The study applied limited proteolysis‑coupled mass spectrometry (LiP‑MS) to map JA‑protein interactions, validating known JA binders and uncovering novel candidates, including several UDP‑glucuronosyltransferases (UGTs). Functional omics, biochemical, enzymatic, and structural analyses demonstrated that two tomato UGTs glucosylate jasmonic acid, revealing a previously missing step in JA catabolism.

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.

The study assessed the impact of adding mammalian growth factors and cytokines to transformation media on CRISPR‑Cas9–mediated genome editing in six tomato (Solanum lycopersicum) accessions with varying regeneration capacities. Over three years, supplementation with these factors significantly increased regeneration rates and the production of stable secondary transgenic lines, especially in recalcitrant genotypes.

The researchers created tomato lines overexpressing the autophagy gene SlATG8f and evaluated their performance under high-temperature stress. qRT‑PCR and physiological measurements revealed that SlATG8f overexpression enhances expression of autophagy‑related and heat‑shock protein genes, accelerates fruit ripening, and improves fruit quality under heat stress.

Proteomic comparison of mock‑ and potato spindle tuber viroid‑infected tomato revealed a broad down‑regulation of nucleoporins and nuclear transport receptors, leading to impaired nuclear import of the immune regulator NPR1. Overexpression of NPR1 or treatment with a salicylic‑acid analog restored defense and reduced PSTVd infection, highlighting nuclear transport repression as a key vulnerability in plant immunity against viroids.

The study isolated an endophytic Pseudomonas aeruginosa strain (SPT08) from tomato cotyledon seedlings that suppressed the wilt pathogen Ralstonia pseudosolanacearum and promoted plant growth, increasing height by 20% and root biomass by 60%. GFP labeling confirmed endophytic colonization, and genomic analysis revealed multiple secretion systems and secondary‑metabolite gene clusters associated with biocontrol and growth‑promoting traits.

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 engineered the linear furanocoumarin pathway in tomato by integrating four biosynthetic genes, aiming to produce psoralen, but instead generated coumarins such as scopoletin. Morphophysiological, metabolomic, and transcriptomic analyses revealed that even low levels of these coumarins can influence plant growth and physiology, highlighting both benefits and costs of coumarin accumulation in crops.

The study investigates hormetic responses of tomato (Solanum lycopersicum) seedlings to low‑dose cadmium, demonstrating enhanced growth through morphological, biochemical, and histochemical analyses. Transcriptomic profiling revealed differential expression of oxidoreductase genes, signaling components, and several long non‑coding RNAs (lncRNAs) that generate miRNAs (sly‑MIR396a and sly‑MIR1063g), which modulate target genes to promote growth. In‑silico analyses of lncRNA targets and miRNA precursors provide mechanistic insight into cadmium‑induced hormesis and its potential for crop improvement.

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.