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. 2022 Apr 11;18(4):e1010021.
doi: 10.1371/journal.pcbi.1010021. eCollection 2022 Apr.

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Szilvia Barsi  1 , Henrietta Papp  2   3 , Alberto Valdeolivas  4 , Dániel J Tóth  1 , Anett Kuczmog  2   3 , Mónika Madai  2   3 , László Hunyady  1   5   6 , Péter Várnai  1   5 , Julio Saez-Rodriguez  4 , Ferenc Jakab  2   3 , Bence Szalai  1
Affiliations

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Szilvia Barsi et al. PLoS Comput Biol. .

Abstract

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity-and not inverse similarity-between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: JSR reports funding from GSK and Sanofi and fees from Travere Therapeutics and Astex. BS is a full time employee of Turbine Ltd., Budapest, Hungary.

Figures

Fig 1
Fig 1. Functional genomic analysis of SARS-CoV-2 infected cell lines.
(A) Inferred pathway and (B) TF activities of SARS-CoV-2 infected samples from lung epithelial cell lines (Calu-3 and A549). Activities were calculated from differential expression signatures (infected—control) using PROGENy and DoRothEA tools for pathway and TF activities, respectively. Only TFs with high absolute level of activity changes (absolute normalized enrichment score > 4) are shown. (C) Causal signalling network in SARS-CoV-2 infected Calu-3 cells (GSE147507) identified by CARNIVAL. RIG-I like receptors (DDX58 and IFIH1) as perturbation targets and DoRothEA inferred TF activities were used as the input of the CARNIVAL pipeline. Color code represents inferred activity of protein nodes (blue: inhibited, red: activated).
Fig 2
Fig 2. Functional genomic analysis of effective drugs treated cell lines.
(A) Inferred pathway and (B) TF activities of anti-SARS-CoV-2 drug-treated cell lines. Activities were calculated from LINCS-L1000 consensus drug-induced signatures, using PROGENy and DoRothEA tools for pathway and TF activities, respectively. Drug clusters in (B) are color coded. Only selected transcription factors (corresponding to Fig 1B) are shown. (C) Relationship between average TF activities induced by drug treatment and SARS-CoV-2 infection for 5 different drug clusters (colors of clusters correspond to panel B). TFs with the highest/lowest average activities are text labeled. (D) Density plot of similarities between SARS-CoV-2- and drug-induced signatures for all LINCS-L1000 drugs and known anti-SARS-CoV-2 drugs (ChEMBL drugs).
Fig 3
Fig 3. Evaluation of similarity-based and machine learning-based models in predicting in vitro effective drugs.
(A, B) ROC analysis of similarity-based predictions of effective drugs against SARS-CoV-2. Drug—SARS-CoV-2 (A) or drug—other virus (B) infection signature similarity was used as prediction score, while known in vitro effective drugs (ChEMBL dataset) were used as true positives. (FPR: false positive rate, TPR: true positive rate) (C) Comparison of predictive performance (ROCAUCs) of similarity-based method (similarity to SARS-CoV-2 infection signature, x-axis) and random forest-based (RF-based, x-axis) prediction. Results of 100 random subsampling cross-validations. In case of similarity-based methods, ROC AUC curves were only calculated for the corresponding cross-validation sets. Boxplots represent the median (central line), first and third quartile (box), minimum and maximum non-outlier values (whiskers) and outliers (diamonds). (D) Feature importances (Gini importance) of the Random Forest model. Top and bottom 10 features (TFs) are shown according to importance.
Fig 4
Fig 4. Cholesterol depleting effect of SREBF activating drugs.
(A) Schematic figure of the hypothesis that antiviral drugs block virus entry into cells by cholesterol depletion from plasma membrane, and are leading to a compensatory increased SREBF1/2 activity. Effects induced by viral infection are marked with black arrows (left side), while orange arrows represent drug-induced changes (right side) The figure was created with BioRender.com. (B) Schematic representation of the used fluorescent constructs. (C) Histogram of SREBF1 activation (left panel) and histogram of predicted probabilities of in vitro antiviral activity of LINCS-L1000 drugs (right panel, according to the Random Forest model). Drugs selected for in vitro experiments are text labeled. (D) Representative confocal microscopy images of D4H-mVenus transfected HEK293A cells treated with DMSO, MβCD, chlorpromazine or amiodarone. White arrows mark plasma membrane, while red arrows show intracellular localised cholesterol sensors. (E) Time-dependent change of log2(PM/IC) ratio of average cholesterol sensor intensity in HEK293A cells treated with DMSO, MβCD, chlorpromazine, amiodarone, loperamide or rosuvastatin. Red line marks drug treatment. *: significant (p<0.001) interaction between drug treatment and elapsed time in linear model.
Fig 5
Fig 5. Cholesterol replenishment inhibits antiviral effect of amiodarone.
(A) Predicted drugs inhibit SARS-CoV-2 replication in infected Vero-E6 cells. Vero-E6 cells were infected with SARS-CoV-2 (top left) and co-treated either with amiodarone (top right), chlorpromazine (bottom left) or loperamide (bottom right). Antiviral effect (reduced cytopathy) was evaluated by microscopic imaging (10x objective) 48 hours after infection. (B) Schematic figure of cholesterol rescue experiments. The figure was created with BioRender.com. (C) Effect of cholesterol rescue on antiviral drug effect. Vero-E6 cells were pretreated with drugs (x-axis), cholesterol was replenished (color code) and cells were infected with SARS-CoV-2. Antiviral effect of drugs was evaluated 48 hours after infection by droplet digital PCR (viral copies, y-axis). *, #: significant (p<0.05) effect of drug treatment and drug treatment-cholesterol interaction in linear model, respectively.
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Grants and funding

BS was supported by the Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences (460044). DJT and PV were supported by the Hungarian Scientific Research Fund (OTKA K134357). On behalf of Project DRUGSENSPRED we thank for the usage of ELKH Cloud (https://science-cloud.hu/) that significantly helped us achieve the results published in this paper. The in vitro SARS-CoV-2 experiments were funded by the Hungarian Scientific Research Fund (OTKA KH129599), by the European Union and the European Social Fund (EFOP-3.6.1.-16-2016-00004), and by the Ministry for Innovation and Technology of Hungary (TUDFO/47138/2019-ITM) to FJ. Also, project no. TKP2021-NVA-07 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the TKP2021-NVA funding scheme to FJ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.