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Biology subjects

Di Donato, S.

Publications and source records attributed to Di Donato, S..

2 recordsLinked to original sources

Molecular glues that inhibit specific Zn2+-dependent DUB activity and inflammation

Deubiquitylases (DUBs) are crucial in cell signalling and are often regulated by interactions within protein complexes. The BRCC36 isopeptidase complex (BRISC) regulates inflammatory signalling by cleaving K63-linked polyubiquitin chains on Type I interferon receptors (IFNAR1). As a Zn2+-dependent JAMM/MPN DUB, BRCC36 is challenging to target with selective inhibitors. We discovered first-in-class inhibitors, termed BRISC molecular glues (BLUEs), which stabilise a 16-subunit BRISC dimer in an autoinhibited conformation, blocking active sites and interactions with the targeting subunit SHMT2. This unique mode of action results in selective inhibition of BRISC over related complexes with the same catalytic subunit, splice variants and other JAMM/MPN DUBs. BLUE treatment reduced interferon-stimulated gene expression in cells containing wild type BRISC, and this effect was absent when using structure-guided, inhibitor-resistant BRISC mutants. Additionally, BLUEs increase IFNAR1 ubiquitylation and decrease IFNAR1 surface levels, offering a potential new strategy to mitigate Type I interferon-mediated diseases. Our approach also provides a template for designing selective inhibitors of large protein complexes by promoting, rather than blocking, protein-protein interactions.

biochemistry↗

Sensitive tumor detection, accurate quantification, and cancer subtype classification using low-pass whole methylome sequencing of plasma DNA

The analysis of circulating tumor DNA (ctDNA) is increasingly used for monitoring disease in patients with metastatic cancer. Here, we introduce a robust and reproducible strategy combining low-pass whole methylome sequencing of plasma DNA with METER, a novel computational tool. Engaging prediction models trained on independent available datasets, METER enables the detection and quantification of tumor content (TC) and performs molecular cancer subtyping. Applied to plasma methylomes from early metastatic breast cancer patients, our method demonstrated reliable quantification, sensitive tumor detection below 3% of TC, and the ability to perform accurate Estrogen Receptor (ER) subtyping. METER provided clinically relevant predictions, underscored by associations with relevant prognostic factors, robust correlation with matched circulating tumor cells, and highly correlated with patients outcomes in challenging scenarios as TC<3%. Our strategy provides comprehensive and sensitive analysis of plasma samples, serving as a valuable yet cost-effective precision oncology tool.

cancer biology↗