The study utilizes a large collection of fluorescently marked Ds-GFP insertional mutations in haploid maize pollen to link gene disruptions with quantitative fitness effects measured as transmission deviations. By integrating genome-derived features (e.g., codon usage) and expression profiling into interpretable machine learning models, they achieve high predictive performance (auROC >90%) for genes influencing pollen fitness, highlighting expression specificity as a key predictor.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study reveals that rice perceives Xanthomonas oryzae pv. oryzae outer membrane vesicles through a rapid calcium signal that triggers plasma‑membrane nanodomain formation and the re‑organisation of defence‑related proteins, establishing an early immune response. Without this Ca2+ signal, OMVs are not recognized and immunity is weakened.

The study compares the iron-poor oceanic diatom Thalassiosira oceanica with the iron-rich coastal species T. pseudonana to uncover how diatoms adapt to low-iron conditions. Using photo‑physiological measurements, proteomic profiling, and focused ion beam scanning electron microscopy, the researchers show that each species remodels chloroplast compartments and exhibits distinct mitochondrial architectures to maintain chloroplast‑mitochondrial coupling under iron limitation.

The study demonstrates that the plastid chaperone CLPC2, but not its paralogue CLPC1, is essential for Arabidopsis responsiveness to microbial volatile compounds and for normal seed and seedling development. Loss of CLPC2 alters the chloroplast proteome, affecting proteins linked to growth, photosynthesis, and embryogenesis, while overexpression of CLPC2 mimics CLPC1 deficiency, highlighting distinct functional roles within the CLP protease complex.

The study investigated how barley (Hordeum vulgare) adjusts mitochondrial respiration under salinity stress using physiological, biochemical, metabolomic and proteomic approaches. Salt treatment increased respiration and activated the canonical TCA cycle, while the GABA shunt remained largely inactive, contrasting with wheat responses.

The study benchmarked over 20 web‑based gRNA on‑target efficiency prediction tools against an experimental plant CRISPR editing dataset, finding several machine‑learning based tools whose scores strongly correlated with observed InDel frequencies. Additionally, the performance of popular platforms such as CRISPOR and CRISPR‑P was assessed, offering guidance for improved gRNA design in plant genome editing.

The study generated deep proteome and phosphoproteome datasets from guard cell‑enriched tissue to examine how phosphorylation regulates stomatal movements. Comparative analysis revealed increased phosphorylation of endomembrane trafficking and vacuolar proteins in closed stomata, supporting a role for phospho‑regulated trafficking in stomatal dynamics.

The study examined seed maturation and germination in the endangered legume Paubrasilia echinata using proteomic and polyamine analyses at 4, 6, and 8 weeks post-anthesis, identifying over 2,000 proteins and linking specific polyamines to developmental stages. Mature seeds (6 weeks) showed elevated proteasome components, translation machinery, LEA proteins, and heat shock proteins, while polyamine dynamics revealed putrescine dominance in early development and spermidine/spermine association with desiccation tolerance and germination. These findings uncover dynamic molecular shifts underlying seed development and provide insights for conservation and propagation.

The study evaluates the use of single-cell RNA sequencing (scRNA-seq) data to predict plant metabolic pathway genes (MPGs) in Arabidopsis thaliana, comparing five multi-label machine‑learning algorithms against traditional bulk RNA‑seq approaches. scRNA‑seq generated co‑expression networks that, while different, yielded significantly higher MPG classification accuracy, especially when data were split by genetic background or tissue type, and deep learning outperformed classical methods. The authors conclude that scRNA‑seq offers superior predictive power and should be incorporated into future MPG discovery pipelines.