Fusarium graminearum is the primary causal agent of Fusarium head blight (FHB) of wheat. The phenylpyrrole fungicide fludioxonil is not currently registered for the management of FHB in China. The current study assessed the fludioxonil sensitivity of a total of 53 F. graminearum isolates collected from the six most important wheat-growing provinces of China during 2018 and 2019. The baseline fludioxonil sensitivity distribution indicated that all of the isolates were sensitive, exhibiting a unimodal cure with a mean effective concentration for 50% inhibition value of 0.13 ± 0.12 μg/ml (standard deviation). Five fludioxonil-resistant mutants were subsequently induced by exposure to fludioxonil under laboratory conditions. Ten successive rounds of subculture in the absence of the selection pressure indicated that the mutation was stably inherited. However, the fludioxonil-resistant mutants were found to have reduced pathogenicity, higher glycerol accumulation, and higher osmotic sensitivity than the parental wild-type isolates, indicating that there was a fitness cost associated with fludioxonil resistance. In addition, the study also found a positive cross resistance between fludioxonil, procymidone, and iprodione, but not with other fungicides such as boscalid, carbendazim, tebuconazole, and fluazinam. Sequence analysis of four candidate target genes (FgOs1, FgOs2, FgOs4, and FgOs5) revealed that the HBXT2R mutant contained two point mutations that resulted in amino acid changes at K223T and K415R in its FgOs1 protein, and one point mutation at residue 520 of its FgOs5 protein that resulted in a premature stop codon. Similarly, the three other mutants contained point mutations that resulted in changes at the K192R, K293R, and K411R residues of the FgOs5 protein but none in the FgOs2 and FgOs4 genes. However, it is important to point out that the FgOs2 and FgOs4 expression of all the fludioxonil-resistant mutants was significantly (P < 0.05) downregulated compared with the sensitive isolates (except for the SQ1-2 isolate). It was also found that one of the resistant mutants did not have changes in any of the sequenced target genes, indicating that an alternative mechanism could also lead to fludioxonil resistance.

The gray mold caused by Botrytis cinerea has a significant impact on tomato production throughout the world. Although the synthetic fungicide fludioxonil can effectively control B. cinerea, there have been several reports of resistance to this fungicide. This study indicated that all of the fludioxonil-resistant strains tested, including one field-resistant isolate and four laboratory strains, had reduced fitness relative to sensitive isolates. In addition to having reduced growth, sporulation, and pathogenicity, the resistant strains were more sensitive to osmotic stress and had significantly (P < 0.05) higher peroxidase activity. BOs1, a kinase in the high-osmolarity glycerol stress response signal transduction pathway, is believed to harbor mutations related to fludioxonil resistance. Sequence analysis of their BOs1 sequences indicated that the fludioxonil-resistant field isolate, XXtom1806, had four point mutations resulting in four amino acid changes (I365S, S531G, T565N, and T1267A) and three amino acids (I365S, S531G, and T565N) in the histidine kinases, adenylyl cyclases, methyl-accepting chemotaxis receptors, and phosphatases domain, which associated with fludioxonil binding. Similarly, two of the laboratory strains, XXtom-Lab1 and XXtom-Lab4, had three (Q846S, I1126S, and G415D) and two (P1051S and V1241M) point mutations, respectively. A third strain, XXtom-lab3, had a 52-bp insertion that included a stop codon at amino acid 256. Interestingly, the BOs1 sequence of the fourth laboratory strain, XXtom-lab5, was identical to those of the sensitive isolates, indicating that an alternative resistance mechanism exists. The study also found evidence of positive cross-resistance between fludioxonil and the dicarboximide fungicides procymidone and iprodione, but no cross-resistance was detected with any other fungicides tested, including boscalid, carbendazim, tebuconazole, and fluazinam.

Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.

Soybean mosaic virus (SMV), a potyvirus, is the most prevalent and destructive viral pathogen in soybean‐planting regions of China. Moreover, other potyviruses, including bean common mosaic virus (BCMV) and watermelon mosaic virus (WMV), also threaten soybean farming. The eukaryotic translation initiation factor 4E (eIF4E) plays a critical role in controlling resistance/susceptibility to potyviruses in plants. In the present study, much higher SMV‐induced eIF4E1 expression levels were detected in a susceptible soybean cultivar when compared with a resistant cultivar, suggesting the involvement of eIF4E1 in the response to SMV by the susceptible cultivar. Yeast two‐hybrid and bimolecular fluorescence complementation assays showed that soybean eIF4E1 interacted with SMV VPg in the nucleus and with SMV NIa‐Pro/NIb in the cytoplasm, revealing the involvement of VPg, NIa‐Pro, and NIb in SMV infection and multiplication. Furthermore, transgenic soybeans silenced for eIF4E were produced using an RNA interference approach. Through monitoring for viral symptoms and viral titers, robust and broad‐spectrum resistance was confirmed against five SMV strains (SC3/7/15/18 and SMV‐R), BCMV, and WMV in the transgenic plants. Our findings represent fresh insights for investigating the mechanism underlying eIF4E‐mediated resistance in soybean and also suggest an effective alternative for breeding soybean with broad‐spectrum viral resistance.

No abstract

Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.

Background Wheat (Triticum aestivum) originated from three different diploid ancestral grass species and experienced two rounds of polyploidization. Exploring how certain wheat gene subfamilies have expanded during the evolutionary process is of great importance. The Lateral Organ Boundaries Domain (LBD) gene family encodes plant-specific transcription factors that share a highly conserved LOB domain and are prime candidates for this, as they are involved in plant growth, development, secondary metabolism and stress in various species. Methods Using a genome-wide analysis of high-quality polyploid wheat and related species genome sequences, a total of 228 LBD members from five Triticeae species were identified, and phylogenetic relationship analysis of LBD members classified them into two main classes (classes I and II) and seven subgroups (classes I a–e, II a and II b). Results The gene structure and motif composition analyses revealed that genes that had a closer phylogenetic relationship in the same subgroup also had a similar gene structure. Macrocollinearity and microcollinearity analyses of Triticeae species suggested that some LBD genes from wheat produced gene pairs across subgenomes of chromosomes 4A and 5A and that the complex evolutionary history of TaLBD4B-9 homologs was a combined result of chromosome translocation, polyploidization, gene loss and duplication events. Public RNA-seq data were used to analyze the expression patterns of wheat LBD genes in various tissues, different developmental stages and following abiotic and biotic stresses. Furthermore, qRT-PCR results suggested that some TaLBDs in class II responded to powdery mildew, regulated reproductive growth and were involved in embryo sac development in common wheat.

Previous evidence suggested the potyviral frame-shift protein P3N-PIPO is required for efficient viral intercellular movement. Host proteins are essential for virus to establish successful infection, many virus proteins play key roles in the viral infection cycle by interacting with that. Here, a yeast two-hybrid screening analysis was completed using Soybean mosaic virus (SMV) P3N-PIPO as the bait and a cDNA library from soybean infected with SMV as the prey to characterize the function of SMV P3N-PIPO. Fifty-four genes were isolated and analyzed by BLAST tools. Several genes encoding proteins that interacted with SMV P3N-PIPO were screened, including genes for inhibitory and transcription factors and those related to defense, transport, and photosynthesis. Some genes encoded proteins involved in metabolic activities in the chloroplast, such as photosystem I subunit PsaD and Calvin cycle protein CP12-2. Some genes were associated with defense responses, such as pathogenesis-related protein 1-like protein, stress-related protein-like protein, and stress enhanced protein 2. Other genes encoded defense-related transcription factors, such as WRKY transcription factor 51, while others were related to signal transduction, including a translationally-controlled tumor protein homolog, calcium-transporting ATPase 12, plasma membrane-type calcium-transporting ATPase 12-like protein, and calcineurin B-like protein 1-like isoform X1. Some genes coding for proteins related to affect viral plasmodesmata tracking, such as glucan endo-1,3-b-glucosidase (acidic isoform GL153). This study is the first to preliminarily delineate the interactions between SMV P3N-PIPO and host proteins related to defense responses during SMV infection.