Mechanistic modeling of the SARS-CoV-2 disease map

Kinza Rian; Marina Esteban-Medina; Marta R Hidalgo; Cankut Cubuk; Matias M Falco; Carlos Loucera; Devrim Gunyel; Marek Ostaszewski; Maria Pena-Chilet; Joaquin Dopazo

Here, we present the first implementation of a mechanistic model of the SARS-CoV-2 infection in a user-friendly interface. The model used here implements the HiPathia 6 algorithm, which has demonstrated to outperform other competing algorithms in a recent benchmarking 7 . The . CC-BY 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.12.025577 doi: bioRxiv preprint mechanistic model implemented in HiPathia has been successfully used to understand the disease mechanisms behind different cancers 6 and was able to predict cancer vulnerabilities with a high precision 8 . The model has been implemented in a user-friendly web application that inputs normalized gene expression values (or similar proteomics or phosphoproteomic values) and can be found at http://hipathia.babelomics.org/covid19/. As an example, we carried out some analyses that involve a case-control differential signaling analysis using a gene expression experiment with lung cell lines infected with SARS-CoV-2 (GEO id: GSE147507). The infected cells showed a differential activation pattern in circuits related to virus entrance to cell, activation of immune, inflammatory and other virus-triggered responses (see Figure 1A and Table 1 for a detailed list of differentially activated signaling circuits and Table 2 for detail on the differentially activated cell functionalities). Interestingly, several of the deregulated pathways include TNF, a target gene of chloroquine, one of the drugs with promising results against COVID-19 9 . Moreover, NF-kB signaling pathway has been highlighted in several studies as one of the main pathways responsible for COVID-19 progression 10 (Figure 1 B) . Figure 1C depicts the heathmap of signaling activity profiles that discriminate the two classes of samples (cases and controls) compared. A very interesting option of the implementation of the model is the Perturbation effect. It allows estimating the effect of interventions (inhibitions or overexpression) across the signaling circuits of the model in a given condition. Moreover, the effect of more than 8000 targeted drugs from DrugBank can be predicted by selecting them, individually or in combinations. Figure 1D shows a simulated knockdown in protein NFKB1A and the cell functionality affected: host-virus interaction. Other options in the application allow building predictors or estimating the relevance of human mutations in the resulting infection phenotype.