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 performed daily large-scale glycome, transcriptome, and proteome profiling of developing fibers from the two cultivated cotton species, Gossypium barbadense and G. hirsutum, across primary and secondary cell wall stages. It identified delayed cellulose accumulation and distinct compositions of xyloglucans, homogalacturonans, rhamnogalacturonan‑I, and heteroxylans in G. barbadense, along with higher expression of specific glycosyltransferases and expansins, suggesting these molecular differences underlie the superior fiber length and strength of G. barbadense.

The study evaluated how acute heat stress affects early-stage rice seedlings, identifying a critical temperature threshold that impairs growth. Transcriptomic profiling of shoots and roots revealed ethylene‑responsive factors (ERFs) as central regulators, with ethylene and jasmonic acid acting upstream, and pre‑treatment with these hormones mitigated heat damage. These findings highlight ERF‑hormone interaction networks as targets for improving rice heat resilience.

Using the Euphorbia peplus genome, the authors performed organ‑specific transcriptomic profiling of the cyathium and combined it with gene phylogenies and dN/dS analysis to investigate floral‑development gene families. They found distinct SEP1 paralog expression, lack of E‑class gene duplications typical of other pseudanthia, and divergent expression patterns for CRC, UFO, LFY, AP3, and PI, suggesting unique developmental pathways in Euphorbia.

The study shows that heatwaves impair the ability of apple (Malus domestica) to mount ASM‑induced immunity against fire blight and apple scab, leading to a loss of protective gene expression. Transcriptomic analysis revealed a broad suppression of ASM‑regulated defense and other biological processes under high temperature, identifying thermo‑sensitive resistance and susceptibility marker genes. The findings highlight that elevated temperature both weakens plant defenses and creates a more favorable environment for pathogens.

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 identified a major QTL (qDTH3) on chromosome 3 responsible for a 7‑10‑day earlier heading phenotype in the rice line SM93, using QTL‑seq, KASP genotyping, association mapping, and transcriptomic analysis to fine‑map the locus to a 2.53 Mb region and pinpoint candidate genes. SNP markers linked to these genes were proposed as tools for breeding early‑maturing, climate‑resilient rice varieties.

The study compares transcriptional, proteomic, and metabolomic responses of wild‑type Arabidopsis and a cyp71A27 mutant to a plant‑growth‑promoting Pseudomonas fluorescens strain and a pathogenic Burkholderia glumeae strain, revealing distinct reprogramming and an unexpected signaling role for the non‑canonical P450 CYP71A27. Mutant analysis showed that loss of CYP71A27 alters gene and protein regulation, especially during interaction with the PGP bacterium, while having limited impact on root metabolites and exudates.