Computational networks of activating transcription factor 3 gene in Huh7 cell lines and hepatitis C virus-infected Huh7 cell lines
- Authors:
- Published online on: March 26, 2015 https://doi.org/10.3892/mmr.2015.3548
- Pages: 1239-1246
Abstract
Introduction
Hepatitis C virus (HCV) infection affects 170 million people worldwide and as a major cause of liver disease worldwide, HCV is a potential cause of substantial morbidity and mortality in the future and therefore, a health serious health concern (1). Approximately 80% of patients with HCV fail to clear the virus and <10% may develop severe liver diseases, such as chronic hepatitis, liver cirrhosis and hepatocarcinoma, which reduce the chance of patient survival (2). Understanding HCV pathology and the development of novel drugs with which to treat patients with HCV is an ongoing challenge.
Microarrays and different types of high-throughput approaches, for example, high-throughput RNA-Seq and high-throughput DNA-Seq, for gene expression analysis have led to an improved understanding of the processes underlying HCV infection. These approaches are advantageous compared with traditional approaches, which are restricted to analyzing small numbers of genes (3). Microarray technology does not require gene selection in advance, meaning that the method is less biased and is capable of identifying genes that are modified when cells are exposed to environmental changes (4). Microarray technology aims to establish gene regulatory networks and to identify interactions among genes and their products. Carefully analyzed networks are used to identify correlated genes that are associated with the same biological processes or pathways, and to infer interactions among molecules, such as physical association, metabolite flow, regulatory associations and co-expression (5). In the present study, GRNinfer software was used in order to establish a gene regulatory network and to identify molecular interactions, including activation and inhibition, in the activating transcription factor 3 (AFT3) signaling pathway in healthy Huh7 (Huh7) and HCV-infected (HCV-Huh7) cell lines.
Materials and methods
Microarray
The gene expression profile data GSE 20948 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20948) were extracted from the public Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/gds/). Microarray analysis was performed for 14 Huh7 samples (2 samples, 6 h; 3 samples, 12 h; 3 samples, 18 h; 3 samples, 24 h; 3 samples, 48 h.) and 14 HCV-Huh7 samples (2 samples, 6 h; 3 samples, 12 h; 3 samples, 18 h; 3 samples, 24 h; 3 samples, 48 h); the latter had been infected with HCV for 6, 12, 18, 24 or 48 h. The platform of the data was GPL 570 (HG-u133-plus-2) affymetrix human Genome u 133 2.0 array.
Gene selection algorithms
Fifty HCV-Huh7 molecular markers were identified using Multiexperiment Viewer (http://www.tm4.org/mev.html; Version 4.8.1-windows), which belongs to TM4, in order to conduct a significant analysis of microarrays (SAM). The TM4 suite consists of four major applications, including Microarray Data Manager, TIGR_Spotfinder, Microarray Data Analysis System. This software is a free, open-source software released under the Artistic license, and is OSI certified. SAM is a statistical technique, used to identify significant genes in a set of microarray experiments. The explanatory variable is gene expression measurement from a set of microarray experiments, and the response variable is a grouping, such as mock-infected or infected. Microarray raw data CEL files were processed using expression console software of Affymetrix, Inc. (Santa Clara, CA, USA). In the present study, 50 significant genes using ATF3 as a target gene, which was on interest, were further analyzed.
Network establishment of candidate genes
ATF3 gene networks were constructed using GRNinfer (6) and GVedit (http://portableapps.com/node/38245; version: 2.38) tools. GRNinfer is a novel mathematical method base on linear programming and a decomposition procedure that infers gene networks. The method ensures derivation of the most consistent network structure reducing the issues associated with data scarcity, yet improving reliability. The following equation represents all of the possible networks for the same dataset: J=(X′−A)U∧1VT+YV=J+YVT. Where J=(Jij)m×m=∂f(x)/∂x is an n×m Jacobian matrix or connectivity matrix, X=(x(t1),…,x(tm)) and all n×m matrices with x′i(tj)=[xi(tj+1)−xi(tj)]/[tj+1−tj] for i=1,…,n; j=1,…m.X(t)=(x1(t),…,xn(t)T∑Rn, a=(a1…,an)T∑Rn,xi(t) is the expression level (mRNA concentrations) of gene i at time instance t.y=(yij) is an nxn matrix, where yij is zero if ej=0. U is a unitary m×n matrix of left eigenvectors, ∧=diag (e1,…,en) is a diagonal n×n matrix containing the n eigenvalues and VT is the transpose of a unitary n×n matrix of right eigenvectors.
The parameters were λ, 0.0 and threshold, 1×10−9.
Functional annotation clustering
The database for annotation, visualization and integrated discovery (DAVID; http://www.david.niaid.nih.gov) was used. The DAVID gene function clustering tool provides representative annotation and gene ontology (GO) term enrichment analysis, which separates genes into different enrichment score groups according to the gene annotation collective frequency occurrence (7,8).
Molecule annotation system 3.0 (MAS 3.0)
MAS (http://bioinfo.capitalbio.com/mas3/) is an analysis platform, which adds biological function annotation to high throughput microarray data. By integrating the relevant annotation information from a number of public information databases, MAS synthesizes biological data, including genes, proteins, functions, expression, protein interactions, signaling pathways, diseases and methylation. MAS enables the incorporation of genetic data from Genebank, European molecular biology laboratory, SwissPort, GO, KEGG, BioCarta, gene map annotator and pathway profiler (GenMapp), mirBase, expected progeny differences, HRPD, MIND, BIND, Intact, TRANSAC, UniGene, single nucleotide polymorphism database, OMIM, InterPro, HUGO, mouse genome informatics and rat genome database.
Results
Gene enrichment analyses
ATF3 was one of fifty genes which were significantly differentially expressed between Huh7 and HCV-Huh7 cell lines (fold change=8.782885; Table I). Gene GO term enrichment analysis for ATF3 demonstrated that the molecular function of ATF3 is associated with transcription corepressor activity, protein binding, sequence-specific DNA binding and protein dimerization activity. ATF3 biological processes are associated with DNA-dependent transcription regulation. ATF3 cellular component is localized in the nucleus and nucleolus. GenMAPP analysis demonstrated that ATF3 is associated with smooth contraction, hypertrophy model, NetPath 5 and Hs transforming growth factor β1 (TGF-β) NetPath 7, and that it exhibits transcription cofactor activity. Disease analysis demonstrated that ATF3 is associated with shock, leukemia, colorectal cancer, neoplasm metastasis, nervous system disease, stomach cancer, necrosis, leukemia T cell, lymphoma non-hodgkins, malignant neoplasm of the breast, hyperalgesia, episodic ataxia type 2 and hereditary ataxia overview.
 | Table IFifty genes exhibiting significant differential expression between Huh7 and HCV-Huh7 cell lines identified using SAM. |
Identification of genes upstream and downstream of ATF3
Using GRNInfer, a cell network was constructed, which included gene clusters upstream and downstream of ATF3 in Huh7 and HCV-Huh7 cell lines. According to DAVID software analysis, 12 gene clusters were identified downstream of ATF3 in HCV-Huh7 cells (10 activation and 2 inhibition; Table II). Using an MAS 3.0 software GO analysis, ATF3 annotation regulation networks were enriched. The upstream pathway of ATF3 in Huh7 cells included the activation of stanniocalcin 2 cationic amino acid transporter, Y+ system member 1 (SLC7A1) and insulin receptor substrate 2 (IRS2), involving 19 GO terms and three KEGG pathways, and inhibition of four and a half LIM domains 2 (FHL2), involving five GO terms. The downstream pathway of ATF3 in Huh7 cells included inhibition of zinc finger protein 295 (ZNF295), involving two GO terms. The upstream pathway of ATF3 in HCV-Huh7 cells included the activation of inhibin β E chain (INHBE), asparagine synthetase (ASNS) and SLC7A1, involving ten GO terms and four KEGG pathways, and the inhibition of reticulocalbin 1, EF-hand calcium binding domain (RCN1), involving one GO term. The downstream pathway of in HCV-Huh7 cells included the activation of the following genes: Brain-derived neurotrophic factor, cold inducible RNA binding protein, FHL2, fatty acid binding protein 3, interferon regulatory factor 9, InaD-like (INADL), IRS2, IRS1, kruppel-like factor 10, LOC100506392, LY6/PLAUR domain containin 1, prion protein, peroxisome proliferator-activated receptor γ coactivator 1-α, RAR-related orphan receptor A, phospholipase A1 member A, SFT2 domain containing 3, SMAD family member 5, TGFβ 1 induced transcript 1, UDP-N-acteylglucosamine pyrophosphorylase 1-like 1, ZNF295 and SLC7A1, involving 128 GO terms and 13 KEGG pathways. The downstream pathway in HCV-Huh7 cells included the inhibition of the following genes: SLC7A1, arrestin domain-containing protein 4, ASNS, chromosome 10 open reading frame 10, INHBE, phosphoenolpyruvate carboxykinase 2, PCK2, RCN1, stanniocalcin 2, suppressor of cytokine signaling 2, SLC1A4 and thioredoxin interacting protein, involving 37 GO terms and 13 KEGG pathways.
| Table IIFunctional annotation clusters (activation and inhibition) downstream of activating transcription factor 3 in hepatitis C virus-infected Huh7 cells. |
Functional annotation clustering analyses
Ten activation and two inhibition annotation clusters were enriched downstream of ATF3, in HCV-Huh7 cells. The enrichment scores of the activation clusters ranged from 0.27–2.93, and the two inhibition clusters were 0.54 and 1.47. Among the activation clusters, the highest enrichment score (cluster 1; 2.93) was associated with positive metabolism regulation, such as fatty acid oxidation, glucose metabolism, carbohydrate metabolism, cellular carbohydrate metabolism, fatty acid metabolism, macromolecule biosynthesis, cellular biosynthesis, lipid metabolism, fatty acid metabolic regulation, macromolecule metabolism and androgen receptor binding. The activation clusters exhibiting enrichment scores 0.27–1.98 were associated with regulating cell proliferation, transcription, DNA-dependent developmental processes, gene expression, responses to stimuli of insulin, endogenous organic substances, peptide hormones, androgen receptor binding, androgen receptor signaling pathways, steroid hormone receptor binding, nuclear hormone receptor binding, intracellular signaling cascades, zinc-finger transcription factors and metal-binding. Therefore, it is hypothesized that ATF3 is inactive in Huh7 cells and is activated upon HCV infection, thereby regulating a number of cytokine functions, metabolic pathways and signal transduction. The present study identified 4 genes that were upstream of ATF3 and 33 that were downstream of ATF3 in HCV-Huh7 cells. Therefore, ATF3 may be an important regulatory factor involved in the pathological reactions of Huh7 cells undergoing the initial stages of infection with HCV.
Fig. 1 demonstrates the ATF3 network construction and analysis processes.
Discussion
In order to understand the involvement of ATF3 in HCV-infected Huh7 cells, an ATF3 GO network was established for Huh7 and HCV-Huh7 cell lines. HCV infection leads to complex biological responses and, therefore, the subsequent genetic interactions in the infected cells are typically non-linear. However, gene networks may only be predicted based on linear equations, due to the limited understanding of biological processes required for more complex statistical analyses (9–12). In the present study, GRNinfer, a linear programming and decomposition software, was used in order to analyze and compare ATF3 networks in Huh7 and HCV-Huh7 cell lines. Constructed networks included upstream and downstream gene clusters of ATF3 in Huh7 and HCV-Huh7 cell lines (Fig. 2). Furthermore, using DAVID and MAS 3.0 software, the KEGG pathways and GO terms of four gene clusters were enriched. The results of the present study demonstrated that downstream pathways of ATF3 in HCV-Huh7 cells were significantly enhanced.
The results of the present study suggested that ATF3 may be inactive in Huh7 cells and activated following HCV infection of Huh7 cells. These results are in accordance with those observed in previous studies. ATF3 expression levels were found to be low in other cell lines or tissue (13) and were shown to rise when cells were subjected to a number of different stresses, including ischemia and trauma, as well as exposure to toxic chemicals. ATF3 mRNA expression was not detected, or remained at low levels, in a number of cell types (14). The present study demonstrated that 4 genes were upstream or downstream of ATF3 in Huh7 cells, whereas 36 genes were upstream or downstream of ATF3 in HCV-Huh7 cells. Therefore, ATF3 may be inactive in healthy Huh7 cells while it is activated in HCV-infected Huh7 cells (Fig. 2). These observations require further investigation.
Four genes were identified upstream, while 33 genes were downstream of ATF3 in HCV-Huh7 cells. Therefore, ATF3 may be associated with HCV pathology in Huh7 cells. A previous study suggested that ATF3 may represent an immediate early responsive gene, which is predominantly activated at a transcriptional level (15).
Extensive studies have shown that ATF3 is involved in immune regulation (16–23), endocrine regulation (24–27), tumorigenicity (28–32), apoptosis, cell cycling (33–37) and inflammation (17,34,38–43). This suggests that ATF3 is associated with host defensive responses to inflammation, viruses and cancer. These processes require the interactions of certain cellular pathways, which influence disease outcomes. ATF3 is an adaptive-response gene, which is involved in a number of cellular processes, and accommodates cellular changes by transducing signals from various receptors via activating or repression of gene expression. ATF3 may also regulate host defense mechanisms (13). According to DAVID functional annotation clustering analysis, no annotation clusters were identified downstream of ATF3 in Huh7 cells nor were there clusters identified upstream in Huh7 and HCV-Huh7 cells. However, ten activation annotation clusters were enriched downstream of ATF3 in HCV-Huh7 cells that exhibited enrichment scores from 0.27–2.93 and two inhibition annotation clusters were enriched downstream of ATF3 in HCV-Huh7 cells that exhibited enrichment scores of 0.54 and 1.47. In addition, analyses using MAS 3.0 GO and KEGG, suggested that 128 GO terms and 13 KEGG pathways are involved downstream of ATF3 in HCV-Huh7 cells and 37 GO terms and 13 KEGG pathways were inhibited. It is hypothesized that ATF3, as an adaptive-response gene, may be involved in the pathophysiological responses of Huh7 cells to HCV infection.
In addition to liver injury and hepatocellular carcinoma, HCV infection is associated with fatty degeneration of the liver, suggesting that hepatitis C is a metabolic disease (44,45). Considerable evidence suggests that HCV infection is associated with metabolic syndromes, which referred to constellation of problems, including insulin resistance, obesity, hypertension and hyperlipaemia. Fatty degeneration of the liver and insulin resistance may be important in HCV-induced metabolic syndrome and a molecular mechanism underlying this process is a virus-induced metabolic disorder of fat in the liver (46). The liver is important for the control of lipogenesis, gluconeogenesis and cholesterol metabolism. A number of investigations into pathological examination have highlighted the importance of metabolic function in liver diseases. The process of lipid metabolism is important in the proliferation of HCV infection (47).
As a cyclic AMP response element-binding protein family member, ATF3 participates in a range of cellular processes. Recent studies have shown that changes in ATF3 activity is associated with energy balance. Homeostasis imbalance may lead to organ growth failure, retardation and metabolic disorder. A loss of ATF3 in Drosophila was found to result in chronic inflammation and primary starvation responses, including lipid overloading in the fat bodies of the larval epithelium, which may lead to energy imbalance and death (48). Kim et al (49) showed that ATF3 was involved in a cholesterol-sensing network.
In the present study, DAVID cluster analysis found that the cluster with the highest enrichment score (2.93) was downstream of ATF3 in HCV-Huh7 cells (Table II). This suggests that ATF3 not only activates lipid metabolism, but also positively regulates fatty acid metabolic, macromolecular biosynthetic and cellular biosynthetic processes. The results of the present study showed that ATF3 may influence metabolic processes in HCV-Huh7 cells. The current study provides a novel perspective for exploring the association of ATF3 in HCV infection of Huh7 cells. However, further research is required in order to explore how ATF3 is associated with metabolic processes and the regulatory functions of ATF3.
In conclusion, an ATF3 network was constructed for Huh7 and HCV-Huh7 cell lines and gene functional annotation and enrichment cluster analyses were performed. The results of the present study suggested that ATF3 is inactive in healthy Huh7 cells and activated following HCV infection. ATF3 may contribute to the initial pathological responses to HCV infection in Huh7 cells. ATF3 may be associated with a number of processes, in particular lipid metabolism, during acute HCV infection of Huh7 cells.
Acknowledgments
This study was supported by the National Natural Science Foundation in China (grant no. 81170393/H0316).
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