Oct 2026· Theoretical and Applied Genetics· Vol 139· 0 citations· 63 references
Medicine
Abstract
We identified digenic control of leaf variegation in a barley mutant. Candidate genes, identified through whole-genome resequencing, are consistent with possible roles in chloroplast proteostasis. Variegation mutants provide valuable insights into chloroplast biogenesis. We characterized a newly identified variegated barley mutant, in which the phenotype is controlled by two epistatic recessive genes. The causal loci, var4 and var5, were mapped on chromosomes 2H and 3H. We used genotyping-by-sequencing, linkage mapping, and PACE genotyping, followed by whole-genome resequencing to identify candidate genes. Our two top candidate genes are an NBR1-like selective autophagy receptor gene and a DNAJ-domain-containing gene, respectively. Based on their homology-based functional annotation, both candidates could be implicated in chloroplast proteostasis, regulating protein import, folding, and/or degradation. While functional confirmation is still required, mild, independent defects in proteostasis from each mutation may act synergistically to surpass a functional threshold, impairing chloroplast development in early leaves while allowing partial recovery in later stages. These findings reveal a novel digenic basis for variegation in barley and identify candidate genes and pathways linked to chloroplast proteostasis.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.