The study measured how plant mass relates to growth rate across 195 European winter wheat cultivars under greenhouse conditions, revealing genetic variation in allometric scaling linked to leaf allocation and development speed. A genetic association with the Photoperiod response-1 (Ppd-1) gene connected greenhouse allometry to genotype‑by‑environment interactions affecting grain yield in field trials, highlighting the agronomic relevance of growth allometry.

The study examined over six decades of USDA Hard Red Spring Wheat Uniform Regional Nursery data to quantify genetic gains in key agronomic traits. It found a modest positive genetic gain of 0.61% per year for grain yield, with stable grain protein levels despite a negative yield‑protein correlation, and highlighted varying gains among breeding programs, especially a ~1% per annum increase in Minnesota's public program.

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 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.