Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.     

LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA)

BackgroundIschemic stroke (IS) was a significant public health concern and long-chain noncoding RNAs (lncRNAs) were gaining particular importance in stroke biology, however, the potential mechanism of lncRNAs in IS was not fully understood.MethodsIn this study, three diagnosed patients with IS and three controls were selected to establish the lncRNA library. Weighted gene co-expression network analysis (WGCNA) was applied to screen key lncRNA modules associated with IS. The key lncRNAs were identified by module membership (MM) and gene significance (GS). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways and protein-protein interaction (PPI) network method was used to identify the key genes.ResultsA total of 3627 lncRNAs were investigated, followed by an analysis of 17 modules, and only one module was highly associated with the IS. The top 10 lncRNAs were identified based on GS and MM. KEGG pathways analysis revealed the top two pathways of the Human T cell Lymphotropic Virus-1 (HTLV-1) infection and the mTOR signaling pathway might influence the progress of IS. Further, genes meeting the top two degree (AKT1 and MAPK14) were selected as the hub genes in the PPI network.ConclusionTo summarize, this study identified the key pathways and genes, which might serve as biomarkers and targets for precise diagnosis and treatment of IS in the future.     

Hepatic miR‐29ab1 expression modulates chronic hepatic injury

MicroRNAs (miRNAs) are small, regulatory non‐coding RNAs that have potent effects on gene expression. Several miRNA are deregulated in cellular processes involved in human liver diseases and regulation of cellular processes. Recent studies have identified the involvement of miR‐29 in hepatic fibrosis and carcinogenesis. Although several targets of miR‐29 have been identified, there is limited information regarding the cell‐type specific roles of miR‐29 in the liver, and we sought to evaluate the role of this miRNA in hepatic pathobiology. We report the generation of a tissue–specific knockout mouse to evaluate the role of miR‐29 in hepatic fibrosis and carcinogenesis in response to injury. We hypothesized that miR‐29 contributes to the hepatocyte driven response to chronic cellular injury that results in fibrosis. In support of this hypothesis, fibrosis and mortality were enhanced in miR29 knockout mice in response to carbon tetrachloride. Genome‐wide gene expression analysis identified an over‐representation of genes associated with fibrosis. The oncofetal RNA H19 was modulated in a miR‐29 dependent manner following exposure to carbon tetrachloride in vivo. The impact of a hepatocyte specific miR‐29 knockout on survival following chronic hepatic injury in vivo implicates this miRNA as a potential target for intervention. These results provide evidence of the involvement of miR‐29 in chronic hepatic injury, and suggest a role for deregulated hepatocyte expression of miR‐29 in the response to hepatic injury, fibrosis and carcinogenesis.     

A predicted protein-protein interaction network of the filamentous fungus Neurospora crassa

The filamentous fungus Neurospora crassa is a leading model organism for circadian clock studies. Computational identification of a protein-protein interaction (PPI) network (also known as an interactome) in N. crassa can provide new insights into the cellular functions of proteins. Using two well-established bioinformatics methods (the interolog method and the domain interaction-based method), we predicted 27,588 PPIs among 3006 N. crassa proteins. To the best of our knowledge, this is the first identified interactome for N. crassa, although it remains problematic because of incomplete interactions and false positives. In particular, the established PPI network has provided clues to further decipher the molecular mechanism of circadian rhythmicity. For instance, we found that clock-controlled genes (ccgs) are more likely to act as bottlenecks in the established PPI network. We also identified an important module related to circadian oscillators, and some functional unknown proteins in this module may serve as potential candidates for new oscillators. Finally, all predicted PPIs were compiled into a user-friendly database server (NCPI), which is freely available at .     

Byakangelicin protects against carbon tetrachloride–induced liver injury and fibrosis in mice

Liver fibrosis is a disease caused by long‐term damage that is related to a number of factors. The current research on the treatment of liver fibrosis mainly focuses on the activation of hepatic stellate cell, in addition to protecting liver cells. byakangelicin has certain anti‐inflammatory ability, but its effect on liver fibrosis is unclear. This study aims to explore whether byakangelicin plays a role in the development of liver fibrosis and to explore its mechanism. We determined that byakangelicin has a certain ability to resist fibrosis and reduce liver cell damage in a model of carbon tetrachloride–induced liver fibrosis in mice. Thereafter, we performed further verification in vitro. The signalling pathways of two important pro‐fibrotic cytokines, transforming growth factor‐β and platelet‐derived growth factor, were studied. Results showed that byakangelicin can inhibit related pathways. According to the hepatoprotective effect of byakangelicin observed in animal experiments, we studied the effect of byakangelicin on 4‐HNE–induced hepatocyte (HepG2) apoptosis and explored its related pathways. The results showed that byakangelicin could attenuate 4‐HNE–induced hepatocyte apoptosis via inhibiting ASK‐1/JNK signalling. In conclusion, byakangelicin could improve carbon tetrachloride–induced liver fibrosis and liver injury by inhibiting hepatic stellate cell proliferation and activation and suppressing hepatocyte apoptosis.     

数据挖掘探究肝-肾-肺纤维化的关键基因

当前用于纤维化治疗的方法很少且疗效有限,为进一步了解纤维化的消退机制以发现潜在的治疗靶点。从Gene Expression Omnibus(GEO)数据库中选取了三个具有代表性的小鼠肝、肾、肺纤维化样本的mRNA数据集,使用GEO2R工具和Venn分析识别了差异表达基因(Differentially Expressed Genes, DEGs)。通过Webgestalt在线工具对DEGs进行基因功能富集。蛋白质-蛋白质相互作用(Protein-Protein Interactions, PPI)网络是由STRING数据库生成的。然后利用CytoHubba插件探索了关键基因,分别选取了三器官共有DEG和肝特异性DEG中MCC (Maximal Clique Centrality)得分最高的前10个作为关键基因。研究中整合分析了基于小鼠模型的肝-肾-肺纤维化的数据集,GSE36066和GSE97546用于第一轮的DEG分析,由于研究除了探究三种器官纤维化共有差异基因,也进一步探究了肝纤维化特有关键基因,所以引入另外一个肝纤维化数据集GSE55747用于验证分析。结果识别出58个肝肺肾纤维化...     

The Chemokine CCL3 Promotes Experimental Liver Fibrosis in Mice

Liver fibrosis is associated with infiltrating immune cells and activation of hepatic stellate cells. We here aimed to investigate the effects of the CC chemokine CCL3, also known as macrophage inflammatory protein-1α, in two different fibrosis models. To this end, we treated mice either with carbon tetrachloride or with a methionine- and choline-deficient diet to induce fibrosis in CCL3 deficient and wild-type mice. The results show that the protein expression of CCL3 is increased in wild-type mice after chronic liver injury. Deletion of CCL3 exhibited reduced liver fibrosis compared to their wild-type counterparts. We could validate these results by treating the two mouse groups with either carbon tetrachloride or by feeding a methionine- and choline-deficient diet. In these models, lack of CCL3 is functionally associated with reduced stellate cell activation and liver immune cell infiltration. In vitro, we show that CCL3 leads to increased proliferation and migration of hepatic stellate cells. In conclusion, our results define the chemokine CCL3 as a mediator of experimental liver fibrosis. Thus, therapeutic modulation of CCL3 might be a promising target for chronic liver diseases.     

A network-based analysis of the human TET Gene Family

Ten-eleven translocation (TET) proteins, a family of Fe²⁺- and 2-oxoglutarate-dependent dioxygenases, are involved in DNA demethylation. Three TET paralogs have been identified (TET1, TET2, and TET3) and they show different patterns of tissue-specific expression. In our previous evolutionary studies, we found that the TET1 and TET2 genes underwent positive selection more frequently than the TET3 gene, possibly due to changes in the selective constraints during their evolutionary process. In this study, we performed a network-based analysis of the mRNA expression profiles of TET knockdown and the TET-containing co-expression modules identified in early human developmental stages. Analyses based on the PPI subnetwork demonstrated that TET DEGs PPI subnetwork genes were more evolutionarily conserved than all the human-chimpanzee orthologs during evolutionary history. GO annotation of gene co-expression modules containing a TET gene ortholog revealed particular features of the potential role of TET gene family members. Our study implicated the TET1 module in fundamental aspects of cellular physiology, such as the regulation of glucose metabolism, and the TET2 module in GPCR signal transduction. The TET3 module was related to signaling pathways involved in developmental regulation. The evolutionary rate and phylogenetic age distribution analysis of network member genes also support these network-based analyses. The present study provides an integrated view of TET gene family properties and might be informative for elucidating the molecular mechanisms of their biological functions.     

Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross

Most common diseases are attributed to multiple genetic variants, and the feasibility of identifying inherited risk factors is often restricted to the identification of alleles with high or intermediate effect sizes. In our previous studies, we identified single loci associated with hepatic fibrosis (Hfib1–Hfib4). Recent advances in analysis tools allowed us to model loci interactions for liver fibrosis. We analysed 322 F2 progeny from an intercross of the fibrosis-susceptible strain BALB/cJ and the resistant strain FVB/NJ. The mice were challenged with carbon tetrachloride (CCl₄) for 6 weeks to induce chronic hepatic injury and fibrosis. Fibrosis progression was quantified by determining histological fibrosis stages and hepatic collagen contents. Phenotypic data were correlated to genome-wide markers to identify quantitative trait loci (QTL). Thirteen susceptibility loci were identified by single and composite interval mapping, and were included in the subsequent multiple QTL model (MQM) testing. Models provided evidence for susceptibility loci with strongest association to collagen contents (chromosomes 1, 2, 8 and 13) or fibrosis stages (chromosomes 1, 2, 12 and 14). These loci contained the known fibrosis risk genes Hc, Fasl and Foxa2 and were incorporated in a fibrosis network. Interestingly the hepatic fibrosis locus on chromosome 1 (Hfib5) connects both phenotype networks, strengthening its role as a potential modifier locus. Including multiple QTL mapping to association studies adds valuable information on gene–gene interactions in experimental crosses and human cohorts. This study presents an initial step towards a refined understanding of profibrogenic gene networks.     

Curcumin attenuates angiogenesis in liver fibrosis and inhibits angiogenic properties of hepatic stellate cells

Hepatic fibrosis is concomitant with sinusoidal pathological angiogenesis, which has been highlighted as novel therapeutic targets for the treatment of chronic liver disease. Our prior studies have demonstrated that curcumin has potent antifibrotic activity, but the mechanisms remain to be elucidated. The current work demonstrated that curcumin ameliorated fibrotic injury and sinusoidal angiogenesis in rat liver with fibrosis caused by carbon tetrachloride. Curcumin reduced the expression of a number of angiogenic markers in fibrotic liver. Experiments in vitro showed that the viability and vascularization of rat liver sinusoidal endothelial cells and rat aortic ring angiogenesis were not impaired by curcumin. These results indicated that hepatic stellate cells (HSCs) that are characterized as liver‐specific pericytes could be potential target cells for curcumin. Further investigations showed that curcumin inhibited VEGF expression in HSCs associated with disrupting platelet‐derived growth factor‐β receptor (PDGF‐βR)/ERK and mTOR pathways. HSC motility and vascularization were also suppressed by curcumin associated with blocking PDGF‐βR/focal adhesion kinase/RhoA cascade. Gain‐ or loss‐of‐function analyses revealed that activation of peroxisome proliferator‐activated receptor‐γ (PPAR‐γ) was required for curcumin to inhibit angiogenic properties of HSCs. We concluded that curcumin attenuated sinusoidal angiogenesis in liver fibrosis possibly by targeting HSCs via a PPAR‐γ activation‐dependent mechanism. PPAR‐γ could be a target molecule for reducing pathological angiogenesis during liver fibrosis.     

Identification of potential mechanism and hub genes for neuropathic pain by expression-based genome-wide association study

Neuropathic pain (NP) is a common pathological pain state with limited effective treatments. This study was designed to identify potential mechanisms and candidate genes using gene expression–based genome-wide association study (eGWAS). All NP-related microarray experiments were obtained from Gene Expression Omnibus and ArrayExpress. Significantly dysregulated genes were identified between experimental and untreated groups, and the number of microarray experiments in which each gene was dysregulated was calculated. Significantly dysregulated genes were ranked according to P values of the chi-square test. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database, we performed functional and pathway enrichment analysis. Protein-protein interaction (PPI) network and module analysis was performed using Cytoscape software. A total of 115 candidate genes were identified from 19 independent microarray experiments by eGWAS based on the Bonferroni threshold ( P < 2.97 × 10 ⁻⁶). Immune and inflammatory responses, and complement and coagulation cascades, were respectively the most enriched biological process and pathways for candidate genes. The hub genes with highest connectivity in PPI network and two modules Ccl2 and Jun, and Ctss application of the eGWAS methodology can identify mechanisms and candidate genes associated with NP. Our results support the validity and prevalence of inflammatory and immune mechanisms across different NP models, and Ccl2, Jun, and Ctss may be the hub genes for NP.     

Network Topology Reveals Key Cardiovascular Disease Genes

The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.     

Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

In this study, we aimed to uncover genes that drive the pathogenesis of liver metastasis in colorectal cancer (CRC), and identify effective genes that could serve as potential therapeutic targets for treating with colorectal liver metastasis patients based on two GEO datasets. Several bioinformatics approaches were implemented. First, differential expression analysis screened out key differentially expressed genes (DEGs) across the two GEO datasets. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we identified the enrichment functions and pathways of the DEGs that were associated with liver metastasis in CRC. Second, immune infiltration analysis identified key immune signature gene sets associated with CRC liver metastasis, among which two key immune gene families (CD and CCL) identified as key DEGs were filtered by protein-protein interaction (PPI) network. Some of the members in these gene families were associated with disease free survival (DFS) or overall survival (OS) in two subtypes of CRC, namely COAD and READ. Finally, functional enrichment analysis of the two gene families and their neighboring genes revealed that they were closely associated with cytokine, leukocyte proliferation and chemotaxis. These results are valuable in comprehending the pathogenesis of liver metastasis in CRC, and are of seminal importance in understanding the role of immune tumor infiltration in CRC. Our study also identified potentially effective therapeutic targets for liver metastasis in CRC including CCL20, CCL24 and CD70.     

Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

Background: Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little is known regarding effective biomarkers for LUAD.Methods: The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from the gene expression omnibus (GEO) datasets. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to extract hub genes from the protein–protein interaction (PPI) network. The expression of the hub genes was validated using expression profiles from TCGA and Oncomine databases and was verified by real-time quantitative PCR (qRT-PCR). The module and survival analyses of the hub genes were determined using Cytoscape and Kaplan–Meier curves. The function of KIF4A as a hub gene was investigated in LUAD cell lines.Results: The PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, and CCNA2, which were significantly upregulated in Oncomine and TCGA LUAD datasets, and were verified by qRT-PCR in our clinical samples. We determined the overall and disease-free survival analysis of the seven hub genes using GEPIA. We further found that CENPF, DLGAP5, and KIF4A expressions were positively correlated with clinical stage. In LUAD cell lines, proliferation and migration were inhibited and apoptosis was promoted by knocking down KIF4A expression.Conclusion: We have identified new DEGs and functional pathways involved in LUAD. KIF4A, as a hub gene, promoted the progression of LUAD and might represent a potential therapeutic target for molecular cancer therapy.     

Restoration of miR-29b exerts anti-cancer effects on glioblastoma

Background

Methylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis.

Methods

Data of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database. Aberrantly methylated-DEGs were obtained by GEO2R. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. MCODE was used for module analysis of the PPI network.

Results

Totally 411 hypomethylation-high expression genes were identified, which were enriched in biological processes of response to wounding or inflammation, cell proliferation and adhesion. Pathway enrichment showed cytokine–cytokine receptor interaction, p53 signaling and cell cycle. The top 5 hub genes of PPI network were CAD, CCND1, ATM, RB1 and MET. Additionally, 239 hypermethylation-low expression genes were identified, which demonstrated enrichment in biological processes including cell–cell signaling, nerve impulse transmission, etc. Pathway analysis indicated enrichment in calcium signaling, maturity onset diabetes of the young, cell adhesion molecules, etc. The top 5 hub genes of PPI network were EGFR, ACTA1, SST, ESR1 and DNM2. After validation in TCGA database, most hub genes still remained significant.

Conclusion

In summary, our study indicated possible aberrantly methylated-differentially expressed genes and pathways in CRC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of CRC. Hub genes including CAD, CCND1, ATM, RB1, MET, EGFR, ACTA1, SST, ESR1 and DNM2 might serve as aberrantly methylation-based biomarkers for precise diagnosis and treatment of CRC in the future.

     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.