Comprehensive Computational Repository of SARS-CoV-2 Virus-Host Interaction Mechanisms: The COVID-19 Disease Map

核心概念

The COVID-19 Disease Map is a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms, supporting data analysis and predictive modelling.

要約

The COVID-19 Disease Map is a collaborative effort involving over 230 biocurators, domain experts, modellers and data analysts from 120 institutions in 30 countries. It aims to build a comprehensive, machine-readable repository of curated computational diagrams and models of molecular mechanisms implicated in COVID-19.

The key aspects of the COVID-19 Disease Map include:

1. Creation and curation of systems biology diagrams by biocurators, following standardized encoding and annotation schemes. The diagrams cover key events in the SARS-CoV-2 infectious cycle and host response, including viral entry and replication, host defense mechanisms, immune response, and metabolic pathways.

2. Integration of relevant knowledge from public databases, interaction repositories, and text mining resources to enrich and validate the curated mechanisms. This allows the Map to be continuously updated as new scientific evidence emerges.

3. Ensuring interoperability of the diagrams through the use of layout-aware systems biology formats (SBML, SBGNML, GPML), enabling computational analysis and modelling workflows.

4. Demonstration of analytical and modelling approaches using the contents of the Map, including network analysis, transcription factor activity profiling, and mechanistic pathway modelling. These workflows generate hypotheses and predictions about the underlying COVID-19 mechanisms.

The COVID-19 Disease Map is a constantly evolving resource, serving as a comprehensive computational repository of SARS-CoV-2 virus-host interaction mechanisms. It supports data interpretation, hypothesis generation, and in silico experimentation, with the potential to identify molecular signatures of disease predisposition and suggest drug repositioning for improved treatments.

統計

"SARS-CoV-2 infection already resulted in the infection of over 106 million people worldwide, of whom 2.3 million have died."
"The COVID-19 Disease Map is a collection of 41 diagrams containing 1836 interactions between 5499 elements, supported by 617 publications and preprints."

引用

"The COVID-19 Disease Map is a constantly evolving resource, refined and updated by ongoing efforts of biocuration, sharing and analysis."
"The COVID-19 Disease Map diagrams are the main building blocks of the Map, composed of biochemical reactions and interactions (altogether called interactions) taking place between different types of molecular entities in various cellular compartments."

抽出されたキーインサイト

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

by Ostaszewski,... 場所 www.biorxiv.org 10-28-2020

https://www.biorxiv.org/content/10.1101/2020.10.26.356014v3

深掘り質問

How can the COVID-19 Disease Map be expanded to incorporate cell-type specific immune responses and host susceptibility factors?

The COVID-19 Disease Map can be expanded to include cell-type specific immune responses and host susceptibility factors by incorporating detailed information about the molecular mechanisms involved in these processes. This expansion would involve curating pathways and interactions that are specific to different immune cell types, such as T cells, B cells, macrophages, and dendritic cells. By including these cell-specific pathways, the map can provide a more comprehensive understanding of how the immune system responds to SARS-CoV-2 infection.
Additionally, the map can be enriched with information about host susceptibility factors, such as genetic variations, underlying medical conditions, age-related factors, and other demographic variables that influence the severity and outcome of COVID-19. By integrating data on these susceptibility factors, the map can help identify key molecular pathways that contribute to increased susceptibility to the virus and severe disease outcomes.
To achieve this expansion, a collaborative effort involving biocurators, domain experts, and data analysts would be essential. Biocurators would be responsible for identifying and curating relevant pathways and interactions, while domain experts would provide insights into the biological relevance of the curated information. Data analysts would play a crucial role in integrating omics data and clinical information to enhance the map's content with cell-specific and susceptibility-related data.

How can the potential limitations of the computational modelling approaches used to analyze the contents of the COVID-19 Disease Map be addressed?

The computational modelling approaches used to analyze the contents of the COVID-19 Disease Map may have limitations that need to be addressed to ensure the accuracy and reliability of the models generated. Some potential limitations of these approaches include the complexity of the biological systems being modeled, the availability of experimentally validated parameters, and the scalability of the models to handle large-scale networks.
To address these limitations, several strategies can be implemented:

Validation and Calibration: It is essential to validate the computational models against experimental data and calibrate the model parameters to ensure that the simulations accurately reflect the biological processes being studied.

Parameter Estimation: Efforts should be made to obtain experimentally validated parameters for the model, either through literature mining or experimental data generation. Sensitivity analysis can help identify the most critical parameters and guide the focus of parameter estimation efforts.

Scalability: For large-scale networks, model reduction techniques can be applied to simplify the complexity of the model while retaining essential features. This can help improve the computational efficiency of the simulations and make the models more manageable.

Integration of Multi-Omics Data: Integrating multi-omics data into the models can provide a more comprehensive view of the molecular mechanisms underlying COVID-19. This integration can help capture the dynamic interactions between different biological components and improve the accuracy of the predictions.

By addressing these limitations and implementing best practices in computational modeling, the COVID-19 Disease Map can generate more reliable and informative insights into the molecular mechanisms of SARS-CoV-2 infection and COVID-19 pathophysiology.

How can the COVID-19 Disease Map be integrated with clinical data and real-world evidence to enhance its utility in supporting drug discovery and repurposing efforts?

Integrating the COVID-19 Disease Map with clinical data and real-world evidence can significantly enhance its utility in supporting drug discovery and repurposing efforts. This integration can provide a more holistic view of the disease mechanisms and help identify potential drug targets and candidates for repurposing with higher precision and efficacy.
Several strategies can be employed to integrate clinical data and real-world evidence into the COVID-19 Disease Map:

Clinical Data Integration: By incorporating clinical data, such as patient demographics, disease severity, treatment outcomes, and comorbidities, into the map, researchers can identify molecular pathways and interactions that are associated with specific clinical phenotypes. This information can guide the selection of potential drug targets and inform personalized treatment strategies.

Real-World Evidence Analysis: Real-world evidence, such as data from electronic health records, patient registries, and population health databases, can be analyzed in conjunction with the map to validate the predicted drug targets and mechanisms of action. This analysis can help prioritize drug candidates for further preclinical and clinical evaluation.

Pharmacogenomics Integration: Incorporating pharmacogenomic data into the map can help identify genetic variations that influence drug response and toxicity. By linking these genetic factors to specific pathways and interactions in the map, researchers can tailor drug treatments to individual patients based on their genetic profiles.

Drug Repurposing Strategies: Leveraging the integrated clinical data and real-world evidence, researchers can identify existing drugs with potential efficacy against COVID-19 by targeting specific pathways and mechanisms implicated in the disease. This approach can expedite the drug discovery process and lead to the rapid identification of novel treatment options.

Overall, the integration of clinical data and real-world evidence into the COVID-19 Disease Map can enhance its predictive power, facilitate the identification of novel drug targets, and support evidence-based decision-making in drug discovery and repurposing efforts.