Abiotic stress in cucumber (Cucumis sativus L.) may trigger distinct transcriptome responses, resulting in significant yield loss. More insight into the molecular underpinnings of the stress response can be gained by combining RNA-Seq meta-analysis with systems biology and machine learning. This can help pinpoint possible targets for engineering abiotic tolerance by revealing functional modules and key genes essential for the stress response. Therefore, to investigate the regulatory mechanism and key genes, a combination of these approaches was utilized in cucumber subjected to various abiotic stresses. Three significant abiotic stress-related modules were identified by gene co-expression network analysis (WGCNA). Three hub genes (RPL18, δ-COP, and EXLA2), ten transcription factors (TFs), one transcription regulator, and 12 protein kinases (PKs) were introduced as key genes. The results suggest that the identified PKs probably govern the coordination of cellular responses to abiotic stress in cucumber. Moreover, the C2H2 TF family may play a significant role in cucumber response to abiotic stress. Several C2H2 TF target stress-related genes were identified through co-expression and promoter analyses. Evaluation of the key identified genes using Random Forest, with an area under the curve of ROC (AUC) of 0.974 and an accuracy rate of 88.5%, demonstrates their prominent contributions in the cucumber response to abiotic stresses. These findings provide novel insights into the regulatory mechanism underlying abiotic stress response in cucumber and pave the way for cucumber genetic engineering toward improving tolerance ability under abiotic stress.

Salinity is a major abiotic stress affecting cereal production. Thus, tritipyrum (x. Tritipyrum), a potential novel salt-tolerant cereal, was introduced as an appropriate alternative for cereal production. The purposes of this study were to evaluate agronomic traits, yield, and yield stability of eight primary tritipyrum lines, five promising triticale lines, and four bread wheat varieties and to screen a stable yielding line. The experiments were conducted in randomized complete block designs with three replicates in three locations during four growing seasons. Analysis of variance in each environment and Bartlett’s test for the variance homogeneity of experimental errors were made. Subsequently, separate experiments were analyzed as a combined experiment. The stability of grain yield was analyzed according to Eberhart and Russell’s regression method, environmental variance, Wrick’s ecovalance, Shokla’s stability variance, AMMI, and Tai methods. Genotype × environment interactions (GEI) and environments were significant for the agronomic traits. Stability analysis revealed that combined primary tritipyrum line (Ka/b)(Cr/b)-5 and triticale 4115, 4108, and M45 lines had good adaptability in all environments. The results of the AMMI3 model and pattern analysis showed that the new cereal, tritipyrum, had the most stable response in various environments. The tritipyrum line (Ka/b)(Cr/b)-5 had the best yield performance and general adaptability. Based on Tai’s method, the contribution of spike number to the stability of grain yield over different environments was higher than that of other yield components. Also, tritipyrum lines demonstrated higher stability compared with wheat and triticale. Totally, M45 triticale and tritipyrum (Ka/b)(Cr/b)-5 lines were the most stable genotypes with high grain yield. Complementary agronomic experiments may then release a new grain crop of triticale and a new pasture line of combined primary tritipyrum for grain and forage. Moreover, the combined tritipyrum line can be used in bread wheat breeding programs for producing salt-tolerant wheat cultivars.

. Maize may be exposed to several abiotic stresses in the field. Therefore, identifying the tolerance mechanisms of natural field stress is mandatory. Gene expression data of maize upon abiotic stress were collected, and 560 differentially expressed genes (DEGs) were identified through meta-analysis. The most significant gene ontology terms in up-regulated genes were ‘response to abiotic stress’ and ‘chitinase activity’. ‘Phosphorelay signal transduction system’ was the most significant enriched biological process in down-regulated DEGs. The co-expression analysis unveiled seven modules of DEGs, with a notable positive correlation between the modules and abiotic stress. Furthermore, the statistical significance was strikingly high for the turquoise, green and yellow modules. The turquoise group played a central role in orchestrating crucial adaptations in metabolic and stress response pathways in maize when exposed to abiotic stress. Within three up-regulated modules, Zm.7361.1.A1_at, Zm.10386.1.A1_a_at and Zm.10151.1.A1_at emerged as hub genes. These genes might introduce novel candidates implicated in stress tolerance mechanisms, warranting further comprehensive investigation and research. In parallel, the R package glmnet was applied to fit a logistic LASSO regression model on the DEGs profile to select candidate genes associated with abiotic responses in maize. The identified hub genes and LASSO regression genes were validated on an independent microarray dataset. Additionally, Differential Gene Correlation Analysis (DGCA) was performed on LASSO and hub genes to investigate the gene-gene regulatory relationship. The P value of DGCA of 16 pairwise gene comparisons was lower than 0.01, indicating a gene–gene significant change in correlation between control and abiotic stress. Integrated weighted gene correlation network analysis and logistic LASSO analysis revealed Zm.11185.1.S1_at, Zm.2331.1.S1_x_at and Zm.17003.1.S1_at. Notably, these 3 genes were identified in the 16 gene-pair comparisons. This finding highlights the notable significance of these genes in the abiotic stress response. Additional research into maize stress tolerance may focus on these three genes.