Weighted Gene Co-expression Network Analysis Reveals Different Immunity but Shared Renal Pathology Between IgA Nephropathy and Lupus Nephritis

Novel insights into the molecular mechanisms of sepsis-associated acute kidney injury: an integrative study of GBP2, PSMB8, PSMB9 genes and immune microenvironment characteristics

BACKGROUND

Sepsis-associated acute kidney injury (SA-AKI) is a prevalent and severe complication of sepsis, but its complex pathogenesis remains unclear. This study aims to identify potential biomarkers for SA-AKI by elucidating its molecular mechanisms through bioinformatics methods.

METHODS

Transcriptional data related to SA-AKI were obtained from the Gene Expression Omnibus (GEO) database. We used differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) to identify characteristic genes associated with SA-AKI and conducted enrichment analyses. Hub genes were determined using protein-protein interaction (PPI) network analysis and the Least Absolute Shrinkage and Selection Operator (LASSO). Additionally, ROC curves were plotted to assess the diagnostic value of these core genes. Immune cell infiltration was analyzed using the CIBERSORT algorithm, and potential associations between the hub genes and clinicopathological features were explored based on the Nephroseq database. Finally, a murine model of SA-AKI was induced with lipopolysaccharide (LPS) to validate the findings, and mRNA abundance and protein production levels of pivotal genes were confirmed via RT-qPCR, Western blotting, and immunohistochemical methods.

RESULTS

We identified 268 characteristic genes associated with SA-AKI that are enriched in immune and inflammation-related pathways. Utilizing machine learning techniques, three key genes were screened: GBP2, PSMB8 and PSMB9. The expression patterns of these three genes were well-validated through animal experiments and databases. Correlation between these genes and clinical indicators was confirmed using the Nephroseq database. Furthermore, immune infiltration analysis provided additional insights into their potential functions.

CONCLUSION

GBP2, PSMB8, and PSMB9 are promising candidate genes for SA-AKI, providing a novel perspective on its pathological mechanisms. Further exploration of the biological roles of these genes in the pathogenesis of SA-AKI is needed in the future.

Molecular Differences in Glomerular Compartment to Distinguish Immunoglobulin A Nephropathy and Lupus Nephritis

Background

Immunoglobulin A nephropathy (IgAN) and lupus nephritis (LN) are the most prevalent primary and secondary glomerular diseases, respectively, with several similarities in clinical presentations. Common pathogenic mechanisms in IgAN and LN have been well investigated by previous studies. However, the manifestation mechanism of these two independent diseases carrying distinct immunofluorescent pathological features is still unknown considering the similarities between them. Therefore, differences in pathogenic mechanisms between IgAN and LN were compared in this study.

Methods

R packages were used for processing the glomerular gene expression datasets acquired from the Gene Expression Omnibus (GEO) database. Least Absolute Selection and Shrinkage Operator (LASSO) and multivariate logistic regression analysis were used to construct models predicting IgAN and LN. Cibersort was used to process the immune cell infiltration analysis. Immunochemistry was used to validate the findings by bioinformatics analysis.

Results

In the predicting models based on differentially expressed genes (DEG) and weighted correlation network analysis (WGCNA), retinoic acid receptor γ (RARG) and prolactin releasing hormone (PRLH) were independent risk factors for IgAN, and HECT domain and RCC1-like domain-containing protein 5 (HERC5) and interferon stimulated exonuclease gene 20 (ISG20) were independent risk factors for LN. Gene Ontology (GO) analysis revealed that DEGs mostly correlated to IgAN were enriched in ligand-receptor activity-induced cellular growth and development, while DEGs mostly correlated to LN were enriched in nucleic acid/nucleotide binding-induced type I interferon-related activity and response to virus infection. Immune infiltration analysis showed CD4+ T-cells and M2 macrophage abundance in the glomerular compartment in IgAN and LN, respectively. Immunochemistry validated the predicting models for IgAN and LN and revealed different expression patterns of RARG, PRLH, HERC5, and ISG20.

Conclusion

We investigated key differences in the pathogenesis between IgAN and LN and provided validated predicting models to distinguish IgAN and LN. RARG and PRLH, HERC5 and ISG20 might play an essential role in the formation of IgAN and LN, respectively.

Identification of toll-like receptor 5 and acyl-CoA synthetase long chain family member 1 as hub genes are correlated with the severe forms of COVID-19 by Weighted gene co-expression network analysis

Since a 25% mortality rate occurred in critical Coronavirus disease 2019 (COVID-19) patients, investigating the potential drivers remains to be important. Here, the authors applied Weighted Gene Co-expression Network Analysis to identify the potential drivers in the blood samples of multiple COVID-19 expression profiles. The authors found that the darkslateblue module was significantly correlated with critical COVID-19, and Gene Ontology analysis indicated terms associated with the inflammation pathway and apoptotic process. The authors intersected differentially expressed genes, Maximal Clique Centrality calculated hub genes, and COVID-19 related genes in the Genecards dataset, and two genes, toll-like receptor 5 (TLR5) and acyl-CoA synthetase long chain family member 1 (ACSL1), were screened out. The Gene Set Enrichment Analysis further supports their core role in the inflammatory pathway. Furthermore, the cell-type identification by estimating relative subsets of RNA transcript demonstrated that TLR5 and ACSL1 were associated with neutrophil enrichment in critical COVID-19 patients. Collectively, the aurthors identified two hub genes that were strongly correlated with critical COVID-19. These may help clarify the pathogenesis and assist the immunotherapy development.

MicroRNA-339-5p inhibits lipopolysaccharide-induced rat mesangial cells by regulating the Syk/Ras/c-Fos pathway

Chronic glomerulonephritis (CGN) is a disease occurred in glomeruli. The mechanism of CGN is regarded to be involved in a range of inflammatory responses. MicroRNA-339-5p (miR-339-5p) has been reported to be involved in inflammatory responses in many diseases. However, the role of miR-339-5p in CGN remains unclear. The purpose of this study was to investigate the role of miR-339-5p in lipopolysaccharide (LPS)-induced nephritis injury in vitro. The real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and western blot (WB) were used to detect the expression of miR-339-5p and Syk/Ras/c-Fos pathway. Double luciferase was performed to identify targeted binding of miR-339-5p to Syk. Cell counting kit-8 (CCK-8) and flow cytometry were used to observe cell viability and cell cycle. Enzyme-linked immunosorbent assay (ELISA) was performed to measure the concentrations of inflammatory cytokines IL-1β, IL-10, IL-6, and TNF-α. Lipopolysaccharide (LPS) could increase HBZY-1 (rat mesangial cells) cell viability, decrease the G2 phase, and promote cell proliferation and accelerate inflammatory cytokine. However, overexpression of miR-339-5p could inhibit LPS-induced HBZY-1 cell viability, decrease the expression of Syk/Ras/c-Fos signaling pathway, downregulate the expression level of inflammatory cytokines, increase the G2 phase, and inhibit cell proliferation. miR-339-5p could inhibit the proliferation and inflammation of the rat mesangial cells through regulating Syk/Ras/c-Fos signaling pathway.

Long Non-Coding NONRATG001910.2 Promotes the Proliferation of Rat Mesangial Cell Line HBZY-1 Through the miR-339-3p/CTNNB1 Axis

Chronic glomerulonephritis (CGN) is one of the leading causes of end-stage renal disease (ESRD). A growing body of literature emphasizes the important role of long non-coding RNAs (lncRNAs) in the development and progression of the disease. However, the function of NONRATG001910.2 in the development of CGN was not well understood. This research aimed to investigate the effect of NONRATG001910.2 on CGN and revealed its potential molecular mechanisms. In this work, the expression of NONRATG001910.2 was detected by quantitative real-time polymerase chain reaction (qRT-RCR) in cell lines. We found that NONRATG001910.2 was significantly up-regulated in lipopolysaccharide (LPS) induced cells. High NONRATG001910.2 levels were associated with the development of CGN. In addition, NONRATG001910.2 knockdown inhibited cell proliferation and cell cycle. At the same time, we found that up-regulation of microRNA-339-3p (miR-339-3p) abrogated the biological roles of NONRATG001910.2 up-regulation. Moreover, the knockdown of CTNNB1 can upregulate miR-339-3p expression, thereby inhibiting cell proliferation. In conclusion, these results demonstrated that NONRATG001910.2 in LPS-stimulated rat mesangial cell line HBZY-1 (HBZY-1) by targeting miR-339-3p, which subsequently promotes the expression of CTNNB1, and suggested that NONRATG001910.2 may be a potential biomarker.

Identification and validation of glomerulotubular crosstalk genes mediating IgA nephropathy by integrated bioinformatics

Background

IgA nephropathy (IgAN), which has been reported as the most prevalent glomerulonephritis globally, is the major contributor to end-stage renal diseases. This bioinformatics study aimed to explore glomerulotubular crosstalk genes and dysregulated pathways relating to the pathogenesis of IgAN.

Methods

The microarray datasets from the Gene Expression Omnibus (GEO) database were searched. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) of both glomeruli and tubulointerstitium were conducted individually. The co-expression gene modules of glomeruli and tubulointerstitium were compared via gene function enrichment analysis. Subsequently, the crosstalk co-expression network was constructed via the STRING database and key genes were mined from the crosstalk network. Finally, key genes were validated using another GEO dataset (GSE99340) containing RNA-seq data of IgAN and lupus nephritis, and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis.

Results

Five hundred eighty-three DEGs and eight modules were identified in glomerular samples, while 272 DEGs and four modules were in tubulointerstitial samples. There were 119 overlapping DEGs between the two groups. Among the distinctive modules, four modules in glomeruli and one module in tubulointerstitium were positively associated with IgAN. While four modules in glomeruli and two modules in tubulointerstitium were negatively associated with IgAN. The top ten key genes screened by CytoHubba were ITGAM, ALB, TYROBP, ITGB2, CYBB, HCK, CSF1R, LAPTM5, FN1, and CTSS. Compared with lupus nephritis, there were significant differences in the expression levels of CYBB, CTSS and TYROBP (P < 0.05), while other key genes showed no significant difference. Meanwhile, CYBB, CTSS, and TYROBP demonstrated possible diagnostic significance.

Conclusions

The crosstalk genes confirmed in this study may provide novel insight into the pathogenesis of IgAN. Immune-related pathways are associated with both glomerular and tubulointerstitial injuries in IgAN. The glomerulotubular crosstalk might perform a role in the pathogenesis of IgAN.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02779-7.