CD36 identified by weighted gene co-expression network analysis as a hub candidate gene in lupus nephritis

Male microbiota-associated metabolite restores macrophage efferocytosis in female lupus-prone mice via activation of PPARγ/LXR signaling pathways

Systemic lupus erythematosus development is influenced by both sex and the gut microbiota. Metabolite production is a major mechanism by which the gut microbiota influences the immune system, and we have previously found differences in the fecal metabolomic profiles of lupus-prone female and lupus-resistant male BWF1 mice. Here we determine how sex and microbiota metabolite production may interact to affect lupus. Transcriptomic analysis of female and male splenocytes showed genes that promote phagocytosis were upregulated in BWF1 male mice. Because patients with systemic lupus erythematosus exhibit defects in macrophage-mediated phagocytosis of apoptotic cells (efferocytosis), we compared splenic macrophage efferocytosis in vitro between female and male BWF1 mice. Macrophage efferocytosis was deficient in female compared to male BWF1 mice but could be restored by feeding male microbiota. Further transcriptomic analysis of the genes upregulated in male BWF1 mice revealed enrichment of genes stimulated by PPARγ and LXR signaling. Our previous fecal metabolomics analyses identified metabolites in male BWF1 mice that can activate PPARγ and LXR signaling and identified one in particular, phytanic acid, that is a very potent agonist. We show here that treatment of female BWF1 splenic macrophages with phytanic acid restores efferocytic activity via activation of the PPARγ and LXR signaling pathways. Furthermore, we found phytanic acid may restore female BWF1 macrophage efferocytosis through upregulation of the proefferocytic gene CD36. Taken together, our data indicate that metabolites produced by BWF1 male microbiota can enhance macrophage efferocytosis and, through this mechanism, could potentially influence lupus progression.

Identification of crucial genes that induce coronary atherosclerosis through endothelial cell dysfunction in AMI-identifying hub genes by WGCNA

OBJECTIVE

To identify the most relevant genes of cardiovascular disease in acute myocardial infarction patients using weighted gene co-expression network analysis (WGCNA).

METHODS

The microarray dataset of GSE66360 was downloaded from the Gene Expression Omnibus (GEO) website. The differential genes with adjusted P < 0.05 and |log2 fold change (FC)| > 0.5 were included in the analysis. The weighed gene co-expression network analysis (WGCNA) was used to build a gene co-expression network and identify the most significant module. Cytoscape was used to filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the hub genes. The key genes were defined as having high statistical and biological significance.

RESULTS

A total of 4751 differentially expressed genes (DEGs) were screened from the dataset. The purple module had the highest significance in AMI. There were 47 hub genes identified from the module. The GO terms "amyloid beta protein metabolism" and "carbohydrate metabolism" and the KEGG terms "phagosome-related pathways" and "Staphylococcus aureus-associated pathways" were the pathways strongly enriched in AMI. Fatty acid translocase cluster of differentiation (CD36), formyl peptide receptor type 2 (FPR2), integrin subunit alpha M (ITGAM), and oxidized low density lipoprotein receptor 1 (OLR1) were considered key genes in AMI.

CONCLUSION

Our research suggested that the underlying mechanism was related to inflammation and lipid formation. The hub genes identified were CD36, FPR2, ITGAM, and OLR1.

Diagnostic test accuracy of novel biomarkers for lupus nephritis-An overview of systematic reviews

INTRODUCTION

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with multiorgan inflammatory involvement and a mortality rate that is 2.6-fold higher than individuals of the same age and sex in the general population. Approximately 50% of patients with SLE develop renal impairment (lupus nephritis). Delayed diagnosis of lupus nephritis is associated with a higher risk of progression to end-stage renal disease, the need for replacement therapy, and mortality. The initial clinical manifestations of lupus nephritis are often discrete or absent and are usually detected through complementary tests. Although widely used in clinical practice, their accuracy is limited. A great scientific effort has been exerted towards searching for new, more sensitive, and specific biomarkers in recent years. Some systematic reviews have individually evaluated new serum and urinary biomarkers tested in patients with lupus nephritis. This overview aimed to summarize systematic reviews on the accuracy of novel serum and urinary biomarkers for diagnosing lupus nephritis in patients with SLE, discussing how our results can guide the clinical management of the disease and the direction of research in this area.

METHODS

The research question is "What is the accuracy of the new serum and urinary biomarkers studied for the diagnosis of LN in patients with SLE?". We searched for systematic reviews of observational studies evaluating the diagnostic accuracy of new serum or urinary biomarkers of lupus nephritis. The following databases were included: PubMed, EMBASE, BIREME/LILACS, Scopus, Web of Science, and Cochrane, including gray literature found via Google Scholar and PROQUEST. Two authors assessed the reviews for inclusion, data extraction, and assessment of the risk of bias (ROBIS tool).

RESULTS

Ten SRs on the diagnostic accuracy of new serum and urinary BMs in LN were selected. The SRs evaluated 7 distinct BMs: (a) antibodies (anti-Sm, anti-RNP, and anti-C1q), (b) cytokines (TWEAK and MCP-1), (c) a chemokine (IP-10), and (d) an acute phase glycoprotein (NGAL), in a total of 20 review arms (9 that analyzed serum BMs, and 12 that analyzed BMs in urine). The population evaluated in the primary studies was predominantly adults. Two SRs included strictly adults, 5 reviews also included studies in the paediatric population, and 4 did not report the age groups. The results of the evaluation with the ROBIS tool showed that most of the reviews had a low overall risk of bias.

CONCLUSIONS

There are 10 SRs of evidence relating to the diagnostic accuracy of serum and urinary biomarkers for lupus nephritis. Among the BMs evaluated, anti-C1q, urinary MCP-1, TWEAK, and NGAL stood out, highlighting the need for additional research, especially on LN diagnostic panels, and attempting to address methodological issues within diagnostic accuracy research. This would allow for a better understanding of their usefulness and possibly validate their clinical use in the future.

REGISTRATION

This project is registered on the International Prospective Registry of Systematic Reviews (PROSPERO) database (CRD42020196693).

CD36 aggravates podocyte injury by activating NLRP3 inflammasome and inhibiting autophagy in lupus nephritis

A major cause of proteinuria in lupus nephritis (LN) is podocyte injury, and determining potential therapeutic targets to prevent podocyte injury is important from a clinical perspective in the treatment of LN. CD36 is involved in podocyte injury in several glomerulopathies and was reported to be a vital candidate gene in LN. Here, we determined the role of CD36 in the podocyte injury of LN and the underlying mechanisms. We observed that CD36 and NLRP3 (NLR family pyrin domain containing 3) were upregulated in the podocytes of lupus nephritis patients and MRL/lpr mice with renal impairment. In vitro, CD36, NLRP3 inflammasome, and autophagy were elevated accompanied with increased podocyte injury stimulated by IgG extracted from lupus nephritis patients compared that from healthy donors. Knocking out CD36 with the CRISPR/cas9 system decreased the NLRP3 inflammasome levels, increased the autophagy levels and alleviated podocyte injury. By enhancing autophagy, NLRP3 inflammasome was decreased and podocyte injury was alleviated. These results demonstrated that, in lupus nephritis, CD36 promoted podocyte injury by activating NLRP3 inflammasome and inhibiting autophagy by enhancing which could decrease NLRP3 inflammasome and alleviate podocyte injury.

Identification of the Key Pathways and Genes in Hypoxia Pulmonary Arterial Hypertension Following Intrauterine Growth Retardation

High-throughput sequencing and weighted gene co-expression network analysis (WGCNA) were used to identify susceptibility modules and genes in liver tissue for the hypoxic pulmonary arterial hypertension (PAH) animal model following intrauterine growth retardation (IUGR). A total of 5,000 genes were clustered into eight co-expression modules via WGCNA. Module blue was mostly significantly correlated with the IUGR–hypoxia group. Gene Ontology analysis showed that genes in the module blue were mainly enriched in the fatty acid metabolic process, lipid modification, and fatty acid catabolic process. The Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that the genes in module blue were mainly associated with fatty acid metabolism, PPAR signaling pathway, and biosynthesis of unsaturated fatty acids. In addition, the maximal clique centrality method was used to identify the hub genes in the subnetworks, and the obtained results were verified using real-time quantitative PCR. Finally, we identified that four genes including Cyp2f4, Lipc, Acadl, and Hacl1 were significantly associated with IUGR-hypoxia. Our study identified a module and several key genes that acted as essential components in the etiology of the long-term metabolic consequences in hypoxia PAH following IUGR.

Identification and Validation of IFI44 as Key Biomarker in Lupus Nephritis

Lupus nephritis (LN) is a common and severe organ manifestation of systemic lupus erythematosus (SLE) and is a major cause of SLE related deaths. Early diagnosis is essential to improve the prognosis of patients with LN. To screen the potential biomarkers associated with LN, we downloaded the gene expression profile of GSE99967 from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to construct a gene co-expression network and identify gene modules associated with LN. Gene Ontology (GO) analysis was also applied to explore the biological function of genes and identify the key module. Differentially expressed genes (DEGs) were identified and Maximal Clique Centrality (MCC) values were calculated to screen hub genes. Furthermore, we selected promising biomarkers for real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) validation in independent cohorts. Our results indicated that five hub genes, including IFI44, IFIT3, HERC5, RSAD2, and DDX60 play vital roles in the pathogenesis of LN. Importantly, IFI44 may considered as a key biomarker in LN for its diagnostic capabilities, which is also a promising therapeutic target in the future.

Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification

Background

The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification.

Material and Methods

We applied weighted gene co-expression network analysis (WGCNA) to analyze dataset GSE55457 and obtained the key module most relevant to the RA phenotype. We then conducted gene function annotation, gene set enrichment analysis (GSEA) and immunocytes quantitative analysis (CIBERSORT). Moreover, the intersection of differentially expressed genes (DEGs) and genes within the key module were entered into the STRING database to construct an interaction network and to mine hub genes. We predicted microRNA (miRNA) using a web-based tool (miRDB). Finally, hub genes and vital miRNAs were validated with independent GEO datasets, RT-qPCR and Western blot.

Results

A total of 367 DEGs were characterized by differential expression analysis. The WGCNA method divided genes into 14 modules, and we focused on the turquoise module containing 845 genes. Gene function annotation and GSEA suggested that immune response and inflammatory signaling pathways are the molecular mechanisms behind RA. Nine hub genes were screened from the network and seven vital regulators were obtained using miRNA prediction. CIBERSORT analysis identified five cell types enriched in RA samples, which were closely related to the expression of hub genes. Through ROC curve and RT-qPCR validation, we confirmed five genes that were specific for RA, including CCL25, CXCL9, CXCL10, CXCL11, and CXCL13. Moreover, we selected a representative gene (CXCL10) for Western blot validation. Vital miRNAs verification showed that only the differences in has-miR-573 and has-miR-34a were statistically significant.

Conclusion

Our study reveals diagnostic genes and vital microRNAs highly related to RA, which could help improve our understanding of the molecular mechanisms underlying the disorder and provide theoretical support for the future exploration of innovative therapeutic approaches.

Identification of MEDAG as a Hub Candidate Gene in the Onset and Progression of Type 2 Diabetes Mellitus by Comprehensive Bioinformatics Analysis

Objectives

We conducted the present study to identify novel hub candidate genes in the pathogenesis of type 2 diabetes mellitus (T2DM) and provide potential biomarkers or therapeutic targets for dealing with the disease.

Methods

We conducted weighted gene coexpression network analysis on a series of the expression profiles of the pancreas islet of T2DM patients obtained from the Gene Expression Omnibus database to construct a weighted coexpression network. After dividing genes into separated coexpression modules, we identified a T2DM-related module using Pearson's correlation analysis. Then, hub genes were identified from the T2DM-related module using the Maximal Clique Centrality method and validated by correlation analysis with clinical traits, differentially expressed gene analysis, validation in other datasets, and single-gene gene set enrichment analysis (GSEA).

Results

Genes were divided into 16 coexpression modules, and one module was identified as a T2DM-related module. Four hub candidate genes were identified, and MEDAG was a novel hub candidate gene. The expression level of MEDAG was positively correlated with hemoglobin A1c (HbA1c) and was evidently overexpressed in the pancreas islet tissue of T2DM patients compared with normal control. Analyses on two other datasets supported the results. GSEA verified that MEDAG plays essential roles in T2DM.

Conclusions

MEDAG is a novel hub candidate of T2DM, and its irregular expression in the pancreas islet plays vital roles in the pathogenesis of T2DM. MEDAG is a potential target of intervention in the future for the treatment of T2DM.

Crucial transcripts predict response to initial immunoglobulin treatment in acute Kawasaki disease

Although intravenous immunoglobulin (IVIG) can effectively treat Kawasaki disease (KD), 10–20% of KD patients show no beneficial clinical response. Developing reliable criteria to discriminate non-responders is important for early planning of appropriate regimens. To predict the non-responders before IVIG treatment, gene expression dataset of 110 responders and 61 non-responders was obtained from Gene Expression Omnibus. After weighted gene co-expression network analysis, we found that modules positively correlated with the non-responders were mainly associated with myeloid cell activation. Transcripts up-regulated in the non-responders, IL1R2, GK, HK3, C5orf32, CXCL16, NAMPT and EMILIN2, were proven to play key roles via interaction with other transcripts in co-expression network. The crucial transcripts may affect the clinical response to IVIG treatment in acute KD. And these transcripts may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of the non-responders.

Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA-lncRNA co-expression network analysis

BACKGROUND

Stomach adenocarcinoma (STAD), is one of the most lethal malignancies around the world. The aim of this study was to find the long noncoding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of STAD.

METHODS

Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between STAD and normal tissue. The machine learning and survival analysis were performed to evaluate the potential diagnostic and prognostic value of lncRNAs for STAD. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real-time polymerase chain reaction (qRT-PCR) and GSE27342 dataset. GSE27342 dataset were also to perform gene set enrichment analysis.

RESULTS

A total of 814 DEmRNAs and 106 DElncRNAs between STAD and normal tissue were obtained. FOXD2-AS1, LINC01235, and RP11-598F7.5 were defined as optimal diagnostic lncRNA biomarkers for STAD. The area under curve (AUC) of the decision tree model, random forests model, and support vector machine (SVM) model were 0.797, 0.981, and 0.983, and the specificity and sensitivity of the three model were 75.0% and 97.1%, 96.9% and 96%, and 96.9% and 97.1%, respectively. Among them, LINC01235 was not only an optimal diagnostic lncRNA biomarkers, but also related to survival time. The expression of three DEmRNAs (ESM1, WNT2, and COL10A1) and three optimal diagnostic lncRNAs biomarkers (FOXD2-AS1, RP11-598F7.5, and LINC01235) in qRT-PCR validation was were consistent with our integrated analysis. Except for FOXD2-AS1, ESM1, WNT2, COL10A1, and LINC01235 were upregulated in STAD, which was consistent with our integration results. Gene set enrichment analysis results indicated that DNA replication, Cell cycle, ECM-receptor interaction, and P53 signaling pathway were four significantly enriched pathways in STAD.

CONCLUSION

Our study identified three DElncRNAs as potential diagnostic biomarkers of STAD. Among them, LINC01235 also was a prognostic lncRNA biomarkers.

Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database

BACKGROUND

Ischemic cardiomyopathy (ICM) is one of the most usual causes of death worldwide. This study aimed to find the candidate gene for ICM.

METHODS

We studied differentially expressed genes (DEGs) in ICM compared to healthy control. According to these DEGs, we carried out the functional annotation, protein-protein interaction (PPI) network and transcriptional regulatory network constructions. The expression of selected candidate genes were confirmed using a published dataset and Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

From three Gene Expression Omnibus (GEO) datasets, we acquired 1081 DEGs (578 up-regulated and 503 down-regulated genes) between ICM and healthy control. The functional annotation analysis revealed that cardiac muscle contraction, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in ICM. SNRPB, BLM, RRS1, CDK2, BCL6, BCL2L1, FKBP5, IPO7, TUBB4B and ATP1A1 were considered the hub proteins. PALLD, THBS4, ATP1A1, NFASC, FKBP5, ECM2 and BCL2L1 were top six transcription factors (TFs) with the most downstream genes. The expression of 6 DEGs (MYH6, THBS4, BCL6, BLM, IPO7 and SERPINA3) were consistent with our integration analysis and GSE116250 validation results.

CONCLUSIONS

The candidate DEGs and TFs may be related to the ICM process. This study provided novel perspective for understanding mechanism and exploiting new therapeutic means for ICM.