The study used transcriptomic profiling to compare tomato (Solanum lycopersicum) responses to three evolutionarily distant pathogens—nematodes, aphids, and oomycetes—during compatible interactions, identifying differentially expressed genes and key host hubs. Integrating public datasets and performing co‑expression and GO enrichment analyses, the authors mapped shared dysregulation clusters and employed Arabidopsis interactome data to place tomato candidates within broader networks, highlighting potential targets for multi‑pathogen resistance.

Using diverse Mexican maize varieties and a MAGIC population, the study demonstrated that leaf vein density is both variable and plastic, correlating positively with photosynthetic rates for small intermediate veins and increasing under heat in drought-adapted lines. Twelve QTLs linked to vein patterning were identified, highlighting candidate genes for intermediate vein development and shedding light on the evolution of high-efficiency C4 leaf architecture.

The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

The study created a system that blocks root‑mediated signaling between wheat varieties in a varietal mixture and used transcriptomic and metabolomic profiling to reveal that root chemical interactions drive reduced susceptibility to Septoria tritici blotch, with phenolic compounds emerging as key mediators. Disruption of these root signals eliminates both the disease resistance phenotype and the associated molecular reprogramming.

The study employed ultra large‑scale 2D clinostats to grow tomato (Solanum lycopersicum) plants beyond the seedling stage under simulated microgravity and upright control conditions across five sequential trials. Simulated microgravity consistently affected plant growth, but the magnitude and direction of the response varied among trials, with temperature identified as a significant co‑variant; moderate heat stress surprisingly enhanced growth under simulated microgravity. These results highlight the utility of large‑scale clinostats for dissecting interactions between environmental factors and simulated microgravity in plant development.

The study demonstrates that the interaction between spliceosomal proteins STA1 and DOT2 controls nuclear speckle organization, pre‑mRNA splicing efficiency, and heat‑stress tolerance in Arabidopsis thaliana. A missense mutation in DOT2 restores the weakened STA1‑DOT2 interaction in the sta1‑1 mutant, linking interaction strength to speckle formation and transcriptome‑wide intron retention under heat stress, while pharmacological inhibition of STA1‑associated speckles reproduces the mutant phenotypes. These findings reveal a heat‑sensitive interaction node that couples spliceosome assembly to nuclear speckle dynamics and splicing robustness.

The study establishes Aeschynomene evenia as a new model for dissecting legume immunity against the soilborne pathogen Aphanomyces euteiches and its relationship with Nod factor-independent symbiosis. Quantitative resistance was assessed through inoculation assays, phenotypic and cytological analyses, and RNA‑seq identified thousands of differentially expressed genes, highlighting immune signaling and specialized metabolism, with mutant analysis confirming dual‑function kinases that modulate resistance. Comparative transcriptomics with Medicago truncatula revealed conserved and unique immune responses, positioning the A. evenia–A. euteiches system as a valuable platform for exploring quantitative resistance and symbiosis integration.

The study characterizes the chloroplast‑localized protein AT4G33780 in Arabidopsis thaliana using CRISPR/Cas9 knockout and overexpression lines, revealing tissue‑specific expression and context‑dependent effects on seed germination, seedling growth, vegetative development, and root responses to nickel stress. Integrated transcriptomic (RNA‑seq) and untargeted metabolomic analyses show extensive transcriptional reprogramming—especially of cell‑wall genes—and altered central energy metabolism, indicating AT4G33780 coordinates metabolic state with developmental regulation rather than controlling single pathways.

The study examined how dual‑purpose hemp (Cannabis sativa) adjusts to different phosphate levels, showing that flower biomass is maintained unless phosphate is completely removed. Integrated physiological measurements and transcriptomic profiling revealed that phosphate is reallocated to flowers via glycolytic bypasses and organic phosphate release, while key regulatory genes followed expected patterns but did not suppress uptake at high phosphate, leading to nitrate depletion that limits growth.

The study evaluated how stomatal anatomy and physiological efficiency influence wheat heat tolerance across multi‑environment field trials with 200 genotypes, using early versus delayed sowing to impose temperature stress. Findings revealed a decoupling between anatomical capacity (gsmax) and actual conductance (gs, gse) under heat, plastic shifts toward smaller, denser stomata, and identified 125 QTL linked to stomatal traits, suggesting targets for breeding climate‑resilient wheat.