bioRxiv2020
The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualisation. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case (S4). In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data (S5). A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. Availabilitywww.zikaknowledgebase.eu FundingEuropean Commissions Seventh Framework Research Programme project PREPARE (FP7-Health n{degrees}602525) and ZIKALLIANCE (MK, H2020; No 734548). Author summaryDuring the recent ZIKV outbreak there was little information about the interactions between Zika virus and the host, however, the massive research response lead to a steep increase in the number of relevant publications within a very short period of time. At the time, there was no structured and comprehensive database available for integrated molecular and physiological data and knowledge about ZIKV infection. Researchers had to manually review the literature (amounting to over 5000 articles on ZIKV during our last update of the ZIKA KB in September 2018) to extract information about host-pathogen interaction and affected molecular, cellular and organ pathways. We explored the use of automated literature analysis and a defined cooperative effort between experts from various scientific, biomedical and information-technology domains to rapidly compile existing pathophysiological knowledge as a potential tool to support investigations during an emergency. This tool is contrasted with conventional approaches that would take months to comb through the massive amount of existing literature. In addition to providing background information for research, scientific publications can be processed to transform textual information into complex networks, which can be integrated with existing knowledge resources to suggest novel hypotheses that potentially contribute to innovative infectious disease research approaches. This study shows that the knowledge extraction and mapping process required to inform clinical and research responses to an emerging epidemic can be efficiently and effectively executed with a dedicated and trained group of experts, a validated process and the necessary tools. Our results further provide an overview of ZIKV biology, allow prediction of drug efficacy and indentify specific host factors and signalling pathways affected by ZIKV.