The study used transcriptomic profiling to compare tomato (Solanum lycopersicum) responses to three evolutionarily distant pathogens—nematodes, aphids, and oomycetes—during compatible interactions, identifying differentially expressed genes and key host hubs. Integrating public datasets and performing co‑expression and GO enrichment analyses, the authors mapped shared dysregulation clusters and employed Arabidopsis interactome data to place tomato candidates within broader networks, highlighting potential targets for multi‑pathogen resistance.

The study utilizes a large collection of fluorescently marked Ds-GFP insertional mutations in haploid maize pollen to link gene disruptions with quantitative fitness effects measured as transmission deviations. By integrating genome-derived features (e.g., codon usage) and expression profiling into interpretable machine learning models, they achieve high predictive performance (auROC >90%) for genes influencing pollen fitness, highlighting expression specificity as a key predictor.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study used paired whole‑genome bisulphite sequencing and RNA‑seq on wheat landraces to investigate how DNA methylation patterns change during drought stress, revealing antagonistic trends across cytosine contexts and a key demethylation role for ROS1a family members. Gene‑body methylation correlated positively with expression but negatively with stress‑responsive changes, while drought‑induced hyper‑methylation of specific transposable elements, especially the RLX_famc9 LTR retrotransposon, appears to modulate downstream gene regulation via siRNA precursors.

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 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 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 adapted high‑throughput transposable‑element sequencing and introduced the deNOVOEnrich pipeline to map somatic TE insertions in Arabidopsis thaliana, uncovering ~200,000 new events across wild‑type and epigenetic mutant lines. Somatic integration is non‑random and TE‑specific, with families like ONSEN, EVADE, and AtCOPIA21 preferentially targeting chromosomal arms, genic regions, and chromatin marked by H2A.Z, H3K27me3, and H3K4me1, especially near environmentally‑responsive genes such as resistance loci and biosynthetic clusters.