Researchers have found a way to control protein levels inside different tissues of a whole, living animal for the first time. The technological advance works while a nematode worm continues to live normally: eating, moving and growing as the system quietly adjusts protein levels inside the tissues of its body. The study paves the way for designing completely new experiments that were impossible to carry out with current techniques and unravel the molecular underpinnings of whole-body processes like ageing.

Powdery mildew poses a major threat to black currant production, yet some cultivars naturally withstand infection far better than others. This study reveals that resistant black currants deploy a multilayered defense system involving physical structures, specialized metabolites, and the assembly of protective microbial communities on leaf surfaces. By integrating metabolomics and phyllosphere microbiome profiling, the research identifies key leaf metabolites—such as salicylic acid, trans-zeatin, and griseofulvin—that help recruit beneficial bacteria and fungi linked to disease suppression. These metabolites also directly reduce pathogen growth. Together, these processes explain how resistant cultivars mount a coordinated defense that limits pathogen invasion and maintains plant health.

Tomato fruit size, a trait that strongly influences market value and yield, is governed by intricate developmental processes. This study uncovers a previously unknown translational regulatory pathway mediated by the RNA-binding protein SlRBP1. Through fruit-specific gene manipulation, researchers show that SlRBP1 is essential for normal cell division and expansion within the tomato pericarp. The findings reveal that SlFBA7 and SlGPIMT are direct downstream gene targets whose translation is controlled by SlRBP1, and silencing either gene produces small fruits similar to SlRBP1-suppressed plants. This work highlights translational regulation as a key but underexplored mechanism for improving fruit size and overall productivity.

This study leverages advanced genomics and machine learning to refine the understanding of key fruit quality traits in peaches. Using whole-genome resequencing data from an F1 progeny of two distant peach cultivars, the researchers constructed an ultra-high-density genetic map, identifying key quantitative trait loci (QTLs) for traits such as fruit shape, color, and maturity. Notably, the study introduces machine learning models for more accurate phenotyping of fruit color, revealing two previously undetectable QTLs for peach flesh color variation. These innovations provide a new framework for precision breeding, enhancing peach quality and other complex traits through improved mapping and phenotyping strategies.