Garadi Suresh, H. | Search bioRxiv

bioRxiv2023

K29-linked unanchored polyubiquitin chains disrupt ribosome biogenesis and direct ribosomal proteins to the Intranuclear Quality control compartment (INQ)

Ribosome assembly requires precise coordination between the production and assembly of ribosomal components. Mutations in ribosomal proteins that inhibit the assembly process or ribosome function are often associated with Ribosomopathies, some of which are linked to defects in proteostasis. In this study, we examine the interplay between several yeast proteostasis enzymes, including deubiquitylases (DUBs), Ubp2 and Ubp14, and E3 ligases, Ufd4 and Hul5, and we explore their roles in the regulation of the cellular levels of K29-linked unanchored polyubiquitin (polyUb) chains. Accumulating K29-linked unanchored polyUb chains associate with maturing ribosomes to disrupt their assembly, activate the Ribosome assembly stress response (RASTR), and lead to the sequestration of ribosomal proteins at the Intranuclear Quality control compartment (INQ). These findings reveal the physiological relevance of INQ and provide insights into mechanisms of cellular toxicity associated with Ribosomopathies.

cell biology↗

bioRxiv2023

PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA, (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

bioinformatics↗