Cowley, K. J. | Search bioRxiv

bioRxiv2025

Local nucleolar surveillance mechanisms maintain rDNA stability

The nucleoli are subdomains of the nucleus that form around actively transcribed ribosomal RNA (rRNA) genes. The highly repetitive and transcribed nature of the rRNA genes (rDNA) by RNA polymerase I (Pol I) poses a challenge for DNA repair and replication machineries. Here, we profile the nucleolar proteome and the chromatin landscape of stalled replication sites upon rDNA damage to characterize the early steps of nucleolar DNA damage response (nDDR). We observed early dynamics in nucleolar-nucleoplasmic proteome localization and identified nucleolar replication stress signatures involving chromatin remodeling networks, transcription-replication conflicts and DNA repair. Our findings define localized surveillance mechanisms that activate the nDDR. Further, we identified that upon rDNA damage, nucleolar RNA Polymerase (Pol) II binds to intergenic rDNA sequences and generates R-loops (DNA:RNA hybrid structures) that are essential for recruiting nDDR factors. Using a boutique CRISPR-Cas9 synthetic lethal screen of DNA repair factors with inhibitors of RNA Pol I transcription, we identified an unexpected protective role for the DNA translocase RAD54L in nDDR. Loss of Rad54L increases nucleolar R-loops and rDNA damage leading to defects in nucleolar structure and enhanced sensitivity to PARP and RNA Pol I inhibitors. Altogether, our study uncovers localized surveillance networks within the nucleolus that respond to rDNA damage. These insights expand our understanding of the molecular mechanisms governing nDDR and opens new avenues for developing nDDR-targeting therapies.

molecular biology↗

bioRxiv2024

A robust unsupervised clustering approach for high-dimensional biological imaging data reveals shared drug-induced morphological signatures

Modern biology increasingly relies on large-scale screening to generate high dimensional datasets with potential to accelerate discovery. However, analysing these complex datasets remains challenging, particularly in applications where the underlying structure and groupings are unknown, and high dimensionality introduces noise and artifacts that make follow up studies difficult to prioritise. Here, we present an unsupervised consensus clustering tool that quantifies biologically meaningful patterns based on multi-scale data organisation to guide decision-making in high-throughput screening. Using large-scale drug screening data in cancer cell lines and bacterium model, we demonstrate its ability to use diverse data inputs to prioritize robust drug clusters with shared biological mechanisms and conserved drug responses. This method addresses key limitations associated with prioritising robust, actionable hits from scalable screening data.

bioinformatics↗