Mechanism-based disease similarity

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Systems level insights into the impact of airborne exposure on SARS-CoV-2 pathogenesis and COVID-19 outcome - A multi-omics big data study

Coronavirus disease 2019 (COVID-19) is a viral pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that led to more than 800,00 deaths and continues to be a major threat worldwide. The scientific community has been studying the risk factors associated with SARS-CoV-2 infection and pathogenesis. Recent studies highlight the possible contribution of atmospheric air pollution, specifically particulate matter (PM) exposure as a co-factor in COVID-19 severity. Hence, meaningful translation of suitable omics datasets of SARS-CoV-2 infection and PM exposure is warranted to understand the possible involvement of airborne exposome on COVID-19 outcome. Publicly available transcriptomic data (microarray and RNA-Seq) related to COVID-19 lung biopsy, SARS-CoV-2 infection in epithelial cells and PM exposure (lung tissue, epithelial and endothelial cells) were obtained in addition with proteome and interactome datasets. System-wide pathway/network analysis was done through appropriate software tools and data resources. The primary findings are; 1. There is no robust difference in the expression of SARS-CoV-2 entry factors upon particulate exposure, 2. The upstream pathways associated with upregulated genes during SARS-CoV-2 infection considerably overlap with that of PM exposure, 3. Similar pathways were differentially expressed during SARS-CoV-2 infection and PM exposure, 4. SARS-CoV-2 interacting host factors were predicted to be associated with the molecular impact of PM exposure and 5. Differentially expressed pathways during PM exposure may increase COVID-19 severity. Based on the observed molecular mechanisms (direct and indirect effects) the current study suggests that airborne PM exposure has to be considered as an additional co-factor in the outcome of COVID-19.