Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA
Heewon Park, Satoru Miyano, Elisabetta Pilotti, Elisabetta Pilotti, Elisabetta Pilotti, Elisabetta Pilotti
TL;DR
This study identifies a molecular network involving HLA class II, CIITA, and CD74 as a marker for severe COVID-19 in the Japanese population using network biology analysis.
Contribution
A novel computational network biology strategy using Kullback–Leibler divergence to detect severity-specific molecular interplays in gene networks.
Findings
The molecular interplay between HLA class II, CIITA, and CD74 is a severity-specific marker for COVID-19.
Computational network biology analysis revealed suppression and activation of this interplay as crucial for understanding severity mechanisms.
Monte Carlo simulations confirmed the effectiveness of the new strategy for differential gene network analysis.
Abstract
COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback–Leibler divergence. Monte Carlo simulations…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsCOVID-19 Clinical Research Studies · SARS-CoV-2 and COVID-19 Research · Diabetes and associated disorders