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medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.12.18.20248226


A recent report found that rare predicted loss-of-function (pLOF) variants across 13 candidate genes in TLR3- and IRF7-dependent type I IFN pathways explain up to 3.5% of severe COVID-19 cases. We performed whole-exome or whole-genome sequencing of 1,934 COVID-19 cases (713 with severe and 1,221 with mild disease) and 15,251 ancestry-matched population controls across four independent COVID-19 biobanks. We then tested if rare pLOF variants in these 13 genes were associated with severe COVID-19. We identified only one rare pLOF mutation across these genes amongst 713 cases with severe COVID-19 and observed no enrichment of pLOFs in severe cases compared to population controls or mild COVID-19 cases. We find no evidence of association of rare loss-of-function variants in the proposed 13 candidate genes with severe COVID-19 outcomes.

biorxiv; 2020.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2020.12.18.423106


The Spike (S)-protein of SARS-CoV-2 binds host-cell receptor ACE2 and requires proteolytic 'priming' (S1/S2) and 'fusion-activation' (S2') for viral entry. The S-protein furin-like motifs PRRAR685{downarrow} and KPSKR815{downarrow} indicated that proprotein convertases promote virus entry. We demonstrate that furin and PC5A induce cleavage at both sites, ACE2 enhances S2' processing, and their pharmacological inhibition (BOS-inhibitors) block endogenous cleavages. S1/S2-mutations (S1/S2) limit S-protein-mediated cell-to-cell fusion, similarly to BOS-inhibitors. Unexpectedly, TMPRSS2 does not cleave at S1/S2 or S2', but it can: (i) cleave/inactivate S-protein into S2a/S2b; (ii) shed ACE2; (iii) cleave S1-subunit into secreted S1', activities inhibited by Camostat. In lung-derived Calu-3 cells, BOS-inhibitors and S1/S2 severely curtail 'pH-independent' viral entry, and BOS-inhibitors alone/with Camostat potently reduce infectious viral titer and cytopathic effects. Overall, our results show that: furin plays a critical role in generating fusion-competent S-protein, and indirectly, TMPRSS2 promotes viral entry, supporting furin and TMPRSS2 inhibitors as potential antivirals against SARS-CoV-2

biorxiv; 2020.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2020.12.20.423533


The Coronavirus Disease 2019 (COVID-19) pandemic has caused millions of deaths and will continue to exact incalculable tolls worldwide. While great strides have been made toward understanding and combating the mechanisms of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection, relatively little is known about the individual SARS-CoV-2 proteins that contribute to pathogenicity during infection and that cause neurological sequela after viral clearance. We used Drosophila to develop an in vivo model that characterizes mechanisms of SARS-CoV-2 pathogenicity, and found ORF3a adversely affects longevity and motor function by inducing apoptosis and inflammation in the nervous system. Chloroquine alleviated ORF3a induced phenotypes in the CNS, arguing our Drosophila model is amenable to high throughput drug screening. Our work provides novel insights into the pathogenic nature of SARS-CoV-2 in the nervous system that can be used to develop new treatment strategies for post-viral syndrome.

COVID-19 , Death , Severe Acute Respiratory Syndrome , Inflammation
researchsquare; 2020.


Background: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.MethodBetween March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. ConclusionWe demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

Severe Acute Respiratory Syndrome