The study investigates autophagy’s protective role against cadmium stress in Arabidopsis thaliana by comparing wild-type, atg5 and atg7 autophagy-deficient mutants, and ATG5/ATG7 overexpression lines. Cadmium exposure triggered autophagy, shown by ATG8a-PE accumulation, GFP-ATG8a fluorescence and ATG gene up-regulation, with atg5 mutants displaying heightened Cd sensitivity and disrupted metal ion homeostasis, whereas overexpression had limited impact. Genotype-specific differences between Col-0 and Ws backgrounds were also observed.

The study introduces a native‑condition method combining cell fractionation and immuno‑isolation to purify autophagic compartments from Arabidopsis, followed by proteomic and lipidomic characterisation of the isolated phagophore membranes. Proteomic profiling identified candidate proteins linked to autophagy, membrane remodeling, vesicular trafficking and lipid metabolism, while lipidomics revealed a predominance of glycerophospholipids, especially phosphatidylcholine and phosphatidylglycerol, defining the unique composition of plant phagophores.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study identifies the Arabidopsis phospholipases LCAT3 and LCAT4 as essential components that hydrolyze membranes of autophagic bodies within the vacuole, a critical step for autophagy completion. Double mutants lacking both enzymes accumulate autophagic bodies and display diminished autophagic activity, while in vivo reconstitution shows LCAT3 initiates membrane hydrolysis, facilitating LCAT4’s function.

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 extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

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 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.