The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function.

BACKGROUND: Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition. RESULTS: We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19. CONCLUSIONS: The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer .

Use of the QIAGEN GeneReader NGS system for detection of KRAS mutations, validated by the QIAGEN Therascreen PCR kit and alternative NGS platform.

BACKGROUND: The detection of somatic mutations in primary tumors is critical for the understanding of cancer evolution and targeting therapy. Multiple technologies have been developed to enable the detection of such mutations. Next generation sequencing (NGS) is a new platform that is gradually becoming the technology of choice for genotyping cancer samples, owing to its ability to simultaneously interrogate many genomic loci at massively high efficiency and increasingly lower cost. However, multiple barriers still exist for its broader adoption in clinical research practice, such as fragmented workflow and complex bioinformatics analysis and interpretation. METHODS: We performed validation of the QIAGEN GeneReader NGS System using the QIAact Actionable Insights Tumor Panel, focusing on clinically meaningful mutations by using DNA extracted from formalin-fixed paraffin-embedded (FFPE) colorectal tissue with known KRAS mutations. The performance of the GeneReader was evaluated and compared to data generated from alternative technologies (PCR and pyrosequencing) as well as an alternative NGS platform. The results were further confirmed with Sanger sequencing. RESULTS: The data generated from the GeneReader achieved 100% concordance with reference technologies. Furthermore, the GeneReader workflow provides a truly integrated workflow, eliminating artifacts resulting from routine sample preparation; and providing up-to-date interpretation of test results. CONCLUSION: The GeneReader NGS system offers an effective and efficient method to identify somatic (KRAS) cancer mutations.