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.

The study examined Xanthomonas strains causing bacterial spot on tomato in Oklahoma fields during 2018‑2019, revealing a shift from X. euvesicatoria pv. euvesicatoria (Xee) to X. euvesicatoria pv. perforans (Xep) race T4, which also expanded to pepper. Phenotypic assays and whole‑genome sequencing highlighted differences in race composition, host range, copper sensitivity, and effector repertoires, and identified a novel species, Xanthomonas oklahomensis.

The authors conducted a comprehensive phylogenetic and sequence analysis of the conserved YUCCA (YUC) gene family across representative plant lineages, classifying the family into six major classes and 41 subclasses. They linked YUC diversification to protein sequence conservation and spatial/temporal gene expression patterns, providing a framework for future functional investigations of auxin biosynthesis.

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 analyzes ancient maize genomes from a 500–600 BP Bolivian offering and compares them with 16 archaeological samples spanning 5,000 years and 226 modern Zea mays lines, revealing close genetic affinity to ancient Peruvian maize and increased diversity during Inca‑local interactions. Phylogenetic and phenotypic analyses of ovule development indicate targeted breeding for seed quality and yield, suggesting culturally driven selection was already established by the 15th century CE.

Using a barley pangenome of 76 genotypes and a pan‑transcriptome subset of 20, the study characterizes the diversity and evolutionary dynamics of CCT motif genes, uncovering novel frameshift variants and clade‑specific domain expansions. Phylogenetic and tissue‑specific expression analyses reveal functional divergence among paralogs, and the unexpected retention of the VRN2 repressor in spring barley suggests additional regulatory mechanisms beyond vernalization.

Phylogenetic analysis reveals that non‑seed plants, exemplified by the liverwort Marchantia polymorpha, possess a streamlined repertoire of cyclin and CDK genes, with only three cyclins active in a phase‑specific manner during vegetative development. Single‑cell RNA‑seq and fluorescent reporter assays, combined with functional overexpression studies, demonstrate the distinct, non‑redundant roles of MpCYCD;1, MpCYCA, and MpCYCB;1 in G1 entry, S‑phase progression, and G2/M transition, respectively.

The study utilizes explainable artificial intelligence (XAI) combined with machine learning to assess how inter‑annual weather variability influences oilseed sunflower yields across the United States from 1976 to 2022. Key climate predictors, especially summer maximum temperature and total precipitation, were identified, and predictive models were projected under various Shared Socioeconomic Pathways to 2080, revealing region‑specific yield declines.