Role of spatial patterning of N-protein interactions in SARS-CoV-2 genome packaging
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Abstract
Viruses must efficiently and specifically package their genomes while excluding cellular nucleic acids and viral sub-genomic fragments. Some viruses use specific packaging signals, which are conserved sequence/structure motifs present only in the full-length genome. Recent work has shown that viral proteins important for packaging can undergo liquid-liquid phase separation (LLPS), where one or two viral nucleic acid binding proteins condense with the genome. The compositional simplicity of viral components lends itself well to theoretical modeling compared to more complex cellular organelles. Viral LLPS can be limited to one or two viral proteins and a single genome that is enriched in LLPS-promoting features. In our previous study, we observed that LLPS-promoting sequences of SARS-CoV-2 are located at the 5ʹ and 3ʹ ends of the genome, whereas the middle of the genome is predicted to consist mostly of solubilizing elements. Is this arrangement sufficient to drive single genome packaging, genome compaction, and genome cyclization? We addressed these questions using a coarse-grained polymer model, LASSI, to study the LLPS of nucleocapsid protein with RNA sequences that either promote LLPS or solubilization. With respect to genome cyclization, we find the most optimal arrangement restricts LLPS-promoting elements to the 5ʹ and 3ʹ ends of the genome, consistent with the native spatial patterning. Genome compaction is enhanced by clustered LLPS-promoting binding sites, while single genome packaging is most efficient when binding sites are distributed throughout the genome. These results suggest that many and variably positioned LLPS-promoting signals can support packaging in the absence of a singular packaging signal which argues against necessity of such a feature. We hypothesize that this model should be generalizable to multiple viruses as well as cellular organelles like paraspeckles, which enrich specific, long RNA sequences in a defined arrangement.
Statement of significance
The COVID-19 pandemic has motivated research of the basic mechanisms of coronavirus replication. A major challenge faced by viruses such as SARS-CoV-2 is the selective packaging of a large genome in a relatively small capsid while excluding host and sub-genomic nucleic acids. Genomic RNA of SARS-CoV-2 can condense with the Nucleocapsid (N-protein), a structural protein component critical for packaging of many viruses. Notably, certain regions of the genomic RNA drive condensation of N-protein while other regions solubilize it. Here, we explore how the spatial patterning of these opposing elements promotes single genome compaction, packaging, and cyclization. This model informs future in silico experiments addressing spatial patterning of genomic features that are experimentally intractable because of the length of the genome.
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SciScore for 10.1101/2021.01.06.425605: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Interpolation was performed using interpolate.griddata from scipy with a two-dimensional piecewise cubic, continuously differentiable, and approximately curvature-minimizing polynomial surface. scipysuggested: (SciPy, RRID:SCR_008058)Plots were made using matplotlib. matplotlibsuggested: (MatPlotLib, RRID:SCR_008624)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study …SciScore for 10.1101/2021.01.06.425605: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Interpolation was performed using interpolate.griddata from scipy with a two-dimensional piecewise cubic, continuously differentiable, and approximately curvature-minimizing polynomial surface. scipysuggested: (SciPy, RRID:SCR_008058)Plots were made using matplotlib. matplotlibsuggested: (MatPlotLib, RRID:SCR_008624)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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