophic pathogen causes severe disease loss in rice and related monocots, including millet, rye, barley, and more recently, wheat. The leaf sheath assay, when performed correctly, yields an optically clear plant section, several layers thick, which allows researchers to perform live-cell imaging during pathogen attack or generate fixed samples stained for specific features. Detailed cellular investigations into the barley-M. oryzae interaction have lagged behind those of the rice host, in spite of the growing importance of this grain as a food source for animals and humans and as fermented beverages. Reported here is the development of a barley leaf sheath assay for intricate studies of M. oryzae interactions during the first 48 h post-inoculation. The leaf sheath assay, regardless of which species is being studied, is delicate; provided is a protocol that covers everything, from barley growth conditions and obtaining a leaf sheath, to inoculation, incubation, and imaging of the pathogen on plant leaves. This protocol can be optimized for high-throughput screening using something as simple as a smartphone for imaging purposes.

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires-Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires-Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R 2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R 2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R 2 =0.99; validation R 2 =0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers' fields for developing better disease management options., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mandal, Adak, Das, Sahoo, Mukherjee, Kumar, Chinnusamy, Das, Mukhopadhyay, Rajashekara and Gakhar.)

The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. To address the above problems, this paper proposes a real-time rice blast disease segmentation method based on a feature fusion and attention mechanism: Deep Feature Fusion and Attention Network (abbreviated to DFFANet). To realize the extraction of the shallow and deep features of rice blast disease as complete as possible, a feature extraction (DCABlock) module and a feature fusion (FFM) module are designed; then, a lightweight attention module is further designed to guide the features learning, effectively fusing the extracted features at different scales, and use the above modules to build a DFFANet lightweight network model. This model is applied to rice blast spot segmentation and compared with other existing methods in this field. The experimental results show that the method proposed in this study has better anti-interference ability, achieving 96.15% MioU, a speed of 188 FPS, and the number of parameters is only 1.4 M, which can achieve a high detection speed with a small number of model parameters, and achieves an effective balance between segmentation accuracy and speed, thereby reducing the requirements for hardware equipment and realizing low-cost embedded development. It provides technical support for real-time rapid detection of rice diseases. [ABSTRACT FROM AUTHOR]

Magnaporthe oryzae causes rice blast disease, which is one of the most devastating and widespread rice diseases. The present study explores the effects of weather factors (temperature, relative humidity, rainfall and bright sunshine hours) on paddy blast severity. During kharif 2019-20 at Zonal Agricultural Research Station, V. C. Farm, Mandya, seven genotypes were studied viz., Jyothi, Jaya, IR 64, PAC 837, PAC 837+, CO 39 and HR 12, all of which showed distinct responses to blast disease. The disease started in the 42nd standard meteorological week of 2019 with a mean severity of 1.29 per cent, gradually increasing to a peak in the 3rd standard meteorological week of 2020 (46.14 %). The HR12 (highly susceptible) genotype showed the highest mean disease severity (64.12 %), whereas the PAC837+ hybrid which is moderately resistant, showed the lowest mean blast severity (3.48 %). The optimal weather conditions for disease development were determined to be maximum temperature (26.75-29.50°C), minimum temperature (16.50-19.25°C), morning relative humidity (80.50 - 94%), evening relative humidity (60.10 - 80.50%) and sunshine (2.50 - 9.50 hours). According to a correlation study between meteorological factors and disease severity, all the variables interacted to play a role in the development of rice blast disease. Further more, the disease development was influenced by the maximum temperature and evening relative humidity. The prediction equations were developed using multiple regression analysis for all seven genotypes, with coeucients of determination (R²) varying from 66 to 79.70 per cent, indicating that 66 to 77.0 per cent of the total variation in the per cent disease index can be explained by the predicted value of disease severity (PDI). [ABSTRACT FROM AUTHOR]

Rice blast is one of the most devastating diseases, causing a significant reduction in global rice production. Developing and utilizing resistant varieties has proven to be the most efficient and cost-effective approach to control blasts. However, due to environmental pressure and intense pathogenic selection, resistance has rapidly broken down, and more durable resistance genes are being discovered. In this paper, a novel wall-associated kinase (WAK) gene, Pb4, which confers resistance to rice blast, was identified through a genome-wide association study (GWAS) utilizing 249 rice accessions. Pb4 comprises an N-terminal signal peptide, extracellular GUB domain, EGF domain, EGF-Ca2+ domain, and intracellular Ser/Thr protein kinase domain. The extracellular domain (GUB domain, EGF domain, and EGF-Ca2+ domain) of Pb4 can interact with the extracellular domain of CEBiP. Additionally, its expression is induced by chitin and polygalacturonic acid. Furthermore, transgenic plants overexpressing Pb4 enhance resistance to rice blast. In summary, this study identified a novel rice blast-resistant gene, Pb4, and provides a theoretical basis for understanding the role of WAKs in mediating rice resistance against rice blast disease. [ABSTRACT FROM AUTHOR]

Recalcitrant rice blast disease is caused by Magnaporthe oryzae, which has a significant negative economic reverberation on crop productivity. In order to induce the disease onto the host, M. oryzae positively generates many types of small secreted proteins, here named as effectors, to manipulate the host cell for the purpose of stimulating pathogenic infection. In M. oryzae, by engaging with specific receptors on the cell surface, effectors activate signaling channels which control an array of cellular activities, such as proliferation, differentiation and apoptosis. The most recent research on effector identification, classification, function, secretion, and control mechanism has been compiled in this review. In addition, the article also discusses directions and challenges for future research into an effector in M. oryzae. [ABSTRACT FROM AUTHOR]

Background: Cellulose is an organic carbon source that can be used as a sustainable alternative for energy, materials, and chemicals. However, the substantial challenge of converting it into soluble sugars remains a major obstacle in its use as a biofuel and chemical feedstock. A new class of enzymes knowns as copperdependent polysaccharide monooxygenases (PMOs) or lytic polysaccharide monooxygenases (LPMOs) can break down polysaccharides such as cellulose, chitin, and starch through oxidation. This process enhances the efficiency of cellulose degradation by cellulase. Results: The genome of the fungus Magnaporthe oryzae, the causal agent of rice blast disease, contains the MGG_00245 gene, which encodes a putative PMO referred to as MoAA16. MoAA16 has been found to be highly expressed in planta during the early stages of fungal infection. The gene was optimized for heterologous expression in Pichia pastoris, and its oxidative cleavage activity on cellulose was characterized by analyzing soluble oligosaccharide products using highperformance anion exchange chromatography (HPAEC-PAD). The reaction catalyzed by MoAA16 requires 2 electrons from an electron donor, such as ascorbic acid, and aerobic conditions. It primarily produces Glc1 to Glc4 oligosaccharides, as well as oxidized cellobionic and cellotrionic acids. MoAA16 has been observed to enhance cellulase hydrolysis on phosphoric acid swollen cellulose (PASC) substrate, resulting in the production of more monosaccharide products. Conclusions: Our findings reveal the successful heterologous expression of MoAA16 in P. pastoris and its cellulose-active PMO properties. These results highlight the potential of MoAA16 as a promising candidate for applications in biofuel production and chemical synthesis. [ABSTRACT FROM AUTHOR]

e rice blast disease caused by Pyricularia oryzae Cavara is among the most severe plant diseases in the world. e utilization of natural antagonists, such as fungal endophytes, can be a good method to control the spread of this disease. In the present study, 68 fungal isolates were obtained from 160 rice samples collected in Guilan province fields, Iran. Morphological and molecular characteristics were used for the identification of these fungi. According to the results, the fungal isolates were Alternaria citri (Penz.) Mussat, Alternaria infectoria Simmons, Aspergillus fumigatus Fresen, Fusarium verticillioides (Sacc.) Nirenberg, Trichoderma harzianum Rifai, Trichoderma virens (Mill., Giddens & Foster) Arx, Trichoderma viride Pers., Ulocladium consortiale (um) Simmons, and Pyricularia oryzae. Eight isolates that did not cause any plant disease were selected for biocontrol studies in vitro. e dual culture, culture filtrate, slide culture, and volatile metabolites methods were used in search of natural endophytic antagonists of Pyricularia oryzae. It was found that T. harzianum, T. virens, T. viride, A. fumigatus, and A. citri isolates caused the highest percentage of mycelial growth inhibition of P. oryzae in vitro. In the greenhouse experiments, all of the studied isolates reduced the P. oryzae disease rating, with T. harzianum isolate being the most effective antagonist (27.36% reduction in the disease rating), followed by T. virens, T. viride, and A. fumigatus (20.75%, 17.92%, and 16.98%, respectively). Furthermore, in greenhouse conditions, these antagonistic fungi increased the height, fresh weight, and dry weight of plants. e results of this research showed that there are various fungi in the natural flora of rice plants that possess potential biological properties against rice blast disease that can be implemented into practice. [ABSTRACT FROM AUTHOR]

Blast disease caused by Magnaporthe sp. is one of the most destructive diseases in cereal crops. Disease infection was studied in farmer fields. Field data was recorded and further information was gathered by interviewing the farmers and agrarian extension officers. Blast infections were assessed in the fields using a standard evaluation system for rice (SES IRRI, 1996). Although symptoms were not observed in seedlings when mature rice fields were infected farmers experienced 100% yield loss. There were no or few leaf symptoms in blast infected mature rice fields yet neck infections were scored as 9 in SES. Unlike rice, leaf blast at seedling stage caused economic losses in finger millet. Mature finger millet fields were susceptible to neck and finger blast. Due to rapid disease development curative fungicide treatment was ineffective in controlling blast. Farmers reported sudden changes in the weather during infections whereby gloomy weather caused a spike in humidity and low temperatures that coincided with the infection. Host susceptibility window synchronized with conducive environments result in severe blast infections therefore, blast disease management needs a coordinated effort and system level interventions whereby control measures and use of resistant germplasm can be combined with climate-based disease predictions to increase the efficiency of disease control. [ABSTRACT FROM AUTHOR]

The spindle assembly checkpoint (SAC) proteins are conserved among eukaryotes safeguarding chromosome segregation fidelity during mitosis. However, their biological functions in plant‐pathogenic fungi remain largely unknown. In this study, we found that the SAC protein MoMad1 in rice blast fungus (Magnaporthe oryzae) localizes on the nuclear envelope and is dispensable for M. oryzae vegetative growth and tolerance to microtubule depolymerizing agent treatment. MoMad1 plays an important role in M. oryzae infection‐related development and pathogenicity. The monopolar spindle 1 homologue in M. oryzae (MoMps1) interacts with MoMad1 through its N‐terminal domain and phosphorylates MoMad1 at Ser‐18, which is conserved within the extended N termini of Mad1s from fungal plant pathogens. This phosphorylation is required for maintaining MoMad1 protein abundance and M. oryzae full virulence. Similar to the deletion of MoMad1, treatment with Mps1‐IN‐1 (an Mps1 inhibitor) caused compromised appressorium formation and decreased M. oryzae virulence, and these defects were dependent on its attenuating MoMad1 Ser‐18 phosphorylation. Therefore, our study indicates the function of Mad1 in rice blast fungal pathogenicity and sheds light on the potential of blocking Mad1 phosphorylation by Mps1 to control crop fungal diseases. [ABSTRACT FROM AUTHOR]

The aim of the present work is to biosynthesize Chitosan nanoparticles (CTNp) using tea (Camellia sinensis) extract, with potent antimicrobial properties towards phytopathogens of rice. Preliminary chemical analysis of the extract showed that they contain carbohydrate as major compound and uronic acid indicating the nature of acidic polysaccharide. The structure of the isolated polysaccharide was analyzed through FTIR and 1H NMR. The CTNp was prepared by the addition of isolated tea polysaccharides to chitosan solution. The structure and size of the CTNp was determined through FTIR and DLS analyses. The surface morphology and size of the CTNp was analysed by SEM and HRTEM. The crystalinity nature of the synthesized nanoparticle was identified by XRD analysis. The CTNp exhibited the antimicrobial properties against the most devastating pathogens of rice viz., Pyricularia grisea, Xanthomonas oryzae under in vitro condition. CTNp also suppressed the blast and blight disease of rice under the detached leaf assay. These results suggest that the biosynthesized CTNp can be used to control the most devastating pathogens of rice. [ABSTRACT FROM AUTHOR]

Mineral nitrogen (N) supply reportedly increases rice susceptibility to the fungal pathogen Magnaporthe oryzae causing blast disease. These biotic and abiotic factors cause changes in spectral reflectance of leaves; however, the effects of N × pathogen interactions on spectral characteristics of rice have not been studied. In this study, hyperspectral imaging was used to assess the effect of N supply on symptoms of rice leaf blast under greenhouse conditions. Three rice genotypes differing in blast susceptibility grown at low, medium, and high N supply were inoculated at the four-leaf stage with three M. oryzae isolates differing in virulence. The reflectance spectra (400 to 1000 nm) of healthy and symptomatic leaves were analyzed using the spectral angle mapper algorithm for supervised classification. Mineral N supply increased the contents of chlorophyll and total N. The number and area of lesions and total blast severity varied depending on rice genotype—M. oryzae isolate interactions and the amount of mineral N applied. The reflectance spectra of healthy tissue and of blast symptom subareas differed with N supply; rice genotypes differed in the response to N supply. Infected plants at high mineral N supply could be distinguished from those at low N supply due to higher differences in the spectra of symptom subareas. Results reveal the potential (and limitations) of hyperspectral imaging for quantifying N effects on rice leaves, disease severity, and symptom expression. The impact of these findings on plant phenotyping and remote sensing under field conditions is discussed. [ABSTRACT FROM AUTHOR]

Rice blast, caused by the filamentous fungus Pyricularia oryzae, has long been one of the major threats to almost all rice-growing areas worldwide. Metconazole, 5-(4-chlorobenzyl)-2, 2-dimethyl-1-(1H-1, 2, 4-triazol-1-ylmethyl) cyclopentanol, is a lipophilic, highly active triazole fungicide that has been applied in the control of various fungal pathogens of crops (cereals, barley, wheat), such as the Fusarium and Alternaria species. However, the antifungal activity of metconazole against P. oryzae is unknown. In this study, metconazole exhibited broad spectrum antifungal activities against seven P. oryzae strains collected from rice paddy fields and the wild type strain P131. Scanning electron microscopic analysis and fluorescein diacetate staining assays revealed that metconazole treatment damaged the cell wall integrity, cell membrane permeability and even cell viability of P. oryzae, resulting in deformed and shrunken hyphae. The supplementation of metconazole in vitro increased fungal sensitivity to different stresses, such as sodium dodecyl sulfate, congo red, sodium chloride, sorbitol and oxidative stress (H2O2). Metconazole could inhibit key virulence processes of P. oryzae, including conidial germination, germ tube elongation and appressorium formation. Furthermore, this chemical prevented P. oryzae from infecting barley epidermal cells by disturbing appressorium penetration and subsequent invasive hyphae development. Pathogenicity assays indicated a reduction of over 75% in the length of blast lesions in both barley and rice leaves when 10 μg/mL of metconazole was applied. This study provides evidence to understand the antifungal effects of metconazole against P. oryzae and demonstrates its potential in rice blast management. [ABSTRACT FROM AUTHOR]

The multifunctional carbon catabolite repression negative on TATA-box-less complex (CCR4-NOT) is a multi-subunit complex present in all eukaryotes, including fungi. This complex plays an essential role in gene expression; however, a functional study of the CCR4-NOT complex in the rice blast fungus Magnaporthe oryzae has not been conducted. Seven genes encoding the putative CCR4-NOT complex were identified in the M. oryzae genome. Among these, a homologous gene, MoNOT3, was overexpressed during appressorium development in a previous study. Deletion of MoNOT3 in M. oryzae resulted in a significant reduction in hyphal growth, conidiation, abnormal septation in conidia, conidial germination, and appressorium formation compared to the wild-type. Transcriptional analyses suggest that the MoNOT3 gene affects conidiation and conidial morphology by regulating COS1 and COM1 in M. oryzae. Furthermore, Δmonot3 exhibited a lack of pathogenicity, both with and without wounding, which is attributable to deficiencies in the development of invasive growth in planta. This result was also observed in onion epidermal cells, which are non-host plants. In addition, the MoNOT3 gene was involved in cell wall stress responses and heat shock. Taken together, these observations suggest that the MoNOT3 gene is required for fungal infection-related cell development and stress responses in M. oryzae. [ABSTRACT FROM AUTHOR]

Rice blast, caused by rice blast fungus (Magnaporthe oryzae), is a global threat to food security, with up to 50% yield losses. Panicle blast is a severe form of rice blast, and disease responses vary between cultivars with different genotypes. Reactive oxygen species (ROS)-mediated signaling reactions and the phenylpropanoid pathway are important defense mechanisms involved in recognizing and resisting against fungal infection. To understand rice-M. oryzae interactions in resistant and susceptible cultivars, we determined dynamic changes in the activities of five defense-related enzymes in resistant cultivar jingsui 18 and susceptible cultivar jinyuan 899 infected with M. oryzae from 4 to 25 days after infection. We then performed untargeted metabolomics analyses to profile the metabolomes of the cultivars under infected and non-infected conditions. Dynamic changes in the activities of five defense-related enzymes were closely related to panicle blast resistance in rice. Metabolome data analysis identified 634 differentially accumulated metabolites (DAMs) between resistant and susceptible cultivars following infection, potentially explaining differences in disease response between varieties. The most enriched DAMs were associated with lipids and lipid-like molecules, phenylpropanoids and polyketides, organoheterocyclic compounds, organic acids and derivatives, and lignans, neolignans, and related compounds. Multiple metabolic pathways are involved in resistance to panicle blast in rice, including biosynthesis of other secondary metabolites, amino acid metabolism, lipid metabolism, phenylpropanoid biosynthesis, arachidonic acid metabolism, arginine biosynthesis, tyrosine metabolism, tryptophan metabolism, tyrosine and tryptophan biosynthesis, lysine biosynthesis, and oxidative phosphorylation. [ABSTRACT FROM AUTHOR]

Agriculture not only plays a vital role in human survival but also contributes to the nation's greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning techniques are designed to improve the productivity and quality of the crops, but the improper irrigation and disease prediction of the existing techniques leads to loss of productivity and quality. Hence, the IoT based wireless communication system is designed for smart irrigation and rice leaf prediction using ANN and ResNeXt-50 model. In this designed model, smart irrigation is controlled by collecting the temperature and moisture of the soil in the agricultural field by using the WSN. Likewise, a surveillance camera is placed in the agricultural field to capture the rice leaf to find the disease such as rice blast, leaf smut, brown spot and bacterial blight. These collected data are processed and classified for smart irrigation and rice leaf disease prediction. For evaluating the performance of both the ANN and ResNeXt-50 trained model, the performance metrics such as accuracy, sensitivity, specificity, precision, error etc. The performance metrics for the ANN and ResNeXt-50 model are 0.9427, 0.925, 0.903, 0.86, 0.0573 and 0.967, 0.935, 0.943, 0.939 and 0.033. Thus, the evaluation of the designed model results that the proposed approach is performing better compared to the current techniques. [ABSTRACT FROM AUTHOR]

Mitophagy is a selective autophagy for the degradation of damaged or excessive mitochondria to maintain intracellular homeostasis. In Magnaporthe oryzae, a filamentous ascomycetous fungus that causes rice blast, the most devastating disease of rice, mitophagy occurs in the invasive hyphae to promote infection. To date, only a few proteins are known to participate in mitophagy and the mechanisms of mitophagy are largely unknown in pathogenic fungi. Here, by a yeast two‐hybrid screen with the core autophagy‐related protein MoAtg8 as a bait, we obtained a MoAtg8 interactor MoAti1 (MoAtg8‐interacting protein 1). Fluorescent observations and protease digestion analyses revealed that MoAti1 is primarily localized to the peripheral mitochondrial outer membrane and is responsible for recruiting MoAtg8 to mitochondria under mitophagy induction conditions. MoAti1 is specifically required for mitophagy, but not for macroautophagy and pexophagy. Infection assays suggested that MoAti1 is required for mitophagy in invasive hyphae during pathogenesis. Notably, no homologues of MoAti1 were found in rice and human protein databases, indicating that MoAti1 may be used as a potential target to control rice blast. By the host‐induced gene silencing (HIGS) strategy, transgenic rice plants targeted to silencing MoATI1 showed enhanced resistance against M. oryzae with unchanged agronomic traits. Our results suggest that MoATI1 is required for mitophagy and pathogenicity in M. oryzae and can be used as a target for reducing rice blast. [ABSTRACT FROM AUTHOR]

Rice is an important staple food crop and a primary energy source for more than half of the world's population. One of the deadly fungal diseases of rice produced by Magnaporthe oryzae is known as "blast disease." It has consistently threatened the world's food supply. This study aimed to dissect the complex blast resistance mechanism of the rice plant against blast pathogens. To understand the molecular mechanisms of blast resistance, we analyzed transcriptome changes in rice cultivars. Using transcriptome data obtained from an RNA-seq analysis of two samples comprising resistant (BR2655) and susceptible (HR12) genotypes under control and stress conditions was carried out. The total RNA sequencing generated a dataset of four samples, representing more than 40 Gb fastq raw data. A total of 145 million filtered reads were generated after the quality check. High-quality clean reads mapped rice reference genome RGAP 7. These aligned reads were taken for further analysis. A total of 11,867 DEGs across the genotypes under stress were identified. A total of 7577 and 4290 genes showed significant differential expression in BRC vs. BRIN library and HRC vs. HRIN library, respectively. The results reveal that differential gene expression analysis clearly shows the changes in the expression profiling of BR2655 and HR12 cultivars. [ABSTRACT FROM AUTHOR]

Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield. Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast-specific spectral vegetation index RBVI = 9.78 R 816 − R 724 − 2.08( ρ 736 / R 724 ) was designed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote sensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and SVM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest detection accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed based on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI had higher accuracy in its ability to qualitatively detect leaf blast in the field. The leaf blast-specific spectral index RBVI proposed in this study can more effectively improve the accuracy of UAV remote sensing for early detection of rice leaf blast in the field and make up for the shortcomings of UAV hyperspectral detection, which is susceptible to interference by environmental factors. The results of this study can provide a simple and effective method for field management and timely control of the disease. [ABSTRACT FROM AUTHOR]

Variance or complete loss of the avirulence gene (Avr) enables the pathogen to escape resistance protein (R) recognition. The field resistance effectiveness of the R gene is determined by its corresponding Avr gene in field isolates. To effectively deploy the rice blast R gene Pi54, the distribution, variation and evolution of the corresponding Avr gene, AvrPi54, were determined through PCR amplification, pathogenicity assay, gene sequences and evolutionary analysis. Among 451 Pyricularia isolates from rice and non-rice hosts, including Oryza rufipogon, Digitaria sanguinalis, Eleusine coracana, E. indica and Musa sp. in Yunnan province, the PCR amplification result showed that AvrPi54 alleles existed among 218 (48.3%) isolates including rice isolates, O. rufipogon isolates and E. coracana isolates. Pathogenicity assay showed that 336 (74.5%) isolates were avirulent to Tetep (holding Pi54). Five AvrPi54 haplotypes were identified among 142 isolates through the gene sequence. These haplotypes were determined to be avirulent to Pi54 through pathogenicity assay. Four novel haplotypes (H2 to H5) of the AvrPi54 gene would provide new target sites for rice blast control. Haplotype diversity analysis indicated that there existed a lower genetic diversity of AvrPi54 for P. oryzae populations (five haplotypes, Hd = 0.127, π = 2.9 × 10−4) in this study. Neutrality tests showed that AvrPi54's genetic variation was affected by purified selection. Haplotype network and phylogeny analysis showed that H1 was an ancestral haplotype and was widely distributed in rice isolates and O. rufipogon isolates, while H5 diverged early and evolved independently. These results indicate that the gene evolves slowly and stably and is a comparatively conserved Avr gene. [ABSTRACT FROM AUTHOR]

Receptor-like kinases (RLKs) are major regulators of the plant immune response and play important roles in the perception and transmission of immune signals. RECEPTOR LIKE KINASE 902 (RLK902) is at the key node in leucine-rich repeat receptor-like kinase interaction networks and positively regulates resistance to the bacterial pathogen Pseudomonas syringae in Arabidopsis. However, the function of RLK902 in fungal disease resistance remains obscure. In this study, we found that the expression levels of OsRLK902-1 and OsRLK902-2 , encoding two orthologues of RLK902 in rice, were induced by Magnaporthe oryzae , chitin, and flg22 treatment. osrlk902-1 and osrlk902-2 knockout mutants displayed enhanced susceptibility to M. oryzae. Interestingly, the osrlk902-1 rlk902-2 double mutant exhibited similar disease susceptibility, hydrogen peroxide production, and callose deposition to the two single mutants. Further investigation showed that OsRLK902-1 interacts with and stabilizes OsRLK902-2. The two OsRLKs form a complex with OsRLCK185, a key regulator in chitin-triggered immunity, and stabilize it. Taken together, our data demonstrate that OsRLK902-1 and OsRLK902-2, as well as OsRLCK185 function together in regulating disease resistance to M. oryzae in rice. [ABSTRACT FROM AUTHOR]

Summary: Rice blast, the most destructive disease of cultivated rice world‐wide, is caused by the filamentous fungus Magnaporthe oryzae. To cause disease in plants, M. oryzae secretes a diverse range of effector proteins to suppress plant defense responses, modulate cellular processes, and support pathogen growth. Some effectors can be secreted by appressoria even before host penetration, while others accumulate in the apoplast, or enter living plant cells where they target specific plant subcellular compartments. During plant infection, the blast fungus induces the formation of a specialized plant structure known as the biotrophic interfacial complex (BIC), which appears to be crucial for effector delivery into plant cells. Here, we review recent advances in the cell biology of M. oryzae–host interactions and show how new breakthroughs in disease control have stemmed from an increased understanding of effector proteins of M. oryzae are deployed and delivered into plant cells to enable pathogen invasion and host susceptibility. [ABSTRACT FROM AUTHOR]

Summary: Brassinosteroids (BRs) play a crucial role in shaping the architecture of rice (Oryza sativa) plants. However, the regulatory mechanism of BR signalling in rice immunity remains largely unexplored. Here we identify a rice mutant dla, which exhibits decreased leaf angles and is insensitive to 24‐epiBL (a highly active synthetic BR), resembling the BR‐deficient phenotype. The dla mutation caused by a T‐DNA insertion in the OsDLA gene leads to downregulation of the causative gene. The OsDLA knockout plants display reduced leaf angles and less sensitivity to 24‐epiBL. In addition, both dla mutant and OsDLA knockout plants are more susceptible to rice blast compared to the wild type. OsDLA is a GRAS transcription factor and interacts with the BR signalling core negative regulator, GSK2. GSK2 phosphorylates OsDLA for degradation via the 26S proteasome. The GSK2 RNAi line exhibits enhanced rice blast resistance, while the overexpression lines thereof show susceptibility to rice blast. Furthermore, we show that OsDLA interacts with and stabilizes the WRKY transcription factor OsWRKY53, which has been demonstrated to positively regulate BR signalling and blast resistance. OsWRKY53 directly binds the promoter of PBZ1 and activates its expression, and this activation can be enhanced by OsDLA. Together, our findings unravel a novel mechanism whereby the GSK2‐OsDLA‐OsWRKY53 module coordinates blast resistance and plant architecture via BR signalling in rice. [ABSTRACT FROM AUTHOR]