The study examined drought tolerance across nine provenances of the conifer Pinus nigra using high‑throughput phenotyping combined with metabolomic and transcriptomic analyses under controlled soil‑drying conditions. Drought tolerance, measured by the decline in Fv/Fm, varied among provenances but was not linked to a climatic gradient and was independent of growth, with tolerant provenances showing distinct flavonoid and diterpene profiles and provenance‑specific gene expression patterns. Integrating phenotypic and molecular data revealed metabolic signatures underlying drought adaptation in this non‑model conifer.

The study systematically identified heterosis-associated genes and metabolites in rice, functionally validated three genes influencing seedling length, and integrated these molecules into network modules to explain heterosis variance. Predominant additive and partially dominant inheritance patterns were linked to parental genomic variants and were shown to affect 17 agronomic traits in rice, as well as yield heterosis in maize and biomass heterosis in Arabidopsis. The work highlights the quantitative contribution of transcriptomic and metabolomic variation, especially in phenylpropanoid biosynthesis, to hybrid vigor.

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 examined five geographically diverse accessions of the hummingbird‑pollinated monkeyflower Mimulus cardinalis, revealing extensive variation in floral morphology, nectar composition, pigment biochemistry, and scent that influence pollinator perception. Integrating metabolomics, morphology, transcriptomics, and whole‑genome sequencing, the authors identified genetic differences underlying the independent evolution of yellow flowers at range edges. These findings highlight how climate, pollinator interactions, and multi‑trait diversification drive early stages of floral divergence.

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.

Phytoplasma infection in sesame (Sesamum indicum) triggers tissue-specific alterations in gene expression and metabolite composition, with floral organs adopting leaf-like traits and distinct changes in porphyrin, brassinosteroid, and phenylpropanoid pathways. Integrated transcriptomic and metabolomic analyses, supported by biochemical, histological, and qRT-PCR assays, reveal differential stress and secondary metabolite responses between infected leaves and flowers.

The study examined how simulated microgravity, using a 2-D clinostat, influences the metabolomic profile of the Starbor Kale (F1) cultivar, focusing on flavonoid content. Proton NMR revealed increased aromatic peaks, and HPTLC showed enhanced banding in medium- and high-polarity extracts, indicating elevated secondary metabolite production under microgravity conditions. These findings suggest kale is a promising candidate for space-based cultivation to mitigate astronaut health risks.

The study compared aquatic and terrestrial morphotypes of the invasive plant Ludwigia grandiflora subsp. hexapetala under aquatic and terrestrial conditions, measuring morphological traits, metabolomic and phytohormonal profiles at 14 and 28 days. Results showed the terrestrial morphotype has higher baseline morphological values, while the aquatic morphotype exhibits greater phenotypic plasticity, with plasticity indices changing over time, indicating pre‑adaptation and potential local adaptation.

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 used wood metatranscriptomics, metabolomics, and metabarcoding to compare grapevine (Vitis vinifera) responses to drought and esca leaf symptom expression, revealing distinct but overlapping transcriptomic and metabolic signatures, including activation of phenylpropanoid and stilbenoid pathways. Drought reduced esca symptom expression, associated with decreased abundance of the wood‑decay fungus Fomitiporia mediterranea and altered fungal virulence factor expression, while increasing the relative abundance and anti‑oxidative gene expression of Phaeomoniella chlamydospora.