Search bioRxiv⌕ Search

Biology subjects

Wagner, J. P.

Publications and source records attributed to Wagner, J. P..

2 recordsLinked to original sources

Understanding the Pathogenicity of Parkin Catalytic Domain Mutants

Mutations in the E3 ubiquitin ligase parkin cause a familial form of Parkinsons disease (PD). Parkin and the mitochondrial kinase PINK1 assure quality control of mitochondria through selective autophagy of mitochondria (mitophagy). Whereas numerous parkin mutations have been functionally characterized and their structural basis revealed, several pathogenic PD mutations found in the catalytic RING2 domain remain poorly understood. Here, we characterize two pathogenic RING2 mutants, T415N and P437L and shed light on the underlying structural causes. For this purpose, we use biochemical in vitro assays in combination with AlphaFold modeling. We demonstrate that both mutants exhibit impaired activity using autoubiquitination and ubiquitin vinyl sulfone assays. After determining the parkin minimal ubiquitin binding region, we show that both mutants display impaired binding to the ubiquitin molecule charged onto the E2 enzyme. Finally, we employ the most recent version of AlphaFold 3 to generate a structural model of the phospho-parkin/phospho-ubiquitin/ubiquitin-charged E2 complex. This model consolidates our findings and provides a structural understanding for the pathogenicity of these two parkin variants. A better understanding of the different PD mutations at the molecular level can pave the way for personalized treatments and the design of small molecule therapeutics for the treatment of PD.

biochemistry↗

Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.

bioinformatics↗