Complexity in correlated cytokine networks associated with COVID-19

Hiroshi Morimoto

COVID-19, graph theory, network, cytokine, cytokine storm.

The COVID-19 pandemic has led to considerable global morbidity and mortality. The cytokine storm is thought to be a major feature among patients with COVID-19. Therefor, profound understanding of cytokine network is necessary. For the study of an inter-correlated cytokine network, identifying the complexity of cytokine network will be an important issue.

Most of the research studies on cytokine networks have attempted to find significant cytokines by observing the change of DNA expression levels in cases and comparing them to controls. However, fewer researches have delved into the features of the network itself. In this paper, we proposed a practical measure to identify the complexity of the cytokine network by applying mathematical graph theory.

Herein, we constructed a correlation network of cytokines from DNA expression data derived from NCBI. By applying the mathematical graph theory to these networks, we looked for prominent sub-networks and extracted several interesting types of sub-networks of cytokines and described their characteristics as a network. In particular, we found a complex type of network in the correlation network associated with COVID-19 patients.

These findings suggest a mechanism by which the correlated cytokine network is induced by COVID-19. Our findings also suggest potential novel prediction of cytokine storm for COVID-19 disease.

https://dx.doi.org/10.31873/IJEAS.8.9.06