Phosphite (Phi) and phosphate (Pi) share the same root uptake system, but Phi acts as a biostimulant that modulates plant growth and disease resistance in a species‑ and Pi‑dependent manner. In Arabidopsis, Phi induces hypersensitive‑like cell death and enhances resistance to Plectosphaerella cucumerina, while in rice it counteracts Pi‑induced susceptibility to Magnaporthe oryzae and Fusarium fujikuroi, accompanied by extensive transcriptional reprogramming.

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 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.

Mutations in the plastid division gene PARC6 and the granule initiation gene BGC1 were combined to generate wheat plants with dramatically enlarged A-type starch granules, some exceeding 50 µm, without affecting plant growth, grain size, or overall starch content. The parc6 bgc1 double mutant was evaluated in both glasshouse and field trials, and the giant granules displayed altered viscosity and pasting temperature, offering novel functional properties for food and industrial applications.

The researchers performed a large‑scale field trial with 105 wheat (Triticum aestivum) genotypes inoculated by Fusarium culmorum, combining quantitative deoxynivalenol (DON) profiling and untargeted metabolomics to uncover molecular signatures of infection. Sesquiterpene‑derived metabolites tracked toxin accumulation, whereas glycosylated diterpene conjugates were enriched in low‑DON samples, indicating a potential defensive metabolic pathway.

The study integrates multi-omics data from six Sorghum bicolor accessions under field drought to link RNA covalent modifications (RCMs) with photosynthetic performance, identifying the enzyme SbDUS2 that produces dihydrouridine (DHU) on transcripts. Loss‑of‑function dus2 mutants in Arabidopsis thaliana reveal that DHU deficiency leads to hyperstability of photosynthesis‑related mRNAs, impairing germination, development, and stress‑induced CO2 assimilation. The authors propose DHU as a post‑transcriptional mark that promotes rapid mRNA turnover during abiotic stress, enhancing plant resilience.

A meta‑analysis of 73 studies on cucumber (Cucumis sativus) under elevated CO₂ (eCO₂) revealed that eCO₂ significantly increased net photosynthetic rate (+56.31%), biomass (+27.75%) and yield (+21.98%), while reducing stomatal conductance (‑36.07%) and transpiration (‑30.42%). The authors recommend maintaining eCO₂ levels between 800–1200 ppm together with higher light, temperature, optimal humidity, and adequate fertilization to optimise greenhouse cucumber production under climate‑change scenarios.

The study investigates the wheat Pm3 NLR allelic series, revealing that near-identical Pm3d and Pm3e alleles confer broad-spectrum resistance by recognizing multiple, structurally diverse powdery mildew effectors. Using chimeric NLR constructs, the authors pinpoint specificity-determining polymorphisms and demonstrate that engineered combinations of Pm3d and Pm3e further expand effector recognition, showcasing the potential for durable wheat protection through NLR engineering.

The study introduces an enhanced crosslinking mass spectrometry workflow that preserves native protein interactions within functional thylakoid membranes of Arabidopsis and spinach, while electron transport remains active. Mapping the obtained crosslinks to known structures validates complex integrity and reveals novel assemblies, facilitating in situ exploration of photosynthetic membrane protein networks.

The study examined how microclimatic factors influence leaf morphology and photosynthetic productivity in Arctostaphylos crustacea ssp. crustacea across two chaparral sites in California, finding that higher light and lower soil moisture increased leaf mass per area, leaf angle steepness, and photosynthetic rates. Linear mixed‑model analysis identified light level as the strongest predictor, with vapor pressure deficit, soil moisture, leaf temperature, and leaf angle also contributing, highlighting the role of combined microclimatic interactions in driving intraspecific trait variation.