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 generated the first single‑nucleus RNA‑sequencing dataset of tomato (Solanum lycopersicum) roots colonized by the arbuscular mycorrhizal fungus Rhizophagus irregularis, revealing distinct transcriptional programs in epidermal and cortical cells across stages of arbuscule development. Using unsupervised subclustering and a Motif‑Informed Network Inference (MINI‑EX) approach, the authors identified candidate transcription factors that may coordinate cell‑cycle reactivation and nutrient integration during symbiosis, offering a resource for future functional genetics.

The study assessed three savory essential oil–based formulations for controlling early blight caused by Alternaria solani in tomato, finding that formulation CC2020 most effectively reduced disease severity in both in vitro and greenhouse trials. CC2020 also helped maintain tomato fruit vitamin C levels and lowered fungal melanin production, indicating dual benefits for disease suppression and fruit quality.

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 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 shows that maize plants carrying autophagy-defective atg10 mutations exhibit delayed flowering and significant reductions in kernel size, weight, and number, culminating in lower grain yield. Reciprocal crossing experiments reveal that the maternal genotype, rather than the seed genotype, primarily drives the observed kernel defects, suggesting impaired nutrient remobilization from maternal tissues during seed development.

Using the bryophyte Physcomitrium patens, the study shows that loss of autophagy enhances auxin‑driven caulonemata differentiation and colony expansion under low nitrogen or imbalanced carbon/nitrogen conditions, accompanied by higher internal IAA, reduced PpPINA expression, and up‑regulated RSL transcription factors. Autophagy appears to suppress auxin‑induced differentiation during nutrient stress, acting as a hub that balances metabolic cues with hormonal signaling.

The study examined the roles of AtKUP2, AtKUP6, AtKUP8, and GORK potassium transport proteins in guard cell function by performing gas-exchange measurements on mature Arabidopsis leaves. Loss of KUP2/6/8 reduced stomatal conductance, whereas a GORK loss‑of‑function mutant showed increased conductance, yet the magnitude of light‑ and ABA‑induced transpiration changes remained similar across genotypes, suggesting a limited dynamic range for rapid stomatal movements that relies on small ionic osmolytes.