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 characterizes tissue-specific elimination of B chromosomes in Sorghum purpureosericeum during embryo development, identifying 28 candidate genes linked to this process. Integrated in situ visualization, genome sequencing, and transcriptomic analyses reveal that the B chromosome originates from multiple A chromosomes, harbors unique repeats, and expresses divergent kinetochore components that likely mediate its selective removal.

The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

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.

Using genome‑wide association studies in Arabidopsis thaliana, the authors identified the chromatin‑associated protein CDCA7 as a trans‑regulator that specifically controls CG methylation (mCG) and TE silencing. CDCA7 and its paralog CDCA7β bind the remodeler DDM1, modulating its activity without broadly affecting non‑CG methylation or histone variant deposition, and natural variation in CDCA7 regulatory sequences correlates with local ecological adaptation.

The study examined how prior light‑acclimation influences the fitness and rapid photoprotective reprogramming of Chlamydomonas during transitions between low and high diurnal light intensities. While high‑light‑acclimated cells struggled to grow and complete the cell cycle after shifting to low light, low‑light‑acclimated cells quickly remodeled thylakoid ultrastructure, enhanced photoprotective quenching, and altered photosystem protein levels, recovering chloroplast function within a single day. Transcriptomic and proteomic profiling revealed swift induction of stress‑response genes, indicating high flexibility in diurnal light acclimation.

The study demonstrates that the chromatin remodeler DDM1 is essential for biomass heterosis in Arabidopsis thaliana hybrids, as loss of DDM1 function leads to reduced rosette growth and extensive genotype‑specific transcriptomic and DNA methylation changes. Whole‑genome bisulfite sequencing revealed widespread hypomethylation in ddm1 mutants, while salicylic acid levels were found unrelated to heterosis, indicating that epigenetic divergence, rather than SA signaling, underpins hybrid vigor.

The study examined molecular responses in grapevine leaves with and without esca symptoms, using metabolite profiling, RNA‑seq and whole‑genome bisulfite sequencing. Metabolic and transcriptomic changes were confined to symptomatic leaves and linked to local DNA‑methylation alterations, while asymptomatic leaves showed distinct but overlapping methylation patterns, some present before symptoms, indicating potential epigenetic biomarkers for early disease detection.

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 GyrB3 as a novel nuclear factor that interacts with histone deacetylases to regulate transposable element silencing in plants, acting as a suppressor of IBM1 deficiency–induced epigenetic defects. Loss of GyrB3 reduces DNA methylation and increases H3 acetylation at TEs, demonstrating the importance of histone deacetylation for genome stability.