Integrated bioinformatics analysis reveals novel hub genes closely associated with pathological mechanisms of immunoglobulin A nephropathy

Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models

Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

Computational Analysis Reveals the Characteristics of Immune Cells in Glomerular and Tubulointerstitial Compartments in IgA Nephropathy Patients

Objective: The commonalities and differences regarding immune states between glomerular and tubulointerstitial compartments of IgA nephropathy (IgAN) remains largely undetermined. We aim to perform bioinformatic analysis for providing a comprehensive insight into the characteristics of immune cells and associated molecular mechanisms in IgAN.

Materials and Methods: We performed integrated bioinformatic analyses by using IgAN-related datasets from the Gene Expression Omnibus database. First, the differentially expressed genes (DEGs) were identified and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, CIBERSORT was employed to determine the landscape of infiltrating immune cells in both glomerular and tubulointerstitial compartments of IgAN patients, followed by Pearson’s correlation analysis and principal component analysis (PCA). Finally, commonly shared DEGs between glomerular and tubulointerstitial entities were recognized, followed by correlation analyses to identify the dominant commonly shared DEGs associated with immune cell infiltration in IgAN.

Results: GO and KEGG enrichment analyses showed apparently distinct biological processes in the glomerular and tubulointerstitial compartments of IgAN. In addition, CIBERSORT analyses revealed a clear trend of increasing proportions of M1 macrophage and M2 macrophage in the glomerular compartment while noticeably higher proportions of resting CD4⁺ memory T cells and M2 macrophages in the tubulointerstitial compartments. The PCA analyses showed that the varying composition of immune cells in both glomerular and tubulointerstitial entities was compelling to distinguish IgAN patients from healthy living controls. In addition, 21 commonly shared DEGs between glomerular and tubulointerstitial entities were recognized as key regulators in the pathogenesis of IgAN, among which the enhanced hemoglobin subunit beta (HBB) gene expression was found to be positively associated with M2 macrophage in the glomerular compartment and resting CD4⁺ memory T cells in the tubulointerstitial compartment. Most importantly, FBJ murine osteosarcoma viral oncogene homolog B (FOSB) gene deficiency was recognized as the dominant alteration in promoting M2 macrophage infiltration in the glomerular compartment of IgAN.

Conclusion: The findings from our current study for the first time reveal commonalities and differences regarding immune states between glomerular and tubulointerstitial compartments, as well as decode the essential role of M2 macrophages and associated molecular patterns within the microenvironments of IgAN.

Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm.

RESULTS

GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease's most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes.

CONCLUSIONS

The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

Prediction and Verification of the Major Ingredients and Molecular Targets of Tripterygii Radix Against Rheumatoid Arthritis

Tripterygii Radix exhibits good clinical efficacy and safety in rheumatoid arthritis (RA) patients, but its effective components and mechanism of action are still unclear. The purpose of this study was to explore and verify the major ingredients and molecular targets of Tripterygii Radix in RA using drug-compounds-biotargets-diseases network and protein-protein interaction (PPI) network analyses. The processes and pathways were derived from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The most important compounds and biotargets were determined based on the degree values. RA fibroblast-like synoviocytes (RA-FLS) were separated from RA patients and identified by hematoxylin and eosin (HE) staining and immunohistochemistry. The purity of RA-FLS was acquired by flow cytometry marked with CD90 or VCAM-1. RA-FLS were subjected to control, dimethyl sulfoxide (control), kaempferol, or lenalidomide treatment. Cell migration was evaluated by the transwell assay. The relative expression of biotarget proteins and cytokines was analyzed by western blotting and flow cytometry. In total, 144 chemical components were identified from Tripterygii Radix; kaempferol was the most active ingredient among 33 other components. Fourteen proteins were found to be affected in RA from 285 common biotargets. The tumor necrosis factor (TNF) signaling pathway was predicted to be one of the most latent treatment pathways. Migration of RA-FLS was inhibited and the expression of protein kinase B (AKT1), JUN, caspase 3 (CASP3), TNF receptor 1 and 2 (TNFR1 and TNFR2), interleukin-6 (IL-6), and TNF-α was significantly affected by kaempferol. Thus, this study confirmed kaempferol as the effective component of Tripterygii Radix against RA-FLS and TNF signaling pathway and its involvement in the regulation of AKT1, JUN, CASP3, TNFR1, TNFR2, IL-6, and TNF-α expression.