Huey, N. | Search bioRxiv

bioRxiv2026

HDAC5-encoded Microprotein NISM Mediates Nucleolar Formation and Ribosomal RNA Synthesis

Ribosome biogenesis is the process by which ribosomal RNA (rRNA) and ribosomal proteins are synthesized, processed, and assembled into functional ribosomes. This process begins in the nucleolus, a multiphase liquid condensate. Here, we discover an arginine-rich disordered nucleolar microprotein encoded within the HDAC5 5'-UTR that we termed Nucleolar Integrity and Stress Microprotein (NISM). NISM overexpression leads to impaired rDNA transcription, triggering nucleolar stress, p53 activation, and suppressed proliferation. NISM knockout causes disruption of nucleolar structure and also induces p53 activation. Mechanistically, NISM interacts with the DExH-box RNA helicase DHX9 and regulates its activities related to pre-rRNA synthesis. Computational analyses and polymer physics-based mathematical modeling revealed that NISM coordinates nucleolar formation and pre-rRNA synthesis by enhancing the liquid-liquid phase separation of DHX9. This study establishes NISM as a regulator of nucleolar biology and deepens our understanding of how disordered microproteins can facilitate the formation of membraneless organelles.

molecular biology↗

bioRxiv2024

De-biased sparse canonical correlation for identifying cancer-related trans-regulated genes

SO_SCPLOWUMMARYC_SCPLOWIn cancer multi-omic studies, identifying the effects of somatic copy number aberrations (CNA) on physically distal gene expressions (trans-associations) can potentially uncover genes critical for cancer pathogenesis. Sparse canonical correlation analysis (SCCA) has emerged as a promising method for identifying associations in high-dimensional settings, owing to its ability to aggregate weaker associations and its improved interpretability. Traditional SCCA lacks hypothesis testing capabilities, which are critical for controlling false discoveries. This limitation has recently been addressed through a bias correction technique that enables calibrated hypothesis testing. In this article, we leverage the theoretical advancements in de-biased SCCA to present a computationally efficient pipeline for multi-omics analysis. This pipeline identifies and tests associations between multi-omics data modalities in biomedical settings, such as the trans-effects of CNA on gene expression. We propose a detailed algorithm to choose the tuning parameters of de-biased SCCA. Applying this pipeline to data on estrogen receptor (ER)-associated CNAs and 10,756 gene expressions from 1,904 breast cancer patients in the METABRIC study, we identified 456 CNAs trans-associated with 256 genes. Among these, 5 genes were identified only through de-biased SCCA and not by the standard pairwise regression approach. Downstream analysis with the 256 genes revealed that these genes were overrepresented in pathways relevant to breast cancer.

genetics↗