Original Article Open Access
Copyright ©2010 Baishideng. All rights reserved.
World J Gastroenterol. Mar 21, 2010; 16(11): 1385-1396
Published online Mar 21, 2010. doi: 10.3748/wjg.v16.i11.1385
Time-series gene expression profiles in AGS cells stimulated with Helicobacter pylori
Yuan-Hai You, Yan-Yan Song, Fan-Liang Meng, Li-Hua He, Mao-Jun Zhang, Xiao-Mei Yan, Jian-Zhong Zhang, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, PO Box 5, Changping District, Beijing 102206, China
Author contributions: You YH and Song YY performed the majority of experiments and wrote the manuscript; Meng FL, He LH, Zhang MJ and Yan XM provided the vital reagents and materials; Zhang JZ designed the study and provided financial support for this work.
Supported by The National Natural Science Foundation of China, No. 39870032; Key Projects in the National Science & Technology Pillar Program in the Eleventh Five-Year Plan Period
Correspondence to: Jian-Zhong Zhang, Professor, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, PO Box 5, Changping District, Beijing 102206, China. zhangjianzhong@icdc.cn
Telephone: +86-10-61739456  Fax: +86-10-61739439
Received: November 22, 2009
Revised: December 14, 2009
Accepted: December 21, 2009
Published online: March 21, 2010


AIM: To extend the knowledge of the dynamic interaction between Helicobacter pylori (H. pylori) and host mucosa.

METHODS: A time-series cDNA microarray was performed in order to detect the temporal gene expression profiles of human gastric epithelial adenocarcinoma cells infected with H. pylori. Six time points were selected to observe the changes in the model. A differential expression profile at each time point was obtained by comparing the microarray signal value with that of 0 h. Real-time polymerase chain reaction was subsequently performed to evaluate the data quality.

RESULTS: We found a diversity of gene expression patterns at different time points and identified a group of genes whose expression levels were significantly correlated with several important immune response and tumor related pathways.

CONCLUSION: Early infection may trigger some important pathways and may impact the outcome of the infection.

Key Words: Helicobacter pylori, Gene expression, Microarray, Time-series


Helicobacter pylori (H. pylori) have been shown to be the principal cause of acute and chronic gastritis and a major risk factor in gastric cancer development. A chronic inflammatory process induced by the pathogen is thought to be the cause of tumor development. It is well known that H. pylori binding to epithelial cells can induce tyrosine phosphorylation of host cell proteins and rearrangement of the cytoskeleton, which may contribute to inflammation and oncogenic transformation[1]. H. pylori colonization to the mucosa may also induce a systemic immune response and be susceptible to Ab-dependent complement-mediated phagocytosis and killing. Infected epithelial cells may also induce a mucosal inflammation under a mechanism of autoantibody-mediated destruction[2]. Some host factors like interleukin (IL)-1β, tumor necrosis factor (TNF)-α, and IL-10 may influence the disease outcome. One investigation on nuclear factor (NF)-κB signaling pathway and iNOS suggests that NF-κB activation may play an important role in protecting mucosol cells from apoptosis through upregulating iNOS[3]. Many previous studies have performed expression profiling to investigate host changes induced by H. pylori infection. These studies have provided some useful and significant information and shed some light for exploring the potential mechanism of H. pylori infection and host immunity[4-10]. However, none of them is designed based on a time-series scheme, the global and sequential profile of H. pylori infection that may be involved in the pathogenetic mechanism by which H. pylori infects and contributes to gastric carcinogenesis remains poorly understood. In this study, human gastric epithelial adenocarcinoma cells (AGS) co-cultured with an H. pylori 26695 strain at different time points were separated and analyzed by a whole genome Illumina microarray. Computer-assisted bioinformatics analysis was conducted to analyze the differential gene expression pattern.

H. pylori and AGS cell co-culture

H. pylori strain 26695 was routinely cultured for 24 h on Columbia agar plates (Oxoid) containing 5% goat blood under microaerophilic conditions at 37°C, following a wash in sterile PBS and estimation of the quantity of bacteria by OD600. The human gastric epithelial adenocarcinoma cell line AGS (ATCC CRL 1739) was cultured in RPMI 1640 without antibiotic or antifungal agents, and supplemented with 4 mmol/L L-glutamine and 10% fetal calf serum (Gibco) at 37°C in a humidified atmosphere of 5% CO2. A monolayer of AGS cells grown to 80% confluence was co-cultured with H. pylori at a multiplicity of infection of 300:1 in culture media for 0.5, 1, 2, 4, and 6 h.

RNA isolation

Co-culture was stopped at each time point and followed by washing three times with PBS. Total RNA was isolated using Trizol extraction (Gibco/BRL). The quality of the RNA was verified by 1% agarose gel containing ethidium bromide.

Microarray expression profiling and data analysis

Illumina Human-6 v2 BeadChips used for this study contains probes for well characterized genes, gene candidates and splice variants for a total number of 48 000 features. The “Detection Score > 0.99” was used to determine the expression. It was a statistical measure in the BeadStudio software, which was computed based on the Z-value of a gene relative to that of the negative controls. The data were normalized using a cubic spline method, which was generally used as a normalization algorithm in BeadStudio. The differentially expressed genes in different time point were identified using the Illumina custom error model implemented in BeadStudio. DiffScore, the expression difference score, takes into account background noise and sample variability[11]. The formula for the calculation of the DiffScore is: DiffScore = 10 sgn(μcond - μref)log10 (p). The differentially expressed genes with a |Diffscore| > 13 were selected for further analysis. The genes with a fold change > 1.5 were integrated and hierarchically clustered using Mev_4_0 (Multiple Experiment Viewer, TIGR). Gene enrichment in KEGG pathways (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology (GO) were accomplished with Onto-Tool (Pathway Express, OE2GO)[12,13], and co-expression gene clustering by short time-series expression miner (STEM, Carnegie Mellon University)[14] with a maximum number of model profiles set as 245, and a maximum unit change in model profiles between time points set at 2. Four interesting co-expression profiles were selected for further analyses. To obtain an optimized GO distribution, we also took all differentially expressed genes including those with a fold change < 1.5 as input for STEM analysis, and chose four profiles for GO enrichment using OE2GO. For pathway level analysis, those genes with a fold change > 1.5 were imported into Pathway-Express to obtain the significantly perturbed pathway list and gene mapping. This program was based on an impact analysis that included the classical statistics but also considered other crucial factors such as the magnitude of each gene’s expression change, their type and position in the given pathways, their interactions, etc. The IF of a pathway is calculated as the sum of the following two terms:

Then a simplified network construction was completed based on the genes enriched and mapped to KEGG pathways using STRING (version 8.2)[15], which is a known Predicted Protein-Protein Interactions Database (http://string.embl.de/).

Real-time polymerase chain reaction for confirmation of microarray results

Real-time reverse-transcriptase polymerase chain reaction (Q-RT-PCR) validation of microarray results was carried out for the GFPT2 gene at the five time points which were significantly altered according to the microarray data. RNA samples of different time points were prepared as previously described in RNA isolation. Briefly, 2 g total RNA of each sample was used for cDNA synthesis. Real time PCR was performed on the Rotor-Gene RG-3000 Real-Time Thermal Cycler with the SYBR Premix Ex Taq™ (TakaRa) and GAPDH was used as an internal control. The relative quantification of mRNA expression at each time point was calculated and compared with that of the untreated AGS cells as control. The primers of selected gene for RT-PCR were: (1) GFPT2 forward primer (5'-GACAAGCAGATGCCCGTCAT-3') and reverse primer (5'-AACTTGGAACTTTCAGTATCGTCCTT-3'); and (2) GAPDH forward primer (5'-AGAAGGCTGGGGCTCATTTG-3') reverse primer (5'-AGGGGCCATCCACAGTCTTC-3').

Definition of differentially expressed genes

Microarray hybridization results showed that about 3577 genes in total (P < 0.05, DiffScore > 13, named dataset1 in this study) expressed differentially compared with 0 h group. This dataset was generated by taking an integration and alignment for the gene list of different time points using Microsoft Excel software, and the repeated genes were thus excluded. Rows were gene names and columns were differential expression values in different time points. Those genes without fold changes in some time points were set as a value equal to 0. The gene numbers at each time point for the 808 genes (P < 0.05, a fold change > 1.5, named dataset2 in this study) are listed in Table 1 and were selected for further emphatically analysis.

Table 1 Number of different genes expressed at different time points compared with those of control AGS cells.
Time point (h)Up-regulation (n)Down-regulation (n)Total
Microarray data analysis

Taking dataset2 as input, hierarchical cluster analysis showed some differentially expressed genes down-regulated at 4 h and up-regulated at 6 h (Figure 1A and B). Eighty of the most differentially expressed genes were extracted by sorting their fold change and were hierarchically clustered as shown in Figure 1C. Immunity and tumor-related genes were labeled with triangles and circles, respectively. Ten significant profiles were obtained by STEM and four interesting profiles were shown with genes in detail (Figure 2 and Table 2). However, GO analysis did not provide significant terms. Taking dataset1 as input, the GO analysis results for the four profiles clustered are listed in Table 3 and Figure 3. Table 4 shows the GO distribution change of each time point by up-regulation and down-regulation, respectively. Analysis of KEGG pathways revealed many enrichment-related pathways including cell adhesion molecules, MAPK signaling, p53 signaling, and TGF-β signaling pathways, complement and coagulation cascades, and epithelial cell signaling in H. pylori infection. The top four significantly perturbed pathways are listed in Table 5. Related networks extracted from significant pathways are shown in Figure 4.

Figure 1
Figure 1 Hierarchical cluster analysis of time-series gene expression alteration after infection of Helicobacter pylori at 5 time points. Genes that significantly changed during infection were included in hierarchical clustering analysis using average linkage and Euclidean dissimilarity methods. Significant clusters A and B show the details of genes including name of the gene down-regulated at 4 h and up-regulated at 6 h. Eighty of the most differentially expressed genes were clustered in C. Immunity and tumor related genes are labeled.
Table 2 Description of selected clustered genes from short time-series expression miner (STEM) using dataset2 as input.
Cluster IDSymbol
Figure 2
Figure 2 Short time-series expression miner (STEM) clustering of the differentially expressed genes. All profiles are ordered based on the P value significance of the number of genes assigned vs expected. A: Profile 123 (0, 0, 0, 0, -1, 0): 126.0 genes assigned, 37.8 genes expected, P-value = 5.4E-32 (significant); B: Profile 3 (0, -2, -2, -2, -4, -3): 11.0 genes assigned, 0.4 genes expected, P-value = 1.9E-12 (significant); C: Profile 144 (0, 0, 1, 0, 2, 3): 16.0 genes assigned, 2.5 genes expected, P-value = 8.5E-9 (significant); D: Profile 121 (0, 0, 0, 0, -2, -3): 21.0 genes assigned, 5.7 genes expected, P-value = 6.3E-7 (significant).
Table 3 Statistically significant changed gene ontology of the four selected profiles.
ProfileGO namenCorrected P valueFunction code
111Apical part of cell20.00842CC
71Nucleic acid binding122.7E-4MF
Zinc ion binding230.00308MF
Regulation of transcription220.01027BP
Myeloid cell differentiation20.01577BP
108Small GTPase binding20.01173MF
Oxido-reductase activity60.02544MF
GPI anchor biosynthetic process20.02591BP
Female pregnancy30.02622BP
Golgi membrane50.03987CC
Cell surface30.03987CC
83DNA binding60.00577MF
Metal binding60.03346MF
Figure 3
Figure 3 STEM clustering of all the 3577 differentially expressed genes labeled by accession number. All profiles were ordered based on the P value significance of the number of genes assigned vs expected. A: Profile 111 (0, 0, 0, -1, 1): 28.0 genes assigned, 4.2 genes expected, P-value = 1.2E-14 (significant); B: Profile 71 (0, -1, 0, 2, 2): 123.0 genes assigned, 19.0 genes expected, P-value = 4.4E-58 (significant); C: Profile 108 (0, 0, 0, -2, -3): 57.0 genes assigned, 16.2 genes expected, P-value = 1.5E-15 (significant); D: Profile 83 (0, -1, 1, 1, 3): 17.0 genes assigned, 2.7 genes expected, P-value = 4.3E-9 (significant).
Table 4 Statistically significant changed gene ontology at each time point.
Time point (h)Up-regulation
GO IDGO nameGenesP valueCodeGO IDGO nameGenesP valueCode
0.5GO:0008201Heparin binding57.1E-4MFGO:0006955Immune response200.00000BP
GO:0008134Transcription factor binding40.01585MFGO:0009615Response to virus100.00000BP
GO:0003700Transcription activity100.02835MFGO:0008150Biological process150.00896BP
GO:0008083Growth factor activit40.03882MFGO:0007267Cell-cell signaling100.00966BP
GO:0005576Extracellular region150.02875CCGO:0006935Chemotaxis60.01581BP
GO:0005634Nucleus280.03452CCGO:0006954Inflammatory response80.01581BP
GO:0008285Negative regulation of cell proliferation70.03430BP
GO:0007275Multicellular organismal development160.03576BP
GO:0008009Chemokine activity70.00000MF
GO:0046870Cadmium ion binding30.00194MF
GO:0016779Nucleotidyl transferase activity50.02486MF
GO:0005576Extracellular region370.00000CC
GO:0005615Extracellular space142.0E-4CC
1GO:0008201Heparin binding50.00265MFGO:0008009Chemokine activity63.5E-4MF
GO:0003700Transcription factor activity130.00886MFGO:0046870Cadmium ion binding30.00264MF
GO:0005515Protein binding380.01716MFGO:0003677DNA binding260.00264MF
GO:0045766Positive regulation of angiogenesis30.01125BPGO:0046872Metal ion binding360.01144MF
GO:0001558Regulation of cell growth60.01502BPGO:0008270Zinc ion binding340.02041MF
GO:0006915Apoptosis80.02591BPGO:0003674Molecular function150.02257MF
GO:0008285Negative regulation of cell proliferation60.02591BPGO:0003676Nucleic acid binding130.02257MF
GO:0005634Nucleus360.00597CCGO:0016779Nucleotidyl transferase activity40.02571MF
GO:0005575Cellular component100.02160CCGO:0005515Protein binding610.03204MF
GO:0003704Specific RNA polymerase II transcription factor activity30.04080MF
GO:0009615Response to virus100.00000BP
GO:0006955Immune response180.00000BP
GO:0006355Regulation of transcription DNA-dependent394.0E-5BP
GO:0008150Biological process180.00348BP
GO:0007267Cell-cell signaling110.00480BP
GO:0006954Inflammatory response80.03385BP
GO:0045087Innate immune response50.04274BP
GO:0005576Extracellular region371.1E-4CC
GO:0005615Extracellular space130.00474CC
GO:0005575Cellular component150.03381CC
2GO:0003700Transcription factor activity181.4E-4MFGO:0009615Response to virus100.00000BP
GO:0008201Heparin binding50.00193MFGO:0006955Immune response160.00000BP
GO:0043565Sequence-specific DNA binding100.01819MFGO:0008150Biological process186.6E-4BP
GO:0008083Growth factor activity50.01885MFGO:0007267Cell-cell signaling100.01111BP
GO:0005178Integrin binding30.02722MFGO:0006954Inflammatory response80.01911BP
GO:0008134Transcription factor binding40.02722MFGO:0045087Innate immune response50.02866BP
GO:0008009Chemokine activity30.02849MFGO:0007565Female pregnancy50.03928BP
GO:0046872Metal ion binding230.04806MFGO:0005576Extracellular region361.0E-5CC
GO:0045944Positive regulation of transcription from RNA polymerase II promoter70.00234BPGO:0005615Extracellular space130.00113CC
GO:0006955Immune response100.00470BPGO:0005634Nucleus510.00899CC
GO:0008285Negative regulation of cell proliferation70.00681BPGO:0046870Cadmium ion binding30.01145MF
GO:0000122Negative regulation of transcription from RNA polymerase II promoter60.00681BPGO:0016831Carboxy-lyase activity30.02198MF
GO:0006915Apoptosis90.00713BPGO:0030674Protein binding bridging40.04373MF
GO:0006954Inflammatory response70.00769BP
GO:0001558Regulation of cell growth50.00914BP
GO:0009611Response to wounding30.01457BP
GO:0005615Extracellular space128E-5CC
GO:0005576Extracellular region224E-4CC
GO:0030173Integral to Golgi membrane30.02101CC
4GO:0008083Growth factor activity81.0E-5MFGO:0009615Response to virus120.00000BP
GO:0005125Cytokine activity63.7E-4MFGO:0007565Female pregnancy91.6E-4BP
GO:0046983Protein dimerization activity60.00123MFGO:0006955Immune response175.0E-4BP
GO:0005100Rho GTPase activator activity30.00268MFGO:0001525Angiogenesis70.02671BP
GO:0008201Heparin binding40.00826MFGO:0007267Cell-cell signaling100.02671BP
GO:0003700Transcription factor activity130.00826MFGO:0008150Biological process190.02928BP
GO:0008047Enzyme activator activity30.01045MFGO:0016477Cell migration50.03984BP
GO:0005178Integrin binding30.01447MFGO:0005576Extracellular region450.00000CC
GO:0016563Transcription activator activity40.02237MFGO:0005577Fibrinogen complex36.0E-4CC
GO:0005515Protein binding330.03960MFGO:0005615Extracellular space140.00843CC
GO:0043565Sequence-specific DNA binding80.03960MFGO:0031093Platelet α granule lumen40.01203CC
GO:0006955Immune response112.4E-4BPGO:0016020Membrane610.03962CC
GO:0006915Apoptosis90.00440BPGO:0005794Golgi apparatus150.03962CC
GO:0030183B cell differentiation30.01798BP
GO:0045944Positive regulation of transcription from RNA polymerase II promoter50.03323BP
GO:0000079Regulation of cyclin-dependent protein kinase activity30.03704BP
GO:0007050Cell cycle arrest40.03704BP
GO:0008284Positive regulation of cell proliferation50.03704BP
GO:0007267Cell-cell signaling60.03704BP
GO:0001558Regulation of cell growth50.0407BP
6GO:0005515Protein binding650.00000MFGO:0046870Cadmium ion binding30.00135MF
GO:0003700Transcription factor activity241.0E-5MFGO:0003674Molecular function130.00852MF
GO:0008083Growth factor activity83.5E-4MFGO:0008009Chemokine activity40.00852MF
GO:0003714Transcription co-repressor activity73.5E-4MFGO:0003950NAD+ADP-ribosyl transferase activity30.01169MF
GO:0005125Cytokine activity83.5E-4MFGO:0030674Protein binding bridging30.04041MF
GO:0005100Rho GTPase activator activity43.5E-4MFGO:0009615Response to virus120.00000BP
GO:0003700Transcription factor activity76.2E-4MFGO:0006955Immune response160.00000BP
GO:0046983Protein dimerization activity76.9E-4MFGO:0007565Female pregnancy90.00000BP
GO:0008270Zinc ion binding320.00504MFGO:0008150Biological process140.00612BP
GO:0046872Metal ion binding320.00827MFGO:0007267Cell-cell signaling80.00676BP
GO:0005085Guanyl-nucleotide exchange factor activity50.02023MFGO:0006952Defense response50.00728BP
GO:0043565Sequence-specific DNA binding110.03272MFGO:0030168Platelet activation30.01864BP
GO:0008201Heparin binding40.03502MFGO:0051258Protein polymerization30.03966BP
GO:0005178Integrin binding30.04652MFGO:0005576Extracellular region440.00000CC
GO:0006915Apoptosis130.00173BPGO:0005615Extracellular space136.0E-5CC
GO:0006950Response to stress70.00173BPGO:0005577Fibrinogen complex36.0E-5CC
GO:0007050Cell cycle arrest60.00788BPGO:0031093Platelet α granule lumen48.1E-4CC
GO:0045944Positive regulation of transcription from RNA polymerase II promoter70.01021BP
GO:0045740Positive regulation of DNA replication30.01720BP
GO:0008360Regulation of cell shape40.02121BP
GO:0008285Negative regulation of cell proliferation80.02486BP
GO:0000122Negative regulation of transcription from RNA polymerase II promoter70.02486BP
GO:0009611Response to wounding30.02698BP
GO:0030183B cell differentiation30.02698BP
GO:0007229Integrin-mediated signaling pathway50.02698BP
GO:0006954Inflammatory response70.02698BP
GO:0007179Transforming growth factor β receptor signaling pathway40.02698BP
GO:0043066Negative regulation of apoptosis40.02698BP
GO:0007010Cytoskeleton organization and biogenesis50.04841BP
GO:0006955Immune response100.04843BP
GO:0005576Extra cellular region319.0E-5CC
GO:0005615Extra cellular space146.6E-4CC
Figure 4
Figure 4 A simplified gene network extracted from significant pathways using STRING database.
Table 5 Top four significantly perturbed pathways at each time point.
Time point0.5 h1 h2 h4 h6 h
Gene mapping
Real-time PCR confirmation of microarray results

Relative expression levels of each time point were consistent with that of the microarray profile except at 0.5 h, for which a little higher fold-change was obtained in microarray data.


Some previous studies have reported that H. pylori type I strains that harbor the cag pathogenicity island (PAI) and cagA are associated with increased bacterial virulence and a more severe inflammatory response in gastric epithelial cells. These virulence factors have also been considered to be associated with induction of interleukin through an NF-κB-dependent pathway in host mucosa[16]. In addition, host protein phosphorylation, cytoskeletal rearrangement, and differential activation of MAP kinases have been described in host cells after infection of type I strains[1]. Although CagA and Cag PAI are considered to be factors highly involved in the development of gastritis and carcinoma, more complex as yet undiscovered mechanisms may exist between H. pylori and host cells. We aimed to take a global view of gene expression profiles of host response to infection in a time-series interaction model, which may help understand the pathogenesis of H. pylori related diseases.

Considering that only genes with fold changes > 1.5 were included in the analysis, the number of differential genes was only 808. This may lead to an ignorance for many important genes. Therefore, we initiated co-expression clustering analysis using STEM for both the 3577 differentially expressed genes (dataset1) and 808 genes (dataset2) with fold-change > 1.5. For the 808 genes, four significant clusters showed four different co-expression profiles (Figure 2). One hundred and twenty-six genes down-regulated at 4 h were clustered into profile 123, but no significant GO terms were enriched for these genes. In profile 3, some genes related to tumors were consistently down-regulated. For instance, cdkn1c had consistently decreased expression of theses genes, which may be involved in promotion of tumor formation. Profile 144 was mainly involved in factors regulating cell bioactivity and morphology such as rflb, gdf15, sqstm1 and adm2. DNA-damage-inducible transcript and csf2 also had increased gene expression at 4 and 6 h, suggesting that some potential mechanisms for cell differentiation and damage may be triggered beginning at 4 h. Hierarchically clustered results also showed two gene clusters with down-regulation at 4 h and up-regulation at 6 h. Analysis of all differentially expressed genes showed four interesting profiles whose GO distributions included nucleic acid binding, regulation of transcription, oxido-reductase activity etc. For the GO distribution of dataset1, profile 71 and profile 83 showed a similar co-expression profile as well as GO terms including nucleus, nucleic acid binding etc. (Table 3, Figure 3B and D). However, profile 83 showed an obvious and continuous up- regulated gene cluster. Profile 111 and 108 mainly focused on cell surface and showed a down-regulated gene cluster (Figure 3A and C). All profiles illustrated an obvious expressional change at 4 h. Statistically significant changes in gene ontology at each time point showed that apoptosis appeared from 1 h in up-regulated genes. At the same time, in down-regulated genes, chemokine activity became the most significant term (Table 4). This seemed consistent with results of the pathway analysis, which showed that the P53 signaling pathway became the most significantly perturbed pathway at 1 h in up-regulated genes. In down-regulated genes, the cytokine-cytokine receptor interaction pathway became more significant. Genes involving immune response and other responses to viruses were at the top of the GO list of down-regulated genes. This suggested an inhibition of immune response by H. pylori during early infection. Tumor-related pathways like P53 and MAPK may play an important role in determining the development of special phenotype and disease outcomes according to the results of pathway analysis. For the top 80 differentially expressed genes, 43 (54%) were related to immunity (29, 36%) and tumor development (14, 18%). Many immune factor-related down-regulated genes showed a consistently increasing expression levels. The cell adhesion molecules (CAM) pathway was the most significantly perturbed pathway at several time-points. The increased expression of CAM induced by H. pylori may contribute to cell adhesion, invasion and cell proliferation in gastric epithelial cells[17].

From the reconstructed simplified pathway, we can inspect some important nodes with several interaction edges like stat1, stat2, fos, csf2, pdgfb and ccl5 genes. These genes may be the trigger and linker of the pathway net during early infection, which however requires further studies. From Figure 4 and the expression value of each gene, we could learn that most immunity-related genes were down-regulated while many tumor-related genes were up-regulated. Il-24 is an important oncogene and could inhibit specifically the tumor growth. The protein encoded by this gene can induce apoptosis selectively in various cancer cells. Overexpression of this gene has been shown to lead to elevated expression of several GADD family genes, which correlates with the induction of apoptosis[18-20]. In this study, we examined il-24 levels which gradually increased more than two-fold from 2 to 6 h. At 6 h, there was a ten-fold change, indicating that after perturbation of P53 and MAPK, il-24 may participate in maintaining the immune defense against invading pathogens. We also examined an increased level of gadd45 which can stimulate DNA excision-repair in vitro and inhibits entry of cells into S phase. This gene is a member of a group of genes whose transcript levels are increased following stressful growth arrest and treatment with DNA-damaging agents. In the network, both c-Fos and c-Jun, two genes considered to mediate inflammation and carcinogenesis, have been found to be up-regulated, which is consistent with the results of this study[21].

We also analyzed expression profiles of some other important infection-related genes that were reported previously and may play an important role in H. pylori-induced diseases, although these genes were not clustered into a special profile in this study using the current analytical tools. MMP is a mucosal matrix metalloproteinase. Previous studies have demonstrated elevated MMP-9 levels in H. pylori-infected gastric mucosa, and eradication of H. pylori can significantly decrease MMP9 expression levels consistently[22,23]. MMP1 has been the subject of studies of inflammatory gene profiles in gastric mucosa[2,24]. MMP7 has been reported to be up-regulated in gastric cancer tissues[25,26]. However, few studies have reported on MMP24. In this study, the profile of MMP24 showed a consistent and increased level from 1 to 6 h, which suggested a similar function with MMP9 during H. pylori infection. Some other genes with similar expression profiles are il-27ra, il-32, il-23a, il-11, il-8 and ccl20. This gene cluster showed down-regulation or no change at the first two or three time points and up-regulation in the last two or three time points. Il-29, ccl5, cxcl10 and cxcl11 showed a consistent down-regulation at all time points with high fold-change. Expression of these genes suggested that the immune defense system may be suppressed during the first 1 or 2 h of H. pylori infection and some tumor-related genes and pathways were activated. After this short interaction and competition for about 2 h, the immune defense system may have regained the advantage with increasing expression levels of inflammatory and tumor suppressor factors. CagA translocation might occur 30 min after infection and may be at its maximum level in a time range of about 4-5 h[27,28]. In this study, the differentially expressed genes significantly increased at the time point of 4 h. This also suggested that it might be an important turning point between infection and host response. Although a model system of the AGS cell line infected with H. pylori was used to explore the host response[5,29], it should be noted that this is an isolated cell culture system, and cannot account for the varied effects of conditions in a human stomach. Therefore, the speculation generated from this study represents a valuable, but a simplified view of the situation. More researches are required to confirm these findings. In addition, we also compared our results with the genes with significant change after H. pylori infection in another report[30]. Several genes in that report are consistent with our results in dataset1 like socs2, stat6, ccl4, cxcl2, hla-dma, hsph1, plat, ifitm1, alox5, tlr4, faim3, cd47, ifngr1 and il8.

Only part of these genes showed a high fold change > 1.5 in differential expressions, including il8, faim3, tlr4, alox5, hla-dma, cxcl2 and ccl4.

In summary, the results from this sequential expression microarray have extended previous studies that were limited to the comparison of normal and diseased tissues. We took a global view on the genes and pathway net related to H. pylori infection, several co-expressional profiles and important new genes like mmp24 and il-24 involved in immune response and tumorigenesis during H. pylori infection were also identified. Our study also suggested that the outcome of H. pylori infection is probably involved in a complex mechanism, and is associated with a number of immune factors. Formation of tumors may be a result of an imbalance between bacterial attack and immune defense of host. We speculate that this competition may occur at 1-2 h after infection, and 4 h may be a first time point at which the balance is upset.


It has been indicated that Helicobacter pylori (H. pylori) infection may highly contribute to gastritis and carcinogenesis in the past two decades since it was recovered from human gastric mucosa in 1983, and many studies have focused on identification of both bacterial factors and host determinants that may contribute to the pathogenic mechanism.

Research frontiers

Gene expression microarray has been widely used in identifying genes associated with H. pylori infection and gastric tumor. However, the time-series gene expression profile of H. pylori infection remains unexplored. In this study, the authors extended the knowledge of the dynamic interaction between H. pylori and host mucosa using a high density human gene microarray and flexible bioinformatics analysis.

Innovations and breakthroughs

Several important genes that have not been reported previously and a pathway net related to H. pylori infection were discovered by the sequential microarrays. Based on the co-expressional profile analysis during infection, a new speculation for the pathogenic mechanism has been set up.


This study has provided a systemic view of expression profile of time-series H. pylori infected AGS cells. The new identified genes and pathway net as well as the hypothesis could help researchers in this field further understand the potential mechanism associated with H. pylori infection and carcinogenesis, and provide important information for prevention and control of H. pylori related diseases.

Peer review

The scientific and innovative contents as well as readability in this manuscript reflect the advanced levels of the clinical and basic researches in gastroenterology both at home and abroad.


Peer reviewer: Dr. Yutao Yan, Medicine Department, Emory University, 615 Michael ST, Whitehead Building/265, Atlanta, GA 30322, United States

S- Editor Wang YR L- Editor Ma JY E- Editor Lin YP

1.  Segal ED, Lange C, Covacci A, Tompkins LS, Falkow S. Induction of host signal transduction pathways by Helicobacter pylori. Proc Natl Acad Sci USA. 1997;94:7595-7599.  [PubMed]  [DOI]  [Cited in This Article: ]
2.  Wen S, Felley CP, Bouzourene H, Reimers M, Michetti P, Pan-Hammarström Q. Inflammatory gene profiles in gastric mucosa during Helicobacter pylori infection in humans. J Immunol. 2004;172:2595-2606.  [PubMed]  [DOI]  [Cited in This Article: ]
3.  Kim JM, Kim JS, Jung HC, Oh YK, Chung HY, Lee CH, Song IS. Helicobacter pylori infection activates NF-kappaB signaling pathway to induce iNOS and protect human gastric epithelial cells from apoptosis. Am J Physiol Gastrointest Liver Physiol. 2003;285:G1171-G1180.  [PubMed]  [DOI]  [Cited in This Article: ]
4.  Conlin VS, Curtis SB, Zhao Y, Moore ED, Smith VC, Meloche RM, Finlay BB, Buchan AM. Helicobacter pylori infection targets adherens junction regulatory proteins and results in increased rates of migration in human gastric epithelial cells. Infect Immun. 2004;72:5181-5192.  [PubMed]  [DOI]  [Cited in This Article: ]
5.  Bach S, Makristathis A, Rotter M, Hirschl AM. Gene expression profiling in AGS cells stimulated with Helicobacter pylori isogenic strains (cagA positive or cagA negative). Infect Immun. 2002;70:988-992.  [PubMed]  [DOI]  [Cited in This Article: ]
6.  DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet. 1996;14:457-460.  [PubMed]  [DOI]  [Cited in This Article: ]
7.  Marchet A, Mocellin S, Belluco C, Ambrosi A, DeMarchi F, Mammano E, Digito M, Leon A, D'Arrigo A, Lise M. Gene expression profile of primary gastric cancer: towards the prediction of lymph node status. Ann Surg Oncol. 2007;14:1058-1064.  [PubMed]  [DOI]  [Cited in This Article: ]
8.  Wu CM, Lee YS, Wang TH, Lee LY, Kong WH, Chen ES, Wei ML, Liang Y, Hwang TL. Identification of differential gene expression between intestinal and diffuse gastric cancer using cDNA microarray. Oncol Rep. 2006;15:57-64.  [PubMed]  [DOI]  [Cited in This Article: ]
9.  Lim JW, Kim H, Kim KH. Cell adhesion-related gene expression by Helicobacter pylori in gastric epithelial AGS cells. Int J Biochem Cell Biol. 2003;35:1284-1296.  [PubMed]  [DOI]  [Cited in This Article: ]
10.  Chang YT, Wu MS, Chang YJ, Chen CC, Lin YS, Hsieh T, Yang PC, Lin JT. Distinct gene expression profiles in gastric epithelial cells induced by different clinical isolates of Helicobacter pylori--implication of bacteria and host interaction in gastric carcinogenesis. Hepatogastroenterology. 2006;53:484-490.  [PubMed]  [DOI]  [Cited in This Article: ]
11.  Chudin E, Kruglyak S, Baker SC, Oeser S, Barker D, McDaniel TK. A model of technical variation of microarray signals. J Comput Biol. 2006;13:996-1003.  [PubMed]  [DOI]  [Cited in This Article: ]
12.  Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz SA, Tainsky MA. Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res. 2003;31:3775-3781.  [PubMed]  [DOI]  [Cited in This Article: ]
13.  Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R. A systems biology approach for pathway level analysis. Genome Res. 2007;17:1537-1545.  [PubMed]  [DOI]  [Cited in This Article: ]
14.  Ernst J, Bar-Joseph Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics. 2006;7:191.  [PubMed]  [DOI]  [Cited in This Article: ]
15.  Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37:D412-D416.  [PubMed]  [DOI]  [Cited in This Article: ]
16.  Censini S, Lange C, Xiang Z, Crabtree JE, Ghiara P, Borodovsky M, Rappuoli R, Covacci A. cag, a pathogenicity island of Helicobacter pylori, encodes type I-specific and disease-associated virulence factors. Proc Natl Acad Sci USA. 1996;93:14648-14653.  [PubMed]  [DOI]  [Cited in This Article: ]
17.  Chen X, Leung SY, Yuen ST, Chu KM, Ji J, Li R, Chan AS, Law S, Troyanskaya OG, Wong J. Variation in gene expression patterns in human gastric cancers. Mol Biol Cell. 2003;14:3208-3215.  [PubMed]  [DOI]  [Cited in This Article: ]
18.  Papathanasiou MA, Kerr NC, Robbins JH, McBride OW, Alamo I Jr, Barrett SF, Hickson ID, Fornace AJ Jr. Induction by ionizing radiation of the gadd45 gene in cultured human cells: lack of mediation by protein kinase C. Mol Cell Biol. 1991;11:1009-1016.  [PubMed]  [DOI]  [Cited in This Article: ]
19.  Jackman J, Alamo I Jr, Fornace AJ Jr. Genotoxic stress confers preferential and coordinate messenger RNA stability on the five gadd genes. Cancer Res. 1994;54:5656-5662.  [PubMed]  [DOI]  [Cited in This Article: ]
20.  Carrier F, Smith ML, Bae I, Kilpatrick KE, Lansing TJ, Chen CY, Engelstein M, Friend SH, Henner WD, Gilmer TM. Characterization of human Gadd45, a p53-regulated protein. J Biol Chem. 1994;269:32672-32677.  [PubMed]  [DOI]  [Cited in This Article: ]
21.  Hofman VJ, Moreilhon C, Brest PD, Lassalle S, Le Brigand K, Sicard D, Raymond J, Lamarque D, Hébuterne XA, Mari B. Gene expression profiling in human gastric mucosa infected with Helicobacter pylori. Mod Pathol. 2007;20:974-989.  [PubMed]  [DOI]  [Cited in This Article: ]
22.  Lee LY, Wu CM, Wang CC, Yu JS, Liang Y, Huang KH, Lo CH, Hwang TL. Expression of matrix metalloproteinases MMP-2 and MMP-9 in gastric cancer and their relation to claudin-4 expression. Histol Histopathol. 2008;23:515-521.  [PubMed]  [DOI]  [Cited in This Article: ]
23.  Kubben FJ, Sier CF, Schram MT, Witte AM, Veenendaal RA, van Duijn W, Verheijen JH, Hanemaaijer R, Lamers CB, Verspaget HW. Eradication of Helicobacter pylori infection favourably affects altered gastric mucosal MMP-9 levels. Helicobacter. 2007;12:498-504.  [PubMed]  [DOI]  [Cited in This Article: ]
24.  Pillinger MH, Marjanovic N, Kim SY, Lee YC, Scher JU, Roper J, Abeles AM, Izmirly PI, Axelrod M, Pillinger MY. Helicobacter pylori stimulates gastric epithelial cell MMP-1 secretion via CagA-dependent and -independent ERK activation. J Biol Chem. 2007;282:18722-18731.  [PubMed]  [DOI]  [Cited in This Article: ]
25.  Ogden SR, Wroblewski LE, Weydig C, Romero-Gallo J, O'Brien DP, Israel DA, Krishna US, Fingleton B, Reynolds AB, Wessler S, Peek RM Jr. p120 and Kaiso regulate Helicobacter pylori-induced expression of matrix metalloproteinase-7. Mol Biol Cell. 2008;19:4110-4121.  [PubMed]  [DOI]  [Cited in This Article: ]
26.  Wei J, O'Brien D, Vilgelm A, Piazuelo MB, Correa P, Washington MK, El-Rifai W, Peek RM, Zaika A. Interaction of Helicobacter pylori with gastric epithelial cells is mediated by the p53 protein family. Gastroenterology. 2008;134:1412-1423.  [PubMed]  [DOI]  [Cited in This Article: ]
27.  Asahi M, Azuma T, Ito S, Ito Y, Suto H, Nagai Y, Tsubokawa M, Tohyama Y, Maeda S, Omata M. Helicobacter pylori CagA protein can be tyrosine phosphorylated in gastric epithelial cells. J Exp Med. 2000;191:593-602.  [PubMed]  [DOI]  [Cited in This Article: ]
28.  Odenbreit S, Gebert B, Püls J, Fischer W, Haas R. Interaction of Helicobacter pylori with professional phagocytes: role of the cag pathogenicity island and translocation, phosphorylation and processing of CagA. Cell Microbiol. 2001;3:21-31.  [PubMed]  [DOI]  [Cited in This Article: ]
29.  Cho SO, Lim JW, Kim KH, Kim H. Involvement of Ras and AP-1 in Helicobacter pylori-Induced Expression of COX-2 and iNOS in Gastric Epithelial AGS Cells. Dig Dis Sci. 2009;Epub ahead of print.  [PubMed]  [DOI]  [Cited in This Article: ]
30.  Kim KK, Kim HB. Protein interaction network related to Helicobacter pylori infection response. World J Gastroenterol. 2009;15:4518-4528.  [PubMed]  [DOI]  [Cited in This Article: ]