Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases. (preprint)

ABSTRACT

Background: COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. Aim: To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. Method: We leveraged the correlation network approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization (WHO) Ordinal Scale (WOS) was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state omics-specific graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Results: Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed more shared biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. We further identified both biosignatures that discriminate between the disease states and interactions between biosignatures that are either common between or associated with COVID-19 disease phases. Interestingly, cross-layer interactions between different omics profiles increased with disease severity. Conclusion: This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions that are either common between or unique to mild, moderate, and severe COVID-19. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. In addition, the approach implemented here can be used for other diseases.