The study investigates the role of the chromatin regulator MpSWI3, a core subunit of the SWI/SNF complex, in the liverwort Marchantia polymorpha. A promoter mutation disrupts male gametangiophore development and spermiogenesis, causing enhanced vegetative propagation, and transcriptomic analysis reveals that MpSWI3 regulates genes controlling reproductive initiation, sperm function, and asexual reproduction, highlighting its ancient epigenetic role in balancing vegetative and reproductive phases.

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 integrated 16 Arabidopsis thaliana whole‑genome bisulfite sequencing datasets from 13 stress experiments using a unified bioinformatic pipeline to map common and stress‑specific DNA methylation changes. Differentially methylated regions varied by stress type and methylation context, with CG DMRs enriched in gene bodies and CHG/CHH DMRs in transposable elements, some of which overlapped loci prone to stable epimutations. Gene ontology and TE enrichment analyses highlighted shared stress pathways and suggest environmental stress can generate heritable epigenetic variation.

High-quality PacBio HiFi draft genome assemblies were generated for three Bouteloua species (B. curtipendula, B. gracilis, B. eriopoda) with >98.5% BUSCO completeness. Gene prediction with Helixer produced inflated gene counts likely reflecting polyploidy and fragmented predictions, and panEDTA identified 25–40% transposable-element content dominated by LTR retrotransposons. These assemblies provide foundational references for comparative genomics within PACMAD grasses.

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 how the bioinoculant Trichoderma afroharzianum T22 influences Pisum sativum growth under iron-sufficient versus iron-deficient conditions, finding pronounced benefits—enhanced photosynthesis, Fe/N accumulation, and stress‑related gene expression—only during iron deficiency. RNA‑seq revealed distinct gene expression patterns tied to symbiosis, iron transport, and redox pathways, and microbiome profiling showed T22 reshapes the root bacterial community under deficiency, suggesting context‑dependent mutualism.

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.