Carmona, M. | Search bioRxiv

bioRxiv2021

Stress-dependent inhibition of cell polarity through unbalancing the GEF/GAP regulation of Cdc42

Cdc42 rules cell polarity and growth in fission yeast. It is negatively and positively regulated by GTPase-activating proteins (GAPs) and by Guanine nucleotide Exchange factors (GEFs), respectively. Active Cdc42-GTP localizes to the poles, where it associates with numerous proteins constituting the polarity module. However, little is known about its down-regulation. We describe here that oxidative stress causes Sty1 kinase-dependent Cdc42 inactivation at cell poles. Both the amount of active Cdc42 at poles and cell length inversely correlate with Sty1 activity, explaining the elongated morphology of{Delta} sty1 cells. We have created stress-blinded cell poles by either eliminating two Cdc42 GAPs or through the constitutive tethering of a GEF to the cell tips, and biochemically demonstrate that Rga3 is a direct substrate of Sty1. We propose that stress-activated Sty1 promotes GTP hydrolysis and prevents GEF activity at the cell tips, thus leading to the inhibition of Cdc42 and polarized growth cessation.

cell biology↗

bioRxiv2020

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published GWAS trait-associated loci

Genome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published human GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

genetics↗