The study investigates the gene regulatory network (GRN) controlling flowering time in the allotetraploid crop Brassica napus by comparing its transcriptome to that of Arabidopsis thaliana. While most orthologous gene pairs show conserved expression dynamics, several flowering‑time genes display regulatory divergence, especially under cold conditions, indicating subfunctionalisation among paralogues. Despite these differences, the overall GRN topology remains similar to Arabidopsis, likely due to retention of multiple paralogues.

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 study generated a dataset of 420 sgRNAs targeting promoters, exons, and introns of 137 tomato genes in protoplasts, linking editing efficiency to chromatin accessibility, genomic context, and sequence features. Open chromatin sites showed higher editing rates, while transcriptional activity had little effect, and a subset of guides produced near‑complete editing with microhomology‑mediated deletions. Human‑trained prediction models performed poorly, highlighting the need for plant‑specific guide design tools.

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 comparative transcriptomics of dorsal and ventral petals across development, alongside expression profiling in floral symmetry mutants, to identify genes linked to dorsal (AmCYC-dependent) and ventral (AmDIV-dependent) identities in Antirrhinum majus. In situ hybridisation validated axis‑specific and boundary‑localized expression patterns, revealing that a conserved NGATHA‑LIKE1‑BRASSINAZOLE‑RESISTANT1‑miR164 module has been co‑opted to regulate AmDIV targets and shape the corolla. These findings delineate regulatory modules coordinating dorsoventral and proximal‑distal patterning in zygomorphic flowers.

The study sequenced genomes of ericoid mycorrhiza‑forming liverworts and experimentally reconstituted the symbiosis, revealing a nutrient‑regulated state that supports intracellular colonization. Comparative transcriptomics identified an ancestral gene module governing intracellular symbiosis, and functional validation in Marchantia paleacea through genetic manipulation, phylogenetics, and transactivation assays confirmed its essential role. The findings suggest plants have retained and independently recruited this ancestral module for diverse intracellular symbioses.

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 integrates genome, transcriptome, and chromatin accessibility data from 380 soybean accessions to dissect the genetic and regulatory basis of symbiotic nitrogen fixation (SNF). Using GWAS, TWAS, eQTL mapping, and ATAC-seq, the authors identify key loci, co‑expression modules, and regulatory elements, and validate the circadian clock gene GmLHY1b as a negative regulator of nodulation via CRISPR and CUT&Tag. These resources illuminate SNF networks and provide a foundation for soybean improvement.