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.

The study presents a chromosome-level reference genome for the invasive fern Lygodium microphyllum and compares the transcriptomic and epigenomic profiles of its haploid gametophyte and diploid sporophyte phases, revealing differential regulation of developmental genes and similar methylation patterns across tissues. Base‑pair resolution methylome data and freezing‑stress experiments show that each life phase employs distinct molecular pathways for stress response, emphasizing the importance of considering both phases in invasive‑species management.

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.