The study examined how soil phosphorus and nitrogen availability influence wheat root-associated arbuscular mycorrhizal fungal (AMF) communities and the expression of mycorrhizal nutrient transporters. Field sampling across two years combined with controlled pot experiments showed that P and N jointly affect AMF colonisation, community composition (with Funneliformis dominance under high P), and regulation of phosphate, ammonium, and nitrate transporters. Integrating metabarcoding and RT‑qPCR provides a framework to assess AMF contributions to crop nutrition.

The study compared aphid resistance and Barley Yellow Dwarf Virus (BYDV) transmission among three wheat varieties (G1, RGT Wolverine, RGT Illustrious). G1 emits the repellent 2‑tridecanone, restricts aphid phloem access, and shows reduced BYDV transmission, whereas RGT Wolverine limits systemic viral infection despite high transmission efficiency. The authors suggest breeding the two resistance mechanisms together for improved protection.

The study investigated whether wheat homoeologous genes actively compensate for each other when one copy acquires a premature termination codon (PTC) mutation. By analyzing mutagenised wheat lines, the authors found that only about 3% of cases exhibited upregulation of the unaffected homoeolog, indicating that widespread active transcriptional compensation is absent in wheat.

The study used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

Overexpression of the wheat bHLH transcription factor TaPGS1 leads to increased flavonol accumulation in the seed coat, which disrupts polar auxin transport and causes localized auxin accumulation, delaying endosperm cellularization and increasing cell number, thereby enlarging grain size. Integrated metabolomic and transcriptomic analyses identified upregulated flavonol biosynthetic genes, revealing a regulatory module that links flavonol-mediated auxin distribution to seed development in wheat.

The study evaluated how alginate oligosaccharide (AOS) chain length influences the levels of seven key phytohormones in wheat seedlings challenged with Botrytis cinerea. Hormone profiling revealed that mid‑range oligomers (DP 4‑6) most strongly up‑regulate defense‑related hormones (JA, SA, ABA, CTK), whereas longer oligomers (DP 7) most effectively suppress ethylene. These findings suggest that tailoring AOS polymerization can optimize disease resistance and growth in cereal crops.

The study used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.

The study introduces ENTRAP-seq, a high‑throughput in‑planta assay that couples protein‑coding libraries with a nuclear magnetic sorting‑based reporter to multiplexively assess transcriptional regulatory activity of thousands of protein variants. Using this platform and machine‑learning analysis, the authors screened 1,495 plant viral proteins, uncovering numerous novel regulatory domains, and applied machine‑guided, semi‑rational design to modify the activity of a plant transcription factor.

The study used phylogeny‑based analyses of 36 legume genomes and a newly created multiparent advanced generation intercross (MAGIC) population of common bean to predict and characterize genome‑wide deleterious mutations. Machine‑learning integration of conservation and protein features identified thousands of potentially deleterious sites, whose variation correlated negatively with flowering time, maturity, and yield, highlighting the impact of genetic load on breeding performance.

The authors introduce S²-PepAnalyst, a web-based tool that leverages plant-specific datasets and advanced machine learning to predict small signaling peptides (SSPs) with 99.5% accuracy and minimal false negatives. By integrating protein language models, geometric‑topological analysis, and reinforcement learning, the tool surpasses existing predictors such as SignalP 6.0 in classifying peptide families like CLE and RALF.