The study investigates the subfunctionalization of duplicated GIGANTEA paralogs in Petunia x hybrida, focusing on PhGI2's role in floral transition. While RNAi silencing of PhGI2 caused mixed vegetative and reproductive phenotypes, CRISPR/Cas9-generated loss‑of‑function alleles showed that PhGI2 specifically promotes flowering without affecting vegetative growth or scent. The findings reveal that PhGI2 is essential for maintaining circadian gene expression dynamics and regulating flowering time, separating vegetative growth from reproductive timing.

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 identifies MtFTb1 and MtFTb2 as essential, redundant regulators of long‑day flowering in the legume Medicago truncatula, demonstrating that they are required for up‑regulating MtFTa1 under vernalised long‑day conditions. Using CRISPR/Cas9‑generated single and double mutants, the authors show that double mutants are specifically delayed in flowering under long days while retaining vernalization responsiveness, and transcriptomic analyses reveal that MtFTb1/2 activate MADS‑box genes and other flowering regulators.

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 reveals that Arabidopsis CRK2 phosphorylates the callose synthases CALS1 and CALS3, influencing callose deposition at plasmodesmata and thereby affecting phloem loading and source‑to‑sink transport. Loss of CRK2 leads to starch accumulation in older leaves, a phenotype rescued by introducing functional CALS1 or CALS3 alleles, indicating that CRK2, CALS1, and CALS3 jointly regulate growth and development through control of intercellular transport.

The study investigates the roles of the receptor-like kinase CRK2 and the RNA-binding protein GRP7 in regulating gibberellin signaling and floral transition in Arabidopsis, using phenotypic analyses of single and double mutants. Exogenous gibberellic acid treatments and transcript profiling reveal that CRK2 and GRP7 jointly modulate GA-responsive pathways, highlighting a novel regulatory layer involving membrane kinases and RNA-binding proteins.

The study investigates how a small molecule that lengthens circadian period can quantitatively adjust the critical day length required for flowering in monocot plants. By modulating the clock's timing, the researchers provide evidence supporting the external coincidence model of photoperiodic control.

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.