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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 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.

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.

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 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.

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.

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.

Bacillus amyloliquefaciens YN201732 is an endophytic bacteria with high biocontrol efficiency and broad-spectrum antimicrobial activities. In order to clarify the main active ingredients and their antifungal mechanisms against powdery mildew of tobacco, this study is focused on lipopeptide obtained through acid precipitation and organic solvent extraction. HPLC and LCMS-IT-TOF were used to separate and identify antimicrobial lipopeptides. Findings revealed that bacillomycin D plays an important role against surrogate fungal pathogen Fusarium solani . Synthetic pathways of sfp, bacillomycin D, and fengycin were separately disrupted. The sfp gene knockout mutant B. amyloliquefaciens YN201732M1 only showed minor antagonistic activity against F. solani . While Erysiphe cichoracearum spore germination was inhibited and pot experiments displayed a significant decrease in tobacco powdery mildew. The spore inhibition rate of YN201732M1 was only 30.29%, and the pot experiment control effect was less than 37.39%, which was significantly lower than that of the wild type. The inhibitory effect of mutant YN201732M2 (deficient in the production of bacillomycin D) and mutant YN201732M3 (deficient in the production of fengycin) on the spore germination of E. cichoracearum were 50.22% and 53.06%, respectively, suggesting that both fengycin and bacillomycin D had potential effects on spore germination of powdery mildew. Interestingly, in a greenhouse assay, both B. amyloliquefaciens YN201732M2 and YN201732M3 mutants displayed less of a control effect on tobacco powdery mildew than wild type. The results from  in vitro , spore germination, and greenhouse-pot studies demonstrated that antimicrobial lipopeptides especially bacillomycin D and fengycin may contribute to the prevention and control of tobacco powdery mildew. In addition, gene mutation related to lipopeptide synthesis can also affect the biofilm formation of strains.

Key messageA single recessive gene for complete resistance to powdery mildew and a major-effect QTL for partial resistance to downy mildew were co-localized in aCucumis hystrixintrogression line of cucumber.Downy mildew (DM) and powdery mildew (PM) are two major foliar diseases in cucumber. DM resistance (DMR) and PM resistance (PMR) may share common components; however, the genetic relationship between them remains unclear. IL52, a Cucumis hystrix introgression line of cucumber which has been reported to possess DMR, was recently identified to exhibit PMR as well. In this study, a single recessive gene pm for PMR was mapped to an approximately 468-kb region on chromosome 5 with 155 recombinant inbred lines (RILs) and 193 F2 plants derived from the cross between a susceptible line ‘changchunmici’ and IL52. Interestingly, pm was co-localized with the major-effect DMR QTL dm5.2 confirmed by combining linkage analysis and BSA-seq, which was consistent with the observed linkage of DMR and PMR in IL52. Further, phenotype–genotype correlation analysis of DMR and PMR in the RILs indicated that the co-localized locus pm/dm5.2 confers complete resistance to PM and partial resistance to DM. Seven candidate genes for DMR were identified within dm5.2 by BSA-seq analysis, of which Csa5M622800.1, Csa5M622830.1 and Csa5M623490.1 were also the same candidate genes for PMR. A single nucleotide polymorphism that is present in the 3ˊ untranslated region (3′UTR) of Csa5M622830.1 co-segregated perfectly with PMR. The GATA transcriptional factor gene Csa5M622830.1 may be a likely candidate gene for DMR and PMR. This study has provided a clear evidence for the relationship between DMR and PMR in IL52 and sheds new light on the potential value of IL52 for cucumber DMR and PMR breeding program.