Determination of the mechanism of action of repetitive halothane exposure on rat brain tissues using a combined method of microarray gene expression profiling and bioinformatics analysis

  • Authors:
    • Jiansheng Wang
    • Xiaojun Yang
    • Huan Xiao
    • Jianqiang Kong
    • Miao Bing
  • View Affiliations

  • Published online on: October 21, 2015     https://doi.org/10.3892/mmr.2015.4462
  • Pages: 8071-8076
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Abstract

The present study aimed to investigate the gene expression profiles of rats brain tissues treated with halothane compared with untreated controls to improve current understanding of the mechanism of action of the inhaled anesthetic. The GSE357 gene expression profile was dowloaded from the Gene Expression Omnibus database, and included six gene chips of samples repeatedly exposed to halothane and 12 gene chips of untreated controls. The differentially expressed genes (DEGs) between these two groups were identified using the Limma package in R language. Subsequently, the Database for Annotation, Visualization and Integrated Discovery was used to annotate the function of these DEGs. In addition, the most significantly upregulated gene and downregulated gene were annotated, to reveal the functional interactions with other associated genes, in FuncBase database. A total of 44 DEGs were obtained between The control and halothane exposure samples. Following Gene Ontology functional classification, these DEGs were found to be involved predominantly in the circulatory system, regulation of cell proliferation and response to endogenous stimulus and corticosteroid stimulus processes. KRT31 and HMGCS2, which were identified as the most significantly downregulated and upregulated DEGs, respectively, were associated with the lipid metabolic process and T cell activation, respectively. These results provided a basis for the development of improved inhalational anesthetics with minimal side effects and are essential for optimization of inhaled anesthetic techniques for advanced surgical procedures.

Introduction

According to statistical reports, there are >200,000,000 individuals worldwide requiring anesthetic care for surgery each year (1). Anesthetics exert the three reversible characteristics of immobility, amnesia and unconsciousness (2). General anesthetic drugs include inhaled gases and intravenous agents, which can cause a reduction in nerve transmission at synapses (3). Halothane, an inhaled anesthetic, is partly metabolized by the liver and the metabolized products are excreted in the urine (4,5). Previous associated studies have indicated that repeated exposure to halothane in adults may result in halothane-associated liver failure (68).

Previous molecular investigations have provided evidence that the mechanism of volatile anesthetic involves a series of molecular modulation. The actions of anesthetics lies within the family of ligand-gated ion channels and the binding of anesthetics may alter the overall motion of a ligand-gatedion channel (9). The ligand-gated ion channel superfamily contains GABAA receptors, glycine receptors, serotonin type 3 receptors and nicotinic acetylcholine receptors. GABAA receptors, neurotransmitter-gated chloride channels, are located on neurons and, when activated, they reduce neuronal excitation (10). Protein kinase C, a soluble cytoplasmic protein, is an important signal transduction enzyme, which is involved in regulating the release of neurotransmitters and ion channel activity (11,12). The results of a study by Maingret et al demonstrated that TREK-1, a two-pore-domain background postassium channel, can be activated by volatile anesthetics and was suggested to be a target in the action of these drugs (13) Associated investigations have reported that the general mechanism of halothane may be associated with competition with endogenous ligands, and mitochondrion are a preferred and saturable site for halothane localization (14). In addition, previous analysis of brain membrane proteins in rats has revealed selective binding of halothane to individual protein subunits of the mitochondrial respiratory chain (15). However, these findings regarding the mechanism of action of volatile anesthetics are limited. Advancements in microarray technology have assisted in providing a comprehensive analysis for gene expression among anesthetics (16). It provides a useful tool for the identification of featured genes associated with anesthetic action.

In the present study, a set of gene expression profiles, including unexposed controls and those of exposure to halo-thane were used to analyze their differentially expressed genes (DEGs). Subsequently, bioinformatics tools were used to identify the functions associated with these DEGs. The aim of this investigation was to identify specific genes involved in the action of anesthetics. The results may assist in providing a more profound understanding of the molecular mechanism of anesthetics and in overcoming the adverse effects arising from their use.

Materials and methods

Affymetrix microarray data

Microarray data from the Gene Expression Omnibus (GEO) database were downloaded under the accession number, GSE357, which were deposited by the University of Pennsylvania (Pennsylvania, USA) (17). A total of 18 gene chips were available. The 18 specimens included 12 control specimens and six repeated halothane exposure specimens. The experiment protocol used by the University of Pennsylvania to obtain these data is briefly described as follows: Rats (n=18; male; weigh, ~250 g) were divided into either a n unexposed control group (n=12) or a repetitive expo -sure group (n=6). Each of the rats in the repetitive exposure group was exposed to 0.8% halothane each day for 90 min, twice daily (with 3 h recovery between exposures) for 2.5 or 5 days, for a total of five or 10 exposures, respectively. None of the animals required intubation. The microarray expression platform was termed GPL85 [RG_U34A] Affymetrix Rat Genome U34 Array. In the present study, the original data was downloaded as well as the annotation of platform.

Data preprocessing and analysis of DEGs

The data in the CEL files were converted into expression profiles using the Affy package (http://www.bioconductor.org/help/search/index.html?q=Affy, Affymetrix,Santa Clara, California, US) in R (18) and data were normalized using the median algorithm. The Limma package (http://www.biocon-ductor.org/packages/release/bioc/html/limma.html) in R language was used to analyze the DEGs between the 12 control samples and six exposure samples (19). A P-value <0.1 and a |log FC|-value >1 were set as the threshold criteria for DEGs.

Gene ontology (GO) enrichment analysis

The GO project (http://www.geneontology.org/) provides structured, controlled vocabularies and classifications, which encompass the three domains of cellular component, molecular function and biological process (20). The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/) consists of an integrated biological knowledge-base and functional annotation charts or tables. It provides a comprehensive set of functional annotation tools for the integration of particular genes of interest with a specific function (21,22). All the DEGs, which were identified in the present study using the Limma Package in R language, were loaded into the DAVID database, and a significant value was calculated for each of the GO terms identified. A count number >2 and a false discovery rate (FDR) <0.05 were selected as the cut-off criteria.

Network analysis

FuncBase is a web resource for viewing quantitative machine learning-based gene function annotations (http://func.mshri.on.ca/) (23). Predictions in FuncBase can be viewed using GO terms. In the present study, the FuncBase database was used to annotate the functional interactions between the significantly DEGs and other genes by calculating their score, which, for the GO function node was based on the number of genes of similar function (24,25). The records with scores >0.8 were retained.

Results

Screening of DEGs

In the present study, the publicly available GSE357 microarray dataset was obtained from the GEO database. Following data preprocessing and normalization (Fig. 1), the data were analyzed using Limma package in R language to identify the DEGs between the 12 control and six exposure samples. According to the threshold criterion (P<0.05 and |log FC|>1) for DEGs, a total of 44 DEGs were obtained, consisting of 19 downregulated and 25 upregulated genes (Table I). Subsequently, the genes exhibiting the most significant upregulation and downregulation were selected, which were HMGCS2 (P=0.00005) and KRT31 (P = 0. 0032), respectively.

Table I

DEGs between the control and exposure groups, consisting of 19 downregulated genes and 25 upregulated genes.

Table I

DEGs between the control and exposure groups, consisting of 19 downregulated genes and 25 upregulated genes.

GeneP-valueLog FC
KRT310.0032−1.4436361
NOS30.00125−1.3555293
CDC25B0.0134−1.3488796
VOM2R320.00813−1.2816925
HMGB10.00994−1.2430645
RAB33B0.00354−1.2112175
PEX100.0026−1.2037957
PA M0.00907−1.1834612
B3GNTL10.0126−1.1714498
GPX30.0076−1.1710799
FCER1A0.0413−1.1358269
TPH10.0219−1.1336363
GLP1R0.00643−1.1331447
MTR0.0015−1.1152652
ACTA 20.0252−1.0915312
VOM1R1010.0103−1.0725745
HAP10.00347−1.0706768
IGSF60.0152−1.0437975
DDX40.0464−1.0081465
HIST1H2AF0.02481.0072684
UBE2D40.02261.0169088
ACOT10.02291.0187761
MMP110.01671.0214673
ALB0.02011.0385379
RGS10.04981.0405889
OLR14960.01981.0563126
UGT2B150.04691.0583942
KLK1C30.01731.077667
TMIGD10.006011.0904489
TSX0.02291.1425503
CNGA30.009841.1530625
IGF2R0.0001981.2007601
NOX40.03781.218972
PCSK50.03381.2191295
LHX10.01241.2359783
TLR40.009221.2781933
RNF40.01981.3328296
SLC1A60.01911.3830303
ZMAT30.01221.4563084
NXT10.005831.4660187
OPA10.003321.7659469
FBXO300.02581.7748347
LEPR0.003192.1167477
HMGCS20.000052.2001709

[i] Thresholds were set at P<0.05 and log FC <1 (downregulation) and log FC >1 (upregulated). DEG, differentially expressed gene.

GO enrichment analysis of the DEGs

In order to investigate the expression of the DEGs in the exposure group at a more functional level, the DEGs (P<0.05 and |log FC|>1) between the control and exposure profiles were classified into GO terms (Table II). A count number >2 and FDR <0.05 were selected as the cut-off criteria. All the DEGs were enriched in 18 GO terms, including response to steroid hormone and endogenous stimulus. GO terms were also associated with metabolic and circulatory system processes.

Table II

Classification of differentially expressed genes between the control and halothane-exposure groups, according to GO terms with FDR <0.05

Table II

Classification of differentially expressed genes between the control and halothane-exposure groups, according to GO terms with FDR <0.05

GO termDescriptionCountFDRGene
GO:0048545Response to steroid hormone stimulus70.000140615PAM, HMGB1, LEPR, GPX3, NOS3, TLR4, CNGA3
GO:0010033Response to organic substance110.000161411PAM, HMGB1,HMGCS2, ALB, ACTA2, LEPR, GPX3, NOS3, TLR4, CNGA3, GLP1R
GO:0009725Response to hormone stimulus80.000454225PAM, HMGB1,HMGCS2, LEPR, GPX3, NOS3, TLR4, CNGA3
GO:0031960Response to corticosteroid stimulus50.000474271PAM, HMGB1, GPX3, TLR4, CNGA3
GO:0003013Circulatory system process50.000892763ALB, ACTA2, NOS3, PCSK5, GLP1R
GO:0008015Blood circulation50.000892763ALB, ACTA2, NOS3, PCSK5, GLP1R
GO:0009719Response to endogenous stimulus80.000909287PAM, HMGB1,HMGCS2, LEPR, GPX3, NOS3, TLR4, CNGA3
GO:0010038Response to metal ion50.001460463PAM, ALB, GPX3, NOS3, CNGA3
GO:0001934Positive regulation of protein amino acid phosphorylation40.002356405ECER1A, HMGB1, TLR4, DDX4
GO:0042327Positive regulation of phosphorylation40.003130896ECER1A, HMGB1, TLR4, DDX4
GO:0045937Positive regulation of phosphate metabolic process40.003390247ECER1A, HMGB1, TLR4, DDX4
GO:0010562Positive regulation of phosphorus metabolic process40.003390247ECER1A, HMGB1, TLR4, DDX4
GO:0051384Response to glucocorticoid stimulus40.004994927PAM, HMGB1, GPX3, TLR4
GO:0010035Response to inorganic substance50.006486276PAM, ALB, GPX3, NOS3, CNGA3
GO:0050731Positive regulation of peptidyl-tyrosine phosphorylation30.007983385ECER1A, TLR4, DDX4
GO:0006518Peptide metabolic process30.008976452PAM, GPX3, PCSK5
GO:0007292Female gamete generation30.009667675 LEPR,NOS3,CDC25B
GO:0042127Regulation of cell proliferation70.009719742NOX4, HMGBl,LHXl,NOS3, TLR4, DDX4, GLP1R

[i] WANG et al: MECHANISM OF THE ACTION OF HALOTHANE ON RAT BRAIN TISSUES

Interaction network construction

The present study used the FuncBase database to annotate the functional interactions between the significantly DEGs and other genes, and screened for significant interactions with a score >0.8. By integrating these associations, interaction networks of these two significant DEGs and their interactive genes were constructed (Fig. 2). The database revealed gene function nodes in two networks from this database, KRT31 and HMGCS2, expressing similar function genes in four function nodes and five function nodes, respectively (Tables III and IV). The function nodes with the highest scores were associated with lipid metabolism (GO: 6629) and T cell activation (GO: 42110).

Table III

Function node scores of KRT31 in the network.

Table III

Function node scores of KRT31 in the network.

GO termDescriptionScore
GO: 6629Lipid metabolic process0.970
GO: 44255Cellular lipid metabolic process0.954
GO: 19752Carboxylic acid metabolic process0.866
GO: 6082Organic acid metabolic process0.846

[i] GO, Gene Ontology.

Table IV

Function node scores of HMGCS2 in the network.

Table IV

Function node scores of HMGCS2 in the network.

GO termDescriptionScore
GO: 42110T cell activation0.898
GO: 42098T cell proliferation0.836
GO: 9913Epidermal cell differentiation0.826
GO: 8544Epidermis development0.808
GO: 42129Regulation of T cell proliferation0.806

[i] GO, Gene Ontology.

Discussion

In the present study, the effects of halothane on the brain tissue of rats were investigated. The results demonstrated that halothane modulated the expression of 44 DEGs, which were involved predominantly in response to endogenous and cortico-steroid stimuli.

The results of the present study described the differential gene expression profiling between unexposed control samples and halothane exposure samples. Certain effects of halothane on regulated genes (HMGB1, TLR4, HMGCS2) were involved in responding to hormone and endogenous stimuli. HMGCS2, was one of the most markedly upregulated genes in tyhe exposure profile compared with the control. The role of this gene in the anesthetic mechanism remains to be fully elucidate, however, HMGCS2 has been demonstrated to correlate with fatty acid metabolism (26). Toll-like receptor 4 (TLR4) is a member of the TLR family, which are pattern recognition receptors that can activate the innate immune response (27). High mobility group box-1 (HMGB1), an endogenous danger signal, is released from injured cells and signals inflammatory responses by binding to pattern recognition receptors, including TLR4 (28). It has been demonstrated that intracellular TFA adducts, metabolized by halothane, can induce a stress response in hepatocytes and activate associated innate immune cells (29). Then activated immune cells release cytotoxic granules leading to hepatocellular necrosis. Meanwhile, HMGB-1 acts as a TLR4 agonist to enhance HAL-induced liver injury (29). It also has been reported that HMGB-1 may be part of a sexually dimorphic innate immune response in halothane-treated mice (30), which may be consistent with the involvement of HMGB-1 in response to hormone stimuli. HMGB-1 is also involved in responding to corticosteroid stimuli. A previous study revealed that glucocorticoids, which act via glucocorticoid receptor (GR) to regulate target gene transcription, may control metabolic energy in hepatic processes (31). There exists a physical interaction between HMGB-1 and GR (32). The present study hypothesized that HMGB-1 may be regulated by glucocorticoids in hepatic processes. KRT31, a member of the keratin gene family, was significantly downregulated in the exposure profile, compared with the control profile. It has been reported that KRT31 is essential for the maintenance of hepatocyte structural and functional integrity (33). Halothane has been demonstrated to induce liver injury and halothane hepatitis (29), and this evidence may account for the downregulation of KRT31 following halothane exposure. In addition, the functional enrichment analysis of the KRT31-centered network suggested the biological process of lipid metabolism was dysregulated following halothane exposure. This result is consistent with that of a previous study, which suggested that keratin polypeptides, obtained from mice, were modified by the covalent attachment of lipids (34). Therefore, the present study hypothesized that the involvement of KRT31 in lipid metabolism may be dysfunctional under halothane exposure.

In conclusion, the data obtained in the present study provided a comprehensive bioinformatics analysis of genes and networks which may be involved in the effect of inhaled anesthetis. A total of 44 DEGs were identified from the GSE357 accession. Furthermore, thee results of the present study demonstrated that genes, including HMGB-1 and TLR4 may be important in the occurrence of halothane-induced hepatotoxicity, and KRT31 may be closely associated with lipid metabolism in the liver. These DEGs may be used as specific therapeutic molecular targets in liver failure. However, there have been no reports on the expression of HMGCS2 in the immune response, therefore, its potential role in anesthetics remains to be elucidated. It may be a specific biomarker in the mechanism of inhaled anesthetics in the brain. Following these observations, further investigations are required to more closely investigate the anesthetic effect, which genes present.

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Spandidos Publications style
Wang J, Yang X, Xiao H, Kong J and Bing M: Determination of the mechanism of action of repetitive halothane exposure on rat brain tissues using a combined method of microarray gene expression profiling and bioinformatics analysis. Mol Med Rep 12: 8071-8076, 2015
APA
Wang, J., Yang, X., Xiao, H., Kong, J., & Bing, M. (2015). Determination of the mechanism of action of repetitive halothane exposure on rat brain tissues using a combined method of microarray gene expression profiling and bioinformatics analysis. Molecular Medicine Reports, 12, 8071-8076. https://doi.org/10.3892/mmr.2015.4462
MLA
Wang, J., Yang, X., Xiao, H., Kong, J., Bing, M."Determination of the mechanism of action of repetitive halothane exposure on rat brain tissues using a combined method of microarray gene expression profiling and bioinformatics analysis". Molecular Medicine Reports 12.6 (2015): 8071-8076.
Chicago
Wang, J., Yang, X., Xiao, H., Kong, J., Bing, M."Determination of the mechanism of action of repetitive halothane exposure on rat brain tissues using a combined method of microarray gene expression profiling and bioinformatics analysis". Molecular Medicine Reports 12, no. 6 (2015): 8071-8076. https://doi.org/10.3892/mmr.2015.4462