Identification of key pathways and genes in different types of chronic kidney disease based on WGCNA

Genome-wide DNA methylome and transcriptome changes induced by inorganic nanoparticles in human kidney cells after chronic exposure

The unique physicochemical properties make inorganic nanoparticles (INPs) an exciting tool in diagnosis and disease management. However, as INPs are relatively difficult to fully degrade and excrete, their unintended accumulation in the tissue might result in adverse health effects. Herein, we provide a methylome-transcriptome framework for chronic effects of INPs, commonly used in biomedical applications, in human kidney TH-1 cells. Renal clearance is one of the most important routes of nanoparticle excretion; therefore, a detailed evaluation of nanoparticle-mediated nephrotoxicity is an important task. Integrated analysis of methylome and transcriptome changes induced by INPs (PEG-AuNPs, Fe3O4NPs, SiO2NPs, and TiO2NPs) revealed significantly deregulated genes with functional classification in immune response, DNA damage, and cancer-related pathways. Although most deregulated genes were unique to individual INPs, a relatively high proportion of them encoded the transcription factors. Interestingly, FOS hypermethylation inversely correlating with gene expression was associated with all INPs exposures. Our study emphasizes the need for a more comprehensive investigation of INPs' biological safety, especially after chronic exposure.

Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach

Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%–16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.

Weighted Gene Co-Expression Network Analysis (WGCNA) Reveals the Functions of Syndecan-1 to Regulate Immune Infiltration by Influenced T Cells in Glioma

Our previous studies shown that syndecan-1 (SDC1) may be a novel class of biomarkers for the diagnosis and treatment of glioma, but its specific roles and the in-depth molecular mechanism remain elusive. Here, we used Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithms and single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms to evaluate the immune score of tumor samples and quantify the relative infiltration of immune cells in the tumor microenvironment (TME), respectively, in different data sets obtained from the Chinese Glioma Genome Atlas and The Cancer Gene Atlas. Next, we calculate the correlation of the immune score and immune cells with SDC1, respectively. To identify the specific process regulated by SDC1, the differentially expressed genes (DEGs) analysis between the high and low expression of SDC1 of glioma samples were used to discover the hub genes through Weighted Gene Coexpression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed cardinal biological processes and pathways involved in genes and tumor grade correlation and survival analysis verified its significance in glioma. The results show that SDC1 is associated with the immune infiltration of glioma in the TME, especially activated CD4+T cells and CD8+T cells. The three data sets filter 8,887 DEGs, the genes in the blue modules were selected as hub genes in WGCNA. GO and KEGG analysis found eight genes in the blue modules involved in antigen processing and presentation in T cells in glioma. Kaplan–Meier estimator and log-rank test statistic determined that the introduced genes are associated with poor prognosis in glioma. Protein–protein network interaction analysis showed that SDC1 may regulate antigen processing and presentation through CTSL or CD4 in glioma. Finally, this study provided insights and clues for the next research direction of SDC1 and identified the key pathways and genes that might participate in the immune escape of glioma. These results might provide a new insight on the study of immune infiltration of glioma in the future.

Evaluating the cytotoxicity and pathogenicity of multi-walled carbon nanotube through weighted gene co-expression network analysis: a nanotoxicogenomics study

Background

Multi-walled carbon nanotube (MWCNT) is one of the most momentous carbonaceous nanoparticles which is widely used for various applications such as electronics, vehicles, and therapeutics. However, their possible toxicity and adverse effects convert them into a major health threat for humans and animals.

Results

In this study, we employed weighted gene co-expression network analysis (WGCNA) to identify the co-expressed gene groups and dysregulated pathways due to the MWCNT exposure. For this purpose, three weighted gene co-expression networks for the microarray gene expression profiles of the mouse after 1, 6, and 12-month post-exposure to MWCNT were constructed. The module-trait analysis specified the significant modules related to different doses (1, 10, 40, and 80 µg) of MWCNT. Afterward, common genes between co-regulated and differentially expressed genes were determined. The further pathway analysis highlighted the enrichment of genes including Actb, Ube2b, Psme3, Ezh2, Alas2, S100a10, Ypel5, Rhoa, Rac1, Ube2l6, Prdx2, Ctsb, Bnip3l, Gp6, Myh9, Ube2k, Mbnl1, Kbtbd8, Riok3, Itgb1, Rap1a, and Atp5h in immune-, inflammation-, and protein metabolism-related pathways.

Conclusions

This study discloses the genotoxicity and cytotoxicity effects of various doses of MWCNT which also affect the metabolism system. The identified genes can serve as potential biomarkers and therapeutic candidates. However, further studies should be performed to validate them in human cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01031-3.

Apolipoprotein D as a Potential Biomarker and Construction of a Transcriptional Regulatory-Immune Network Associated with Osteoarthritis by Weighted Gene Coexpression Network Analysis

OBJECTIVE

Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA).

DESIGN

A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments.

RESULTS

The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell.

CONCLUSIONS

Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.

Identifying Conserved Generic Aspergillus spp. Co-Expressed Gene Modules Associated with Germination Using Cross-Platform and Cross-Species Transcriptomics

Aspergillus spp. is an opportunistic human pathogen that may cause a spectrum of pulmonary diseases. In order to establish infection, inhaled conidia must germinate, whereby they break dormancy, start to swell, and initiate a highly polarized growth process. To identify critical biological processes during germination, we performed a cross-platform, cross-species comparative analysis of germinating A. fumigatus and A. niger conidia using transcriptional data from published RNA-Seq and Affymetrix studies. A consensus co-expression network analysis identified four gene modules associated with stages of germination. These modules showed numerous shared biological processes between A. niger and A. fumigatus during conidial germination. Specifically, the turquoise module was enriched with secondary metabolism, the black module was highly enriched with protein synthesis, the darkgreen module was enriched with protein fate, and the blue module was highly enriched with polarized growth. More specifically, enriched functional categories identified in the blue module were vesicle formation, vesicular transport, tubulin dependent transport, actin-dependent transport, exocytosis, and endocytosis. Genes important for these biological processes showed similar expression patterns in A. fumigatus and A. niger, therefore, they could be potential antifungal targets. Through cross-platform, cross-species comparative analysis, we were able to identify biologically meaningful modules shared by A. fumigatus and A. niger, which underscores the potential of this approach.

Identification of Hub Genes Associated with the Development of Acute Kidney Injury by Weighted Gene Co-Expression Network Analysis

BACKGROUND

Acute kidney injury (AKI) is a severe clinical syndrome, causing a profound medical and socioeconomic burden worldwide. This study aimed to explore underlying molecular targets related to the progression of AKI.

METHODS

A public database originated from the NCBI GEO database (serial number: GSE121190) and a well-established and unbiased method of weighted gene co-expression network analysis (WGCNA) to identify hub genes and potential pathways were used. Furthermore, the unbiased hub genes were validated in 2 classic models of AKI in a rodent model: chemically established AKI by cisplatin- and ischemia reperfusion-induced AKI.

RESULTS

A total of 17 modules were finally obtained by the unbiased method of WGCNA, where the genes in turquoise module displayed strong correlation with the development of AKI. In addition, the results of gene ontology revealed that the genes in turquoise module were involved in renal injury and renal fibrosis. Thus, the hub genes were further validated by experimental methods and primarily obtained Rplp1 and Lgals1 as key candidate genes related to the progression of AKI by the advantage of quantitative PCR, Western blotting, and in situ tissue fluorescence. Importantly, the expression of Rplp1 and Lgals1 at the protein level showed positive correlation with renal function, including serum Cr and BUN.

CONCLUSIONS

By the advantage of unbiased bioinformatic method and consequent experimental verification, this study lays the foundation basis for the pathogenesis and therapeutic agent development of AKI.

Potential Prognostic Predictors and Molecular Targets for Skin Melanoma Screened by Weighted Gene Co-expression Network Analysis

AIMS AND OBJECTIVES

Among skin cancers, malignant skin melanoma is the leading cause of death. Identification of gene markers of malignant skin melanoma associated with survival may provide new clues for prognosis prediction and treatment. This research aimed to screen out potential prognostic predictors and molecular targets for malignant skin melanoma.

INTRODUCTION

Information regarding gene expression in skin melanoma and patients' clinical traits was obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to build co-expression modules and investigate the association between the modules and clinical traits. Moreover, functional enrichment analysis was performed for clinically significant co-expression modules. Hub genes of these modules were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (http:// www.proteinatlas.org).

METHODS

First, using WGCNA, 9 co-expression modules were constructed by the top 25% differentially expressed genes (4406 genes) from 77 human melanoma samples. Two co-expression modules (magenta and blue modules) were significantly correlated with survival months (r = -0.27, p = 0.02; r = 0.27, p = 0.02, respectively). The results of functional enrichment analysis demonstrated that the magenta module was mainly enriched in the cell cycle process and the blue module was mainly enriched in the immune response process. Additionally, the GEPIA and Human Protein Atlas results suggested that the hub genes CCNB2, ARHGAP30, and SEMA4D were associated with relapse-free survival and overall survival (all p-values < 0.05) and were differentially expressed in melanoma tumors and normal skin.

RESULTS AND CONCLUSION

The results provided the framework of co-expression gene modules of skin melanoma and screened out CCNB2, ARHGAP30, and SEMA4D associated with survival as potential prognostic predictors and molecular targets of treatment.