Identification and analysis of co-disease genes between COVID-19 and osteoarthritis (preprint)

Background: The molecular mechanisms of corona virus disease 2019 (COVID-19) and osteoarthritis (OA) are unclear, and there is an urgent need to identify new biomarkers and explore their potential molecular mechanisms in COVID-19 and OA. Methods: The GSE57218, GSE157103 training sets and the GSE82107, GSE171110 validation sets were acquired via gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between disease and normal samples in the GSE57218 and GSE157103 training sets were respectively sifted out by differential expression analysis. The modules with the highest correlation with OA and normal, COVID-19 and non COVID-19 were gained by weighted gene co-expression network analysis (WGCNA), individually. Then, OA-DEGs were intersected with the module genes that had significant correlation with OA, and COVID-19-DEGs were intersected with the module genes which were dramatically correlated with COVID-19 to yield OA-intersected genes and COVID-19 intersected genes, respectively. The OA-intersected genes and COVID-19 intersected genes were intersected to yield candidate genes, and they were analyzed for function enrichment analysis. Next, the seven algorithms (Closeness, MCC, Degree, MNC, Radality, Stress and EPC) were performed on candidate genes to sift out biomarkers. Finally, we constructed the competing endogenous RNA (ceRNA), transcription factor (TF)/miRNA-mRNA and drug-target regulatory networks. Results:There were 1135 OA-DEGs and 4336 COVID-19-DEGs between disease and normal samples in the GSE57218 and GSE157103 training sets, respectively. The pink, blue and brown modules had significant correlations with OA in the GSE57218 training set, while in the GSE157103 training set, the pink and brown modules were notably correlated with COVID-19. We finally yield 715 OA-intersected genes and 2282 COVID-19-intersected genes. After intersecting the above two intersected genes, we gained 106 candidate genes, and they were involved in ADP metabolic process, nucleoside diphosphate phosphorylation, etc.. The 7 biomarkers, namely AK1, APP, ENO1, TPI1, HSP90B1, HSPB1 and ESR1, were acquired based on seven algorithms. Finally, we successfully constructed the ceRNA, TF/miRNA-mRNA and drug-target networks. Conclusion: Through bioinformatic methods, we explored the biomarkers (AK1, APP, ENO1, TPI1, HSP90B1, HSPB1 and ESR1) of COVID-19 combined OA, providing new ideas for studies related to molecular mechanisms and treatment of comorbidity.

A prognostic dynamic model applicable to infectious diseases providing easily visualized guides -- A case study of COVID-19 in the UK (preprint)

A reasonable prediction of infectious diseases transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, single intervention is effective in disease control but at huge expense while combined interventions would be more efficient, among which, enhancing detection number is crucial in control strategy of COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection number in real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata, it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimick the effect of multiple factors in infectious disease control.