Identification and characterization of potential NBS-encoding resistance genes and induction kinetics of a putative candidate gene associated with downy mildew resistance in Cucumis

<p>Abstract</p> <p>Background</p> <p>Due to the variation and mutation of the races of <it>Pseudoperonospora cubensis</it>, downy mildew has in recent years become the most devastating leaf disease of cucumber worldwide. Novel resistance to downy mildew has been identified in the wild <it>Cucumis </it>species, <it>C. hystrix </it>Chakr. After the successful hybridization between <it>C. hystrix </it>and cultivated cucumber (<it>C. sativus </it>L.), an introgression line (IL5211S) was identified as highly resistant to downy mildew. Nucleotide-binding site and leucine-rich repeat (NBS-LRR) genes are the largest class of disease resistance genes cloned from plant with highly conserved domains, which can be used to facilitate the isolation of candidate genes associated with downy mildew resistance in IL5211S.</p> <p>Results</p> <p>Degenerate primers that were designed based on the conserved motifs in the NBS domain of resistance (R) proteins were used to isolate NBS-type sequences from IL5211S. A total of 28 sequences were identified and named as cucumber (<it>C. sativus </it>= CS) resistance gene analogs as CSRGAs. Polygenetic analyses separated these sequences into four different classes. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis showed that these CSRGAs expressed at different levels in leaves, roots, and stems. In addition, introgression from <it>C. hystrix </it>induced expression of the partial CSRGAs in cultivated cucumber, especially CSRGA23, increased four-fold when compared to the backcross parent CC3. Furthermore, the expression of CSRGA23 under <it>P. cubensis </it>infection and abiotic stresses was also analyzed at different time points. Results showed that the <it>P. cubensis </it>treatment and four tested abiotic stimuli, MeJA, SA, ABA, and H₂O_2,triggered a significant induction of CSRGA23 within 72 h of inoculation. The results indicate that CSRGA23 may play a critical role in protecting cucumber against <it>P. cubensis </it>through a signaling the pathway triggered by these molecules.</p> <p>Conclusions</p> <p>Four classes of NBS-type RGAs were successfully isolated from IL5211S, and the possible involvement of CSRGA23 in the active defense response to <it>P. cubensis </it>was demonstrated. These results will contribute to develop analog-based markers related to downy mildew resistance gene and elucidate the molecular mechanisms causing resistance in IL5211S in the future.</p

Comparative Genomic Analysis Reveals Extensive Genetic Variations of WRKYs in Solanaceae and Functional Variations of CaWRKYs in Pepper

As a conserved protein family, WRKY has been shown to be involved in multiple biological processes in plants. However, the mechanism of functional diversity for WRKYs in pepper has not been well elucidated. Here, a total of 223 WRKY members from solanaceae crops including pepper, tomato and potato, were analyzed using comparative genomics. A tremendous genetic variation among WRKY members of different solanaceous plants or groups was demonstrated by the comparison of some WRKY features, including number/size, group constitution, gene structure, and domain composition. The phylogenetic analysis showed that except for the known WRKY groups (I, IIa/b/c/d/e and III), two extra WRKY subgroups specifically existed in solanaceous plants, which were named group IIf and group IIg in this study, and their genetic variations were also revealed by the characteristics of some group IIf and IIg WRKYs. Except for the extensive genetic variations, certain degrees of conservatism for solanaceae WRKYs were also revealed. Moreover, the variant zinc-finger structure (CX4,7CX22-24HXC) in group III of solanaceae WRKYs was identified. Expression profiles of CaWRKY genes suggested their potential roles in pepper development and stress responses, and demonstrated a functional division pattern for pepper CaWRKYs. Furthermore, functional analysis using virus induced gene silencing (VIGS) revealed critical roles of two CaWRKYs (CaWRKY45 and CaWRKY58) in plant responses to disease and drought, respectively. This study provides a solid foundation for further dissection of the evolutionary and functional diversity of solanaceae WRKYs in crop plants

Two-Dimensional Exponential Sparse Discriminant Local Preserving Projections

The two-dimensional discriminant locally preserved projections (2DDLPP) algorithm adds a between-class weighted matrix and a within-class weighted matrix into the objective function of the two-dimensional locally preserved projections (2DLPP) algorithm, which overcomes the disadvantage of 2DLPP, i.e., that it cannot use the discrimination information. However, the small sample size (SSS) problem still exists, and 2DDLPP processes the whole original image, which may contain a large amount of redundant information in the retained features. Therefore, we propose a new algorithm, two-dimensional exponential sparse discriminant local preserving projections (2DESDLPP), to address these problems. This integrates 2DDLPP, matrix exponential function and elastic net regression. Firstly, 2DESDLPP introduces the matrix exponential into the objective function of 2DDLPP, making it positive definite. This is an effective method to solve the SSS problem. Moreover, it uses distance diffusion mapping to convert the original image into a new subspace to further expand the margin between labels. Thus more feature information will be retained for classification. In addition, the elastic net regression method is used to find the optimal sparse projection matrix to reduce redundant information. Finally, through high performance experiments with the ORL, Yale and AR databases, it is proven that the 2DESDLPP algorithm is superior to the other seven mainstream feature extraction algorithms. In particular, its accuracy rate is 3.15%, 2.97% and 4.82% higher than that of 2DDLPP in the three databases, respectively

SAR-PC: Edge Detection in SAR Images via an Advanced Phase Congruency Model

Edge detection in Synthetic Aperture Radar (SAR) images has been a challenging task due to the speckle noise. Ratio-based edge detectors are robust operators for SAR images that provide constant false alarm rates, but they are only optimal for step edges. Edge detectors developed by the phase congruency model provide the identification of different types of edge features, but they suffer from speckle noise. By combining the advantages of the two edge detectors, we propose a SAR phase congruency detector (SAR-PC). Firstly, an improved local energy model for SAR images is obtained by replacing the convolution of raw image and the quadrature filters by the ratio responses. Secondly, a new noise level is estimated for the multiplicative noise. Substituting the SAR local energy and the new noise level into the phase congruency model, SAR-PC is derived. Edge response corresponds to the max moment of SAR-PC. We compare the proposed detector with the ratio-based edge detectors and the phase congruency edge detectors. Receiver Operating Characteristic (ROC) curves and visual effects are used to evaluate the performance. Experimental results of simulated images and real-world images show that the proposed edge detector is robust to speckle noise and it provides a consecutive edge response

Phylogenetic Relationship of Plant MLO Genes and Transcriptional Response of MLO Genes to Ralstonia solanacearum in Tomato

As a broad-spectrum disease resistance factor, MLO is involved in a variety of biotic and abiotic stress responses in plants. To figure out the structural features, phylogenetic relationships, and expression patterns of MLO genes, we investigated the genome and transcriptome sequencing data of 28 plant species using bioinformatics tools. A total of 197 MLO genes were identified. They possessed 5&ndash;7 transmembrane domains, but only partially contained a calmodulin-binding domain. A total of 359 polymorphic sites and 142 haplotypes were found in 143 sequences, indicating the rich nucleotide diversity of MLO genes. The MLO genes were unevenly distributed on chromosomes or scaffolds and were mainly located at the ends, forming clusters (24.1% genes), tandem duplicates (5.7%), and segment duplicates (36.2%). The MLO genes could be classified into three groups by phylogenetic analysis. The angiosperm genes were mainly in subgroup IA, Selaginella moellendorffii genes were in subgroup IA and IIIB, Physcomitrella patens genes were in subgroup IB and IIIA, and almost all algae genes were in group II. About half of the MLO genes had homologs within and across species. The Ka/Ks values were all less than 1, varying 0.01&ndash;0.78, suggesting that purifying selection had occurred in MLO gene evolution. In tomato, RNA-seq data indicated that SlMLO genes were highly expressed in roots, followed by flowers, buds, and leaves, and also regulated by different biotic stresses. qRT&ndash;PCR analysis revealed that SlMLO genes could respond to tomato bacterial wilt, with SlMLO1, SlMLO2, SlMLO4, and SlMLO6 probably involved in the susceptibility response, whereas SlMLO14 and SlMLO16 being the opposite. These results lay a foundation for the isolation and application of related genes in plant disease resistance breeding

PML-ED : A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation

Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the Encoder-Decoder Framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal Encoder-Decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria