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Abstract Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial–immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial–immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial–immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

Cancer: Key proteins hiding in the neighbourhood Cancer is considered a systems disease in which the interactors of cancer-related proteins have a key role, also as targets to fight cancer. New therapeutic approaches are needed to improve success rates and to identify suitable proteins as novel, alternative drug targets. We designed a computational approach, combining mutation and differential expression data with network information, to analyse the interactions of cancer-related proteins in colon, breast, liver and lung cancer. We found that first (direct) neighbours, not linked previously to the given cancer type, are similarly important as mutated proteins known to be involved in cancer development. We found 223 drugs already in the clinic targeting these proteins but not yet used against cancer as their oncology relevance was hidden so far. Our observations open up new strategies for target selection and anti-cancer drug discovery.