The authors used computational simulations of plant cellular metabolism under historical atmospheric conditions to demonstrate that reduced CO₂ and increased aridity can drive the evolutionary transition from C₃ to CAM photosynthesis. Their results suggest that while future elevated CO₂ may favor a reversion to C₃-like behavior, drought consistently promotes CAM regardless of CO₂ or temperature, and a minimum O₂ level is required for nocturnal respiration in CAM.

The study employed immunofluorescence labeling and fluorescence intensity quantification to examine tissue-specific cellular modifications in plants under drought stress, revealing targeted alterations in proteoglycans, polysaccharides, and AGPs in leaves and roots. These findings highlight the importance of in planta analyses for accurately capturing stress-induced structural changes.

The study examined how osmotic stress and cytosolic Ca²⁺ signaling regulate autophagy in plants by monitoring the dynamics of RFP‑tagged ATG8i. Both stimuli altered the accumulation of RFP‑ATG8i‑labeled autophagosomes in an organ‑specific way, and colocalization with the ER marker HDEL indicated that ATG8i participates in ER‑phagy during stress.

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 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 engineered plastid-targeted genetically encoded biosensors to visualize several phosphoinositide species (PI3P, PI4P, PI5P, PI(4,5)P2, PI(3,5)P2) within chloroplasts and confirmed their specificity via immunological assays. Co‑expression with PPI‑modifying enzymes revealed altered biosensor distribution, an association between PI3P and the chloroplast protein VIPP1, and that elevated PPI levels under stress change localization and increase drought sensitivity, highlighting PPIs in plant stress responses.

The study reveals that the bacterial pathogen Pseudomonas syringae suppresses host plant translation by targeting processing bodies (P‑bodies) through two liquid-like effectors, linking this repression to the ER stress response. It further demonstrates that autophagic clearance of P‑bodies is essential for balancing translationally active and inactive mRNAs, uncovering new connections among translation, ER stress, and autophagy during plant immunity.