Synthetic Lethality-Based Prediction of Anti-SARS-CoV-2 Targets (preprint)

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.Funding: This research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI, CCR; and used the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). We acknowledge and thank the National Cancer Institute for providing financial and infrastructural support. This work was also supported by the following grants to the Sanford Burnham Prebys Medical Discovery Institute: DoD: W81XWH-20-1-0270; DHIPC: U19 AI118610; Fluomics/NOSI: U19 AI135972. K.C. and S.S is supported by the NCI-UMD Partnership for Integrative Cancer Research Program.Declaration of Interests: The other authors declare no competing interests.

Synthetic lethality-based prediction of anti-SARS-CoV-2 targets (preprint)

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets (preprint)

Tremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral Spike vaccines (preprint)

The SARS-CoV-2 Spike glycoprotein mediates virus entry and is a major target for neutralizing antibodies. All current vaccines are based on the ancestral Spike with the goal of generating protective neutralizing antibodies. Several novel SARS-CoV-2 variants with multiple Spike mutations have emerged, and their rapid spread and potential for immune escape have raised concerns. One of these variants, first identified in the United Kingdom, B.1.1.7 (also called VUI202012/01), contains eight Spike mutations with potential to impact antibody therapy, vaccine efficacy and risk of reinfection. Here we employed a lentivirus-based pseudovirus assay to show that variant B.1.1.7 remains sensitive to neutralization, albeit at moderately reduced levels (~2-fold), by serum samples from convalescent individuals and recipients of two different vaccines based on ancestral Spike (mRNA-1273, Moderna, and protein nanoparticle (NVX-CoV2373, Novavax). Some monoclonal antibodies to the receptor binding domain (RBD) of Spike were less effective against the variant while others were largely unaffected. These findings indicate that B.1.1.7 is not a neutralization escape variant that would be a major concern for current vaccines, or for risk of reinfection.

Systematic analysis of electronic health records identifies drugs reducing risk of COVID-19 hospitalization and severity (preprint)

Background SARS-Cov-2 is a new virus causing a pandemic of primarily respiratory illness designated as Coronavirus Disease 2019 (COVID-19). This disease is associated with excess mortality, particularly among the elderly, raising concerns for public health. It is crucial to identify whether existing medications could protect against adverse outcomes of COVID-19 infection. Methods We performed a population-based study among members of Clalit Health Services (CHS), the largest healthcare provider in Israel. CHS centrally manages electronic health records (EHR) including medication purchases for over 4.5 million insurees. Since the disease outbreak through October 10, 2020, 8,681 adult patients aged between 18 and 95 have been hospitalized for COVID-19, among them 3,777 in severe condition. Two case-control matched cohorts were assembled to assess which drugs taken by patients in the month preceding a SARS-CoV-2 positive test affected risks of COVID-19 hospitalization and disease severity. Significance of the associations was assessed using Fisher's exact test and Benjamini-Hochberg correction for multiple testing. Findings We identified several drugs and products sold in pharmacies that are significantly associated with reduced odds ratios of SARS-CoV-2 hospitalization and disease severity: notably ubiquinone (OR:0.25, p<0.001), ezetimibe (OR=0.51, P<0.001), rosuvastatin (OR=0.75, p<0.001) and flecainide (OR=0.30, p<0.01). Additionally, acquisition of surgical masks, latex gloves and several ophthalmological products, including eye wipes were associated with decreased risk for hospitalization. Interpretation Ubiquinone, ezetimibe and rosuvastatin, all related to the cholesterol synthesis pathway, are associated with a protective effect against COVID-19 complications. These medications are associated with reduced hospitalization rate and decreased severity in hospitalized patients. These findings set the basis for specific prospective randomized control trials that should be carried out to carefully assess their protective effects.

Systematic Cell Line-Based Identification of Drugs Modifying ACE2 Expression (preprint)

The COVID-19 pandemic caused by SARS-COV-2 has infected over 500,000 people causing over 25,000 deaths in the last 10 weeks. A key host cellular protein required for the virus entry is angiotensin-converting enzyme 2 (ACE2). Recent studies have reported that patients with hypertension and diabetes treated with ACE inhibitors or angiotensin receptor blockers might be at a higher risk of COVID-19 infection as these drugs have been reported to increase ACE2 expression. This has raised the need to systematically investigate the effect of different drugs including antihypertensives on modulating ACE2 expression. Here, we analyzed a publicly available CMAP dataset of pre/post transcriptomic profiles for drug treatment in cell lines for over 20,000 small molecules. We show that only one subclass of antihypertensives drugs - ACE inhibitors, are significantly enriched for drugs up-regulating ACE2 expression. Studying the effects of the 672 clinically approved drugs in CMAP, we chart the drug categories that affect ACE2 expression. Specifically, we find that panobinostat (an HDAC inhibitor) confers the highest up-regulation of ACE2 expression while isotretinoin (a vitamin A derivative) is its strongest down-regulator. Our results provide initial candidates guiding further in vitro and in vivo studies aimed at assessing drug effects on ACE2 expression.