Transcriptome Analyses in a Selected Gene Set Indicate Alternative Oxidase (AOX) and Early Enhanced Fermentation as Critical for Salinity Tolerance in Rice

Exploring salt tolerance mechanisms using machine learning for transcriptomic insights: case study in Spartina alterniflora

Salt stress poses a significant threat to global cereal crop production, emphasizing the need for a comprehensive understanding of salt tolerance mechanisms. Accurate functional annotations of differentially expressed genes are crucial for gaining insights into the salt tolerance mechanism. The challenge of predicting gene functions in under-studied species, especially when excluding infrequent GO terms, persists. Therefore, we proposed the use of NetGO 3.0, a machine learning-based annotation method that does not rely on homology information between species, to predict the functions of differentially expressed genes under salt stress. Spartina alterniflora, a halophyte with salt glands, exhibits remarkable salt tolerance, making it an excellent candidate for in-depth transcriptomic analysis. However, current research on the S. alterniflora transcriptome under salt stress is limited. In this study we used S. alterniflora as an example to investigate its transcriptional responses to various salt concentrations, with a focus on understanding its salt tolerance mechanisms. Transcriptomic analysis revealed substantial changes impacting key pathways, such as gene transcription, ion transport, and ROS metabolism. Notably, we identified a member of the SWEET gene family in S. alterniflora, SA_12G129900.m1, showing convergent selection with the rice ortholog SWEET15. Additionally, our genome-wide analyses explored alternative splicing responses to salt stress, providing insights into the parallel functions of alternative splicing and transcriptional regulation in enhancing salt tolerance in S. alterniflora. Surprisingly, there was minimal overlap between differentially expressed and differentially spliced genes following salt exposure. This innovative approach, combining transcriptomic analysis with machine learning-based annotation, avoids the reliance on homology information and facilitates the discovery of unknown gene functions, and is applicable across all sequenced species.

Molecular Basis of Crops and Fruit Plants in Response to Stress

This editorial summarizes the main scientific contributions from 11 papers comprising the Special Issue (SI) "Molecular Basis of Crops and Fruit Plants in Response to Stress". Here, we collected papers from different research groups encompassing molecular studies from monocots (ginger, rice, maize) and eudicots (common hazel, cowpea, pepper, soybean, tomato) species submitted to abiotic stresses as heat, cold, salt, drought, and heavy metals or biotic stresses induced by different viruses, such as BPEV, PepGMV, PMMoV, and TEV. These studies explored different aspects of molecular mechanisms involved in plant stress tolerance, establishing comparative analyses among genotypes/cultivars to identify potential molecular markers of stresses that are now available for future application in biotechnological studies. This SI presents a collection of advanced concepts and emerging strategies for readers and researchers aiming to accelerate plant breeding.

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.

Polymorphisms in alternative oxidase genes from ecotypes of Arabidopsis and rice revealed an environment-induced linkage to altitude and rainfall

We investigated SNPs in alternative oxidase (AOX) genes and their connection to ecotype origins (climate, altitude, and rainfall) by using genomic data sets of Arabidopsis and rice populations from 1190 and 90 ecotypes, respectively. Parameters were defined to detect non-synonymous SNPs in the AOX ORF, which revealed amino acid (AA) changes in AOX1c, AOX1d, and AOX2 from Arabidopsis and AOX1c from rice in comparison to AOX references from Columbia-0 and Japonica ecotypes, respectively. Among these AA changes, Arabidopsis AOX1c_A161E&G165R and AOX1c_R242S revealed a link to high rainfall and high altitude, respectively, while all other changes in Arabidopsis and rice AOX was connected to high altitude and rainfall. Comparative 3D modeling showed that all mutant AOX presented structural differences in relation to the respective references. Molecular docking analysis uncovered lower binding affinity values between AOX and the substrate ubiquinol for most of the identified structures compared to their reference, indicating better enzyme-substrate binding affinities. Thus, our in silico data suggest that the majority of the AA changes found in the available ecotypes will confer better enzyme-subtract interactions and thus indicate environment-related, more efficient AOX activity.

Effect of Salt Stress on the Activity, Expression, and Promoter Methylation of Succinate Dehydrogenase and Succinic Semialdehyde Dehydrogenase in Maize (Zea mays L.) Leaves

The effect of salt stress on the expression of genes, the methylation of their promoters, and the enzymatic activity of succinate dehydrogenase (SDH) and succinic semialdehyde dehydrogenase (SSADH) was investigated in maize (Zea mays L.). The incubation of maize seedlings in a 150 mM NaCl solution for 24 h led to a several-fold increase in the activity of SSADH that peaked at 6 h of NaCl treatment, which was preceded by an increase in the Ssadh1 gene expression and a decrease in its promoter methylation observed at 3 h of salt stress. The increase in SDH activity and succinate oxidation by mitochondria was slower, developing by 24 h of NaCl treatment, which corresponded to the increase in expression of the genes Sdh1-2 and Sdh2-3 encoding SDH catalytic subunits and of the gene Sdh3-1 encoding the anchoring SDH subunit. The increase in the Sdh2-3 expression was accompanied by the decrease in promoter methylation. It is concluded that salt stress results in the rapid increase in succinate production via SSADH operating in the GABA shunt, which leads to the activation of SDH, the process partially regulated via epigenetic mechanisms. The role of succinate metabolism under the conditions of salt stress is discussed.