An enhanced RRM2 siRNA delivery to rheumatoid arthritis fibroblast-like synoviocytes through a liposome‑protamine-DNA-siRNA complex with cell permeable peptides

Epigenome-wide association study of long-term psychosocial stress in older adults

Long-term psychosocial stress is strongly associated with negative physical and mental health outcomes, as well as adverse health behaviours; however, little is known about the role that stress plays on the epigenome. One proposed mechanism by which stress affects DNA methylation is through health behaviours. We conducted an epigenome-wide association study (EWAS) of cumulative psychosocial stress (n = 2,689) from the Health and Retirement Study (mean age = 70.4 years), assessing DNA methylation (Illumina Infinium HumanMethylationEPIC Beadchip) at 789,656 CpG sites. For identified CpG sites, we conducted a formal mediation analysis to examine whether smoking, alcohol use, physical activity, and body mass index (BMI) mediate the relationship between stress and DNA methylation. Nine CpG sites were associated with psychosocial stress (all p < 9E-07; FDR q < 0.10). Additionally, health behaviours and/or BMI mediated 9.4% to 21.8% of the relationship between stress and methylation at eight of the nine CpGs. Several of the identified CpGs were in or near genes associated with cardiometabolic traits, psychosocial disorders, inflammation, and smoking. These findings support our hypothesis that psychosocial stress is associated with DNA methylation across the epigenome. Furthermore, specific health behaviours mediate only a modest percentage of this relationship, providing evidence that other mechanisms may link stress and DNA methylation.

IGF2BP3 regulates the expression of RRM2 and promotes the progression of rheumatoid arthritis via RRM2/Akt/MMP-9 pathway

BACKGROUND

Rheumatoid arthritis (RA) is a common inflammatory and autoimmune disease. Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) is a crucial and a rate-limiting enzyme responsible for deoxynucleotide triphosphate(dNTP) production. We have found a high expression level of RRM2 in patients with RA, but the molecular mechanism of its action remains unclear.

METHODS

We analyzed the expression of hub genes in RA using GSE77298 datasets downloaded from Gene Expression Omnibus database. RRM2 and insulin-like growth factor-2 messenger ribonucleic acid (mRNA)-binding protein 3 (IGF2BP3) gene knockdown was achieved by infection with lentiviruses. The expression of RRM2, IGF2BP3, matrix metalloproteinase (MMP)-1, and MMP-9 were detected via western blotting assay. Cell viability was detected via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. MeRIP-qRT-PCR was performed to test the interaction of IGF2BP3 and RRM2 mRNA via m6A modification. Cell proliferation was determined by clone formation assay. Migration and invasion assays were performed using transwell Boyden chamber.

RESULTS

RRM2 and IGF2BP3 were highly expressed in clinical specimens and tumor necrosis factor alpha (TNF-α) and interleukin (IL)-1β-stimulated synovial cells. RRM2 and IGF2BP3 knockdown inhibited the proliferation, migration, and invasion of MH7A cells. The inhibitory effects of IGF2BP3 knockdown were effectively reversed by simultaneously overexpressing RRM2 in MH7A cells. By analyzing N6-methyladenosine (m6A)2Target database, five m6A regulatory target binding sites for IGF2BP3 were identified in RRM2 mRNA, suggesting a direct relationship between IGF2BP3 and RRM2 mRNA. Additionally, in RRM2 small hairpin (sh)RNA lentivirus-infected cells, the levels of phosphorylated Akt and MMP-9 were significantly decreased compared with control shRNA lentivirus-infected cells.

CONCLUSION

The present study demonstrated that RRM2 promoted the Akt phosphorylation leading to high expression of MMP-9 to promote the migration and invasive capacities of MH7A cells. Overall, IGF2BP promotes the expression of RRM2, and regulates the migration and invasion of MH7A cells via Akt/MMP-9 pathway to promote RA progression.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration.

Methods

We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis.

Results

Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis.

Conclusion

RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

A model-based clustering via mixture of hierarchical models with covariate adjustment for detecting differentially expressed genes from paired design

The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts on a whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. However, not all known confounding factors can be controlled in a paired/match design. Model-based clustering, such as mixtures of hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. To the best of our knowledge, no model-based gene clustering methods have the capacity to adjust for the effects of covariates yet. In this article, we proposed a novel mixture of hierarchical models with covariate adjustment in identifying differentially expressed transcripts using high-throughput whole-genome data from paired design. Both simulation study and real data analysis show the good performance of the proposed method.

Identification of CKS2 and RRM2 as potential markers of vitiligo using bioinformatics analysis

Previous studies have attempted to elucidate the molecular mechanism of vitiligo; however, its pathogenesis remains unclear. This study aimed to explore biomarkers related to vitiligo through bioinformatic analysis. The microarray datasets GSE53146 and GSE65127 were downloaded from the Gene Expression Omnibus database. Firstly, differentially expressed genes (DEGs) in GSE53146 were screened, and then an enrichment analysis was performed. Secondly, the protein-protein interaction (PPI) network of DEGs was constructed using the STRING database, and the key genes were screened using the MCODE plugin in Cytoscape and verified using GSE65127. Finally, quantiseq was used to evaluate immune cell infiltration in vitiligo, then to observe the correlation between biomarkers and immune cells. In total, 544 DEGs were identified, including 342 upregulated and 202 downregulated genes. Gene Ontology (GO) enrichment showed that DEGs were related to inflammatory and immune responses, and Kyoto Encyclopedia of Genes and Genomes enrichment showed that DEGs were involved in many autoimmune diseases. In the PPI network, 7 key genes, CENPN, CKS2, PLK4, RRM2, TPX2, CCNA2, and CDC45 were identified by MCODE cluster and verified using the GSE65127 dataset. With an area under the curve (AUC) > 0.8 as the standard, 2 genes were screened, namely CKS2 and RRM2. Further immune infiltration analysis showed that M2 macrophages were involved in the pathogenesis of vitiligo, whereas CKS2 and RRM2 were both related to M2 macrophages. This study shows that CKS2 and RRM2 have potential as biomarkers of vitiligo and provides a theoretical basis for a better understanding of the pathogenesis of vitiligo.

SnoopLigase Enables Highly Efficient Generation of C-C-Linked Bispecific Nanobodies Targeting TNF-α and IL-17A

Bispecific antibodies (bis-Nbs) have been extensively developed since the concept was devised over the decades. Taking advantage of the superior characteristics of nanobodies, bis-Nbs exhibit an emerging tendency to become the new generation of research and diagnostic tools. Traditional strategies to connect the homo- or heterogeneous monomers are commonly applied, but there are still technical issues to generate the bispecific molecules as efficiently as designed. Here, we utilize SnoopLigase to directly tether the C terminus (C-C) of the tagged nanobodies against tumor necrosis factor-α (TNF-α) and interleukin-17A (IL-17A). Under optimal conditions, the yield of C-C-linked bis-Nbs can reach as high as 70% due to the existence of SnoopLigase. The prepared bis-Nbs possessed similar or even higher affinity as the monomers and significantly inhibited the proliferation and migration of rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) induced by TNF-α and IL-17A. This study provides an innovative route for using SnoopLigase to realize a highly efficient generation of C-C-linked bis-Nbs. The approach can be applied to different and multicomponent systems for their potential applications in disease diagnosis and treatment.

Intra-articular Injection of Lornoxicam and MicroRNA-140 Co-loaded Cationic Liposomes Enhanced the Therapeutic Treatment of Experimental Osteoarthritis

Osteoarthritis is a chronic joint disease characterized by chronic inflammation, progressive destruction of articular cartilage, and subchondral bone sclerosis. When compared to individual treatment, the combined administration of genes and small-molecule drugs for osteoarthritis may not only provide superior inflammation control and pain relief, but may also repair cartilage damage. Here, cationic liposomes (CL) were used to deliver small hydrophobic drugs and microRNA into chondrocytes to treat osteoarthritis. Lornoxicam cationic liposomes (Lnxc-CL) were prepared by film dispersion, and loaded with microRNA-140 (miR-140) by electrostatic interaction to obtain cationic liposomes co-loaded with lornoxicam and miR-140 (Lnxc-CL/miR-140). The prepared Lnxc-CL/miR-140 had a particle size of 286.6 ± 7.3 nm, polydispersity index (PDI) of 0.261 ± 0.029 and zeta potential of 26.5 ± 0.5 mV and protected miR-140 from RNase degradation for 24 h. Lnxc-CL/miR-140 was evaluated for its ability to regulate gene expression in chondrocytes in vitro and to provide in vivo therapeutic effects for knee osteoarthritis in rats. The results of in vitro uptake experiments and polymerase chain reaction (PCR) analysis showed that Lnxc-CL/miR-140 efficiently delivered miR-140 into chondrocytes and up-regulated the expression of miR-140 and COL2A1 mRNA. Pharmacodynamics studies demonstrated that Lnxc-CL/miR-140 effectively treated osteoarthritis by eliminating joint inflammation and repairing damaged cartilage cells, with superior therapeutic effects compared to Lnxc or miR-140 alone. Overall, the findings of this study support the co-delivery of Lnxc and miR-140 with cationic liposomes as a potential new therapeutic strategy for the treatment of osteoarthritis.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.