The soilborne pathogen Ralstonia solanacearum is the causal agent of bacterial wilt (BW), a major disease of pepper (Capsicum annuum). The genetic basis of resistance to this disease in pepper is not well known. This study aimed to identify BW resistance markers in pepper. Analysis of the dynamics of bioluminescent R. solanacearum colonization in reciprocal grafts of a resistant (BVRC 1) line and a susceptible (BVRC 25) line revealed that the resistant rootstock effectively suppressed the spreading of bacteria into the scion. The two clear-cut phenotypic distributions of the disease severity index in 440 F2 plants derived from BVRC 25 × BVRC 1 indicated that a major genetic factor as well as a few minor factors that control BW resistance. By specific-locus amplified fragment sequencing combined with bulked segregant analysis, two adjacent resistance-associated regions on chromosome 10 were identified. Quantitative trait (QTL) mapping revealed that these two regions belong to a single QTL, qRRs-10.1. The marker ID10-194305124, which reached a maximum log-likelihood value at 9.79 and accounted for 19.01% of the phenotypic variation, was located the closest to the QTL peak. A cluster of five predicted R genes and three defense-related genes, which are located in close proximity to the significant markers ID10-194305124 or ID10-196208712, are important candidate genes that may confer BW resistance in pepper.

Introduction: It is well-known that maize and wheat are main food crops in the world. Thus, promoting high quality and abundant maize and wheat crops guarantees the development of the grain industry, which is needed to support world hunger. Weeds seriously affect the growing environment of maize, wheat, and their seedlings, resulting in low crop yields and poor seedling quality. This paper focuses on the identification of maize and wheat seedlings and field weeds using deep learning.Methods: Maize and wheat seedlings and field weeds are the research objects. A weed identification model based on the UNet network model and ViT classification algorithm is proposed. The model uses UNet to segment images. A Python Imaging Library algorithm is used to segment green plant leaves from binary images, to enhance the feature extraction of green plant leaves. The segmented image is used to construct a ViT classification model, which improves the recognition accuracy of maize and wheat seedlings and weeds in the field.Results: This paper uses average accuracy, average recall, and F1 score to evaluate the performance of the model. The accuracy rate (for accurately identifying maize and wheat seedlings and weeds in the field) reaches 99.3%. Compared with Alexnet, VGG16, and MobileNet V3 models, the results show that the recognition effect of the model trained using the method presented in this paper is better than other existing models.Discussion: Thus, this method, which accurately disambiguates maize and wheat seedlings from field weeds can provide effective information support for subsequent field pesticide spraying and mechanical weeding.

Pericarp color is a crucial commercial trait influencing consumer preferences for bitter gourds. However, until now, the gene responsible for this trait has remained unidentified. In this study, we identified a gene (McAPRR2) controlling pericarp color via a genome-wide association study (GWAS) utilizing the resequencing data of 106 bitter gourd accessions. McAPRR2 exhibits three primary haplotypes: Hap1 is a wild type with a green pericarp, Hap2 is a SA (South Asian) and SEA (Southeast Asia) type with a green pericarp, and Hap3 is primarily a SEA type with a light green pericarp. The McAPRR2 haplotype is significantly correlated with both pericarp color and ecological type. Importantly, McAPRR2 with the light green pericarp demonstrated premature termination due to a 15 bp sequence insertion. The phylogenetic tree clustered according to pericarp color and ecological type, using SNPs located in the McAPRR2 gene and its promoter. High π_wild/SEA and π_SA/SEA values indicate high nucleotide diversity between wild and SEA types and between SA and SEA types in the McAPRR2 gene. The haplotypes, phylogenetic tree, and nucleotide diversity of McAPRR2 suggest that McAPRR2 has undergone domestication selection. This study identifies McAPRR2 as the key gene determining pericarp color in bitter gourds and introduces a novel insight that McAPRR2 is subject to domestication selection.

Transcriptome analysis is an efficient way to explore molecular markers in plant species, for which genome sequences have not been published. To address the limited number of markers published for the Welsh onion, this study found 6486 loci of genic simple sequence repeats (SSR), which consisted of 1–5 bp repeat motifs, based on next-generation sequencing (NGS) technology and the RNA-Seq approach. The most abundant motif was mononucleotide (52.33%), followed by trinucleotide (31.96%), and dinucleotide (14.57%). A total of 2525 primer pairs were successfully designed, and 91 out of 311 tested primers were polymorphisms. Overall, 38 genic SSR markers were randomly selected to further validate the degree of genetic diversity, and 22 genic SSR markers (57.89%) showed high levels of polymorphism. The average polymorphism information content (PIC) value and the number of alleles (Na) were 0.63 and 5.27, respectively, and the unweighted pair-group method with arithmetic average (UPGMA) cluster analysis grouped the 22 Allium accessions into three groups with Nei’s similarity coefficients ranging from 0.37 to 0.99. This result suggested that these genic SSR markers could be used to develop a higher resolution genetic map and/or to analyze the phylogenetic relationships among Allium plants in the near future.

Seeds are major vehicles of propagation and dispersal in plants. A number of transcription factors, including APETALA2 (AP2), play crucial roles during the seed development process in various plant species. However, genes essential for seed development and the regulatory networks that operate during seed development remain unclear in lettuce. Here, we identified a lettuce AP2 (LsAP2) gene that was highly expressed during the early stages of seed development. LsAP2 knockout plants obtained by the CRISPR/Cas9 system were used to explore the biological function of LsAP2. Compared with the wild type, the seeds of Lsap2 mutant plants were longer and narrower, and developed an extended tip at the seed top. After further investigating the structural characteristics of the seeds of Lsap2 mutant plants, we proposed a new function of LsAP2 in seed dispersal. Moreover, we identified several interactors of LsAP2. Our results showed that LsAP2 directly interacted with the lettuce homolog of BREVIPEDICELLUS (LsBP) and promoted the expression of LsBP. Transcriptome analysis revealed that LsAP2 might also be involved in brassinosteroid biosynthesis and signaling pathways. Taken together, our data indicate that LsAP2 has a significant function in regulating seed shape in lettuce.

Foliage diseases are prevalent in cucumber production and cause serious yield reduction across the world. Identifying resistance or susceptible genes under foliage-disease stress is essential for breeding resistant varieties, of which leaf-specific expressed susceptible genes are extremely important but rarely studied in crops. This study performed an in-depth mining of public transcriptome data both in different cucumber tissues and under downy mildew (DM) inoculation, and found that the expression of leaf-specific expressed transcription factor CsTCP14 was significantly increased after treatment with DM, as well as being upregulated under stress from another foliage disease, watermelon mosaic virus (WMV), in susceptible cucumbers. Furthermore, the Pearson correlation analysis identified genome-wide co-expressed defense genes with CsTCP14. A potential target CsNBS-LRR gene, Csa6M344280.1, was obtained as obviously reduced and was negatively correlated with the expression of the susceptible gene CsTCP14. Moreover, the interaction experiments of electrophoretic mobility shift assay (EMSA) and yeast one-hybrid assay (Y1H) were successfully executed to prove that CsTCP14 could transcriptionally repress the expression of the CsNBS-LRR gene, Csa6M344280.1, which resulted in inducing susceptibility to foliage diseases in cucumber. As such, we constructed a draft model showing that the leaf-specific expressed gene CsTCP14 was negatively regulating the defense gene Csa6M344280.1 to induce susceptibility to foliage diseases in cucumber. Therefore, this study explored key susceptible genes in response to foliage diseases based on a comprehensive analysis of public transcriptome data and provided an opportunity to breed new varieties that can resist foliage diseases in cucumber, as well as in other crops.