Identification of a circRNA-miRNA-mRNA network to explore the effects of circRNAs on pathogenesis and treatment of systemic lupus erythematosus.

OBJECTIVES: Systemic lupus erythematosus (SLE) is an autoimmune disease characterised by immune inflammation. It involves multiple organs. Many studies have demonstrated that circRNAs are closely associated with SLE. Nonetheless, the potential mechanism by which circRNAs impacts SLE is not fully understood. The aim of this study was to explore the regulatory roles of circRNAs, the key genes and pathways governing the pathophysiological processes of SLE, and to screen therapeutic agents. METHODS: The sequencing data of circRNA, miRNA and mRNA were obtained from Gene Expression Omnibus (GEO) datasets. Candidates were identified to construct a circRNA-miRNA-mRNA network based on circRNA-miRNA interactions and miRNA-mRNA interactions. Gene functional enrichments were performed on the DAVID database. Protein-protein interaction (PPI) network was constructed by STRING database and visualised in Cytoscape software. The hub genes were explored by the MCODE plugin app. The Connectivity Map L1000 platform was used to identify potential agents. RESULTS: A total of 1093 differentially expressed circRNAs (DEcircRNAs), 42 differentially expressed miRNAs (DEmiRNAs) and 1431 differentially expressed mRNAs (DEmRNAs) were identified. We integrated 3 overlapped circRNAs, 13 miRNAs and 352 target mRNAs into a circRNA-miRNA-mRNA network. We next identified 44 hub genes based on the PPI network. KEGG pathway analysis revealed that the DEGs were mainly associated with MAPK signalling pathway. In addition, we discovered several chemicals as potential treatment options for SLE. CONCLUSIONS: Through this bioinformatics analysis, we suggest a regulatory role for circRNAs in the pathogenesis and treatment of SLE from the view of a competitive endogenous RNA (ceRNA) network.

The psychological impact of COVID-19 on university students in China and Africa.

The COVID-19 pandemic is alarmingly a global health catastrophe that has created an unprecedented mental health decline especially in young adults, who have been noted to be a vulnerable population. In this study, we investigated the prevalence of depression and anxiety in university students in China and Africa during the COVID-19 pandemic, the significant factors contributing to the prevalence of anxiety and depression, the differences in factors affecting the different groups being investigated and to emphasize that psychological intervention are as important as the physical interventions during and after the pandemic. The study was conducted through online surveys, with 684 participants using Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 standardized scales. Comparing all groups combined, of the 636 participants, 361 (56.8%) had depression and 227 (35.7%) had anxiety. Chi squared tests at significance level (P<0.05) showed that country of citizenship, religion, parents' educational background, household monthly income and, having family members with COVID-19 variables were strongly associated with depression and anxiety. In contrast, age, gender, educational background, and major showed no significant association. Comparing the individual groups separately using chi square (P<0.05), the Chinese students in China group had 35.6% with depression and 13.1% with anxiety. The variable associated with both depression and anxiety was education major, with depression only was parent's educational background and with anxiety only was gender. The African students in China group had 70.3% with depression and 45.0% with anxiety. Gender was strongly associated with both depression and anxiety, and religion and having family members with COVID-19 with anxiety only. Africans in Africa had 66.0% with depression and 50.5% with anxiety. Educational background was strongly associated with depression. There was no statistically significant variable for anxiety. Chi square test showed a statistically significant difference in depression and anxiety levels with the Chinese group compared to both African groups, and no significant difference between both African groups. Our findings demonstrated that COVID-19 had a negative psychological impact on university students. Therefore, more attention should be put on youth's mental health during this pandemic.

Glomerular Expression of S100A8 in Lupus Nephritis: An Integrated Bioinformatics Analysis.

Introduction: Lupus nephritis (LN) is a major risk factor of morbidity and mortality. Glomerular injury is associated with different pathogeneses and clinical presentations in LN patients. However, the molecular mechanisms involved are not well understood. This study aimed to explore the molecular characteristics and mechanisms of this disease using bioinformatics analysis. Methods: To characterize glomeruli in LN, microarray datasets GSE113342 and GSE32591 were downloaded from the Gene Expression Omnibus database and analyzed to determine the differentially expressed genes (DEGs) between LN glomeruli and normal glomeruli. Functional enrichment analyses and protein-protein interaction network analyses were then performed. Module analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software. Immunofluorescence staining was performed to identify the glomerular expression of S100A8 in various International Society of Nephrology/Renal Pathology Society (ISN/RPS) class LN patients. The image of each glomerulus was acquired using a digital imaging system, and the green fluorescence intensity was quantified using Image-Pro Plus software. Results: A total of 13 DEGs, consisting of 12 downregulated genes and one upregulated gene (S100A8), were identified in the microarray datasets. The functions and pathways associated with the DEGs mainly include inflammatory response, innate immune response, neutrophil chemotaxis, leukocyte migration, cell adhesion, cell-cell signaling, and infection. We also found that monocytes and activated natural killer cells were upregulated in both GSE113342 and GSE32591. Glomerular S100A8 staining was significantly enhanced compared to that in the controls, especially in class IV. Conclusions: The DEGs identified in the present study help us understand the underlying molecular mechanisms of LN. Our results show that glomerular S100A8 expression varies in different pathological types; however, further research is required to confirm the role of S100A8 in LN.