Comparative Phylogenomics and Transcriptional Regulatory Networks of AQPs, HSPs, and LEA Proteins in Salt-Stressed Portulaca oleracea.

Genome-Wide Characterization of the ZIP Transporter Family in Sea Island Cotton (Gossypium barbadense L.) and Expression Profiling Under Heavy Metal and Pathogen Stresses

G. barbadense represents an indispensable germplasm resource for high-quality textile fiber and disease resistance; nevertheless, systematic information regarding its ZRT/IRT-like protein (ZIP) gene family remains limited. Here, a total of 46 GbZIP genes were identified across the G. barbadense genome. Comprehensive bioinformatic investigations revealed uneven chromosomal distribution and confirmed that segmental/whole-genome duplications, supplemented by localized tandem duplications, drove family expansion. Members clustered within the same phylogenetic clades shared conserved motif organization and gene architecture, while promoter regions harbored abundant cis-acting elements associated with phytohormone and stress signaling. Transcriptome profiling indicated distinct expression patterns across vegetative/reproductive tissues, fiber and ovule developmental stages, and diverse abiotic stress conditions (cold, hot, drought, and salt). Quantitative Real-Time PCR (qRT-PCR) further validated that several GbZIP candidates exhibited temporal expression variations upon exposure to cadmium toxicity, V. dahliae infection, and combined Cd-V. dahliae stress. Specifically, GbZIP13, GbZIP18, GbZIP27, and GbZIP36 displayed prominent broad-spectrum responses to all three stress conditions, whereas GbZIP16, GbZIP29, and GbZIP30 showed stress-specific regulatory divergence. Overall, this study aims to systematically analyze the evolutionary characteristics and expression patterns of the GbZIP family, and to specifically evaluate the response differences under Cd stress, V. dahliae stress, and combined stress, in order to identify potential key candidate genes.

Integrative Multi-Scale Analysis Identifies the Hub Gene TRE1 Associating Trehalose Metabolism With Salinity Tolerance in Rice.

Salinity is a major constraint on rice (Oryza sativa L.) production, yet the regulatory hubs that distinguish tolerant from susceptible genotypes remain largely unknown. Using a computational and experimental pipeline combining time-series meta-transcriptomics, weighted gene co-expression network analysis (WGCNA), fuzzy clustering, network centrality metrics, promoter analysis, and molecular and physicochemical evaluations, we identified genotype-specific hub genes associated with salinity tolerance in rice. Analysis of RNA-Seq data from tolerant (Pokkali) and susceptible (IR29) genotypes across five time points after salinity treatment yielded 3369 meta-differentially expressed genes. WGCNA revealed a salinity-responsive co-expression module, within which fuzzy clustering uncovered a cluster of 48 genes showing striking opposite temporal patterns between genotypes. Multi-metric centrality ranking prioritized Trehalase1 (TRE1), encoding a trehalase enzyme, among the identified hub genes. Promoter analysis of TRE1 revealed the presence of stress-related cis-elements (ABRE, TGACG-motif, MYB/MYC, DRE). Independent greenhouse validation confirmed the opposite expression pattern of TRE1 between Pokkali and IR29 under salinity, correlating with increased trehalose accumulation in Pokkali and trehalose depletion in IR29. Physicochemical assessments showed that Pokkali exhibited lower oxidative stress markers, higher relative water content, better photosynthetic performance, and a more favorable K+/Na+ ratio than IR29. Principal component analysis distinctly separated the two genotypes, with TRE1 expression clustering with susceptibility-associated traits, while trehalose clustered with tolerance-associated traits. Our network-based discovery and experimental validation together indicate that the contrasting regulation of the hub gene TRE1 links trehalose metabolism to salinity tolerance in rice, highlighting TRE1 as a possible target for further functional analysis to enhance salinity tolerance.

Transcriptomic insights into renal osmoregulation and environmental adaptation in two hammerhead shark species (Sphyrna zygaena and Sphyrna lewini)

Phylogenomics and expression profiling of wheat (Triticum aestivum L.) magnesium transporters under dual drought and salinity stress conditions

Proteomic Insights into Salt-Stress Tolerance in Zygosaccharomyces rouxii Revealed by Data-Independent Acquisition Mass Spectrometry.

Under severe stress, GO and KEGG enrichment consistently revealed the reinforcement of central carbon and energy metabolism, peroxisome-associated fatty acid turnover, oxidoreductase/redox activities, and translation and nucleotide metabolic pathways.

Selaginella sellowii gene regulatory networks reveal distinct transcriptional strategies for dehydration stress and recovery.

Vegetative desiccation tolerance (VDT) enables resurrection plants to withstand extreme water loss, yet its system-level regulation remains incompletely understood. Here, we interrogated the VDT landscape in Selaginella sellowii using time-series RNA-seq and a computational pipeline that integrates ensemble network inference, graph attention networks (GATs), and protein language models (PLMs). Specifically, we captured the transcriptional response to water potentials characteristic of resurrection plants but lethal to most species, including -4, -10, and -150 MPa. Our analysis revealed two distinct strategies for dehydration tolerance based on stress severity. Intermediate dehydration elicits a complex transcriptional response associated with active damage control, while extreme dehydration shifts to a minimalist, stable transcriptome. Network analysis identified 12 persistent regulators bridging quiescence and recovery. GAT embedding further demonstrated that these regulators undergo context-specific functional reprogramming to drive rapid cellular reconstruction. This study establishes a regulatory blueprint for VDT, identifies key mechanisms linking quiescence to coordinated recovery, and provides a robust methodological framework for decoding complex gene regulatory networks (GRNs).