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.

The study examined leaf pavement cell shape complexity across a natural European aspen (Populus tremula) population, using GWAS to pinpoint the transcription factor MYB305a as a regulator of cell geometry. Functional validation showed that MYB305a expression is induced by drought and contributes to shape simplification, with cell complexity negatively correlated with water-use efficiency and climatic variables of the genotypes' origin.

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.

A genome‑wide association study of 187 bread wheat genotypes identified 812 significant loci linked to 25 spectral vegetation indices under rainfed drought conditions, revealing a major QTL hotspot on chromosome 2A that accounts for up to 20% of variance in greenness and pigment traits. Candidate gene analysis at this hotspot uncovered stress‑responsive genes, demonstrating that vegetation indices are heritable digital phenotypes useful for selection and genetic analysis of drought resilience.

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 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 used untargeted metabolomics and QTL mapping in a tomato recombinant inbred line population to characterize root exudate composition and identify genetic loci controlling specific metabolites. It reveals domestication-driven changes in exudate profiles and links metabolic QTLs with previously reported microbial QTLs, suggesting a genetic basis for shaping the root microbiome.

The study cloned the resistance genes Rmo2 and Rwt7 from barley and wheat, revealing them as orthologous tandem kinase proteins (TKPs) with an N‑terminal heavy metal‑associated (HMA) domain. Domain‑swapping experiments indicated that the HMA domain dictates effector specificity, supporting a model of TKP diversification into paralogs and orthologs that recognize distinct pathogen effectors.