A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

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

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. O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/459786v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@7cd823org.highwire.dtl.DTLVardef@51f699org.highwire.dtl.DTLVardef@114c555org.highwire.dtl.DTLVardef@a774ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

Reprogramming of the intestinal epithelial-immune cell interactome during SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents an unprecedented worldwide health problem. Although the primary site of infection is the lung, growing evidence points towards a crucial role of the intestinal epithelium. Yet, the exact effects of viral infection and the role of intestinal epithelial-immune cell interactions in mediating the inflammatory response are not known. In this work, we apply network biology approaches to single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids to investigate how altered intracellular pathways upon infection in intestinal enterocytes leads to modified epithelial-immune crosstalk. We point out specific epithelial-immune interactions which could help SARS-CoV-2 evade the immune response. By integrating our data with existing experimental data, we provide a set of epithelial ligands likely to drive the inflammatory response upon infection. Our integrated analysis of intra- and inter-cellular molecular networks contribute to finding potential drug targets, and suggest using existing anti-inflammatory therapies in the gut as promising drug repurposing strategies against COVID-19.

Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell epitope mapping

The current COVID-19 pandemic represents a global challenge. A better understanding of the immune response against SARS-CoV-2 is key to unveil the differences in disease severity and to develop future vaccines targeting novel SARS-CoV-2 variants. Feature barcode technology combined with CITE-seq antibodies and DNA-barcoded peptide-MHC I Dextramer reagents enabled us to identify relevant SARS-CoV-2-derived epitopes and compare epitope-specific CD8+ T cell populations between mild and severe COVID-19. We identified a strong CD8+ T cell response against an S protein-derived epitope. CD8+ effector cells in severe COVID-19 displayed hyperactivation, T cell exhaustion and were missing characteristics of long-lived memory T cells. We identify A*0101 WTAGAAAYY as an immunogenic CD8+ T cell epitope with the ability to drive clonal expansion. We provide an in-depth characterization of the CD8+ T cell-mediated response to SARS-CoV-2 infection which will be relevant for the development of molecular and targeted therapies and potential adjustments of vaccination strategies.

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Whither systems medicine?

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.