Background: Salt and drought are the main abiotic stresses that restrict the yield of crops. Peroxidases (PRXs) are involved in various abiotic stress responses. Furthermore, only few wheat PRXs have been characterized in the mechanism of the abiotic stress response. Results: In this study, a novel wheat peroxidase (PRX) gene named TaPRX-2A, a member of wheat class III PRX gene family, was cloned and its response to salt stress was characterized. Based on the identification and evolutionary analysis of class III PRXs in 12 plants, we proposed an evolutionary model for TaPRX-2A, suggesting that occurrence of some exon fusion events during evolution. We also detected the positive selection of PRX domain in 13 PRXs involving our evolutionary model, and found 2 or 6 positively selected sites during TaPRX-2A evolution. Quantitative reverse transcription–polymerase chain reaction (qRT–PCR) results showed that TaPRX-2A exhibited relatively higher expression levels in root tissue than those exhibited in leaf and stem tissues. TaPRX-2A expression was also induced by abiotic stresses and hormone treatments such as polyethylene glycol 6000, NaCl, hydrogen peroxide (H₂O₂), salicylic acid (SA), methyljasmonic acid (MeJA) and abscisic acid (ABA). Transgenic wheat plants with overexpression of TaPRX-2A showed higher tolerance to salt stress than wild-type (WT) plants. Confocal microscopy revealed that TaPRX-2A-eGFP was mainly localized in cell nuclei. Survival rate, relative water content, and shoot length were higher in TaPRX-2A-overexpressing wheat than in the WT wheat, whereas root length was not significantly different. The activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were enhanced in TaPRX-2A-overexpressing wheat compared with those in the WT wheat, resulting in the reduction of reactive oxygen species (ROS) accumulation and malondialdehyde (MDA) content. The expression levels of downstream stress-related genes showed that RD22, TLP4, ABAI, GST22, FeSOD, and CAT exhibited higher expressions in TaPRX-2A-overexpressing wheat than in WT under salt stress. Conclusions: The results show that TaPRX-2A plays a positive role in the response to salt stress by scavenging ROS and regulating stress-related genes.

Background: The receptor-like kinase (RLK) gene families in plants contains a large number of members. They are membrane proteins with an extracellular receptor domain and participate in biotic and abiotic stress responses. Results: In this study, we identified RLKs in 15 representative plant genomes, including wheat, and classified them into 64 subfamilies by using four types of phylogenetic trees and HMM models. Conserved exon‒intron structures with conserved exon phases in the kinase domain were found in many RLK subfamilies from Physcomitrella patens to Triticum aestivum. Domain distributions of RLKs were also diagrammed. Collinearity events and tandem gene clusters suggested that polyploidization and tandem duplication events contributed to the member expansions of T. aestivum RLKs. Global expression pattern analysis was performed by using public transcriptome data. These analyses were involved in T. aestivum, Aegilops tauschii and Brachypodium distachyon RLKs under biotic and abiotic stresses. We also selected 9 RLKs to validate the transcriptome prediction by using qRT‒PCR under drought treatment and with Fusarium graminearum infection. The expression trends of these 9 wheat RLKs from public transcriptome data were consistent with the results of qRT‒PCR, indicating that they might be stress response genes under drought or F. graminearum treatments. Conclusion: In this study, we identified, classified, evolved, and expressed RLKs in wheat and related plants. Thus, our results will provide insights into the evolutionary history and molecular mechanisms of wheat RLKs.

Recently, convolutional neural networks (CNNs) and self-attention mechanisms have been widely applied in plant disease identification tasks, yielding significant successes. Currently, the majority of research models for tomato leaf disease recognition rely solely on traditional convolutional models or Transformer architectures and fail to capture both local and global features simultaneously. This limitation may result in biases in the model’s focus, consequently impacting the accuracy of disease recognition. Consequently, models capable of extracting local features while attending to global information have emerged as a novel research direction. To address these challenges, we propose an Eff-Swin model that integrates the enhanced features of the EfficientNetV2 and Swin Transformer networks, aiming to harness the local feature extraction capability of CNNs and the global modeling ability of Transformers. Comparative experiments demonstrate that the enhanced model has achieved a further increase in training accuracy, reaching an accuracy rate of 99.70% on the tomato leaf disease dataset, which is 0.49~3.68% higher than that of individual network models and 0.8~1.15% higher than that of existing state-of-the-art combined approaches. The results show that integrating attention mechanisms into convolutional models can significantly enhance the accuracy of tomato leaf disease recognition while also offering the great potential of the Eff-Swin backbone with self-attention in plant disease identification.

Cucumber (Cucumis sativus L.) is an important vegetable crop worldwide and gibberellins (GAs) play important roles in the regulation of cucumber developmental and growth processes. GA oxidases (GAoxs), which are encoded by different gene subfamilies, are particularly important in regulating bioactive GA levels by catalyzing the later steps in the biosynthetic pathway. Although GAoxs are critical enzymes in GA synthesis pathway, little is known about GAox genes in cucumber, in particular about their evolutionary relationships, expression profiles and biological function. In this study, we identified 17 GAox genes in cucumber genome and classified them into five subfamilies based on a phylogenetic tree, gene structures, and conserved motifs. Synteny analysis indicated that the tandem duplication or segmental duplication events played a minor role in the expansion of cucumber GA2ox, GA3ox and GA7ox gene families. Comparative syntenic analysis combined with phylogenetic analysis provided deep insight into the phylogenetic relationships of CsGAox genes and suggested that protein homology CsGAox are closer to AtGAox than OsGAox. In addition, candidate transcription factors BBR/BPC (BARLEY B RECOMBINANT/BASIC PENTACYSTEINE) and GRAS (GIBBERELLIC ACID-INSENSITIVE, REPRESSOR of GAI, and SCARECROW) which may directly bind promoters of CsGAox genes were predicted. Expression profiles derived from transcriptome data indicated that some CsGAox genes, especially CsGA20ox1, are highly expressed in seedling roots and were down-regulated under GA₃ treatment. Ectopic over-expression of CsGA20ox1 in Arabidopsis significantly increased primary root length and lateral root number. Taken together, comprehensive analysis of CsGAoxs would provide a basis for understanding the evolution and function of the CsGAox family.

One of the main abiotic stressors affecting agricultural output is drought stress, which has a substantial impact on wheat growth, development, and yield. This study aims to uncover transcriptomic changes in wheat leaves under drought stress using machine learning and bioinformatics approaches, thereby providing new research perspectives and solutions for understanding the mechanisms of abiotic stress responses in wheat and identifying drought-tolerant genes. First, publicly available RNA sequencing data on wheat drought stress were retrieved from databases, followed by sequence alignment and quantitative expression analysis. Differentially expressed genes (DEGs) under drought stress were identified through differential expression analysis. Subsequently, a weighted gene co-expression network was constructed to determine key gene modules, and multiple machine learning models were compared for their performance. Finally, an improved Random Forest-Boruta (RF-Boruta) algorithm was employed to identify key genes closely associated with drought stress responses. The differential expression analysis identified 16,754 DEGs, and the constructed gene co-expression network successfully identified modules related to drought stress responses. Among the various machine learning models, the random forest algorithm performed best in identifying drought stress-responsive genes. The improved RF-Boruta algorithm further selected candidate genes highly related to drought stress, improving model accuracy from 0.889 to 0.942 and the area under the curve (AUC) from 0.968 to 0.978. Gene enrichment analysis was also conducted. By integrating bioinformatics and machine learning techniques, this study identified key genes highly associated with drought stress responses in wheat, providing important insights into the potential mechanisms of drought responses in wheat.