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.

The study investigates how miR394 influences flowering time in Arabidopsis thaliana by combining transcriptomic profiling of mir394a mir394b double mutants with histological analysis of reporter lines. Bioinformatic analysis identified a novel lncRNA overlapping MIR394B (named MIRAST), and differential promoter activity of MIR394A and MIR394B suggests miR394 fine‑tunes flower development through transcription factor and chromatin remodeler regulation.