GSE162569
Genomic RNA elements drive phase separation of the SARS-CoV-2 nucleocapsid
We report that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with viral RNA. N-protein condenses with specific RNA genomic elements under physiological buffer conditions and condensation is enhanced at human body temperatures (33°C and 37°C) and reduced at room temperature (22°C). RNA sequence and structure in specific genomic regions regulate N-protein condensation while other genomic regions promote condensate dissolution, potentially preventing aggregation of the large genome. At low concentrations, N-protein preferentially crosslinks to specific regions characterized by single-stranded RNA flanked by structured elements and these features specify the location, number, and strength of N-protein binding sites (valency). Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is RNA sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules, and presents a screenable process for identifying antiviral compounds effective against SARS-CoV-2. Overall design: In vitro transcribed RNA was treated with 5NIA or DMSO (control) for SHAPE-MaP RNA structure probing experiments.  The binding of recombinantly expressed and purified SARS-CoV-2 N-protein at 160x, 80x, 20x, or 0x (control) molar excess concentrations to in vitro transcribed RNA was chemically probed using the bivalent chemical crosslinker SDA for RNP-MaP experiments. Chemically modified RNA samples from SHAPE-MaP and RNP-MaP experiments were subjected to MaP reverse transcription, which encodes the position of chemical adducts as non-templated nucleotides or deletions in the product cDNA. Sequencing libraries were generated using the Nextera XT workflow and sequenced on an Illumin MiSeq instrument. The ShapeMapper 2 software was used to align reads and call modification-induced mutations. Software analysis pipelines were employed to create SHAPE and RNP-MaP profiles, find in-cell reactivity perturbations, locate correlated mutations, and model RNA structures.