Francis E. Agamah ¹ Thomas H. Ederveen ² Michelle Skelton ¹ Darren Martin ¹ Emile Rugamika Chimusa ^3* Peter A. 'T Hoen ^4*

• ¹ Division of Computational Biology, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
• ² Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, Netherlands
• ³ Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
• ⁴ Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, Netherlands

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 a multi-omics network-based 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 specific omics-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 distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4(CCL4), and Interferon Regulatory Factor 1(IRF1). Hepatocyte Growth Factor(HGF), Matrix Metallopeptidase 12(MMP12), Interleukin 10(IL10), Nuclear Factor Kappa B Subunit 1(NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1(STAT1), Superoxide Dismutase 2(SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. Conclusion: This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions that are unique to mild, moderate, and severe COVID-19 disease states.