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Abstract Background Syndrome coronavirus-2 (SARS-CoV-2) has developed various strategies to evade the antiviral impact of type I IFN. Non-structural proteins and auxiliary proteins have been extensively researched on their role in immune escape. Nevertheless, the detailed mechanisms of structural protein-induced immune evasion have not been well elucidated. Methods Human alveolar basal epithelial carcinoma cell line (A549) was stimulated with polyinosinic-polycytidylic acid (PIC) and independently transfected with four structural proteins expression plasmids, including nucleocapsid (N), spike (S), membrane (M) and envelope (E) proteins. By RT-qPCR and ELISA, the structural protein with the most pronounced inhibitory effects on IFN-β induction was screened. RNA-sequencing (RNA-Seq) and two differential analysis strategies were used to obtain differentially expressed genes associated with N protein inhibition of IFN-β induction. Based on DIANA-LncBase and StarBase databases, the interactive competitive endogenous RNA (ceRNA) network for N protein-associated genes was constructed. By combining single-cell sequencing data (GSE158055), lncRNA-miRNA-mRNA axis was further determined. Finally, RT-qPCR was utilized to illustrate the regulatory functions among components of the ceRNA axis. Results SARS-CoV-2 N protein inhibited IFN-β induction in human alveolar epithelial cells most significantly compared with other structural proteins. RNA-Seq data analysis revealed genes related to N protein inhibiting IFNs induction. The obtained 858 differentially expressed genes formed the reliable ceRNA network. The function of LINC01002-miR-4324-FRMD8 axis in the IFN-dominated immune evasion was further demonstrated through integrating single-cell sequencing data. Moreover, we validated that N protein could reverse the effect of PIC on LINC01002, FRMD8 and miR-4324 expression, and subsequently on IFN-β expression level. And LINC01002 could regulate the production of FRMD8 by inhibiting miR-4324. Conclusion SARS-CoV-2 N protein suppressed the induction of IFN-β by regulating LINC01002 which was as a ceRNA, sponging miR-4324 and participating in the regulation of FRMD8 mRNA. Our discovery provides new insights into early intervention therapy and drug development on SARS-CoV-2 infection.

Abstract Background As a histone methyltransferase, suppressor of variegation 3–9 homolog 1 (SUV39H1) plays an important role in the occurrence and development of cancer. To explore the mechanism and biological function of SUV39H1 in hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) can gain an insight into the pathogenesis of HBV-HCC. Methods The effect of HBV infection on SUV39H1 in hepatoma cells was detected. CCK-8, colony growth assay and wound healing assay were used to assess the proliferation and migration of HBV-positive hepatoma cells. RNA sequencing (RNA-seq) was applied to find differential genes and enriched pathways. The serum SUV39H1 level in HBV-HCC patients was detected and its correlation with clinical indicators was analyzed. Results SUV39H1 was increased by HBV infection and promoted the proliferation and migration of hepatoma cells. SUV39H1 could upregulate the expression of mitochondrial oxidative phosphorylation (OXPHOS) pathway-related genes. OXPHOS pathway inhibitors could reduce the capacity of proliferation and migration of hepatoma cells after overexpressing SUV39H1. Serum SUV39H1 levels were higher in chronic hepatitis B (CHB) patients than in healthy controls and higher in HBV-HCC patients than in CHB patients. In the diagnosis of HCC, the predictive value of SUV39H1 combined with alpha-fetoprotein (AFP) was better than that of AFP alone. Conclusion SUV39H1 is regulated by HBV infection and promotes the proliferation and migration of hepatoma cells by targeting OXPHOS pathway. It indicates that SUV39H1 may be a new biomarker of the diagnosis of HCC.

Abstract Aim Hydrogels with excellent biocompatibility and biodegradability can be used as the desirable dressings for the therapy of diabetic foot ulcer (DFU). This review aimed to summarize the biological functions of hydrogels, combining with the pathogenesis of DFU. Methods The studies in the last 10 years were searched and summarized from the online database PubMed using a combination of keywords such as hydrogel and diabetes. The biological functions of hydrogels and their healing mechanism on DFU were elaborated. Results In this review, hydrogels were classified by their active substances such as drugs, cytokines, photosensitizers, and biomimetic peptide. Based on this, the biological functions of hydrogels were summarized by associating the pathogenesis of DFU, including oxidative stress, chronic inflammation, cell phenotype change, vasculopathy, and infection. This review also pointed out some of the shortcomings of hydrogels in present researches. Conclusions Hydrogels were classified into carrier hydrogels and self‐functioning hydrogels in this review. Besides, the functions and components of existing hydrogels were clarified to provide assistance for future researches and clinical applications.

Cell migration–inducing protein (CEMIP), also known as KIAA1199 and hyaluronan-binding protein involved in hyaluronan depolymerization, is a new member of the hyaluronidase family that degrades hyaluronic acid (HA) and remodels the extracellular matrix. In recent years, some studies have reported that CEMIP can promote the proliferation, invasion, and adhesion of various tumor cells and can play an important role in bacterial infection and arthritis. This review focuses on the pathological mechanism of CEMIP in a variety of diseases and expounds the function of CEMIP from the aspects of inhibiting cell apoptosis, promoting HA degradation, inducing inflammatory responses and related phosphorylation, adjusting cellular microenvironment, and regulating tissue fibrosis. The diagnosis and treatment strategies targeting CEMIP are also summarized. The various functions of CEMIP show its great potential application value.

BackgroundChronic hepatitis B (CHB) remains a significant global health problem, leading to recurrent inflammation and liver-damaging diseases such as fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Currently, although diagnostic markers for CHB are well established, the indicators for predicting liver injury caused by hepatitis B virus (HBV) infection still need to be further explored. Thus, the identification of credible infectious indicators is urgently needed to facilitate timely clinical intervention and avoid the progression of disease malignancy.MethodsThe Gene Expression Omnibus (GEO) database GSE83148 data set was used to explore the hub genes for HBV infection. The quantitative real-time polymerase chain reaction (qPCR) was used to identify the impact of HBV infection on the expression of hub gene at the cell level. At the same time, serum samples and clinical information were collected from healthy, HBV-free and CHB patients. The enzyme-linked immunosorbent assay (ELISA) was used to verify the results of cell experiments and Pearson correlation analysis was used to clarify hub genes correlation with HBV infection indicators and liver injury-related indicators. Finally, the Gene Expression Profiling Interactive Analysis (GEPIA) database was used to analyze the differences in the expression of hub gene in liver injury diseases.ResultsChemokine (C-X-C motif) ligand (CXCL)8, CXCL9, CXCL10, and CXCL11 were identified as hub genes in HBV infection. After HBV infection, the expression of the four chemokines was significantly increased and the concentrations secreted into serum were also increased. Moreover, the four chemokines were significantly correlated with HBV infection-related indicators and liver injury-related indicators, which were positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST) and hepatitis B e antigen (HBeAg), and negatively correlated with AST/ALT ratio and hepatitis B core antibody (HBcAb). In addition, the expression of CXCL9, CXCL10, and CXCL11 in HCC tissues was significantly higher than in normal tissues.ConclusionUsing a combination of bioinformatics, cell experiments, and clinical correlation analysis, this study showed that CXCL8, CXCL9, CXCL10, and CXCL11 can be used as serum biomarkers to forecast liver injury caused by HBV infection.