A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections

Figures

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  Figure 1.. Proposed mechanism of high multiplicity of RNA viral infections. Newly synthesized host RNA binding proteins (SRSF1, RNPS1) are required to stabilize nascent transcripts throughout the nucleus. During influenza or other viral infections, these proteins can be bound to viral genomes and transcriptomes. As viral replication and transcription proceeds, these nucleic acids containing strong binding sites for these RBPs in the cytoplasm (SARS-CoV-2) and nucleus (Influenza) that complete with host RNAs and deplete these proteins from the nucleus. This enables nascent transcripts to reanneal with transcription templates, and R-loops are formed. If not removed by RNAse H or other helicases, unresolved R-loops at numerous genomic loci triggers genomic instability. Their frequency and density of unrepaired chromosome damage would be expected to overwhelm DNA repair components (BRCA1/2, FANC complex, and XPC), inducing multiple chromosomal strand breaks in each cell 10. These breakage events initiate apoptosis, releasing a high multiplicity of infectious viral particles.

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  Figure 2.. R i of SRSF1 and RNPS1 Binding Sites in the SARS-CoV-2 and Human Genomes. Histograms display the distribution of R i values for SRSF1 [“Replicate 1” and “Replicate 2” models] and RNPS1 binding sites strengths identified in A) the SARS-CoV-2 viral genome, and B) all transcribed regions in the human genome.

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  Figure 3.. Distribution of SRSF1 and RNPS1 Binding Sites Across SARS-CoV-2 and Influenza A. The viral genomes of A) SARS-CoV-2 (NCBI Reference Sequence: NC_045512.2) and B) Influenza A virus (A/swine/Ontario/104-25/2012[H3N2]) were scanned for strong pre-existing binding sites for the RBP RNPS1 and SRSF1 (newly derived “Replicate 2” model). Custom wiggle tracks which contained those RBP of R i ≥ R sequence were generated and visualized by NCBI Nucleotide. Track images were manually adjusted to indicate the strand in which the binding site was identified (blue vertical lines indicate sites on the positive strand, orange on the negative strand). The majority of sites predicted by the RNPS1 model were simultaneously predicted by the SRSF1 model, however the SRSF1 model identifies additional unique binding sites.

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  Figure 4.. SRSF1 information dense clusters in the THSD4 gene. A) Lollipop plot of information density of clusters annotated by coordinate range and number of sites comprising that cluster using the SRSF1 “Replicate 2” information-based weight models (all R i ≥ R sequence) for the NM_024817 mRNA splice form of THSD4 (some clusters counted in Section 1 – Table 5 (extended data 39) are found in other THSD4 splice forms which span beyond the range of this particular mRNA). B) Information dense SRSF1 clusters within THSD4 that overlap a DRIPc-seq interval (GSE70189 DRIPc-seq dataset). One additional overlapping cluster is not displayed, as is located immediately upstream of the 5’ untranslated region of the NM_024817.2 splice form. No intervals from the GSE68845 DRIP-seq dataset overlap this gene.

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  Figure 5.. Violin Plots of DDB2 Expression in Influenza- and Radiation-Exposed Patients. DDB2 expression for Model 1 including Influenza patients, controls and radiation patients plotted using GraphPad. Each colour represents a different dataset. The left distribution of the radiation data (shaded grey) represents the expression of the radiated patients and the right distribution represents unirradiated controls. For all Influenza datasets (coloured), the left-most distribution represents the true negatives, the middle distribution represents the false positives, and the right-most distribution represents uninfected controls.

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  Figure 6.. Inhibition of Host SRSF1 Binding by Viral Genome Replication. As the fraction of SARS-CoV-2 genomes increase in the host cell, the fraction of SRSF1 bound to the host transcriptome versus the viral genome decreases, resulting in R-loops. Strong SRSF1 binding sites (“Replicate 2” model) were identified in both SARS-CoV-2 (N=60 on the positive strand) and in the human transcriptome. A Scatchard plot (right) was created and used to determine the theoretical number of viral SRSF1 binding sites expected at different viral genome (inhibitor) to host (substrate) ratios (left).