Open Access

Global transcriptome‑wide analysis of the function of GDDR in acute gastric lesions

  • Authors:
    • Ziqiang Zhang
    • Jie Zhu
    • Yuanqiang Dong
    • Hongyuan Xu
    • Tao Jiang
    • Wenshuai Li
    • Diannan Xu
    • Liubin Shi
    • Jianghong Yu
    • Jun Zhang
    • Jianjun Du
  • View Affiliations

  • Published online on: October 2, 2017
  • Pages: 8673-8684
  • Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Acute gastric lesions induced by stress are frequent occurrences in medical establishments. The gastric dramatic downrelated gene (GDDR) is a secreted protein, which is abundantly expressed in normal gastric epithelia and is significantly decreased in gastric cancer. In our previous study, it was found that GDDR aggravated stress‑induced acute gastric lesions. However, the role of GDDR in acute gastric lesions remains to be fully elucidated. In the present study, RNA sequencing was performed in order to examine the gene expression profile regulated by GDDR in acute gastric lesions. The dataset comprised four stomach samples from wild-type (WT) mice and four stomach samples from GDDR‑knockout mice. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to analyze the differentially-expressed genes (DEGs). Weighted correlation network analysis was used to identify clusters of highly correlated genes. Cytoscape was used to construct a protein‑protein interaction network (PPI) of the DEGs. Based on the GO analysis, the upregulated DEGs were distinctly enriched in muscle contraction and response to wounding; and the downregulated DEGs were significantly enriched in the regulation of nitrogen compound metabolic process and regulation of RNA metabolic process. The results of the KEGG pathway analysis showed that the upregulated DEGs were enriched in ECM‑receptor interaction and the signaling pathway of cGMP‑PKG, and the downregulated DEGs were enriched in the renin‑angiotensin system and glycerolipid metabolism. The co‑expression network revealed a group of genes, which were associated with increased wound healing in the WT mice. Significant pathways were identified through the PPI network, including negative regulation of the signaling pathway of glucocorticoid receptor, regulation of cellular stress response, and regulation of hormone secretion. In conclusion, the present study improves current understanding of the molecular mechanism underlying acute gastric lesions and may assist in the treatment of gastric lesions.


The disruption of homeostasis causes stress in response to damage to the body. However, sustained stress can lead to organ injury and diseases, including hypertension, diabetes, gastric ulcers and cancer. Gastric lesions are a typical organ injury associated with stress (1). A water immersion restraint stress (WRS) model, which has been widely used in investigating stress-associated organ injury, imitates the clinical acute gastric lesions resulting from surgery, trauma and sepsis (2). Elucidation of the mechanism underlying gastric injury induced by stress, and the development of specific therapeutic drugs are practically and clinically important. Physiological and psychological stress are involved in the pathogenesis of gastric ulceration. Anxiety, depression, helplessness and fear are considered to be the psychological responses (3). Neurohormonal and immunological activation are incorporated in the physiological responses, and these two systems can interact under circumstances of stress (4,5). However, the mechanisms underlying stress-induced acute gastric lesions remain to be elucidated.

The novel gastric dramatic downrelated gene (GDDR), which was also known as gastrokine 2, was first cloned using the Ends-Marathon Racein method, described in our previous study (6). The expression of GDDR has been confirmed to be high in gastric mucosa epithelial cells in particular (7), however, the expression of GDDR is significantly reduced in gastric cancer (8). In the gastrointestinal tract, the secretion of GDDR is regulated by a series of cytokines, including interleukin (IL)-8 and IL-6, in addition to transforming growth factors (TGF)s (9). The loss of GDDR drives premalignant gastric inflammation and tumor progression (10). Poor prognosis and lymph node metastasis in gastric cancer have been closely linked to the loss of GDDR (11). The expression of GDDR may suppress cancer cell proliferation through a TFF1-dependent manner, and induces apoptosis through the extrinsic apoptotic pathway (12,13). Therefore, GDDR contributes to gastric mucosa homeostasis (14) and acts as a putative tumor suppressor (15). The function of GDDR remains to be fully elucidated unclear, however our previous study (16) found that GDDR aggravated stress-induced gastric lesions, however, no significant differences were found between wild-type (WT) mice and GDDR-knockout (KO) mice without stress.

As it was previously shown that the expression of GDDR in gastric cancer significantly aggravated gastric lesions, based on biological functions and pathways analysis, the present study aimed to examine the process underlying gastric lesions at the molecular level, and provide insight for the identification of potential candidate biomarkers for drug targets, diagnosis and prognosis.

Materials and methods

Formation of WRS-induced gastric mucosal lesions in mice

A total of 8 KO mice (male; age, 6–8 weeks; weight, ~20 g) were purchased from the Mutant Mouse Resource Research Center (Sacramento, CA, USA). The handling and care of the animals was implemented with reference to the National Institutes of Health guidelines. The temperature within the cages was controlled at 25±3°C, and the humidity was kept at 50±5%. Mice were given free access to water and food. The present study was performed following the approval of the Animal Experimentation Ethics Committee of Fudan University (Shanghai, China). The WRS model used in the present study has been widely used in investigations of gastric lesions (17). The WRS model can mimic acute gastric lesions in humans to a high degree. GDDR shows stomach-restricted expression and is highly conserved among mammals (18); therefore, GDDR shares similar functions among mammals. Prior to each experiment, the animals were starved for 24 h. The immersing of animals in water was performed to the extent of the xiphoid process in a restraint cage as previously described (19). Following exposure to stress for the indicated duration (0 to 8 h), the animals were sacrificed. The stomachs of the animals were removed and immersed in RNAlater (Thermo Fisher Scientific, Inc., Waltham, MA, USA) for 1 day. By cutting along the greater curvature, the stomachs were cut open and used for RNA extraction.

RNA extraction and quality control

TRIzol® reagent (Thermo Fisher Scientific, Inc.) was used to extract total RNA from each sample, according to the manufacturer's protocol. The concentrations of all samples were determined using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Inc.). The Agilent Bioanalyzer 2100 (Agilent Technologies, Inc., Santa Clara, CA, USA) was used to evaluate the quality.

Preparation of whole transcriptome libraries and deep sequencing

The Ion Total RNA-Seq kit v2 (Thermo Fisher Scientific, Inc.) was used to construct the sequencing library of the RNA samples in accordance with the manufacturer's protocol (Thermo Fisher Scientific, Inc.). In brief, Dynabeads mRNA purification (Thermo Fisher Scientific, Inc.) was utilized for refining poly(A)-containing mRNA. Fragmentation of the mRNA was implemented with the use of RNaseIII, following which the mRNA was purified. Subsequently, reverse transcription of the RNA fragments was performed by SuperScript™ II Reverse Transcriptase (Thermo Fisher Scientific, Inc.). The reverse transcribed mRNA was then amplified into double-stranded cDNA (42°C, 50 min; 70°C, 15 min), and was purified using a magnetic bead-based approach, following which concentrations of the samples were detected for the cDNA library. Sequencing quality was analyzed by RSeQC (version 2.6;

Raw read filtering and mapping

The filtered raw reads ≥50 bp were used for the mapping. The TopHat (version 2.0.9; tool was used for the RNA sequencing (RNA-seq) data mapping analysis based on spliced mapping, which is able to immediately and accurately identify the eukaryotic splicing (20). EdgeR (version 3.5; was used to identified the differentially-expressed genes (DEGs).

Gene Ontology (GO) and pathway enrichment analysis of DEGs

For the functional analysis of the DEGs, GO analysis was used to annotate genes and gene products, and to identify characteristic biological properties for high-throughput transcriptome or genome data (21). Kyoto Encyclopedia of Genes and Genomes pathway (KEGG; was used for systematic genetic functional analysis, to connect higher-order functional information with genomic information (22). The genes were mapped to the relevant biological annotation in the Database for Annotation, Visualization and Integrated Discovery (DAVID; to perform high-throughput gene functional analysis (23). P<0.05 was considered to indicate a statistically significant difference.

Co-expression network

For each gene pair, the Pearson correlation coefficient was calculated and important correlation pairs were selected in order to establish the network (24). The degree of centrality of a gene in the network was measured to determine the relative significance. By definition, the degree of centrality is the number of links of one node relative to another (25).

Integrative analysis of protein-protein interaction (PPI) networks and modules

The Search Tool for the Retrieval of Interactive Genes (STRING) database is an online tool for evaluating PPI information. The STRING database (version 10.0; contains 9,600,000 types of protein in 2,000 organisms. For the assessment of interactions among DEGs in the present study, the DEGs were uploaded to the STRING database, and only interactions with a composite score >0.4, which were experimentally validated, were considered to be significant. Cytoscape software (version 3.5.1; was then used to construct the PPI networks. Plug-in Molecular Complex Detection (MCODE) was then utilized for screening the PPI network modules in Cytoscape. The outcomes of the pathway and functional enrichment analysis were used for analyzing DEGs in the modules.

Reverse-transcription-quantitative polymerase chain reaction (RT-qPCR) analysis

The RT-qPCR assay was performed on the CFX96 Touch™ (Bio-Rad Laboratories, Inc., Hercules, CA, USA). According to the manufacturer's protocol, TRIzol® reagent (Thermo Fisher Scientific, Inc.) was used to extract RNA from each sample. According to the manufacturer's protocol, cDNA synthesis was performed from 1 µg RNA with the use of a FastQuant RT kit (Tiangen Biotech Co., Ltd., Beijing, China). Subsequently, the PCR mixtures (25 µl) were prepared in triplicate, each containing 12.5 µl SYBR® Premix Ex Taq (Takara Bio, Inc., Otsu, Japan), 1 µl of cDNA template, 0.5 µl primer (10 mM; Genewiz, Inc., Suzhou, China) and 3.6 ml DEPC-treated water. The primers included the following: Chemokine (C-X-C motif) ligand 2 (Cxcl2) forward, 5′-CCAACCACCAGGCTACAGG-3′ and reverse, 5′-GCGTCACACTCAAGCTCTG-3′; Tgf-β1 forward, 5′-CTCCCGTGGCTTCTAGTGC-3′ and reverse, 5′-GCCTTAGTTTGGACAGGATCTG-3′; IL-1β forward, 5′GCA ACT GTT CCT GAA CTC AAC T-3′ and reverse, 5′-ATCTTTTGGGGTCCGTCAACT-3′; insulin II (Ins2) forward, 5′-GCTTCTTCTACACACCCATGTC-3′ and reverse, 5′-AGCACTGATCTACAATGCCAC-3′; serine/threonine kinase 2 (Sgk2) forward, 5′-CCATTGGTTACCTTCACTCTCTC-3′ and reverse, 5′-GTCTCCTCAGGCTCTACACAT-3′; tuberous sclerosis 1 (Tsc1) forward, 5′-ACTCTCCCTTCTACCGAGACA-3′ and reverse, 5′-GAGGCTGCCGAATGAGTCTTC-3′; erb-B2 receptor tyrosine kinase 3 (Erbb3) forward, 5′-AAGTGACAGGCTATGTACTGGT-3′ and reverse, 5′-GCTGGAGTTGGTATTGTAGTTCA-3′; heparin-binding epidermal growth factor-like growth factor (Hbegf) forward, 5′-CGGGGAGTGCAGATACCTG-3′ and reverse, 5′-TTCTCCACTGGTAGAGTCAGC-3′; prostaglandin-endoperoxide synthase 2 (Ptgs2) forward, 5′-TTCAACACACTCTATCACTGGC-3′ and reverse, 5′-AGAAGCGTTTGCGGTACTCAT-3′; adhesion G protein-coupled receptor B1 (Adgrb1) forward, 5′-TTGCTCCACTCCTGCTGTTAC-3′ and reverse, 5′-GTAGCCGAAGAACTTTCCCTG-3′; glyceraldehyde 3-phosphate dehydrogenase (GAPDH) forward, 5′-TGGATTTGGACGCATTGGTC-3′ and reverse, 5′-TTTGCACTGGTACGTGTTGAT-3′. The conditions included initial denaturation at a temperature of 95°C for 30 sec, followed by 40 cycles (95°C, 5 sec; 60°C, 30 sec). Finally all quantified values were normalized to the endogenous GAPDH control. The 2−∆∆Cq method was used to analyze gene expression levels (26).

Statistical analysis

The statistical analyses of data were performed using Student's t-test with SPSS software version 17.0 (SPSS, Inc., Chicago, IL, USA). Data are presented as the mean ± standard deviation. P<0.05 was considered to indicate a statistically significant difference.


Overview of sequencing data from RNA-seq analyses

The overall raw reads of the eight samples fell in the range of 46,000,000–82,000,000. Through strict quality checks, >95% of the reads had a quality score of ≥Q20. The RSeQC package was used to analyze the sequencing quality (27). The raw sequence data of each sample produced ~6 GB of data. Of the total raw reads, 96.5% (~5.87±2.15×107 reads) were mapped onto the mouse genome sequence in the eight separate samples (Table I) and 89.1% of the total raw reads (5.42±1.92×107 reads) were uniquely aligned to the mouse genome. The RNA-seq data was normalized to RPKM values for quantifying transcript expression levels.

Table I.

Statistical results of raw and mapped reads from RNA sequencing analysis of the two sample groups.

Table I.

Statistical results of raw and mapped reads from RNA sequencing analysis of the two sample groups.

Sample IDAll reads (n)Mapped reads (n)Mapped pair reads (n)Mapped unique reads (n)Mapped multi reads (n)Mapping ratio (%)

[i] WT, wild-type mouse; KO, gastric dramatic downrelated gene-knockout mouse.

Selection of DEGs

The total samples analyzed in the present study comprised four WT stomach samples and four KO stomach samples. The data were analyzed individually with the use of edgeR and the lists of DEGs were identified. According to the results of the analyses, using the criteria of fold control >2.0 and P<0.05, a total of 1,704 genes were identified, of which 710 were downregulated and 994 were upregulated, respectively. The DEG gene expression heatmap is shown in Fig. 1, comprising the top 50 downregulated and upregulated genes. For targeting the DEGs, the DEGs were screened by GO terms, which were associated with phenotype (Table II).

Table II.

Differentially-expressed genes associated with functions and phenotypes.

Table II.

Differentially-expressed genes associated with functions and phenotypes.

Gene symbolDescriptionLog2FCP-valueGene ontology term
HbegfHeparin binding EGF-like growth factor−2.86 3.79×10−34Angiogenesis; extracellular space; epidermal growth factor receptor signaling pathway; growth factor activity
Ptgs2 Prostaglandin-endoperoxide synthase 2−1.88 2.83×10−09Negative regulation of toll-like receptor signaling pathway; prostaglandin biosynthetic process;
Cxcl2C-X-C motif chemokine ligand 2−3.97 3.80×10−07Cytokine activity; extracellular region; inflammatory response; G-protein coupled receptor signaling pathway; neutrophil chemotaxis
Tgf-β1Transforming growth factor-β1−1.48 1.55×10−06Transcription coactivator activity; focal adhesion; positive regulation of epithelial to mesenchymal transition; Wnt signaling pathway
IL-1βInterleukin-1β−2.58 2.28×10−04Activation of MAPK activity; positive regulation of protein phosphorylation cytokine activity
Ins2Insulin II5.01 7.42×10−11Negative regulation of transcription from RNA polymerase II promoter; MAPK cascade; negative regulation of acute inflammatory response; extracellular region
Tsc1Tuberous sclerosis 11.31 4.44×10−09Regulation of cell-matrix adhesion; adaptive immune response; protein binding; cytoplasm
Sgk2Serine/threonine kinase 21.22 1.06×10−08Nucleotide binding; regulation of cell growth; protein serine/threonine kinase activity; cytoplasm
Erbb3Erb-B2 receptor tyrosine kinase 31.07 3.75×10−07Nucleotide binding; protein kinase activity; transmembrane receptor protein tyrosine kinase activity; receptor signaling protein tyrosine kinase activity; protein binding
Adgrb1Adhesion G protein-coupled receptor B11.17 1.24×10−06Transmembrane signaling receptor activity; G-protein coupled receptor activity; protein binding; plasma membrane

[i] FC, fold change; MAPK, mitogen-activated protein kinase.

GO term enrichment analysis

In order to identify the overrepresented categories (GO and KEGG pathways), all DEGs were uploaded to the online DAVID software. Based on the results of the GO analysis, there was significant enrichment of upregulated DEGs in biological processes (BP) encompassing muscle contraction, cell adhesion, and locomotion and response to wounding (Table III); there was significant enrichment of the downregulated DEGs in BPs encompassing regulation of nitrogen compound metabolic process, regulation of RNA metabolic process, and response to peptide hormone (Table III). For molecular function (MF), there was enrichment of the upregulated DEGs in transcription factor activity, cytoskeletal protein binding, sequence-specific binding by RNA polymerase II core promoter region, and calcium ion binding, and there was enrichment of the downregulated DEGs in serine-type peptidase activity, and serine-type endopeptidase activity, and serine hydrolase activity (Table III). In addition, based on the GO cell component (CC) analysis, there was significant enrichment of the upregulated DEGs in contractile fiber, myofibril, and contractile fiber part, and also significant enrichment of downregulated DEGs in extracellular space, zymogen granule and extracellular region part (Table III).

Table III.

GO analysis of differentially-expressed genes associated with gastric lesions.

Table III.

GO analysis of differentially-expressed genes associated with gastric lesions.

ExpressionCategoryTerm/gene functionGene count%P-value
UpregulatedBPGO:0006936-muscle contraction  69   7.2 3.1×10−32
BPGO:0007155-cell adhesion14414.9 1.9×10−16
BP GO:0040011-locomotion13814.3 1.5×10−15
BPGO:0009611-response to wounding  69   7.2 3.7×10−15
BP GO:0030198-extracellular matrix organization  42   4.4 1.5×10−14
CC GO:0043292-contractile fiber  72   7.5 1.7×10−36
CC GO:0030016-myofibril  70   7.3 5.3×10−36
CC GO:0044449-contractile fiber part  67   7.0 1.1×10−34
CC GO:0030017-sarcomere  65   6.7 2.3×10−34
CC GO:0005578-proteinaceous extracellular matrix  78   8.1 2.5×10−27
MF GO:0008092-cytoskeletal protein binding  87   9.0 1.9×10−12
MF GO:0000982-transcription factor activity, RNA polymerase II core promoter region sequence-specific binding  53   5.5 6.1×10−12
MFGO:0005509-calcium ion binding  76   7.9 6.9×10−12
MFGO:0000981-RNA polymerase II transcription factor activity, sequence-specific DNA binding  74   7.7 1.6×10−11
MFGO:0003779-actin binding  50   5.2 1.0×10−10
DownregulatedBP GO:0051252-regulation of RNA metabolic process10518.5 3.1×10−06
BP GO:0051171-regulation of nitrogen compound metabolic process11520.3 2.9×10−05
BP GO:0010468-regulation of gene expression11520.3 4.8×10−05
BPGO:0044242-cellular lipid catabolic process  12   2.5 8.5×10−04
BPGO:0043434-response to peptide hormone  17   2.6 1.5×10−02
CC GO:0005615-extracellular space  6210.9 1.5×10−08
CCGO:0042588-zymogen granule     5   0.9 1.0×10−03
CC GO:0044421-extracellular region part  9616.9 1.2×10−03
CC GO:0005576-extracellular region10718.9 1.3×10−03
CCGO:0005576-zymogen granule membrane     4   0.7 2.3×10−03
MF GO:0004252-serine-type endopeptidase activity  29   5.1 1.6×10−15
MF GO:0008236-serine-type peptidase activity  29   5.1 2.2×10−14
MFGO:0017171-serine hydrolase activity  29   5.1 3.0×10−14
MFGO:0043169-cation binding13924.5 4.4×10−12
MFGO:0046872-metal ion binding13824.5 4.6×10−12

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

KEGG pathway analysis

Based on the results of the KEGG pathway analysis, the most significantly enriched pathways of the downregulated DEGs and the upregulated DEGs were identified, as listed in Table IV. There was significant enrichment of the upregulated DEGs in the cGMP-PKG signaling pathway, focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathway and gastric acid secretion. There was enrichment of the downregulated DEGs in the endocrine, renin-angiotensin system, and other factor-regulated calcium reabsorption, PPAR signaling pathway, glycerolipid metabolism, and neuroactive ligand-receptor interaction.

Table IV.

Kyoto Encyclopedia of Genes and Genomes pathway analysis of differentially-expressed genes associated with gastric lesions

Table IV.

Kyoto Encyclopedia of Genes and Genomes pathway analysis of differentially-expressed genes associated with gastric lesions

Pathway IDNameGene count%P-valueGenes
mmu04512ECM-receptor interaction22   2.3 1.6×10−10Col1a1, Col1a2, Col3a1, Col4a, Col4a2, Col4a, Col4a6, Col5a1, Col5a2, Col5a3, Col6a1, Col6a, Col6a3, Fn1, Itga11, Itga3, Lamb2, Npnt, Hspg2, Tnxb, Thbs1, Thbs4
mmu04510Focal adhesion32   3.3 2.1×10−09Shc2, Actn2, Actn3, Col1a1, Col1a2, Col3a1, Col4a1, Col4a2, Col4a5, Col4a6, Col5a1, Col5a2, Col5a, Col6a1, Col6a2, Col6a3, Fn1, Flnc, Itga11, Itga3, Jun, Lamb2, Mylk3, Mylk4, Mylpf, Mylk2, Mylk, Tnxb, Thbs1, Thbs4, Vegfc, Zyx
mmu04022Cgmp-PKG signaling pathway23   2.5 6.7×10−06Atp2a1, Atp1a2, Atp1b2, Atf6b, Adcy1, Adcy3, Adcy7, Adra1a, Cacna1s, Gucy1b3, Irs2, MEF2C, Mylk3, Mylk4, Mylk2, Mylk, Nfatc4, Pde5a, Pln, Kcnmb1, Srf, Slc8a2, Tprc6
mmu05146PI3K-Akt signaling pathway34   3.5 3.7×10−05Epha2, Atf6b, Chrm2, Col1a1, Col1a2, Col3a1, Col4a1, Col4a2, Col4a5, Col4a6, Col5a1, Col5a2, Col5a3, Col6a1, Col6a2, Col6a3, Efna3, Fgf10, Fgf11, Fgf1, Fn1, Gnb4, Itga11, Itga3Lamb2, Ngfr, Ngf, Osm, Nr4a, Ppp2r5b, Tnxb, Thbs1, Thbs4, Vegfc
mmu05414Gastric acid secretion12   1.2 3.2×10−04Atp1a2, Atp1b2, Adcy1, Adcy3, Adcy7, Camk2a, Gast, Mylk3, Mylk4, Mylk2, Myl, Kcne2
mmu04614Renin-angiotensin system  8   1.4 1.7×10−06Mas1, Klk1, Klk1b11, Klk1b24, Klk1b, Klk1b5, Klk1b8, Klk1b26
mmu04961Endocrine and other factor-regulated calcium reabsorption  7   1.2 2.5×10−04Klk1, Klk1b11, Klk1b24, Klk1b3, Klk1b5, Klk1b8, Klk1b26
mmu00561Glycerolipid metabolism  6   1.3 2.9×10−03Cel, Lpin1, Lpin2, Phliprp1, Pnli, Pnliprp2
mmu03320PPAR signaling pathway  5   1.0 4.7×10−02Acox2, Angptl4, Cpt1a, Cyp4a32, Ehhdh
mmu04080Neuroactive ligand-receptor interaction10   2.1 5.3×10−02Gpr156, Mas, 1810009j06Rik, 2210010c04Rik, Oprd1, Try5, Gm10334, Prss2, Sctr, Try4
Gene co-expression network

Gene co-expression network analysis was performed between the highlighted groups of DEGs in synergy with possible involvement in BPs resulting in phenotypic changes. As shown in Fig. 2A, there was a positive correlation between the expression levels of insulin-like growth factor-binding protein 1 and plasminogen (Pearsons r=0.99), and these genes were involved in wound healing (Fig. 2A). In addition, a family of genes were associated with wound healing, including calponin 2, enolase 3, fibrinogen β chain, myogenic differentiation 1 and myosin heavy chain 2 (Myh2), in the WT mice (Fig. 2A). A group of genes associated with tight junction and focal adhesion, including actinin α2, actinin α3, myosin light chain, phosphorylatable, fast skeletal muscle and Myh2, were also correlated in the WT group. In the GDDR-/-group, it was found that 12 gene pairs of genes with similar expression profiles had involvement with proteolysis, which included CUB and zona pellucida-like domains 1/carboxypeptidase A2 (Cpa2), 2210010c04Rik/carboxypeptidase B1 (Cpb1), 2210010c04Rik/trypsin 5 (Try5), Cpa1/2210010c04Rik, Cpa2/protease, serine, 2 (Prss2), Cpb1/chymotrypsin-like elastase family member 1 (Cela1), Cela1/kallikrein 1 (Klk1), Cela2a/chymotrypsin-like (Ctrl), Try4/Cela3a, Ctrb1/Ctrl and Try4/Try5 (Fig. 2B).

Module screening from the PPI network

According to the STRING database results, the screening of top hub nodes with high degrees higher linked to phenotype was implemented. These encompassed period circadian clock 3 (Per3), period circadian clock 2 (Per2), Ptgs2, period circadian clock 3 (Per1), cryptochrome circadian clock 1 (Cry1), α-actin-3 (Actg2), cardiac muscle 1 (Actc1), nitric oxide synthase 2 (Nos2), mitogen-activated protein kinase kinase kinase 1 (Map3k1), and growth arrest specific 7 (Gas7). In addition, 317 edges and 282 nodes were analyzed with the use of MCODE plug-ins. In this process, the top three significant modules were selected, and the functional genetic annotation of these modules was analyzed (Fig. 3). Based on the enrichment analysis, the genes in the top three modules were highly associated with negative regulation of the signaling pathway of glucocorticoid receptor, mesenchyme migration and response to organic substance (Table V).

Table V.

Pathways enriched in modules of the protein-protein interaction network.

Table V.

Pathways enriched in modules of the protein-protein interaction network.

Gene setP-valueFDRNodes
Module 1
Negative regulation of glucocorticoid receptor signaling pathway 1.79×10−07 9.81×10−07Per1, Cry1, Arntl
Regulation of hormone secretion 4.21×10−03 2.01×10−04Per2, Nr1d1, Cry1, Arntl
Regulation of cellular response to stress 3.48×10−03 7.03×10−03Sfpq, Per1, Cry1, Arntl
Module 2
Mesenchyme migration 1.23×10−08 1.30×10−09Acta1, Actc1, Acta2, Actg2
Mesenchyme morphogenesis 5.54×10−05 6.76×10−06Acta1, Actc1, Acta2, Actg2
Cytoskeleton organization 4.26×10−04 4.64×10−04Gas7, Cap2, Cnn2, Fscn1, Myh10, Acta1
Module 3
Response to organic substance 1.34×10−02 4.48×10−10Zyx, Pygm, Slc11a2, Nos2, Ptgs2, Slc7a11, Ampd1, Epha2, Zx3h12a, Hspa1a, Hsph1, Afp, Myod1, Map3k1, Jun, Dusp1, Cpt1a, Dusp10, Tsc1, Fosb, Fos1, Junb, Atf3, Ddit3, HmgcS2, Hipk2, Oprd1, E2f1, Bysl, Cad, Dpysl2, Abcc2, Fasn, Sel1l, Klf2, Trib1, Abcc2, Atp1a2, Melf2c
Response to lipid 5.13×10−03 2.73×10−09Fosb, Fosl1, Fos, Jun, Nos2, Ptgs2, Dusp1, Junb, Dusp10, Hmgcc2, Jund, Cebpa, Nr4a2, Trib1, Myod1, Sox9, Atp1a2, E2f1, Bysl, Cad, Mef2c, Zc3h12a, Abcc2, Cpt1a
Regulation of cell death 8.13×10−03 5.26×10−08Nr4a2, Fosl1, Ptgs2, Slc11a2, Map3k1, Birc3, Junb, Jund, Atf3, Ddit3, Hspb1, Gadd45a, Erbb3, Bag3, Hsph1, Plk, Plk2, Sfn, Sox, Hipk2, Mef2, Vdr, Pdc, Igf1r, Rhob, Spry2, Steap3, Siah2, Ngfr

[i] FDR, false discovery rate.

Gene validation via RT-qPCR

The gene expression profiles of the DEGs were examined (Table II). Based on the results of the RT-qPCR analysis, the gene expression profiles of the DEGs in the two groups of samples were consistent with the RNA-seq data, with the exception of Adgrb (Fig. 4). Of note, the expression level of Ptgs2 in the WT group was ~5-fold of that in the KO group. Adgrb1 in the KO mice was marginally higher than that in the WT mice (P>0.05). Therefore, Adgrb1 may not contribute to the protection of the gastric mucosa under stress.


In the present study, the gene expression data of four WT mice and four KO mice were retrieved using the RNA-seq technique. In total, 1,704 DEGs were identified between the four KO samples and WT samples, of which 710 were downregulated and 994 were upregulated. The upregulated genes included Hbegf, Ptgs2, Cxcl2, Tgf-β1 and IL-1β. These genes are involved in the inflammatory response and immune response, and may aggravate gastric lesions. The downregulated genes included Ins2, Tsc1, Sgk2, Erbb3 and Map3k1. These genes are essential in proliferation, response to oxidative stress and negative regulation of acute inflammatory response. These genes can enhance the resistance of the gastric epithelia against damage and inhibit the acute inflammatory response. It has been reported that co-expressed genes are generally composed of a family of genes, which have similar expression profiles, and are frequently involved in parallel biological process. In the present study, a co-expression network was constructed among the DEGs from the WT mice and KO mice. The network may indicate how GDDR upregulates or downregulates DEGs, however, the results obtained in the present study were obtained from the whole stomach, containing various types of cells, therefore differences observed in the present study cannot be repeated in a single cell line. This indicates the merit of further investigation of the differences in one type of cell in the stomach. GO and KEGG pathway analyses were also performed to further elucidated the interactions of the DEGs. Based on the GO term analysis, the upregulated DEGs were predominantly associated with muscle contraction, cell adhesion, locomotion and response to wounding, whereas the downregulated DEGs were associated with regulation of nitrogen compound metabolic process, regulation of RNA metabolic process, and response to peptide hormone. These results are consistent with the knowledge that multiple processes, including increased nitrite/nitrate concentrations, the breakdown products of nitric oxide by inducible nitric oxide synthase in the gastric mucosa (28), gene expression, response to peptide hormone (3), microcirculation (29,30) and the vasodilator effect (31) are involved in acute gastric lesions. The KEGG pathways enriched in upregulated DEGs included focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathway, cGMP-PKG signaling pathway and gastric acid secretion (32), The KEGG pathways enriched in the downregulated DEGs included the endocrine, renin-angiotensin system, other factor-regulated calcium reabsorption, glycerolipid metabolism, PPAR signaling pathway and neuroactive ligand-receptor interaction. The gastric lesion is the result of the disturbance between defensive and aggressive factors in the gastric mucosa (30), including mucus secretion, mucosal blood flow and repair processes. Previous studies have shown that the renin-angiotensin system is important in gastric mucosal protection (33). Evidence also indicates that the ECM-receptor interaction is involved during wound repair (34,35). Activation in the PI3K-Akt signaling pathway has also been reported to be involved in protecting the gastric mucosal epithelium from damage (36). Therefore, investigation of these signaling pathways may assist in predictions of gastric lesions and wound repair.

In the present study, a PPI network was also constructed with the DEGs, and the top degree hub genes were identified, which were involved in phenotype: Per3, Ptgs2, Per2, Cry1, Per1, Actg2, Actc1, Nos2, Map3k1 and Gas7. Ptgs2, also known as cyclooxygenase 2 (Cox-2), serves as the key enzyme in the process of prostaglandin biosynthesis, and is a peroxidase and dioxygenase. Ptgs2 is involved in the production of inflammatory prostaglandins, and the upregulation of Ptgs2 is involved in phenotypic changes, increased cell adhesion, resistance to apoptosis and tumor angiogenesis. Previous studies have reported that the expression of Cox-2 is induced in inflammatory cells at sites of inflammation (37), and the presence of Cox-2 in the intact gastric mucosa is crucial for protection against injury caused by non-steroidal anti-inflammatory agents (38). The second hub gene, Cry1, is a core component of the circadian clock, which regulates various physiological processes through gene expression according to circadian rhythms of ~24 h. It is an important regulator of a broad set of physiological functions, including immune and endocrine functions, which are involved in gastric lesions (3941). The third hub gene, Per1, is a member of the period family of genes and functions in the repression of glucocorticoid receptor Nr3c1/Gr-induced transcriptional activity. It is important in gastric mucosal defense (42,43) by reducing the association between Nr3c1/Gr and glucocorticoid response elements. In addition, Per1 is involved in modulating the inflammatory state via regulating the release of inflammatory mediators, including Ccl2 and IL6. Per2 is also a member of the period family, and is involved in maintaining cardiovascular functions through the regulation of NO and vasodilatatory prostaglandin production. Cry1 and Cry2 are transported into the nucleus by Per1 and Per2 proteins, with appropriate circadian timing. Cry1 and cry2 exhibit repressing activity, which has a direct effect on clock-controlled target genes by interacting with groups of RNA-binding proteins, helicases and other transcriptional repressors (44). There is increasing evidence indicating that the functional disturbance of Nos2 can affect the response to hypoxia (45) and innate immune response in the mucosa (46). It can release NO, serving as a messenger molecule with distinct biological functions in the body. Map3k1 encodes a serine/threonine kinase and is known to be involved in certain signal-transduction cascades encompassing the c-Jun N-terminal kinase and extracellular signal-regulated kinase kinase pathways, and the nuclear factor-kB pathway. Reactive oxygen species are signaling molecules, which function in stimulating the protein synthesis of hypoxia-inducible factor 1 α (Hif-1α) through activating the mitogen-activated protein kinase (MAPK) pathways (47). To date, the biological functions of MAPK in acute gastric lesion remains to be elucidated.

Based on module analysis of the PPI networks, the process of gastric lesions in WT mice were associated with regulation of the glucocorticoid receptor signaling pathway, regulation of the cellular stress response to stress, and regulation of hormone secretion. It has been established that acutely increased corticosterone has a protective effect on the stomach against injury induced by stress (48). It has also been demonstrated that the gastroprotective activity of glucocorticoids results from the maintenance of mucus secretion, repair processes and gastric mucosal blood flow, and the attenuation of harmful factors, including increasing microvascular permeability and gastric motility (47,49). In addition, glucocorticoids have a compensatory gastroprotective role if the gastro-protective mechanisms rendered by NO, capsaicin-sensitive sensory neurons and prostaglandins are impaired (48).

The present study focused on the differences caused by GDDR under stress-induced conditions. The WT and KOmice groups were used to compare and analyze the differences. The correlation between the expression of DEGs and the degree of pathologic damage was not examined in the present study and requires further investigation. The present study focused on examining the differences between WT mice and GDDR-/- mice associated with phenotype. The DEGs analyzed in the present study were also based on phenotype. As, in the absence of stress, no significant differences are found between the WT mice and KO mice, the gene expression profiles in WT mice with and without stress were not examined in the present study.

In conclusion, comprehensive bioinformatics analysis of the DEGs was performed in the present study, which may be associated with acute gastric lesions and regulated by GDDR. The results provided an array of potent targets for future investigations of the molecular mechanisms. The functions of the genes identified in gastric lesions require confirmation in further molecular biological experiments.


This study was supported by the National Natural Science Foundation of China (grant no. 81270440).



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Zhang, Z., Zhu, J., Dong, Y., Xu, H., Jiang, T., Li, W. ... Du, J. (2017). Global transcriptome‑wide analysis of the function of GDDR in acute gastric lesions. Molecular Medicine Reports, 16, 8673-8684.
Zhang, Z., Zhu, J., Dong, Y., Xu, H., Jiang, T., Li, W., Xu, D., Shi, L., Yu, J., Zhang, J., Du, J."Global transcriptome‑wide analysis of the function of GDDR in acute gastric lesions". Molecular Medicine Reports 16.6 (2017): 8673-8684.
Zhang, Z., Zhu, J., Dong, Y., Xu, H., Jiang, T., Li, W., Xu, D., Shi, L., Yu, J., Zhang, J., Du, J."Global transcriptome‑wide analysis of the function of GDDR in acute gastric lesions". Molecular Medicine Reports 16, no. 6 (2017): 8673-8684.