Molecular Correlates of the Disease Outcome

The recent emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting coronavirus disease 2019 (COVID-19) has led to more than 649,000 deaths world-wide, a number that is still increasing. A vast number of recent studies have identified associations between various chronic diseases and lifestyle factors and COVID-19. High-throughput assays (for example, metabolomics, proteomics, and transcriptomics) are being used to investigate underlying molecular processes and to identify targets for treatment. Analysis of these data to obtain useful markers of disease is nontrivial and requires complex analytical techniques for data processing, integration, and interpretation. Thus, research focusing on multiomics data to identify COVID19 drivers would be of interest to this Special Issue. Broadly, this topic is related to human immunology, systems biology, and personalized medicine.

Dear Colleagues,

Recent breakthroughs in high-throughput technologies such as proteomics, metabolomics, and transcriptomics enable measurements of thousands of biomolecules to investigate correlates of severe infections caused by SARS-CoV-2. Moreover, collaborations with clinicians have facilitated sampling of multiple human tissues in order to study disease pathology in humans. Different high-throughput assays provide complimentary information to investigate underlying molecular mechanisms and thus identify novel disease drivers that can be targeted for treatment. However, obtaining relevant information from huge amounts of data, its interpretation, and its integrations is nontrivial.

In the pathophysiology of the disease, defining molecular signatures underlying disease outcome is difficult due to variations in inter- and intra-individual measurement, history, and other demographic variables, among other variables introduced by study-design. Advanced computational techniques for data interpretation, the integration of multiomics data, and the incorporation of existing data and knowledge are required.

Thus, the purpose of this Issue is to identify disease drivers from high-throughput multiomics approaches and to present innovative ways to quantitatively describe the molecular data that are most relevant to study disease outcome in humans.

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• translational studies
• multiomics data
• data integration and interpretation
• data analytics
• systems biology