Prioritization of Diagnostic and Prognostic Biomarkers for Lupus Nephritis Based on Integrated Bioinformatics Analyses

Genetic association between celiac disease and chronic kidney disease: a two-sample Mendelian randomization study

OBJECTIVE

A two-sample Mendelian randomization (MR) analysis was performed to elucidate the causal impact of celiac disease on the risk of chronic kidney disease (CKD).

METHODS

The study comprised data from three genome-wide association studies involving individuals of European ancestry. The study groups included participants with celiac disease (n = 24,269), CKD (n = 117,165), and estimated glomerular filtration rate levels based on serum creatinine (eGFRcrea, n = 133,413). We employed four widely recognized causal inference algorithms: MR-Egger, inverse variance weighted (IVW), weighted median, and weighted mode. To address potential issues related to pleiotropy and overall effects, MR-Egger regression and the MR-PRESSO global test were performed. Heterogeneity was assessed using Cochran's Q test.

RESULTS

We identified 14 genetic variants with genome-wide significance. The MR analysis provided consistent evidence across the various methodologies, supporting a causal relationship between celiac disease and an elevated risk of CKD (odds ratio (OR)IVW = 1.027, p = 0.025; ORweighted median = 1.028, P = 0.049; ORweighted mode = 1.030, p = 0.044). Furthermore, we observed a causal link between celiac disease and a decreased eGFRcrea (ORIVW = 0.997, P = 2.94E-06; ORweighted median = 0.996, P = 1.68E-05; ORweighted mode = 0.996, P = 3.11E-04; ORMR Egger = 0.996, P = 5.00E-03). We found no significant evidence of horizontal pleiotropy, heterogeneity, or bias based on MR-Egger regression, MR-PRESSO, and Cochran's Q test.

CONCLUSION

The results of this study indicate a causal relationship between celiac disease and an increased risk of CKD.

Urinary exosomal mRNAs as biomarkers for predicting the therapeutic effect of renin-angiotensin system inhibitors in IgA nephropathy patients

BACKGROUND

Renin-angiotensin system inhibitors (RASi) treatment is the basic therapy for IgA nephropathy (IgAN) patients. However, there is few of biomarker that can predict the efficacy of RASi. This study aimed to find urinary exosomal mRNAs related to the therapeutic effect of RASi in the treatment of proteinuria in IgAN patients.

METHODS

We divided IgAN patients in screening cohort into A1 (proteinuria increase at 3 months), B1 (proteinuria decrease less than 50 % at 3 months), C1 (proteinuria decrease more than 50 % at 3 months) groups according to changes of proteinuria after treatment. The urinary exosomes were collected before biopsy, RNAs were extracted and analyzed with the microarray assay. The candidate genes were screened by differentially expressed genes (DEGs) analysis and then validated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort. A receiver operating characteristic (ROC) curve was used to evaluate gene performance in predicting therapeutic effect on RASi reducing proteinuria in IgAN patients.

RESULTS

ECE1 and PDE1A mRNAs were significantly different among the three groups, and were gradually decreased among A1, B1 and C1 groups. In the validation cohort, the level of urinary exosomal ECE1 and PDE1A mRNAs were also significantly lower in A2 group compared with C2 group(ECE1, P < 0.001;PDE1A, P < 0.01). Besides, the level of ECE1 mRNA was also lower in B2 group compared with C2 group (P < 0.01). The ROC curve verified that urinary exosomal ECE1 and PDE1A gene level predicted RASi efficacy in IgAN patients with area under curve (AUC) 0.68 and 0.63 respectively.

CONCLUSION

Urinary exosomal ECE1 and PDE1A mRNAs expression can serve as potential biomarkers for predicting the RASi efficacy to reduce proteinuria in IgAN patients.

Identification and analysis of differently expressed transcription factors in aristolochic acid nephropathy

BACKGROUND

Aristolochic acid nephropathy (AAN) is a rapidly progressive interstitial nephropathy caused by Aristolochic acid (AA). AAN is associated with the development of nephropathy and urothelial carcinoma. It is estimated that more than 100 million people worldwide are at risk of developing AAN. However, the underlying mechanisms driving renal deterioration in AAN remain poorly understood, and the treatment options are limited.

METHODS

We obtained GSE27168 and GSE136276 series matrix data from the Gene Expression Omnibus (GEO) related to AAN. Using the R Studio environment, we applied the limma package and WGCNA package to identify co-differently expressed genes (co-DEGs). By GO/KEGG/GSVA analysis, we revealed common biological pathways. Subsequently, co-DEGs were subjected to the String database to construct a protein-protein interaction (PPI) network. The MCC algorithms implemented in the Cytohubba plugin were employed to identify hub genes. The hub genes were cross-referenced with the transcription factor (TF) database to identify hub TFs. Immune infiltration analysis was performed to identify key immune cell groups by utilizing CIBERSORT. The expressions of AAN-associated hub TFs were verified in vivo and in vitro. Finally, siRNA intervention was performed on the two TFs to verify their regulatory effect in AAN.

RESULTS

Our analysis identified 88 co-DEGs through the "limma" and "WGCNA" R packages. A PPI network comprising 53 nodes and 34 edges was constructed with a confidence level >0.4. ATF3 and c-JUN were identified as hub TFs potentially linked to AAN. Additionally, expressions of ATF3 and c-JUN positively correlated with monocytes, basophils, and vessels, and negatively correlated with eosinophils and endothelial cells. We observed a significant increase in protein and mRNA levels of these two hub TFs. Furthermore, it was found that siRNA intervention targeting ATF3, but not c-JUN, alleviated cell damage induced by AA. The knockdown of ATF3 protects against oxidative stress and inflammation in the AAN cell model.

CONCLUSION

This study provides novel insights into the role of ATF3 in AAN. The comprehensive analysis sheds light on the molecular mechanisms and identifies potential biomarkers and drug targets for AAN treatment.

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Background

Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods

We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results

A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion

CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Association of class II HLA alleles with susceptibility to develop immune-mediated diseases in Paraguayan patients

Genetic and nongenetic factors are involved in the pathogenesis of immune-mediated inflammatory diseases (IMIDs). The best-known genetic factor for susceptibility to IMIDs is the human leukocyte antigen (HLA). The aim of the present study was to evaluate the association of HLA class II genes with the risk of systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and systemic sclerosis (SSc) in the Paraguayan population. We included 254 patients with IMIDs (101 SLE, 103 RA, and 50 SSc) and 50 healthy controls. The haplotypes of five genes corresponding to HLA class II genes and their relationship to the IMIDs studied were determined. Note that 84.6% were women, with a mean age of 43.4 ± 14 years. Among the associated HLA alleles, we found the previously identified risk factors in other populations like HLA-DRB1*03:01 and HLA-DRB1*14:02 for RA, as well as new ones not previously identified, such as DPA1*02:01 for SLE and, DB1*02:01 for RA and SSc. In the genetic association analysis, already known associations have been replicated, and unpublished associations have been identified in Paraguayan patients with IMIDs. This is the first genetic association study in Paraguayan patients with IMIDs.

Potential biomarkers for active renal involvement in systemic lupus erythematosus patients

OBJECTIVE

This study aimed to identify the key genes related to active renal involvement in patients with systemic lupus erythematosus (SLE).

METHODS

Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between SLE patients with active renal involvement and those who did not have active renal involvement were identified by R software. Hub genes were identified using protein-protein interaction networks. The relationships between the expression levels of identified hub genes and SLEDAI were subjected to linear correlation analysis. The diagnostic accuracy of the hub genes was evaluated with the area under the curve of the receiver operating characteristic curve (ROC-AUC). Transcription factors (TFs) were predicted. The expression levels of different hub genes and histopathological patterns were also examined.

RESULTS

A total of 182 DEGs were identified. Enrichment analysis indicated that DEGs were primarily enriched in neutrophil degranulation, neutrophil activation involved in immune response and neutrophil activation. The expression levels of 12 identified hub genes were verified. Ten of the 12 hub genes were positively associated with SLEDAI. The combination model of DEFA4, CTSG, RETN, CEACAM8, TOP2A, LTF, MPO, ELANE, BIRC5, and LCN2 had a certain diagnostic accuracy in detecting renal involvement with high disease activity in SLE patients. The expressions of five predicted TFs were validated by GSE65391 dataset.

CONCLUSION

This work explored the pathogenesis of renal involvement in SLE. These results may guide future experimental research and clinical transformation.

GBP2 acts as a member of the interferon signalling pathway in lupus nephritis

Lupus nephritis (LN) is a common and serious clinical manifestation of systemic lupus erythematosus. However, the pathogenesis of LN is not fully understood. The currently available treatments do not cure the disease and appear to have a variety of side effects in the long term. The purpose of this study was to search for key molecules involved in the LN immune response through bioinformatics techniques to provide a reference for LN-specific targeted therapy. The GSE112943 dataset was downloaded from the Gene Expression Omnibus database, and 20 of the samples were selected for analysis. In total, 2330 differentially expressed genes were screened. These genes were intersected with a list of immune genes obtained from the IMMPORT immune database to obtain 128 differentially expressed immune-related genes. Enrichment analysis showed that most of these genes were enriched in the interferon signalling pathway. Gene set enrichment analysis revealed that the sample was significantly enriched for expression of the interferon signalling pathway. Further analysis of the core gene cluster showed that nine genes, GBP2, VCAM1, ADAR, IFITM1, BST2, MX2, IRF5, OAS1 and TRIM22, were involved in the interferon signalling pathway. According to our analysis, the guanylate binding protein 2 (GBP2), interferon regulatory factor 5 and 2′-5′-oligoadenylate synthetase 1 (OAS1) genes are involved in three interferon signalling pathways. At present, we do not know whether GBP2 is associated with LN. Therefore, this study focused on the relationship between GBP2 and LN pathogenesis. We speculate that GBP2 may play a role in the pathogenesis of LN as a member of the interferon signalling pathway. Further immunohistochemical results showed that the expression of GBP2 was increased in the renal tissues of LN patients compared with the control group, confirming this conjecture. In conclusion, GBP2 is a member of the interferon signalling pathway that may have implications for the pathogenesis of LN and serves as a potential biomarker for LN.

Plasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response to renin-angiotensin system inhibitors in patients with IgA nephropathy

Background

Renin-angiotensin system inhibitors (RASi) are the first choice and basic therapy for the treatment of IgA nephropathy (IgAN) with proteinuria. However, approximately 40% of patients have no response to RASi treatment. The aim of this study was to screen potential biomarkers for predicting the treatment response of RASi in patients with IgAN.

Methods

We included IgAN patients who were treatment-naive. They received supportive treatment, including a maximum tolerant dose of RASi for 3 months. According to the degree of decrease in proteinuria after 3 months of follow-up, these patients were divided into a sensitive group and a resistant group. The plasma of the two groups of patients was collected, and the exosomes were extracted for high-throughput sequencing. The screening of hub genes was performed using a weighted gene co-expression network (WGCNA) and filtering differentially expressed genes (DEGs). We randomly selected 20 patients in the sensitive group and 20 patients in the resistant group for hub gene validation by real-time quantitative polymerase chain reaction (qRT−PCR). A receiver operating characteristic (ROC) curve was used to evaluate hub genes that predicted the efficacy of the RASi response among the 40 validation patients.

Results

After screening 370 IgAN patients according to the inclusion and exclusion criteria and the RASi treatment response evaluation, there were 38 patients in the sensitive group and 32 patients in the resistant group. IRAK1, ABCD1 and PLXNB3 were identified as hub genes by analyzing the high-throughput sequencing of the plasma exosomes of the two groups through WGCNA and DEGs screening. The sequencing data were consistent with the validation data showing that these three hub genes were upregulated in the resistant group compared with the sensitive group. The ROC curve indicated that IRAK1 was a good biomarker to predict the therapeutic response of RASi in patients with IgAN.

Conclusions

Plasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response of RASi in patients with IgAN.

Identifying key genes in CD4+ T cells of systemic lupus erythematosus by integrated bioinformatics analysis

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall signature genes in CD4⁺ T cells and how they affect the pathological process of SLE remain to be elucidated. This study aimed to identify the crucial genes, potential biological processes and pathways underlying SLE pathogenesis by integrated bioinformatics. The gene expression profiles of isolated peripheral CD4⁺ T cells from SLE patients with different disease activity and healthy controls (GSE97263) were analyzed, and 14 co-expression modules were identified using weighted gene co-expression network analysis (WGCNA). Some of these modules showed significantly positive or negative correlations with SLE disease activity, and primarily enriched in the regulation of type I interferon and immune responses. Next, combining time course sequencing (TCseq) with differentially expressed gene (DEG) analysis, crucial genes in lupus CD4⁺ T cells were revealed, including some interferon signature genes (ISGs). Among these genes, we identified 4 upregulated genes (PLSCR1, IFI35, BATF2 and CLDN5) and 2 downregulated genes (GDF7 and DERL3) as newfound key genes. The elevated genes showed close relationship with the SLE disease activity. In general, our study identified 6 novel biomarkers in CD4⁺ T cells that might contribute to the diagnosis and treatment of SLE.

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.