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

Understanding the molecular mechanisms that mediate SARS-CoV-2 infection is key for the rapid development of efficient preventive or therapeutic interventions against the COVID-19. A comprehensive description of such molecular mechanisms is represented in the corresponding disease map, that is, the fragment of the whole network of known human protein interactions that are relevant for the disease 2 . The recent availability of a detailed catalog of viral-human protein interactions 3 has facilitated the construction of a first version of the map of human molecular pathways involved in the viral infection. To do so, we firstly expanded the SARS-CoV-2 virus-human interactome from existing KEGG pathways maps to obtain a subset of signaling circuits (a circuit is the sub-pathway that defines the chain of signal transduction that connects a receptor protein to an effector protein) that represent a comprehensive model of the direct and indirect interactions of the virus with the cell. In particular, we selected those signaling circuits with at least one UniProt function that fit in one of these virus-related categories: 1) Host-virus interaction, 2) inflammatory response, 3) immune activity, 4) antiviral defense, 5) endocytosis. Our model is a part of a detailed repository of SARS-CoV-2 mechanisms, the COVID-19 Disease Map, constructed by an international community, and is available at: http://doi.org/10.17881/covid19-disease-map. Disease maps are repositories of knowledge of disease-relevant mechanisms that provide qualitative guidance for the interpretation of experimental findings. Actually, disease maps are the supporting foundation of different tools able to model the information contained in them in order to provide a detailed quantitative explanation for experimental results. In particular, mechanistic models of disease maps are becoming increasingly relevant for genomic data interpretation because they provide a natural link between omics data measurements and cell behavior and outcome, which ultimately accounts for the phenotype of the infection. The knowledge of these links allows a better understanding of the molecular mechanisms of the viral infection and the responses to drugs. Basically, mechanistic models map experimental values in the context of the disease map information, which is used to point out the relevant experimental, thus revealing aspects of the molecular mechanisms behind the experiment. It is important to note that this assessment is made from a systems biology perspective, in the holistic context of the disease map, and considers the functional interactions among the gene products as described in the map. Typically, these experimental values are gene expression transcriptomic data, although other data such as proteomic, phosphoproteomic, genomic, or even methylomics, can also be used.