Interactions Of Differentially Expressed Genes Research Articles

Targeted RNAseq Revealed the Gene Expression Signature of Ferroptosis-Related Processes Associated with Disease Severity in Patients with Multiple Sclerosis.

Detrimental molecular processes in multiple sclerosis (MS) lead to the cellular accumulation of lipid peroxidation products and iron in the CNS, which represents the main driving force for ferroptosis. Ferroptosis is an iron-dependent form of regulated cell death, with proposed roles in neurodegeneration, oligodendrocyte loss and neuroinflammation in the pathogenesis of MS. Ferroptosis-related gene expression signature and molecular markers, which could reflect MS severity and progression, are currently understudied in humans. To tackle these challenges, we have applied a curated approach to create and experimentally analyze a comprehensive panel of ferroptosis-related genes covering a wide range of biological processes associated with ferroptosis. We performed the first ferroptosis-related targeted RNAseq on PBMCs from highly distinctive MS phenotype groups: mild relapsing-remitting (RR) (n = 24) and severe secondary progressive (SP) (n = 24), along with protein detection of GPX4 and products of lipid peroxidation (MDA and 4-HNE). Out of 138 genes, 26 were differentially expressed genes (DEGs), indicating changes in both pro- and anti-ferroptotic genes, representing a molecular signature associated with MS severity. The top three DEGs, as non-core ferroptosis genes, CDKN1A, MAP1B and EGLN2, were replicated by qPCR to validate findings in independent patient groups (16 RR and 16 SP MS). Co-expression and interactions of DEGs were presented as additional valuable assets for deeper understanding of molecular mechanisms and key targets related to MS severity. Our study integrates a wide genetic signature and biochemical markers related to ferroptosis in easily obtainable PBMCs of MS patients with clinical data and disease severity, thus providing novel molecular markers which can complement disease-related changes in the brain and undergo further research as potential therapeutic targets.

Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches.

Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis. The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR). Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway. The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.

Identification of NETs-related biomarkers and molecular clusters in systemic lupus erythematosus.

Neutrophil extracellular traps (NETs) is an important process involved in the pathogenesis of systemic lupus erythematosus (SLE), but the potential mechanisms of NETs contributing to SLE at the genetic level have not been clearly investigated. This investigation aimed to explore the molecular characteristics of NETs-related genes (NRGs) in SLE based on bioinformatics analysis, and identify associated reliable biomarkers and molecular clusters. Dataset GSE45291 was acquired from the Gene Expression Omnibus repository and used as a training set for subsequent analysis. A total of 1006 differentially expressed genes (DEGs) were obtained, most of which were associated with multiple viral infections. The interaction of DEGs with NRGs revealed 8 differentially expressed NRGs (DE-NRGs). The correlation and protein-protein interaction analyses of these DE-NRGs were performed. Among them, HMGB1, ITGB2, and CREB5 were selected as hub genes by random forest, support vector machine, and least absolute shrinkage and selection operator algorithms. The significant diagnostic value for SLE was confirmed in the training set and three validation sets (GSE81622, GSE61635, and GSE122459). Additionally, three NETs-related sub-clusters were identified based on the hub genes' expression profiles analyzed by unsupervised consensus cluster assessment. Functional enrichment was performed among the three NETs subgroups, and the data revealed that cluster 1 highly expressed DEGs were prevalent in innate immune response pathways while that of cluster 3 were enriched in adaptive immune response pathways. Moreover, immune infiltration analysis also revealed that innate immune cells were markedly infiltrated in cluster 1 while the adaptive immune cells were upregulated in cluster 3. As per our knowledge, this investigation is the first to explore the molecular characteristics of NRGs in SLE, identify three potential biomarkers (HMGB1, ITGB2, and CREB5), and three distinct clusters based on these hub biomarkers.

Differential gene screening and bioinformatics analysis of epidermal stem cells and dermal fibroblasts during skin aging

To explore the differentially expressed genes (DEGs) and potential therapeutic targets of skin aging in GEO database by bioinformatics methods. Dermal fibroblasts and skin aging related data sets GSE110978 and GSE117763 were downloaded from GEO database, and epidermal stem cells and skin aging related data sets GSE137176 were downloaded. GEO2R was used to screen DEGs of candidate samples from the three microarrays, GO function analysis and KEGG pathway analysis were performed. Protein interaction network was constructed using String database, and hub gene was obtained by Cytoscape. NetworkAnalys was used to analyze the coregulatory network of DEGs and MicroRNA (miRNA), interaction with TF, and protein-chemical interactions of DEGs. Finally, DSigDB was used to determine candidate drugs for DEGs. Six DEGs were obtained. It mainly involves the cytological processes such as response to metal ion, and is enriched in mineral absorption and other signal pathways. Ten genes were screened by PPI analysis. Gene-miRNA coregulatory network found that Peg3 and mmu-miR-1931 in DEGs were related to each other, and Cybrd1 was related to mmu-miR-290a-5p and mmu-miR-3082-5p. TF-gene interactions found that the transcription factor UBTF co-regulated two genes, Arhgap24 and Mpzl1. Protein-chemical Interactions analysis and identification of candidate drugs show results for candidate drugs. Try to explore the mechanism of hub gene action in skin aging progression, and to discover the key signaling pathways leading to skin aging, which may be a high risk of skin aging.

Identification and Integrated Analysis of the miRNA-mRNA Regulatory Network in Lens from an H2O2-Induced Zebrafish Cataract Model

Purpose This study aimed to explore the regulatory mechanisms of age-related cataract (ARC) formation. Methods Cataracts in zebrafish were induced by injecting hydrogen peroxide into the fish anterior chamber. The mRNA and miRNA expression profiles of the lens from H2O2-injected and PBS-injected zebrafishes were detected by RNA sequencing. The LIMMA package was applied to identify differentially expressed genes (DEGs). Gene Ontology categories were enriched by the R “cluster Profiler” package and Kyoto Encyclopedia of Genes and Genomes pathway enrichment was performed based on hypergeometric distribution using the R “phyper” function. The protein-protein interaction network of DEGs was built via the STRING. Target genes of differentially expressed miRNAs (DEmiRs) were predicted by miRanda. Furthermore, DEGs were selected as DEmiR targets and a DEmiR-DEG regulatory network was constructed via Cytoscape. Results In total, 3689 DEGs (such as opn1mw4, LOC103908930, si:dkeyp-1h4.8, crispld1b, cyp1a, and gdpd3a) including 2478 upregulated and 1211 downregulated genes were identified. 177 DEmiRs (such as dre-miR-96-3p, dre-miR-182-5p, dre-miR-9-7-3p, and dre-miR-124-4-5p) including 108 upregulated and 69 downregulated miRNAs were detected. The DEGs are involved in cell death, DNA repair, and cell development-related pathways. A protein-protein interaction network including 79 node genes was constructed to explore the interactions of DEGs. Furthermore, a DEmiR-DEG regulatory network focusing on the DNA repair process was constructed, including 21 hub DEGs and 15 hub DEmiRs. Conclusions We identified several DEGs and constructed a miRNA-mRNA regulatory network related to the DNA repair process in a zebrafish cataract model. These genes participate in the oxidative stress response of lens epithelium cells and finally contribute to the formation of zebrafish cataracts. The hub DEGs and hub DEmiRs could be potential therapeutic targets for ARC.

Bioinformatics analysis of potential core genes for glioblastoma.

Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes.Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool.Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis.Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

The effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the transcriptome of aryl hydrocarbon receptor (AhR) knock-down porcine granulosa cells.

Background2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a toxic man-made chemical, adversely affecting reproductive processes. The well-characterized canonical mechanism of TCDD action involves the activation of aryl hydrocarbon receptor (AhR) pathway, but AhR-independent mechanisms were also suggested. By applying RNA interference technology and Next Generation Sequencing (NGS) we aimed to identify genes involved in the mechanism of TCDD action in AhR knock-down porcine granulosa cells.MethodsPorcine granulosa cells were transfected with small interfering RNAs targeting mRNA of AhR. After transfection, medium was exchanged and the AhR knock-down cells were treated with TCDD (100 nM) for 3, 12 or 24 h, total cellular RNA was isolated and designated for NGS. Following sequencing, differentially expressed genes (DEGs) were identified. To analyze functions and establish possible interactions of DEGs, the Gene Ontology (GO) database and the Search Tool for the Retrieval of Interacting Genes (STRING) database were used, respectively.ResultsThe AhR gene expression level and protein abundance were significantly decreased after AhR-targeted siRNAs transfection of the cells. In TCDD-treated AhR knock-down cells we identified 360 differentially expressed genes (DEGs; P-adjusted < 0.05 and log2 fold change [log2FC] ≥ 1.0). The functional enrichment analysis of DEGs revealed that TCDD influenced the expression of genes involved, among other, in the metabolism of vitamin A, follicular development and oocyte maturation, proliferation and differentiation as well as inflammation, stress response, apoptosis and oncogenesis. The three-time point study demonstrated that TCDD-induced changes in the transcriptome of AhR knock-down porcine granulosa cells were especially pronounced during the early stages of the treatment (3 h).ConclusionsTCDD affected the transcriptome of AhR knock-down porcine granulosa cells. The molecules involved in the AhR-independent action of TCDD were indicated in the study. The obtained data contribute to better understanding of molecular processes induced by xenobiotics in the ovary.

Transcriptome profiling reveals the association of multiple genes and pathways contributing to hormonal control in celery leaves.

Celery is a vital vegetable belonging to the Apiaceae family. The leaves of celery are its main edible part with high nutritional value. Hormone signaling plays diverse and critical roles in controlling plant growth and development. However, the molecular mechanism of hormone regulating growth and development in celery leaves has not been investigated. Here, we aimed to understand the molecular functions of genes related to hormone metabolism in celery leaf growth and development. A total of 77 hormone-related differentially expressed genes (DEGs) were identified from the transcriptome of celery leaves at three development stages. The roles and interactions of DEGs in the growth and development of celery leaves were discussed. The contents of multiple hormones (IAA, ZR, ABA, BR, GA3, and MeJA) in celery leaf development were also detected. The changes of endogenous hormone level during the development of celery leaves could be regulated by the expressions of hormone-related genes. Our results indicated that the plant hormones had a complex regulatory mechanism for the growth of celery leaves. Our current findings will provide potential valuable references for the future research on celery leaf development.

Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells.

BACKGROUNDStudies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells (HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use “omics” methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIMTo establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODSWe used microarrays, bioinformatics, protein-protein interaction (PPI) network, and sub-modules to investigate taurine-induced changes in gene expression in human HSCs (LX-2). Subsequently, all of the differentially expressed genes (DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTSA total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1 (ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase (MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway, estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21, TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSIONTaurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

Distinguishable Prognostic Signatures of Left- and Right-Sided Colon Cancer: a Study Based on Sequencing Data

Background/Aims: Left- and right-sided colon cancers are considered to be two different diseases and have altered outcomes. However, specific molecules to predict the prognosis of left- and right-sided colon cancers are currently lacking. Methods: Expression profiling of colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) of left- and right-sided colon cancers were compared by DESeq analysis. The prognostic values of DEGs were assessed by univariate and multivariate Cox regression. Prognostic index models of two side colon cancers were conducted with prognostic values genes, respectively. Interaction of DEGs was then analyzed by the protein-protein interaction (PPI). Different biology function of two sides of colon cancer was assessed by Gene Set Enrichment Analysis (GSEA). Results: A total of 167 DEGs were identified between left- and right-sided colon cancers based on TCGA data. Using univariate COX regression analysis, five genes (PHACTR3, CKMT2, CYP2W1, ERFE, HOXC4) were related to overall survival in left-sided, and eight distinguishable genes (EREG, ERFE, HOXC6, SLC22A31, TFF1, GFI1, ZG16, RASL10B) in right-sided. Further, left-sided prognostic model was established with PHACTR3 and CKMT2 (HR=2.040; 95%CI=1.004-4.145; P=0.049). Distinguishable prognostic signature for right-sided colon cancer was established based on EREG, ERFE, GFI1, and RASL10B (HR=3.530; 95%CI: 1.934-6.444; P< 0.001) in multivariate analysis. PPI analysis of 167 DEGs showed that CCL5, GNG4, GNLY, GZMH, DRD2, and FASLG genes were at the core of interaction network. In GSEA function analysis, four pathways, including antigen processing and presentation, natural killer cell mediated cytotoxicity, intestinal immune network for Iga production, and type I diabetes mellitus, were significantly enriched in the DEGs of the right-sided colon cancer. Conclusions: This study constructs a panel of potential prognostic model of left- and right-sided colon cancers, respectively. We also provide molecular biological alterations between left- and right-sided colon cancers.

A time-course microarray data analysis reveals consistent dysregulated genes and upstream microRNAs in autoantibody-mediated arthritis

BackgroundThe purpose of this study is to identify key genes and microRNAs (miRNAs) involved in autoantibody-mediated arthritis (AMA).MethodsA time-course microarray data (ID: GSE27492) of peripheral blood leukocytes, ankle tissue, and synovial fluid from K/BxN mouse serum-transferred mice were downloaded from Gene Expression Omnibus. Those samples were collected at days 0, 1, 3, 7, 12, and 18 after serum injection. Limma of R was employed to identify differentially expressed genes (DEGs) in samples collected at days 1–18 compared with those collected at day 0. Consistent DEGs were obtained by taking the interaction of DEGs from different time points, followed by functional enrichment analysis. MiRNAs were screened out and constructed into regulatory network with DEGs using Cytoscape.ResultsIn total, 17 consistent DEGs were obtained, including downregulated Ephx1 and upregulated AF251705, Adam8, Arg1, Basp1, Ccl2, Ccl7, Ccl9, Ccr2, Clec4a2, Clec4d, Cxcl1, Fabp5, Fcgr1, Gp49a, Il1rn, and Saa3. Those DEGs were associated with biological processes of immune response, inflammatory response, and defense response; chemokine signaling pathway; cytokine-cytokine receptor interaction; and NOD-like receptor signaling pathway. Additionally, 202 miRNAs were identified to have a regulatory effect on 9 of the 17 DEGs. Notably, miR-944, miR-374a, and miR374b were found to regulate the expression of Cxcl1, Ccl7, and Ccl2. Clec4d was targeted by 78 miRNAs.ConclusionsOur study reveals that 17 DEGs and 202 miRNAs may be associated with autoimmune disorder in the progression of AMA, which could guide future researches.

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy.