Mechanistic insights into Qiteng Xiaozhuo Granules' regulation of autophagy for chronic glomerulonephritis treatment: Serum pharmacochemistry, network pharmacology, and experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: Qiteng Xiaozhuo Granules (QTXZG), a traditional Chinese medicine prescription, is widely acknowledged for its therapeutic efficacy and lack of discernible toxicity in clinical practice, substantiating its potential in the treatment of chronic glomerulonephritis (CGN). Nevertheless, the specific effectiveness and underlying mechanisms of QTXZG remain insufficiently explored. AIM OF THE STUDY: The purpose of this study was to explore the mechanism of the QTXZG in the treatment of CGN via targeting autophagy based on serum pharmacochemistry, network pharmacology, and experimental validation. METHODS: Serum samples from SD rats orally administered QTXZG were analyzed using UPLC-QE/MS to identify contained compounds. Network and functional enrichment analyses elucidated QTXZG's targets and biological mechanisms. Reliability was ensured through molecular docking, in vivo and in vitro experiments. RESULTS: After oral administration of QTXZG, 39 enriched compounds in serum samples collected 1 h later were identified as potential active agents, with 508 potential targets recognized as QTXZG-specific targets. Through integration of various databases, intersection analysis of QTXZG targets, CGN-related genes, and autophagy-related targets identified 10 core autophagy-related targets for QTXZG in CGN. GO and KEGG analyses emphasized their roles in autophagy, inflammation, and immune processes, particularly emphasizing the enrichment of the AMPK/mTOR signaling pathway. Molecular docking results demonstrated strong binding affinities between QTXZG's key compounds and the predicted core targets. In animal experiments, QTXZG was found to ameliorate renal tissue damage in CGN model mice, significantly reducing serum creatinine (Scr) and blood urea nitrogen (BUN) levels. Importantly, both animal and cell experiments revealed QTXZG's ability to decrease excessive ROS and inflammatory factor release in mesangial cells. Furthermore, in vitro and in vivo experiments confirmed QTXZG's capacity to upregulate Beclin1 and LC3II/I expression, decrease p62 expression, and induce CGN autophagy through modulation of the AMPK/mTOR pathway. CONCLUSIONS: This study indicated that QTXZG can induce autophagy in CGN by affecting the AMPK/mTOR pathway, and induction of autophagy may be one of the possible mechanisms of QTXZG's anti-CGN.

Identifying Aging-Related Biomarkers and Immune Infiltration Features in Diabetic Nephropathy Using Integrative Bioinformatics Approaches and Machine-Learning Strategies.

BACKGROUND: Aging plays an essential role in the development of diabetic nephropathy (DN). This study aimed to identify and verify potential aging-related genes associated with DN using bioinformatics analysis. METHODS: To begin with, we combined the datasets from GEO microarrays (GSE104954 and GSE30528) to find the genes that were differentially expressed (DEGs) across samples from DN and healthy patient populations. By overlapping DEGs, weighted co-expression network analysis (WGCNA), and 1357 aging-related genes (ARGs), differentially expressed ARGs (DEARGs) were discovered. We next performed functional analysis to determine DEARGs' possible roles. Moreover, protein-protein interactions were examined using STRING. The hub DEARGs were identified using the CytoHubba, MCODE, and LASSO algorithms. We next used two validation datasets and Receiver Operating Characteristic (ROC) curves to determine the diagnostic significance of the hub DEARGs. RT-qPCR, meanwhile, was used to confirm the hub DEARGs' expression levels in vitro. In addition, we investigated the relationships between immune cells and hub DEARGs. Next, Gene Set Enrichment Analysis (GSEA) was used to identify each biomarker's biological role. The hub DEARGs' subcellular location and cell subpopulations were both identified and predicted using the HPA and COMPARTMENTS databases, respectively. Finally, drug-protein interactions were predicted and validated using STITCH and AutoDock Vina. RESULTS: A total of 57 DEARGs were identified, and functional analysis reveals that they play a major role in inflammatory processes and immunomodulation in DN. In particular, aging and the AGE-RAGE signaling pathway in diabetic complications are significantly enriched. Four hub DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) were further screened using the interaction network, CytoHubba, MCODE, and LASSO algorithms. The results above were further supported by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infiltration, DN had significantly more resting mast cells and delta gamma T cells but fewer regulatory T cells and active mast cells. Four DEARGs have statistical correlations with them as well. Further investigation revealed that four DEARGs were implicated in immune cell abnormalities and regulated a wide range of immunological and inflammatory responses. Furthermore, the drug-protein interactions included four possible therapeutic medicines that target four DEARGs, and molecular docking could make this association practical. CONCLUSIONS: This study identified four DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) associated with DN, which might play a key role in the development of DN and could be potential biomarkers in DN.

Construction of chronic glomerulonephritis­related lncRNA­mRNA regulatory network and lncRNA­-miRNA­mRNA ceRNA network by bioinformatics analysis.

Long non-coding RNAs (lncRNAs) are ncRNA transcripts >200 nucleotides that are important genetic regulators. LncRNAs can directly regulate mRNA through a lncRNA-mRNA regulatory mode and can also regulate mRNA through competitive binding to micro (mi)RNA, which is generally known as the competitive endogenous RNA (ceRNA) network. The present study evaluated the functional roles and regulatory networks of lncRNAs in chronic glomerulonephritis (CGN). The proliferative ability of mouse glomerular mesangial cells (GMCs) induced by different concentrations of lipopolysaccharide (LPS) was assessed using the Cell Counting Kit-8 assay, and RNA sequencing (RNA-seq) was performed to identify differentially expressed lncRNAs in LPS-induced GMCs. Based on the sequencing results, six lncRNAs were selected for validation using reverse transcription-quantitative PCR (RT-qPCR). Furthermore, the lncRNA-mRNA regulatory network and the lncRNA-miRNA-mRNA ceRNA network were constructed to assess the role and mechanism of CGN-related lncRNAs. To elucidate the biological functions of lncRNAs, Gene Ontology (GO) biological process term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on all mRNAs involved in the lncRNA-mRNA regulatory network and in the ceRNA network. A total of 1,532 differentially expressed lncRNAs, including 594 upregulated lncRNAs and 938 downregulated lncRNAs, were identified using RNA-seq. The results of RT-qPCR validation were consistent with RNA-seq results. An lncRNA-mRNA regulatory network, including 236 lncRNAs and 556 mRNAs, and a ceRNA network, including 6 lncRNAs, 18 miRNAs and 419 mRNAs, were successfully constructed. The GO biological process term enrichment and KEGG pathway enrichment analyses demonstrated that those lncRNAs were often related to inflammatory response and substance metabolism. The present study identified key CGN-related lncRNAs in LPS-induced GMCs, and further demonstrated a global view of the lncRNA-mRNA regulatory network and ceRNA network involved in CGN. These results offered novel insights into the roles of lncRNAs in the pathogenesis of CGN and identified potential diagnostic biomarkers.

The potential role of N6-methyladenosine modification of LncRNAs in contributing to the pathogenesis of chronic glomerulonephritis.

BACKGROUND: Increasing evidence indicates that N6-methyladenosine (m6A) modification of mRNAs has been shown to play a critical role in the occurrence and development of many diseases, while little is known about m6A modification in long non-coding RNAs (LncRNAs). Our study aims to investigate the potential functions of LncRNA m6A modifications in lipopolysaccharide (LPS)-induced mouse mesangial cells (MMCs), providing us with a new perspective on the molecular mechanisms of chronic glomerulonephritis (CGN) pathogenesis. METHODS: Differentially methylated LncRNAs were identified by Methylated RNA immunoprecipitation sequencing (MeRIP-seq). LncRNA-mRNA and LncRNA-associated LncRNA-miRNA-mRNA (CeRNA) networks were constructed by bioinformatics analysis. Furthermore, we utilized gene ontology (GO) and pathway enrichment analyses (KEGG) to explore target genes from co-expression networks. In addition, the total level of m6A RNA methylation and expression of methyltransferase and pro-inflammatory cytokines were detected by the colorimetric quantification method and western blot, respectively. Cell viability and cell cycle stage were detected by cell counting kit-8 (CCK-8) and flow cytometry. RESULTS: In total, 1141 differentially m6A-methylated LncRNAs, including 529 hypermethylated LncRNAs and 612 hypomethylated LncRNAs, were determined by MeRIP-seq. The results of GO and KEGG analysis revealed that the target mRNAs were mainly enriched in signal pathways, such as the NF-kappa B signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway, and apoptosis signaling pathway. In addition, higher METTL3 expression was found in CGN kidney tissues using the GEO database. METTL3 knockdown in MMC cells drastically reduced the levels of m6A RNA methylation, pro-inflammatory cytokines IL6 and TNF-α, and inhibited cell proliferation and cycle progression. CONCLUSIONS: Our findings provide a basis and novel insight for further investigations of m6A modifications in LncRNAs for the pathogenesis of CGN.

Alteration of N6-methyladenosine epitranscriptome profile in lipopolysaccharide-induced mouse mesangial cells.

N6-Methyladenosine (m6A) is the most prevalent internal modification of messenger RNA (mRNA) in eukaryotes. The underlying molecular mechanisms of m6A modification in chronic glomerulonephritis (CGN) remain unexplored. Here, we performed methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) analyses to assess the alterations of epitranscriptome-wide m6A profile in lipopolysaccharide (LPS)-induced mouse mesangial cells (MMC). The results of our data showed 2153 significantly differential m6A peaks and 358 significantly differentially expressed genes. Furthermore, integrated analysis from MeRIP-seq and RNA-seq identified a total of 64 genes with differential m6A modification and expressed levels, of which 5 genes displayed hypermethylation and upregulation, 42 genes displayed hypermethylation and downregulation, 11 genes displayed hypomethylation and upregulation, and 8 genes displayed hypomethylation and downregulation. Many of them (including Fosl1, Sorbs1, Ambp, Fgfr3, Nedd9, Fgg, Trim13, Fgf22, Mylk, and Muc6) are implicated in the regulation of the immune and inflammatory response. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis found that differential 64 genes were mainly enriched in fatty acid oxidation, apoptosis signaling pathway, complement and coagulation cascades, and PPAR signaling pathway. Together, our study provided a new perspective on the understanding of molecular features of m6A modification in CGN pathogenic pathogenesis.

Identifying effective diagnostic biomarkers and immune infiltration features in chronic kidney disease by bioinformatics and validation.

Background: Chronic kidney disease (CKD), characterized by sustained inflammation and immune dysfunction, is highly prevalent and can eventually progress to end-stage kidney disease. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets for CKD. Methods: First, we merged data from GEO microarrays (GSE104948 and GSE116626) to identify differentially expressed genes (DEGs) in CKD and healthy patient samples. Then, we conducted GO, KEGG, HPO, and WGCNA analyses to explore potential functions of DEGs and select clinically significant modules. Moreover, STRING was used to analyse protein-protein interactions. CytoHubba and MCODE algorithms in the cytoscape plug-in were performed to screen hub genes in the network. We then determined the diagnostic significance of the obtained hub genes by ROC and two validation datasets. Meanwhile, the expression level of the biomarkers was verified by IHC. Furthermore, we examined immunological cells' relationships with hub genes. Finally, GSEA was conducted to determine the biological functions that biomarkers are significantly enriched. STITCH and AutoDock Vina were used to predict and validate drug-gene interactions. Results: A total of 657 DEGs were screened and functional analysis emphasizes their important role in inflammatory responses and immunomodulation in CKD. Through WGCNA, the interaction network, ROC curves, and validation set, four hub genes (IL10RA, CD45, CTSS, and C1QA) were identified. Furthermore, IHC of CKD patients confirmed the results above. Immune infiltration analysis indicated that CKD had a significant increase in monocytes, M0 macrophages, and M1 macrophages but a decrease in regulatory T cells, activated dendritic cells, and so on. Moreover, four hub genes were statistically correlated with them. Further analysis exhibited that IL10RA, which obtained the highest expression level in hub genes, was involved in abnormalities in various immune cells and regulated a large number of immune system responses and inflammation-related pathways. In addition, the drug-gene interaction network contained four potential therapeutic drugs targeting IL10RA, and molecular docking might make this relationship viable. Conclusion: IL10RA and its related hub molecules might play a key role in the development of CKD and could be potential biomarkers in CKD.