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 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 authors introduce S²-PepAnalyst, a web-based tool that leverages plant-specific datasets and advanced machine learning to predict small signaling peptides (SSPs) with 99.5% accuracy and minimal false negatives. By integrating protein language models, geometric‑topological analysis, and reinforcement learning, the tool surpasses existing predictors such as SignalP 6.0 in classifying peptide families like CLE and RALF.