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 authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study investigates late‑stage effector genes of the fungus Leptosphaeria maculans that infect Brassica napus stems, assessing whether these effectors are more conserved than early‑stage ones and thus may confer more durable resistance. Six candidate late effectors were selected and screened across an expanded set of semi‑winter B. napus genotypes, revealing new resistance sources predominantly within this genetic pool, supporting the hypothesis of greater stability of late effectors.

The study presents an optimized Agrobacterium-mediated transformation protocol for bread wheat that incorporates a GRF4‑GIF1 fusion to enhance regeneration and achieve genotype‑independent transformation across multiple cultivars. The approach consistently improves transformation efficiency while limiting pleiotropic effects, offering a versatile platform for functional genomics and gene editing in wheat.

The study employed deep learning–based image analysis to monitor 6,889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars in field conditions, generating over 27,000 precise measurements of lesion growth. Lesion expansion was found to be a significant, moderately heritable component of quantitative resistance (QR) in most cultivars, and its variation correlated strongly with overall QR after exclusion of an outlier. These findings highlight lesion growth as a valuable target for breeding durable QR in wheat.