The study integrated genetic architecture derived from maize GWAS into phenotypic simulations of hybrid populations, using ≥200 top GWAS hits and adjusting marker effect sizes, which increased the correlation between simulated and empirical trait data across environments (r = 0.397–0.915). These informed simulations produced realistic trait distributions and genomic prediction results that closely matched empirical observations, demonstrating improved utility for digital breeding programs.

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.

Populus VariantDB v3.2 is a searchable database that catalogs sequence polymorphisms across heterozygous poplar genomes, enabling precise design of guide RNAs and primers for CRISPR genome editing in outcrossing tree species. The tool accepts short sequence inputs, supports multiple Populus genomes (including hybrids), and provides customizable search parameters to improve gRNA/primer design accuracy. Its platform‑agnostic and expandable architecture makes it broadly applicable to sequence‑sensitive functional genomics studies.

The study compared physiological, ion‑balance, and metabolic responses of two maize inbred lines—salt‑sensitive C68 and salt‑tolerant NC326—under salinity stress. Untargeted metabolomics identified 56 metabolites and, together with genetic analysis, linked 10 candidate genes to key protective metabolites, revealing constitutive and inducible mechanisms of salt tolerance.

The study models maize flowering time plasticity using a physiological reaction norm derived from multi-environment trial data, revealing genotype-specific differences in temperature-driven development and photoperiod perception. It introduces an envirotyping metric that shows genotypes can experience markedly different photoperiods even within the same environment, and demonstrates distinct adaptive strategies between tropical and temperate germplasm.

The study evaluated 11 intermediate phenotypes linked to stalk lodging resistance in a diverse panel of 566 maize (Zea mays L.) inbred lines across four environments, preserving individual stalk identity to capture plant-level variation. This high-density phenotypic dataset enabled statistical genomics, predictive modeling, and machine learning to uncover genetic factors underlying lodging resistance, offering insights applicable to other grass species.

The study generated a temporal physiological and metabolomic map of leaf senescence in diverse maize inbred lines differing in stay‑green phenotype, identifying 84 metabolites associated with senescence and distinct metabolic signatures between stay‑green and non‑stay‑green lines. Integration of metabolite data with genomic information uncovered 56 candidate genes, and reverse‑genetic validation in maize and Arabidopsis demonstrated conserved roles for phenylpropanoids such as naringenin chalcone and eriodictyol in regulating senescence.