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 examines how ectopic accumulation of methionine in Arabidopsis thaliana leaves, driven by a deregulated AtCGS transgene under a seed‑specific promoter, reshapes metabolism, gene expression, and DNA methylation. High‑methionine lines exhibit increased amino acids and sugars, activation of stress‑hormone pathways, and reduced expression of DNA methyltransferases, while low‑methionine lines show heightened non‑CG methylation without major transcriptional changes. Integrated transcriptomic and methylomic analyses reveal a feedback loop linking sulfur‑carbon metabolism, stress adaptation, and epigenetic regulation.

The study generated the first high-quality avocado pangenome from seven genetically diverse accessions using PacBio HiFi long‑read sequencing and partially phased, near chromosome‑level assemblies. Comparative analyses revealed lineage‑specific expansions and extensive diversity of NLR immune genes, with accessory regions enriched for pathogen response and secondary metabolism, providing a graph‑based framework for resistance breeding.

The study generated a watermelon super‑pangenome from 138 high‑quality, near‑gapless assemblies covering all seven species, uncovering ~1 million structural variants (SVs) and enabling precise SV genotyping across ~900 accessions. Incorporating SVs into genome‑wide association studies pinpointed a dosage‑dependent copy‑number variant affecting flesh color, and the comprehensive variant map supported accurate genomic prediction for 18 agronomic traits, providing a framework for precision breeding in watermelon.

The study constructs a graph-based cucumber pangenome from 39 high-quality assemblies, identifying 171,892 high-confidence structural variants (SVs) and genotyping them across 443 diverse accessions. Analyses reveal that SVs are more strongly purged than mildly deleterious SNPs during domestication and expansion, with gene flow contributing to increased SV burden in African and European populations, and demonstrate that incorporating SV burden improves genomic prediction of key agronomic traits.

The study shows that the SnRK1 catalytic subunit KIN10 directs tissue-specific growth‑defense programs in Arabidopsis thaliana by reshaping transcriptomes. kin10 knockout mutants exhibit altered root transcription, reduced root growth, and weakened defense against Pseudomonas syringae, whereas KIN10 overexpression activates shoot defense pathways, increasing ROS and salicylic acid signaling at the cost of growth.

The study generated high‑quality genome assemblies for 12 indica and japonica rice accessions and demonstrated that structural variants (SVs) are abundant and strongly associated with heterosis across 17 agronomic traits. Correlation analyses revealed that SV count between parental lines predicts hybrid performance, and functional validation of SVs in S5‑ORF5 and OsBZR1 confirmed their contributions to seed setting rate and yield heterosis, supporting an overdominance model for inter‑subspecific hybrid vigor.

The study combined long‑read whole‑genome assembly, multi‑omics profiling (DNA methylation, mRNA, ribosome‑associated transcripts, tRNA abundance, and protein levels) in two Arabidopsis thaliana accessions to evaluate how genomic information propagates through the Central Dogma. Codon usage in gene sequences emerged as the strongest predictor of both mRNA and protein abundance, while methylation, tRNA levels, and ribosome‑associated transcripts contributed little additional information under stable conditions.

The study performed a comprehensive computational analysis of the Arabidopsis thaliana proteome, classifying 48,359 proteins by melting temperature (Tm) and melting temperature index (TI) and linking thermal stability to amino acid composition, molecular mass, and codon usage. Machine‑learning and evolutionary analyses revealed that higher molecular mass and specific codon pairs correlate with higher Tm, and that gene duplication has driven the evolution of high‑Tm proteins, suggesting a genomic basis for stress resilience.