Integrated Transcriptomics and Network Analysis of Potential Mechanisms and Health Effects of Convalescent COVID-19 Patients

Suthipong Chujan; Watanyoo Nakareangrit; Tawit Suriyo; Jutamaad Satayavivad

doi:10.1177/11779322231206684

Integrated Transcriptomics and Network Analysis of Potential Mechanisms and Health Effects of Convalescent COVID-19 Patients

Bioinform Biol Insights. 2023 Oct 24:17:11779322231206684. doi: 10.1177/11779322231206684. eCollection 2023.

Authors

Suthipong Chujan^{1

2}, Watanyoo Nakareangrit³, Tawit Suriyo^{1

2}, Jutamaad Satayavivad^{1

2}

Affiliations

¹ Laboratory of Pharmacology, Chulabhorn Research Institute, Bangkok, Thailand.
² Center of Excellence on Environmental Health and Toxicology (EHT), OPS, MHESI, Bangkok, Thailand.
³ Translational Research Unit, Chulabhorn Research Institute, Bangkok, Thailand.

Abstract

Coronaviral disease 2019 (COVID-19) is a recent pandemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, there are still cases of COVID-19 around the world that can develop into persistent symptoms after discharge. The constellation of symptoms, termed long COVID, persists for months and can lead to various diseases such as lung inflammation and cardiovascular disease, which may lead to considerable financial burden and possible risk to human health. Moreover, the molecular mechanisms underlying the post-pandemic syndrome of COVID-19 remain unclear. In this study, we aimed to explore the molecular mechanism, disease association, and possible health risks in convalescent COVID-19 patients. Gene expression data from a human convalescent COVID-19 data set was compared with a data set from healthy normal individuals in order to identify differentially expressed genes (DEGs). To determine biological function and potential pathway alterations, the GO and KEGG databases were used to analyze the DEGs. Disease association, tissue, and organ-specific analyses were used to identify possible health effects. A total of 250 DEGs were identified between healthy and convalescent COVID-19 subjects. The biological function alterations identified revealed cytokine interactions and increased inflammation through NF-κB1, RELA, JUN, STAT3, and SP1. Interestingly, the most significant pathways were cytokine-cytokine receptor interaction, altered lipid metabolism, and atherosclerosis that play a crucial role in convalescent COVID-19. In addition, we also found pneumonitis, dermatitis, and autoimmune diseases. Based on our study, convalescent COVID-19 is associated with inflammation in a variety of organs that could lead to autoimmune and inflammatory diseases, as well as atherosclerosis. These findings are a first step toward fully exploring the disease mechanisms in depth to understand the relationship between post-COVID-19 infection and potential health risks. This is necessary for the development of appropriate strategies for the prevention and treatment of long COVID.

Keywords: COVID-19; bioinformatics analysis; convalescent COVID-19; inflammation; network analysis.