Woo, I. | Search bioRxiv

bioRxiv2026

Ume6 Coordinates Lysosomal Remodeling and Autophagy Through Transcriptional Repression in S. cerevisiae

Lysosome function and autophagy are highly regulated processes that must be coordinated to enable cellular adaptation to stress. However, the transcriptional mechanisms linking these pathways remain incompletely understood. Here, we identify Ume6 as a transcriptional repressor that regulates vacuolar protein composition and autophagy in Saccharomyces cerevisiae (budding yeast). Dysregulation of Ume6 alters vacuolar protein composition through two distinct mechanisms: increased abundance of multiple vacuolar components and enhanced degradation of selected vacuolar membrane proteins by elevated expression of the vacuolar E3 ubiquitin ligases. UME6 deletion also increases autophagic activity, whereas UME6 overexpression delays autophagy induction. Furthermore, we find that Ume6 coordinates with another transcriptional repressor, Rph1, and its regulatory activity is regulated by multiple protein kinases, including Rim15, Rim11, and Mck1. Together, our study reveal a broad transcriptional regulatory mechanism through which Ume6 coordinates vacuolar protein composition and autophagy, providing new insights into the regulation of lysosome-related functions in response to cellular stress.

cell biology↗

bioRxiv2026

A scalable approach to resolving variants of uncertain significance

Over 90% of missense variants across [~]4,000 disease-associated genes are variants of uncertain significance (VUS). Experimental variant effect measurements provide critical evidence about pathogenicity and inform disease biology, but most variants lack data and clinical translation has been limited. The Impact of Genomic Variation on Function Consortium generated experimental data for 62,215 variants across ten genes using multiplexed assays and 1,407 variants across 163 genes using arrayed assays, curated 193,139 additional community-generated variant effect measurements across 30 additional genes, and developed automated calibration methods for translating experimental data and variant effect predictions into clinical evidence. To reduce current VUS, we developed a scalable workflow using only experimental and predictive evidence, enabling reclassification of 75% of the 16,115 VUS in these genes as pathogenic or benign with <1% error. To minimize future VUS, we analyzed >90,000 unobserved variants; 62% had enough evidence to be "preclassified" as pathogenic or benign. We validated our data, evidence and classifications using All of Us and created interactive resources to enable clinical use of the calibrated data. Thus, for 40 genes, representing 1% of the clinical genome, we resolve most existing VUS and future variants, illustrating how systematic use of scalable evidence can empower genomic medicine.

genomics↗