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Key messageHost resistance conferred by Pm genes provides an effective strategy to control powdery mildew. The study of Pm genes helps modern breeding develop toward more intelligent and customized.Powdery mildew of wheat is one of the most destructive diseases seriously threatening the crop yield and quality worldwide. The genetic research on powdery mildew (Pm) resistance has entered a new era. Many Pm genes from wheat and its wild and domesticated relatives have been mined and cloned. Meanwhile, modern breeding strategies based on high-throughput sequencing and genome editing are emerging and developing toward more intelligent and customized. This review highlights mining and cloning of Pm genes, molecular mechanism studies on the resistance and avirulence genes, and prospects for genomic-assisted breeding for powdery mildew resistance in wheat.

Background Powdery mildew is a major disease that causes great losses in soybean yield and seed quality. Disease-resistant varieties, which are generated by reducing the impact of susceptibility genes through mutation in host plants, would be an effective approach to protect crops from this disease. The Mildew Locus O (MLO) genes are well-known susceptibility genes for powdery mildew in plant. In this study, we utilized the CRISPR/Cas9 system to induce targeted mutations in the soybean GmMLO genes to improve powdery mildew resistance. Results A dual-sgRNA CRISPR/Cas9 construct was designed and successfully transferred into the Vietnamese soybean cultivar DT26 through Agrobacterium tumefaciens -mediated transformation. Various mutant forms of the GmMLO genes including biallelic, chimeric and homozygous were found at the T0 generation. The inheritance and segregation of CRISPR/Cas9-induced mutations were confirmed and validated at the T1 and T2 generations. Out of six GmMLO genes in the soybean genome, we obtained the Gmmlo02/Gmmlo19/Gmmlo23 triple and Gmmlo02/Gmmlo19/Gmmlo20/Gmmlo23 quadruple knockout mutants at the T2 generation. When challenged with Erysiphe diffusa , a fungus that causes soybean powdery mildew, all mutant plants showed enhanced resistance to the pathogen, especially the quadruple mutant. The powdery mildew severity in the mutant soybeans was reduced by up to 36.4% compared to wild-type plants. In addition, no pleiotropic effect on soybean growth and development under net-house conditions was observed in the CRISPR/Cas9 mutants. Conclusions Our results indicate the involvement of GmMLO02 , GmMLO19 , GmMLO20 and GmMLO23 genes in powdery mildew susceptibility in soybean. Further research should be conducted to investigate the roles of individual tested genes and the involvement of other GmMLO genes in this disease infection mechanism. Importantly, utilizing the CRISPR/Cas9 system successfully created the Gmmlo transgene-free homozygous mutant lines with enhanced resistance to powdery mildew, which could be potential materials for soybean breeding programs.

Key messageNew powdery mildew resistance gene Pm68 was found in the terminal region of chromosome 2BS of Greek durum wheat TRI 1796. The co-segregated molecular markers could be used for MAS.Durum wheat (Triticum turgidum L. var. durum Desf.) is not only an important cereal crop for pasta making, but also a genetic resource for common wheat improvement. In the present study, a Greek durum wheat TRI 1796 was found to confer high resistance to all 22 tested isolates of Blumeria graminis f. sp. tritici (Bgt). Inheritance study on the F1 plants and the F2 population derived from the cross TRI 1796/PI 584832 revealed that the resistance in TRI 1796 was controlled by a single dominant gene, herein designated Pm68. Using the bulked segregant RNA-Seq (BSR-Seq) analysis combined with molecular analysis, Pm68 was mapped to the terminal part of the short arm of chromosome 2B and flanked by markers Xdw04 and Xdw12/Xdw13 with genetic distances of 0.22 cM each. According to the reference genome of durum wheat cv. Svevo, the corresponding physical region spanned the Pm68 locus was about 1.78-Mb, in which a number of disease resistance-related genes were annotated. This study reports the new powdery mildew resistance gene Pm68 that would be a valuable resource for improvement of both common wheat and durum wheat. The co-segregated markers (Xdw05–Xdw11) developed here would be useful tools for marker-assisted selection (MAS) in breeding.

Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky–Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS–SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R 2  = 0.741–0.852), while the SVR and PLSR models performed similarly ( R 2  = 0.733–0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R 2 was 0.849–0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R 2 increased by 14.39%, and the R 2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.

Strawberry powdery mildew (SPM), caused by Podosphaera aphanis , severely reduces strawberry yield and quality. Due to the pathogen’s obligate parasitism, the screening of biocontrol bacteria is challenging, and effective strains remain scarce. In this study, we isolated a novel strain, Bacillus altitudinis DXHS, exhibiting strong control efficacy against SPM. Genomic analysis revealed abundant biosynthetic gene clusters for secondary metabolites in DXHS, including lichenysin, siderophore, and terpenes. Strawberry leaves treated with DXHS showed a 77.05% reduction in SPM incidence versus the infected control, marking the first report, to our knowledge, of B. altitudinis efficacy against SPM. Transcriptomic analysis of strawberry leaves revealed 3,722 upregulated and 1,729 downregulated differentially expressed genes (DEGs) following treatment with the DXHS strain. GO enrichment analysis showed that these DEGs were significantly enriched in biological processes including defense response and the jasmonic acid (JA) signaling pathway. KEGG pathway analysis indicated that the DEGs were enriched in pathways including plant-pathogen interaction, MAPK signaling pathway, and plant hormone signal transduction. Furthermore, GSEA demonstrated a significant enrichment and upregulation of the MAPK signaling pathway. The qPCR validation confirmed significant upregulation of key genes, including MPK3 , MPK6 , PR1 , and ERF1 . These results demonstrate that the DXHS strain activates the salicylic acid (SA), JA, and ethylene (ET) pathways in strawberry leaves, enhancing resistance to SPM. These findings elucidate key aspects of the molecular mechanism underlying the resistance induced by B. altitudinis DXHS against SPM and the application of DXHS strain in the biological control of strawberry diseases.

Background Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with more than 200 nucleotides in length, which play vital roles in a wide range of biological processes. Powdery mildew disease (PM) has become a major threat to the production of melon. To investigate the potential roles of lncRNAs in resisting to PM in melon, it is necessary to identify lncRNAs and uncover their molecular functions. In this study, we compared the lncRNAs between a resistant and a susceptible melon in response to PM infection. Results It is reported that 11,612 lncRNAs were discovered, which were distributed across all 12 melon chromosomes, and > 85% were from intergenic regions. The melon lncRNAs have shorter transcript lengths and fewer exon numbers than protein-coding genes. In addition, a total of 407 and 611 lncRNAs were found to be differentially expressed after PM infection in PM-susceptible and PM-resistant melons, respectively. Furthermore, 1232 putative targets of differently expressed lncRNAs (DELs) were discovered and gene ontology enrichment (GO) analysis showed that these target genes were mainly enriched in stress-related terms. Consequently, co-expression patterns between LNC_018800 and CmWRKY21 , LNC_018062 and MELO3C015771 (glutathione reductase coding gene), LNC_014937 and CmMLO5 were confirmed by qRT-PCR. Moreover, we also identified 24 lncRNAs that act as microRNA (miRNA) precursors, 43 lncRNAs as potential targets of 22 miRNA families and 13 lncRNAs as endogenous target mimics (eTMs) for 11 miRNAs. Conclusion This study shows the first characterization of lncRNAs involved in PM resistance in melon and provides a starting point for further investigation into the functions and regulatory mechanisms of lncRNAs in the resistance to PM.

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.

Background Powdery mildew, a widespread fungal disease caused by Blumeria graminis f. sp. tritici ( Bgt ), seriously threatens the yield and quality of wheat. The most effective and sustainable approach to control this disease is utilizing resistance genes and unraveling their underlying molecular mechanisms. Spelt ( Triticum aestivum ssp. Spelta , 2n = 6x = 42, AABBDD), an ancient hexaploid wheat subspecies, has emerged as a valuable genetic resource for enhancing powdery mildew resistance in modern wheat breeding programs. Results Spelt accession lsy-93 demonstrated resistance against powdery mildew at the whole-growth stage. Genetic analysis revealed that the seedling resistance is conferred by a single dominant gene, tentatively designated as PmLsy-93 . Bulked segregant RNA sequencing (BSR-seq) and molecular markers positioned PmLsy-93 within a 1.5 cM (genetic) and 10.34 Mb (physical) interval on chromosome 2BL. Six genes were directly associated with disease resistance in this interval and hence were considered as the candidate genes for PmLsy-93 . Furthermore, a total of 3,140 differentially expressed genes (DEGs) were identified between the two bulks, with 2,214 down-regulated and 916 up-regulated ones relative to the susceptible bulk. The integration of gene ontology and kyoto encyclopedia of genes and genomes pathway analysis underscores the multifaceted roles of these DEGs in plant defense, stress response, and metabolic regulation. Then, expression pattern of six genes, encoding disease resistance protein, serine threonine-protein kinase, or protein kinase domain, were profiled with Bgt invasion, and analyzed their potential roles in immune pathway. Three closely linked or co-segregated markers were confirmed to be available for marker-assisted selection of PmLsy-93 in breeding programs. Conclusions This study successfully pinpointed critical genetic loci and candidate genes associated with powdery mildew resistance in the spelt wheat accession Lsy-93. The results provide valuable insights into plant-pathogen defense mechanisms and lay an foundation for subsequent molecular breeding efforts to enhance crop disease resistance.

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R2-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection.

Powdery mildew fungi (Erysiphales) are among the most common and important plant fungal pathogens. These fungi are obligate biotrophic parasites that attack nearly 10,000 species of angiosperms, including major crops, such as cereals and grapes. Although cultural and biological practices may reduce the risk of infection by powdery mildew, they do not provide sufficient protection. Therefore, in practice, chemical control, including the use of fungicides from multiple chemical groups, is the most effective tool for managing powdery mildew. Unfortunately, the risk of resistance development is high because typical spray programs include multiple applications per season. In addition, some of the most economically destructive species of powdery mildew fungi are considered to be high-risk pathogens and are able to develop resistance to several chemical classes within a few years. This situation has decreased the efficacy of the major fungicide classes, such as sterol demethylation inhibitors, quinone outside inhibitors and succinate dehydrogenase inhibitors, that are employed against powdery mildews. In this review, we present cases of reduction in sensitivity, development of resistance and failure of control by fungicides that have been or are being used to manage powdery mildew. In addition, the molecular mechanisms underlying resistance to fungicides are also outlined. Finally, a number of recommendations are provided to decrease the probability of resistance development when fungicides are employed.