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American Journal of Translational Research logoLink to American Journal of Translational Research
. 2021 Aug 15;13(8):9195–9207.

Inflammation is involved in response of gastric mucosal epithelial cells under simulated microgravity by integrated transcriptomic analysis

Shaobin Chai 1, Song Guo 2, Jiaqi Yang 2, Shengyu Lu 2, Yuan Yue 2, Hao Li 2, Peiming Sun 2, Tao Zhang 2, Binxin Xu 3, Hongwei Sun 2, Shaoyan Si 3, Jinlian Zhou 4, Jianwu Yang 2, Heming Yang 2, Zhengpeng Li 5, Yan Cui 1,2
PMCID: PMC8430122  PMID: 34540035

Abstract

Astronauts suffer from inflammatory changes induced by microgravity during space flight. Microgravity can significantly affect the inflammatory response of various cell types and multiple systems of the human body, such as cardiovascular system, skeletal muscle system, and digestive system. The aim of this research was to identify the key genes and pathways of gastric mucosa affected by microgravity. Human gastric mucosal epithelial GES-1 cells were cultured in a rotary cell culture system (RCCS) bioreactor to simulate microgravity. The gene expression profiles of GES-1 cells were obtained using Illumina sequencing platform and differentially expressed genes were identified by DESeq2 software, then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Subsequently, a protein-protein interaction (PPI) network was constructed. Compared with a normal gravity (NG) group, a total of 943 DEGs, including 192 downregulated genes and 751 upregulated genes, were identified. These DEGs were associated with findings that included response to interleukin-1, positive regulation of inflammatory response, and positive regulation of neuroinflammatory response. Furthermore, these DEGs were mainly enriched in herpes simplex virus 1 infection, cytokine-cytokine receptor interaction, and NOD-like receptor signaling pathway. Thus, 21 hub genes were identified from PPI network, including IL6, IL1B, ITGAM, CXCL8, ITGAX, CCL5, SERPINA1, APOE, CSF1R, VWF, GBP1, APOB, CYBB, HLA-DRB1, CD68, FGG, FGA, OASL, NOD2, OAS2 and FCGR2A. These findings suggested that simulated microgravity upregulated inflammation-related genes and pathways of GES-1 cells, which may play important roles in the response to microgravity and provide useful information for preventing mucosal damage in astronauts. In conclusion, this study revealed the key genes and pathways associated with simulated microgravity and indicated that simulated microgravity induced an inflammatory response in gastric mucosal epithelial cells.

Keywords: Weightlessness simulation, bioinformatics analysis, inflammatory response, interleukin 6, interleukin 1β

Introduction

Manned space flight projects not only bring technical challenges, but also face astronaut’s physical health problems caused by microgravity. The weightlessness environment affects human physiologic functions and causes diseases, such as decreased absorption of cerebrospinal fluid [1], electrolyte disorders [2], skeletal muscle atrophy [3], space motion sickness [4], space anemia [5], decreased immune function [6] and bone loss [7]. Additionally, research achievements have been made in the study of microgravity on the digestive system [8]; however gastric dysfunction under weightlessness needs to be further clarified. Gastric mucosal epithelium is a single cell layer lining the inner side of the gastric wall and there are a large number of gastric glands at the bottom of gastric pit, which can secrete mucus to cover the surface of gastric mucosa and prevent damage from gastric acid and pepsin to gastric mucosa. Weightlessness caused micro-focal lesions in rat gastric mucosa, and dystrophic developments in the glands, as well as mucous barrier and biosynthesis dysfunction [9]. A previous study of our group showed that physiologic structure and function of gastric mucosa were affected significantly by simulated microgravity. With the increase of stomach excretory and incretory activity under weightlessness [10], astronauts are more likely to develop gastric ulcers. Therefore, it is important to explore the effects of simulated weightlessness on function of gastric mucosa. This study focused on the effects of simulated microgravity on human gastric mucosa GES-1 cells by a rotary cell culture system (RCCS). Bioinformatic analysis based on RNA sequencing was performed to identify the profile of DEGs after simulated microgravity induction and explore the regulatory mechanism of microgravity. In conclusion, the study provides a basis for prevention and treatment of astronaut gastric mucosal diseases.

Materials and methods

GES-1 cell culture

Human gastric mucosal epithelial GES-1 cell line was procured from iCell Bioscience Inc (China). Cells were cultured in DMEM (Thermo Fisher Scientific, USA) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin (100 units/ml penicillin and 100 mg/ml streptomycin) (both Thermo Fisher Scientific, USA), and were incubated at 37°C with 5% CO2. The medium was changed every two days.

Microgravity simulation and induction with RCCS

RCCS (Synthecon, USA) was used to simulate microgravity (SMG) condition, which could create an optimized suspension culture environment to support 3-D cell growth on microcarrier bead scaffolds. First, Cytodex-3 microcarrier beads (Sigma, USA) were soaked in PBS buffer and stored at 4°C for 4 hours; then, the beads were washed 20 seconds with 75% alcohol for 3 times and immersed in alcohol at 4°C for at least 24 hours. When the microcarriers were used, they were washed 20 seconds 3 times with PBS. Subsequently, 3.5×106 GES-1 cells with 250 mg cytodex-3 microcarrier beads were seeded into 50 ml high-aspect-ratio vessels (HARVs) in the RCCS. The rotating culture vessels were filled with complete medium and air bubbles were removed using a 5 ml syringe. Finally, HARVs were fixed in RCCS with 12 rpm rotational speed and the cell-beads complexes were incubated at 37°C with 5% CO2 for 7 days. GES-1 cells were stored and cultured in the same incubator with normal gravity under the same conditions as controls (NG group) [11]. Three independent experiments were performed to confirm the results.

Sample collection and processing

The solution in HARVs was transferred to a 50 mL centrifuge tube with a pipette and left to stand 3 minutes and the supernatant was discarded after precipitation of cell-microcarrier complexes. After that, the complexes were washed 20 seconds with PBS for three times, and the supernatant was discarded. Then 4 ml 0.25% trypsin-EDTA was added to the centrifuge tube to digest at 37°C for 5 min and complete medium was added to harvest cells. The cell-microcarrier medium was blown repeatedly with a pipette to detach the cells from the microcarrier and terminate digestion reaction. Thereafter, the samples were centrifuged at 1000 rpm/min for 5 min and cells were obtained through 70 μm cell sieve filtration followed by 5 min 1000 rpm centrifugation. The control group cells were obtained after discarding culture medium, washing with PBS, digesting with 3 ml 0.25% trypsin, and centrifugation at 1000 rpm for 5 min. Cells collected from NG and SMG groups were frozen in liquid nitrogen for 5 minutes and stored at -80°C.

RNA extraction and sequencing

Total RNA was extracted from the cells using Trizol® Reagent (Invitrogen, USA) and genomic DNA was removed using DNase I (TaKara, Japan). Then RNA quality was determined by 2100 Bioanalyser (Agilent, USA) and quantified using the ND-2000 (Nano Drop Technologies, USA). Only a high-quality RNA sample (OD260/280=1.8~2.2, OD260/230≥2.0, RIN≥6.5, 28S: 18S≥1.0, >1 μg) was used to construct the sequencing library. RNA-seq transcriptome library was prepared following TruSeqTM RNA sample preparation Kit (Illumina, USA) by using 1 μg of total RNA. First, messenger RNA was isolated according to polyA selection method by oligo (dT) beads and then fragmented by fragmentation buffer. Second, double-stranded cDNA was synthesized using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, USA) with random hexamer primers (Illumina, USA). Then the synthesized cDNA was subjected to end-repair, phosphorylation, and poly ‘A’ base addition according to Illumina’s library construction protocol. Libraries were size selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose followed by PCR amplified using Phusion DNA polymerase (NEB) for 15 PCR cycles. After being quantified by TBS380, paired-end RNA-seq library was sequenced with the Illumina NovaSeq 6000 sequencer (2×150 bp read length). The raw paired end reads were trimmed and quality controlled by Seq Prep (https://github.com/jstjohn/SeqPrep) and Sickle (https://github.com/najoshi/sickle) with default parameters. Then clean reads were separately aligned to the reference genome with orientation mode using HISAT2 (http://ccb.jhu.edu/software/hisat2/index.shtml) software [12]. The mapped reads of each sample were assembled by String Tie (https://ccb.jhu.edu/software/stringtie/index.shtml?t=example) in a reference-based approach [13].

Differentially expressed genes and functional enrichment analyses

To identify DEGs between the different groups, the expression level of each transcript was calculated according to the transcripts per million reads (TPM) method. RSEM (http://deweylab.biostat.wisc.edu/rsem/) [14] was used to quantify gene abundances. Essentially, DEGs was selected using the DESeq2 [15] with a Q (P-adjust) value of less than 0.05 (FDR adjusted for multiple testing) and the absolute value of log2 (fold change) greater than or equal to 3.32. In addition, functional-enrichment analysis including GO and KEGG were performed to identify GO terms and KEGG pathways in which DEGs were significantly enriched with BH-corrected P-value < 0.05 compared to the whole-transcriptome background. GO functional enrichment and KEGG pathway analyses were carried out by Goatools (https://github.com/tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/home.do) [16].

Protein-protein interaction (PPI) network analysis

The Search Tool for the Retrieval of Interacting Genes (STRING, string-db.org) is an online database of known and predicted gene interactions designed to evaluate PPI network information. 943 DEGs were mapped to STRING database to construct a PPI network, and the cut-off criterion for validated interactions was a combined score >0.4. Subsequently, Cytoscape v3.8.0 (http://www.cytoscape.org/) was used to statistically analyze the topology property of the network to obtain the significant hub genes and modules. Nodes with degree ≥15 were defined as hub genes. Modules were extracted from the PPI network by the Molecular Complex Detection (MCODE) [17] plugin of Cytoscape with degree cutoff ≥2 and node score cutoff ≥0.2. Then, the module with the highest score was selected and the genes in this module were used for additional GO and KEGG analysis.

Results

Transcriptional profile differences of GES-1 cells between NG and SMG

The raw sequence data was uploaded to Sequence Read Archive (SRA) database (Bioproject accession PRJNA722265). The gene numbers with an expression level >1 TPM in GES-1 cells under NG and SMG are presented in Figure 1A. The Venn diagram suggested differences between two groups. 12785 genes were shared by the two groups (Figure 1A). On the other hand, 785 and 1330 genes were specifically expressed in the NG or SMG group, respectively (Figure 1A). The principal component analysis (PCA) showed that the cumulative contribution rate reached 82.27%. In the two-dimensional PCA score plot, PCA achieved good separation between samples under NG and SMG, as well as compact aggregation of the repeated samples (Figure 1B), which indicated that the constructed model was effective.

Figure 1.

Figure 1

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A. Venn diagram. Co-expressed and uniquely expressed genes in GES-1 cells before and after simulated microgravity treatment. B. PCA plot. Principal component analysis of NG and SMG samples. NG, normal gravity; SMG, simulated microgravity.

Identification of DEGs between NG and SMG

Gene expression profiles of GES-1 cells between NG and SMG were analyzed. Based on the cut-off criteria of adjusted P < 0.05 and |log2FC|>3.32, 943 differentially expressed genes (DEGs) were identified between NG and SMG, among which 751 DEGs (79.6%) were up-regulated and 192 DEGs (20.4%) were down-regulated. The volcano plot of all DEGs is presented in Figure 2.

Figure 2.

Figure 2

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Volcano Plot of DEGs. Red dots: upregulated genes with log2FC>3.32, and adjusted P < 0.05. The Green dots: downregulated genes with log2FC < -3.32 and adjusted P < 0.05. FC, fold change; DEGs, differentially expressed genes.

GO functional enrichment analysis

DEGs between NG and SMG were annotated by Gene Ontology (GO) database. Afterward, GO functional enrichment analysis was performed to investigate the biologic functions of DEGs. GO analysis is a powerful tool designed to reveal biologic functions underlying observed patterns in genomics or transcriptomics. The GO functions of the DEGs were classified into biological process (BP), cellular component (CC) and molecular function (MF). A total of 89 GO terms (69 BP, 8 CC and 12 MF) were enriched. The 20 most significantly enriched GO terms (adjust P < 0.05) are presented in Figure 3. Response to interleukin-1, positive regulation of inflammatory response, and positive regulation of neuroinflammatory response were the most important biologic processes; receptor regulator activity, receptor ligand activity, signaling receptor activator activity, cytokine activity and signaling receptor binding were the most important molecular functions. Furthermore, significantly enriched cellular components revealed that enriched DEGs were largely located in the extracellular region (121 DEGs), cell surface (38 DEGs), and extracellular space (86 DEGs). Additionally, DEGs enriched in the GO terms with higher rich factor are shown in Figure 4 to reveal important genes that may be sensitive to microgravity and red marks are used to display the hub genes that shown in Figure 8.

Figure 3.

Figure 3

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Bubble plot of GO enrichment analysis of DEGs. The top 20 enriched GO terms (adjusted P < 0.05) are shown. The bubble size and color represent the number of DEGs and the adjusted p value, respectively. Red, blue, and green terms indicate biologic processes, cellular components, and molecular function, respectively. The enrichment-factor indicates the ratio of enriched differentially expressed gene number to background gene number in a certain GO term. DEGs, differentially expressed genes; GO, gene ontology.

Figure 4.

Figure 4

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Heatmap of genes enriched in the GO terms with the higher enrichment factor. The log10FC values were used to generate heatmaps. Each row represents a gene and each column represents a group in the figure. Red, high expression, and blue, low expression in the group, respectively. FC, fold change; DEGs, differentially expressed genes.

Figure 8.

Figure 8

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A. The most important module of the protein-protein interaction network. B. The enriched cellular pathways of module genes.

KEGG pathway enrichment analysis

KEGG analysis was performed to further study the biologic pathways of DEGs. The 13 most significantly enriched pathways of DEGs are displayed in Figure 5. Cytokine-cytokine receptor interaction was the most important pathway (adjust P=3.25×10-7). The pathway with the largest number of DEGs was the Herpes simplex virus 1 infection (33 DEGs) followed by Cytokine-cytokine receptor interaction (26 DEGs) and NOD-like receptor signaling pathway (18 DEGs). TNF signaling pathway (adjust P=0.0295; 9 DEGs) was also an important pathway. DEGs enriched in above pathways are presented in Figure 6 and red marks are used to display the hub genes, shown in Figure 8.

Figure 5.

Figure 5

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Bubble plot of KEGG pathway enrichment analysis of DEGs. The bubble size and color represent the number of DEGs and the adjusted p value, respectively. DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 6.

Figure 6

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Heatmap of genes enriched in pathways with a larger number of DEGs. The log10FC values were used to generate heatmaps. Each row represents a gene and each column represents a group in the figure. Red: high expression, and blue: low expression in the sample. FC, fold change; DEGs, differentially expressed genes.

PPI network analysis of DEGs

Based on the STRING database, the PPI network of these DEGs was established and subsequently visualized by Cytoscape v3.80 software. The PPI network consisted of 296 nodes (protein-coding genes) and 779 edges (interactions) (Figure 7). In the integrated PPI network, degrees represented the number of interactions between a node and its neighbors, and nodes with higher degrees were defined as hub genes. Among the 296 nodes, 21 hub genes were identified by the filtering criteria: node degree ≥15. These hub genes were IL6, IL1B, ITGAM, CXCL8, ITGAX, CCL5, SERPINA1, APOE, CSF1R, VWF, GBP1, APOB, CYBB, HLA-DRB1, CD68, FGG, FGA, OASL, NOD2, OAS2 and FCGR2A (Table 1). We found that these hub genes were mainly involved in inflammation response. Then, MCODE plugin of Cytoscape was used to identify the hub gene functions, and the module with the highest score was selected as a focus for follow-up analysis. The module contained 27 DEGs and 147 edges (Figure 8A). GO analysis of the module genes revealed that hub genes were mainly associated with cellular response to interleukin-1, regulation of chemokine production and regulation of inflammatory response (Table 2). Furthermore, KEGG analysis showed that hub genes were chiefly related to NOD-like receptor signaling pathway (9 DEGs), Influenza A (8 DEGs), Cytokine-cytokine receptor interaction (7 DEGs), Toll-like receptor signaling pathway (4 DEGs) and TNF signaling pathway (4 DEGs) (Figure 8B). These data suggested that simulated microgravity was strongly associated with the inflammatory response.

Figure 7.

Figure 7

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Protein-protein interaction network of DEGs. Ellipse nodes represent upregulated DEGs and diamond nodes represent downregulated DEGs. Node color represents the log2FC value; redder nodes indicate a higher value and bluer nodes indicate a lower value. Yellow nodes suggest the most significant module position in the PPI network. Node size represents the connectivity degree; larger nodes determine a more important gene. Yellow, green and purple edges represent coexpression relationship from weak to strong. DEGs, differentially expressed genes; FC, fold change.

Table 1.

Degrees of the top 21 hub genes in the PPI network

Order Node Degree Order Node Degree
1 IL6 58 12 APOB 21
2 IL1B 44 13 CYBB 19
3 ITGAM 37 14 HLA-DRB1 18
4 CXCL8 36 15 CD68 18
5 ITGAX 30 16 FGG 18
6 CCL5 26 17 FGA 18
7 SERPINA1 24 18 OASL 17
8 APOE 23 19 NOD2 17
9 CSF1R 22 20 OAS2 16
10 VWF 22 21 FCGR2A 15
11 GBP1 21
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Table 2.

Enriched GO terms of module genes

Description GO ID P value P adjust Nodes
clathrin-coated endocytic vesicle membrane GO:0030669 1.29E-08 6.71E-05 APOB, APOE, HLA-DRB1, HLA-DPA1
cellular response to interleukin-1 GO:0071347 1.46E-08 6.71E-05 CXCL8, GBP1, CCL5, GBP1, FGG
regulation of chemokine production GO:0032642 2.71E-08 6.71E-05 IL6, IL1B, CSF1R, TSLP, IL33
regulation of ERK1 and ERK2 cascade GO:0070372 4.28E-08 6.71E-05 IL1B, APOE, CSF1R, GBP1, FGA, HLA-DRB1, FGG, NOD2
regulation of peptide hormone secretion GO:0090276 4.46E-08 6.71E-05 IL6, IL1B, CCL5, FGA, HLA-DRB1, FGG
regulation of neuroinflammatory response GO:0150077 4.71E-08 6.71E-05 IL6, IL1B, IL33, ITGAM
response to virus GO:0009615 4.94E-08 6.71E-05 IL6, IL33, GBP1, ITGAX, APOB, OAS2, OASL
regulation of inflammatory response GO:0050727 6.00E-08 6.71E-05 IL6, IL1B, IL33, NOD2, HLA-DRB1, ITGAM, APOE, TSLP
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Discussion

Microgravity has profound effects on human health. With the rapid development of manned spaceflight projects, the effect of space environment on human health become a focus of research. Recent studies showed that microgravity induced stomach hypersecretion [10] and space motion sickness presenting as stomach discomfort, nausea, and vomiting in most astronauts during space flight [18]. Also, microgravity resulted in increased pepsinogen, lower pH, and decreased glycoproteins of the stomach [19]. Additionally, previous research showed that the structure and function of gastric mucosa is highly sensitive to microgravity variation. Collagen fibers in mice gastric mucosa were reduced after a 30-day space flight, and the fibrous reduction was restored following a 7-day land readaptation [20]. Moreover, microgravity also led to mucous lesions, dystrophic developments in the acid glands, dissociation of the mucous barrier function, and deterioration of its biosynthetic function after the 12-d Foton-M3 flight [9]. However, the effects and associated mechanisms of microgravity to gastric mucosa cells remain unknown. These affect the prevention of gastric mucosal damage under microgravity. Due to the infrequency of space flights and the high cost of direct research in space, ground-based microgravity simulators [21] such as RCCS designed by NASA are proposed for investigation, and these can achieve the same effects as that in space. Such instruments provide valuable tools for productive research to better understand the effects of microgravity on cells. This study found that simulated microgravity induced gastric mucosal epithelial inflammation which would be beneficial to prevention of gastric mucosal damage.

In this study, the gene expressions of GES-1 cells before and after simulated microgravity treatment were sequenced by second-generation sequencing technology to identify key genes related to microgravity. In GO analysis, we found that response to interleukin-1, positive regulation of inflammatory response, and positive regulation of neuroinflammatory response were the most important biologic processes, while receptor regulator activity, receptor ligand activity, and signaling receptor activator activity were the most important molecular functions. KEGG pathway analysis revealed that DEGs were mainly enriched in herpes simplex virus 1 infection, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and TNF signaling pathway. 21 hub genes were identified from the PPI network, including IL6, IL1B, ITGAM, CXCL8, ITGAX, CCL5, SERPINA1, APOE, CSF1R, VWF, GBP1, APOB, CYBB, HLA-DRB1, CD68, FGG, FGA, OASL, NOD2, OAS2 and FCGR2A. These findings illustrated that inflammation related genes and pathways were affected by simulated microgravity in GES-1 cells. It is reported that spaceflight induced a proinflammatory state [22] that enhanced IL-6, IL-1β, TNF-α; and IL-8 levels were observed in astronauts [23-25]. This was validated in other recent studies [26,27]. IL-1β, IL-6, and IL-8 are primary pro-inflammatory cytokines, which can initiate and propagate the inflammatory response [28]. Inflammation is essentially an adaptive response that aims to restore homeostasis, and it is triggered upon tissue damage or microbial invasion [29]. Self-limiting inflammation in appropriate amounts triggers regeneration and repair, but excessive inflammation can easily become detrimental because of its tissue-damaging potential [29].

IL-6 is a prototypical pro-inflammatory cytokine, which influences cell proliferation, survival, migration, invasion, angiogenesis, and inflammation [30]. Excessive IL-6 production causes severe inflammatory diseases [31]. While classic IL-6 signaling by the membrane-bound receptor is mainly regenerative and protective, IL-6 trans-signaling by the soluble IL-6R is rather pro-inflammatory [32]. The complex of IL-6 and IL-6R binds to two molecules of gp130 and leads to IL-6-signal transduction, which includes activation of JAK/STAT, SHP-2-Ras-ERK, and PI3K-AKT-mTORC1 pathways by the common co-receptor gp130 [33]. STAT3 is the major effector that links inflammation to cell proliferation, survival, and cancer. IL-6 directly affects epithelial-cell proliferation and confers resistance to tissue injuries through phosphorylation of STAT3 [34] or the activation of YAP-Notch signaling [30]. IL-6 (-/-) mice and blockade of IL-6 trans-signaling both showed severe tissue damage, while administration of recombinant IL-6 promoted cell proliferation [35,36]. Balanced IL-6 cytokine signaling through SHP2/Ras/Erk and STAT3 leads to gastric mucosal homeostasis [37]. Mucosal wound healing depends on activation of STAT1/3, such that a mutation abrogating SHP2-Ras-ERK signaling caused gastric adenomas, and a mutation ablating STAT1/3 signaling or IL-6 (-/-) mice showed impaired mucosal wound healing [38,39]. Loss of one STAT3 allele reduced inflammatory infiltration and cytokine and chemokine expression, and it inhibited capability of macrophage and neutrophil infiltration [39].

IL-1β can lead to spontaneous gastritis in the stomach of IL-6-transgenic mice [40] and recurrence of gastric ulcer in rats with healed ulcers [41]. IL-1β level is positively associated with gastric mucosal inflammation, severe hyperplasia, chronic inflammation, atrophy, metaplasia, and dysplasia [40-44], which is also evidenced by IL-1β polymorphisms [43,45]. However, IL-1β (-/-) knockout mice and IL-1 receptor antagonism exhibited a decreased capability to develop gastritis [40,44]. IL-1β has emerged as a central mediator of gastric mucosal inflammation, which also is a powerful inhibitor of gastric acid secretion by suppression of membrane protein H+/K+/ATPase and the peptide hormone gastrin, resulting in gastric hypochlorhydria [46]. IL-1β has been shown to enhance the release of proinflammatory cytokines such as IL-6, and chemotactic IL-8 from gastric mucosal epithelium [47]. The release of IL-8 from the epithelium elicits acute inflammation by neutrophil accumulation and activation [48]. Variation in IL-8 genetic diversity is related to the intensity of the inflammatory response [49] and gastric ulcers, gastric atrophy, and eventually GC development [50]. Treatment with anti-CD11b to adhesion molecules inhibited both neutrophil infiltration into the scarred mucosa and ulcer recurrence caused by IL-1β [41]. CD11b is encoded by the ITGAM gene, which is associated with autoimmunity and targets of interest in the treatment of autoimmune diseases [51].

The current study also found increased secretion of IL-6, IL-1β, and IL-8 in gastric mucosal cells under simulated microgravity, indicating that the gastric mucosa GES-1 cells may be in a pro-inflammatory or inflammatory state. In practical terms, microgravity can induce an inflammatory response for adaption while an excessive reaction could damage gastric mucosal epithelial cells. Therefore, it is necessary to intervene in microgravity-induced inflammation of gastric mucosal epithelial cells. This may be beneficial for the health of astronauts, and the genes we found may become targets for intervention.

In conclusion, this study provided useful information for identifying key genes and the associated mechanism in gastric mucosal epithelial cells under simulated microgravity. This could support prevention and control of gastric injury in astronauts.

Acknowledgements

We are grateful to The National Basic Research Program Grants of China (2013CB945501).

Disclosure of conflict of interest

None.

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