IP3 R attenuates oxidative stress and inflammation damage in smoking-induced COPD by promoting autophagy.

Tobacco smoking is one of the most important risk factors for chronic obstructive pulmonary disease (COPD). However, the most critical genes and proteins remain poorly understood. Therefore, we aimed to investigate these hub genes and proteins in tobacco smoke-induced COPD, together with the potential mechanism(s). Differentially expressed genes (DEGs) were analysed between smokers and patients with COPD. mRNA expression and protein expression of IP3 R were confirmed in patients with COPD and extracted smoke solution (ESS)-treated human bronchial epithelial (HBE) cells. Moreover, expression of oxidative stress, inflammatory cytokines and/or autophagy-related protein was tested when IP3 R was silenced or overexpressed in ESS-treated and/or 3-MA-treated cells. A total of 30 DEGs were obtained between patients with COPD and smoker samples. IP3 R was identified as one of the key targets in tobacco smoke-induced COPD. In addition, IP3 R was significantly decreased in patients with COPD and ESS-treated cells. Loss of IP3 R statistically increased expression of oxidative stress and inflammatory cytokines in ESS-treated HBE cells, and overexpression of IP3 R reversed the above functions. Furthermore, the autophagy-related proteins (Atg5, LC3 and Beclin1) were statistically decreased, and p62 was increased by silencing of IP3 R cells, while overexpression of IP3 R showed contrary results. Additionally, we detected that administration of 3-MA significantly reversed the protective effects of IP3 R overexpression on ESS-induced oxidative stress and inflammatory injury. Our results suggest that IP3 R might exert a protective role against ESS-induced oxidative stress and inflammation damage in HBE cells. These protective effects might be associated with promoting autophagy.

INTRODUCTION

Chronic obstructive pulmonary disease (COPD), a general term for a series of airflow limitation diseases, has been predicted to become the third leading cause of death in 2030 by the World Health Organization (WHO). In recent years, COPD seriously threatened the health of humans and reduced the quality of normal life. The symptoms of patients with COPD always include chronic cough, sputum production and dyspnoea, which are mainly associated with the inflammatory response in the airway and alveolar epithelium. Symptoms could be induced by a variety of reasons, among them, tobacco smoking, especially in elderly smokers, is one of the main risk factors for COPD. More than 4000 chemicals are contained in tobacco smoke, of which at least 20 are carcinogenic substances. The pathogenic mechanisms of tobacco smoke mainly include inflammation, oxidative damage and cell senescence. In addition, previous studies have reported that changes in gene expression and epigenetic modification are related to tobacco smoke–induced inflammation; , meanwhile, a large number of protein‐coding genes are associated with COPD exacerbation. , However, the gene and protein whose alteration is most critical in tobacco smoke–induced COPD remain poorly understood. Therefore, first, this study used bioinformatics methods to explore the biomarkers of COPD induced by tobacco smoke based on the gene chip databases. We found inositol 1,4,5‐trisphosphate receptor was one of the common different expression genes of patients with COPD and normal smokers compared with the non‐smoker control, separately.

IP3R can regulate the release of intracellular Ca2+ through binding the second messenger IP3 from the endoplasmic reticulum (ER). In the molecule, IP3R is essentially regulated by IP3 and Ca2+, and intracellular signals such as the redox state, ATP and cAMP can modulate its regulation. Moreover, IP3R was found to play an important role in both normal physiological processes and disease. The central function of IP3R is its delivery of Ca2+ to mitochondria or lysosomes, by which it participates in regulatory processes such as oxidative phosphorylation and cell apoptosis. , , Dysregulation or mutation of IP3R is associated with neurodegenerative disorders. , In addition, IP3R signalling pathway might be a target for cancer therapeutics by inducing autophagy. The latest studies demonstrated that IP3R‐associated calcium signalling plays an essential role in the oxidative stress process in human endothelial cells. However, whether IP3R contributes to the smoke‐induced respiratory inflammatory response and oxidative stress remains not obvious.

Airway inflammation, which might be related to increased oxidative stress, is widespread in patients with tobacco smoke–induced COPD. , Numerous oxidants can be directly measured in tobacco smoke, and tobacco smoking can deplete antioxidants. , Moreover, oxidative stress remains high due to the presence of active inflammatory cells in tobacco smoke–induced COPD. The inflammatory response was also shown to be further amplified in this process by activation of the NF‐κB pathway. Multiple cell and proinflammatory cytokines participate in the inflammatory response. IL‐4, IL‐5, IL‐9 and IL‐13 mediate inflammation, whereas TNF‐α and IL‐1β further amplify the inflammatory response. , In tobacco smoke–induced COPD patients, an imbalance between oxidative and antioxidative processes caused by the inflammation response eventually leads to disease progression.

Autophagy is a widespread basic activity in eukaryotic cells that plays an essential role in the inflammatory response to stress in the airway and lung. , Furthermore, there is increasing evidence that the autophagy process plays critical roles in the progression of COPD‐emphysema. , , , , , It has been demonstrated that exposure to tobacco and/or e‐cig/nicotine vapour promotes oxidative stress reaction and inflammatory response that could lead to autophagy‐flux impairment, further impeding the important cellular homeostatic processes involved in the removal of misfolded proteins and bacterial/viral pathogens, ultimately affecting cell survival. , , In this process, Ca2+ signalling is one of the basic mechanistic targets for modulating autophagic flux. Previous studies proposed that IP3R could mediate Ca2+ signalling as an essential target in starvation‐induced autophagy. Cytosolic Ca2+ buffering could impede autophagy through the mTOR‐related pathway. However, whether cell autophagy–related protein mediated by IP3R participates in the regulation of oxidative stress and inflammation induced by tobacco smoke requires further investigation.

In the present study, we performed a bioinformatics analysis to identify the potential molecular targets of tobacco smoke–induced COPD. A subsequent study was performed to verify the differential expression of one of these selected targets (IP3R). Finally, we aimed to explore the role of IP3R in the process of oxidative stress and inflammation in a tobacco smoke–induced human bronchial epithelial (HBE) cell injury model and determine whether IP3R regulation is associated with autophagy.

MATERIALS AND METHODS

Data resources

Gene expression data used in this study were obtained from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI‐GEO) database. We obtained the gene expression data sets GSE54837 and GSE37768. Microarray data from these two data sets were collected with the GPL570 platform ([HG‐U133_Plus_2]). The GSE54837 data set comprised data from 136 COPD patient, 84 smoker control and six non‐smoker control samples. The GSE37768 data set comprised data from 18 COPD patient, 11 smoker control and nine non‐smoker control samples. The batch effects in the expression data were adjusted using distance‐weighted discrimination methods. Human blood samples were from Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University. The research ethics committee at Shengjing Hospital of China Medical University, China (ethics number: 2015PS173K), approved the study and its methodology. All study participants provided written informed consent. The study was conducted in accordance with the principles of the Declaration of Helsinki (2013).

Data pre‐processing of differentially expressed genes

Differentially expressed genes (DEGs) between COPD/non‐smoker controls and smoker/non‐smoker controls were identified via GE2O online tools. The DEGs between these pairs of groups were aggregated. A P‐value <.05 and a |logFC| > 2 were used as the cut‐off criteria for DEG screening. Then, raw data were analysed with the Venn online website to determine common DEGs between the COPD/non‐smoker control and smoker/non‐smoker pairs of groups.

Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

DAVID is an online bioinformatics tool that is widely used to identify gene and protein functions. , In the present study, gene ontology (GO) analysis of the enrichment of biological process (BP), molecular function (MF) and cellular component (CC) terms in the DEGs and KEGG pathway analysis were performed with the DAVID online tool.

Reagents

Human bronchial epithelial cells and lentiviruses were purchased from GeneChem. FBS, RPMI‐1640 medium and penicillin/streptomycin were obtained from HyClone. Commercial kits used for the analysis of CAT, MDA, SOD and GSH‐Px were purchased from Nanjing Jiancheng Bioengineering Institute. The 2′7′‐dichlorofluorescein diacetate (DCFH‐DA) used to measure reactive oxygen species (ROS) formation was obtained from Beyotime Biotechnology. A cell proliferation assay kit (MTS method) was obtained from Promega Biotech Company. TRIzol used for RNA extraction, a PrimeScript RT reagent kit, a SYBR Premix TaqTM kit and dT primers were all purchased from TaKaRa Biotechnology Company. Total protein extraction kits were purchased from Invent Biotechnologies Institution. A BCA reagent kit and SDS‐PAGE kit were obtained from Beyotime Biotechnology. PVDF membranes were purchased from Millipore. Antibodies against IP3R and fluorescently labelled secondary antibody were obtained from Cell Signaling Technology. Antibodies against Atg5, LC3, P62, Beclin1 and β‐actin were purchased from CST, and goat anti‐rabbit IgG HRP secondary antibody was obtained from the Absin Bioscience Institution. 3‐Methyladenine (3‐MA) was purchased from MedChemExpress.

HBE cell cultures and transfection

The HBE cell line was used in the present study. HBE cells were thawed and cultured in six‐well cell culture plates. RPMI‐1640 medium containing 10% FBS and 1% penicillin/streptomycin was used to culture the cells in an incubator set at 37°C and 5% CO2.

To explore the function of IP3R in tobacco smoke–induced oxidative stress and inflammation, we purchased lentiviruses expressing si‐RNA against IP3R, which were transduced into HBE cells according to the manufacturer’s instructions. In addition, HBE cells transduced with lentiviruses expressing control si‐RNA were used as a control group. Subsequently, IP3R was overexpressed in the experimental group with the same method, and HBE cells transfected with lentiviruses containing a scrambled sequence were used as the control group.

Pre‐treatment with aqueous cigarette smoke extract

Tobacco smoke was extracted based on previous reports. , Three cigarettes from different companies (Liqun, Nanjing, China; Hongtashan, Yunnan, China; and Jiaozi, Sichuan, China) were burned, and the smoke mixtures were extracted through a vacuum pump and dissolved in 10 mL of PBS. The pH of the extracted smoke solution (ESS) was adjusted to 7.4, and the ESS was filtered through a 0.22‐μm filter. HBE cells in the experimental group were cultured in medium containing 5% ESS. HBE cells cultured in medium containing a 5% extract of normal air prepared in a manner similar to that used to prepare the experimental extract were used as a control group.

IP3R double immunofluorescence of HBE cells

Human bronchial epithelial cells were pre‐treated for 1 hour with 5% ESS, and the medium was then replaced. After pre‐treatment for 24 hours, the HBE cells and medium were collected. HBE cells after ESS exposure and control group were fixed with formaldehyde, and treated with 0.1% Triton X‐100 to permeabilize the cell membrane. The cells were then blocked with non‐immunized animal serum and incubated with antibodies against IP3R (1:500; Cell Signaling Technology) at 4°C and incubated with a fluorescently labelled secondary antibody (1:200; Cell Signaling Technology) for 2 hours at room temperature. DAPI was applied to stain the nuclei, and results were imaged using a fluorescence microscope.

Determination of CAT, MDA, SOD and GSH‐Px levels

Human bronchial epithelial cells were pre‐treated for 1 hour with 5% ESS, and the medium was then replaced. After pre‐treatment for 24 hours, the HBE cells and medium were collected. The supernatant of the cell and medium mixture was recycled after sonication and centrifugation. Protein concentrations in the supernatant were determined with the BCA method. The absorbance of CAT, MDA, SOD and GSH‐Px at 405, 530, 450 and 412 nm, respectively, was determined and recorded according to the specifications of the assay kit manufacturer. The levels of MDA and the activities of CAT, SOD and GSH‐Px were calculated based on equations.

Measurement of ROS formation

Human bronchial epithelial cells were collected 24 hours after pre‐treatment for 1 hour with 5% ESS. Digested cells were harvested after centrifugation, and precipitated cells were resuspended in binding buffer after washing twice. The final concentration of HBE cells was 1 × 105/mL. As shown in previous research, HBE cells were incubated with 10 μmol/L DCFH‐DA for 30 min in an incubator. Finally, ROS formation was quantified via flow cytometry (Becton Dickinson) and is shown as the fluorescence intensity.

Quantification of HBE cell viability

Human bronchial epithelial cell viability was quantified using the MTS method. HBE cells were evenly cultured in a 96‐well assay plate and pre‐treated for 1 hour with 5% ESS, following which the cell medium was recycled with fresh medium, and the incubation was continued for 24 hours. Subsequently, the cells were cultured in the incubator for 2 hours after the addition of 20 μL of MTS assay reagent to each well. Absorbance values at 490 nm were determined using a microplate reader (BioTek Epoch).

Real‐time quantitative PCR (RT‐qPCR) analysis

Human bronchial epithelial cells were collected after pre‐treatment with ESS as previously described. Gene expression levels of TNF‐α, IL‐1β, IL‐4, IL‐6 and IP were determined via RT‐qPCR, and the primer sequences are provided in Table 1. β‐Actin was used as a housekeeping gene. Total RNA was isolated with TRIzol reagent, and cDNA was synthesized with a PrimeScript RT kit according to the manufacturer’s instructions. RT‐qPCR was performed with a SYBR Premix TaqTM Kit using a 7500 real‐time PCR system (Applied Biosystem). After pre‐denaturation for 30 seconds at 90oC, RT‐qPCR was performed as 40 cycles of denaturation for 5 seconds at 95°C and annealing for 34 seconds at 60°C. Relative target genes were quantified with the comparative CT () method and then compared with target gene expression in the control group. The gene expression of each target in each sample was measured in three independent experiments.

TABLE 1

Primer sequences used for RT‐qPCR in this study

Gene name	Gene ID		Primer sequences (5'‐3')
IP3R	NM_002222	Forward	GCGGAGGGATCGACAAATGG
		Reverse	TGGGACATAGCTTAAAGAGGCA
TNF‐α	NM_000594	Forward	CCTCTCTCTAATCAGCCCTCTG
		Reverse	GAGGACCTGGGAGTAGATGAG
IL‐1β	NM_000576	Forward	ATGATGGCTTATTACAGTGGCAA
		Reverse	GTCGGAGATTCGTAGCTGGA
IL‐4	NM_000589	Forward	CCAACTGCTTCCCCCTCTG
		Reverse	TCTGTTACGGTCAACTCGGTG
IL‐6	NM_000600	Forward	ACTCACCTCTTCAGAACGAATTG
		Reverse	CCATCTTTGGAAGGTTCAGGTTG
β‐Actin	NM_001101	Forward	CATGTACGTTGCTATCCAGGC
		Reverse	CTCCTTAATGTCACGCACGAT

Western blot

After pre‐treatment with ESS for 1 hour, HBE cells were collected 24 hour later. Total protein was extracted using an Invent protein extraction kit (containing 1% PMSF), and the protein concentration was quantified using BCA kits. Equal quantities of protein (30‐60 μg) were separated by 8%‐12% SDS‐PAGE and transferred to PVDF membranes. The membranes were blocked for 2 hours, followed by incubation with primary antibodies at 4°C overnight. The membranes were washed three times and incubated with HRP‐conjugated anti‐rabbit antibodies (1:5000) for 1 hour at room temperature. An enhanced ECL substrate was used to measure the optical densities of the bands. β‐Actin was used as the loading control. The relative optical densities of bands in the Western blots were determined with Fluor Chem 2.0. Representative protein bands from one single experiment are shown in the figures, and integrated density values indicating the relative expression of each protein are shown as a bar chart. Each protein western blot analysis was performed at least three times. Primary antibodies against the following were used in the present study: IP3R (1:800), Atg5 (1:1000), LC3 (1:1000), P62 (1:1000), Beclin1 (1:1000) and β‐actin (1:2500).

Statistical analysis

Statistical analyses in this study were performed with SPSS 17.0, and GraphPad Prism software was used to prepare figures. Student’s t test was used to analyse the variance between experimental groups and the control group. One‐way analysis of variance followed by the Student–Newman–Keuls (SNK) test was used for comparisons between multiple means. Sample data from independent experiments are shown as the mean ± standard deviation (SD). Differences for which *P < .05 or **P < .01 were considered statistically significant.

RESULTS

Determination of the DEGs common to smokers and COPD patients compared with non‐smoker control

A total of 154 COPD patient samples, 95 smoker control samples and 15 non‐smoker control samples were used in the present study. From the GSE54837 and GSE37768 data sets, we obtained DEGs between COPD patient and non‐smoker control samples and DEGs between smoker and non‐smoker control samples via GEO2R. The results after preliminary screening are shown as volcano plots in Figure 1A‐D. Forty‐one DEGs between COPD patient samples and non‐smoker control samples were extracted, while 39 DEGs between smoker and non‐smoker control samples were extracted. Subsequently, we used the Venn diagram website to determine the DEGs common to smokers and COPD patients, and finally obtained 30 common DEGs (Table 2 and Figure 1E).

FIGURE 1

Determination of DEGs among COPD patients and smoker samples. A and B, Volcano plots of DEGs between COPD patient and non‐smoker control samples; C and D, Volcano plots of DEGs between smoker control and non‐smoker control samples; E, DEGs common to COPD patients and smoker samples via a Venn diagram

TABLE 2

All 30 commonly DEGs in COPD and smoker samples from GEO datasets

Gene name	DEGs	log FC	log FC
		(COPD patients & control)	(Smoker & control)
Calcium and integrin binding family member 4	CIB4	−3.54	−3.59
Guanylate cyclase 1 soluble subunit alpha 2	GUCY1A2	−2.51	−2.72
heparan sulfate 2‐O‐sulfotransferase 1	HS2ST1	−2.35	−2.43
Retinal G protein coupled receptor	RGR	−2.16	−2.13
Eukaryotic translation initiation factor 1A, Y‐linked	EIF1AY	−2.88	−2.83
Protocadherin related 15	PCDH15	−2.16	−2.01
HOXA distal transcript antisense RNA	HOTTIP	−2.57	−2.65
Phenylalanyl‐tRNA synthetase beta subunit	FARSB	−2.83	−3.08
Wilms tumor 1 associated protein	WTAP	−2.08	−2.04
Glucuronidase, beta pseudogene 4	GUSBP4	−2.73	−2.95
Centromere protein N	CENPN	−2.26	−2.19
Inositol 1,4,5‐Trisphosphate Receptor Type 1	ITPR1	−2.23	−2.18
Gap junction protein gamma 1	GJC1	−2.3	−2.13
X inactive specific transcript	XIST	4.93	4.89
Notch 3	NOTCH3	−3.32	−3
Zinc finger protein, Y‐linked	ZFY	−2.33	−2.51
Fibrillin 1	FBN1	−2.43	−2.32
Ubiquitously transcribed tetratricopeptide repeat containing, Y‐linked	UTY	−2.08	−2.4
Taxilin gamma pseudogene, Y‐linked	TXLNGY	−2.24	−2.42
Glycoprotein M6A	GPM6A	−2.11	−3.15
Phospholipase A2 group IIA	PLA2G2A	−2.03	−2.42
Long intergenic non‐protein coding RNA 491	LINC00491	−2.76	−2.47
MIR670 host gene	MIR670HG	−2.9	−2.86
Solute carrier family 24 member 3	SLC24A3	−2.6	−2.32
semaphorin 3A	SEMA3A	2.42	2.25
microRNA 675///H19, imprinted maternally expressed transcript	MIR675///H19	−2.13	−2.54
Uncharacterized LOC100288911	LOC100288911	−2.08	−2.35
SPANX family member A2///SPANX family member C///sperm protein associated with the nucleus, X‐linked, family member A1	SPANXA2///SPANXC///SPANXA1	−2.31	−2.09
Uncharacterized LOC100505902///uncharacterized LOC285389	LOC100505902///LOC285389	−2.46	−2.03
Long intergenic non‐protein coding RNA 1568	LINC01568	−2.28	−2.51

GO and KEGG pathway enrichment analyses of DEGs in smokers and COPD patients

The results of GO analysis and KEGG pathway related to the selected DEGs in tobacco smoke‐induced COPD are shown in Table 3 and Figure S1. For BP analysis, DEGs were particularly enriched in BP related to the process of visual perception. For MF analysis, DEGs were significantly enriched in MF related to calcium ion binding and ion channel activity. For CC analysis, DEGs were markedly enriched in CC related to the ER membrane and plasma membrane. For the KEGG pathway, the selected DEGs were significantly enriched in the vascular smooth muscle contraction process. In addition, the results showed that three genes, PLA2G2A, GUCY1A2 and IP3R, were enriched in this pathway. Based on the results of GO and KEGG pathway analyses, the IP gene was selected for subsequent experimental verification in the present study.

TABLE 3

GO and KEGG analyses of DEGs in tobacco smoke–induced COPD

Category	Term	Count	P‐value	Genes
GOTERM_MF_DIRECT	GO:0005509~calcium ion binding	7	.0001	NOTCH3, CIB4, SLC24A3, FBN1, PLA2G2A, PCDH15, ITPR1*
GOTERM_MF_DIRECT	GO:0005216~ion channel activity	2	.0449	ITPR1*, GJC1
GOTERM_CC_DIRECT	GO:0005789~endoplasmic reticulum membrane	4	.0373	NOTCH3, PLA2G2A, ITPR1*, GJC1
GOTERM_CC_DIRECT	GO:0005886~plasma membrane	8	.0493	NOTCH3, GPM6A, SLC24A3, PLA2G2A, GUCY1A2, PCDH15, ITPR1*, GJC1
GOTERM_BP_DIRECT	GO:0007601~visual perception	3	.0172	PCDH15, RGR, GJC1
KEGG_PATHWAY	hsa04270:Vascular smooth muscle contraction	3	.0075	PLA2G2A, GUCY1A2, ITPR1*

ITPR1 = IP3R.

IP3R was decreased in COPD and ESS‐treated HBE cells

To verify the differential expression of the IP gene, we analysed its mRNA levels in tobacco smoke–induced COPD lung samples and non‐smoker control samples using a GEO data set (GES103174, 11739563_a_at). Figure 2A shows that the IP3R mRNA was significantly decreased in tobacco smoke–induced COPD patients compared with the controls (P < .05). In addition, we performed RT‐qPCR analysis to further determine the IP3R mRNA level in peripheral blood samples from patients with COPD (who had smoked for more than 15 years) and volunteer non‐smokers, which showed that the transcription of IP3R was also significantly reduced in the patients with COPD (P < .05, Figure 2B). Subsequently, we analysed both the mRNA expression and protein expression of IP3R in ESS‐treated HBE cells. The results in Figure 2C,D indicated that both the mRNA expression (P <.01) and protein expression (P <.05) of IP3R were markedly decreased after ESS exposure. In addition, microphotographs showed that the number of HBE cells was decreased and that the area of intercellular adhesion was thinner in the ESS‐treated group compared with the control (Figure 2E). Moreover, we found that ESS significantly decreased cell viability but increased ROS formation compared with the control group (both P <.01; Figure 2F‐H). These results showed that ESS exposure could cause HBE cell injury and decrease the transcription of IP3R.

FIGURE 2

Verification of differences in IP3R expression and the effects of ESS on HBE cells. A, The analysis of IP3R mRNA expression data from smoking COPD patient and non‐smoker control samples in a GEO data set (GSE3365); B, the difference in IP3R gene expression in the peripheral blood of patients with COPD (who had smoked for more than 15 y) and volunteer non‐smokers (n = 6); C and D, the mRNA and protein expression of IP3R after treatment with ESS in HBE cells; E, morphological changes after treatment with ESS in HBE cells (200×); F, changes in HBE cell viability after exposure to ESS; G, ROS formation in HEB cells after exposure to ESS; H, immunofluorescence data of IP3R expression in HEB cells after exposure to ESS (400×). All experiments were performed at least three times, and similar results were obtained; the data are shown as the mean ± SD. *P < .05, **P <.01 compared with the control group

Silencing of IP3R enhanced oxidative stress and inflammation damage in ESS‐treated HBE cells

To assess the effect of IP3R in ESS‐treated HBE cells, the IP3R was silenced by lentiviral transfection. As shown in Figure 3A‐C, both the mRNA expression and protein expression of IP3R were significantly decreased after ESS treatment and si‐IP3R transfection compared with the control group (P < .05 or P < .01). Moreover, we detected that ESS exposure significantly elevated MDA but decreased levels of CAT, SOD and GSH‐Px (P < .05 or P < .01), while si‐IP3R further enhanced these functions. However, there were no significant differences in SOD and GSH‐Px (Figure 3D‐G). Furthermore, as shown in Figure 3H‐K, the relative mRNA expression levels of TNF‐α, IL‐1β, IL‐4 and IL‐6 were all significantly increased by silencing of IP3R under ESS exposure compared with the control group (P < .05 or P < .01). These findings confirmed that silencing of IP3R could enhance oxidative stress and inflammation damage in ESS‐treated HBE cells.

FIGURE 3

Oxidative stress level and the inflammatory response in si‐IP3R HBE cells pre‐treated with ESS. A and B, The mRNA and protein expression of IP3R in transfected HBE cells. CM The variation in ROS formation in si‐IP3R HBE cells after ESS treatment. D‐G, The level of MDA and the activities of CAT, SOD and GSH‐Px after exposure to ESS. H‐K, The results of RT‐qPCR analysis of TNF‐α, IL‐1β, IL‐4 and IL‐6 gene expression in si‐IP3R HBE cells pre‐treated with ESS. One single experimental band was selected to represent the results. Experiments were performed at least three times, and data are shown as the mean ± SD. *P < .05, **P < .01 compared with the control group; #P < .05, ##P < .01 compared with the single ESS‐treated HBE cells

Overexpression of IP3R decreased oxidative stress and inflammatory damage in ESS‐treated HBE cells

Next, we overexpressed the IP3R gene by lentiviral transfection in si‐IP3R HBE cells. The mRNA and protein expression levels of IP3R are shown in Figure 4A,B, respectively. The results demonstrated that both the mRNA and protein levels of IP3R were statistically increased in the overexpression (IP3R+) group compared with the control group (P < .01). The fluorescence intensity was significantly lower in the IP3R+ group than that in the control group after treatment with ESS, which indicated that ROS formation was decreased with the overexpression of IP3R (Figure 4C). As shown in Figure 4D, the MDA level was obviously decreased in the IP3R+ group compared with the control group (P < .01). Furthermore, the CAT, SOD and GSH‐Px activities were specifically increased in the IP3R+ group compared with the control group (P < .05 or P < .01; Figure 4E‐G). Additionally, the relative gene expression levels of TNF‐α, IL‐1β, IL‐4 and IL‐6 were all obviously decreased in the IP3R+ group compared with the control group (P < .05 or P < .01; Figure 4H‐K). These data implied that overexpression of IP3R could decrease oxidative stress and inflammatory damage in ESS‐treated HBE cells.

FIGURE 4

Oxidative stress level and inflammatory response in overexpressing IP3R HBE cells pre‐treated with ESS. A and B, The mRNA and protein expression of IP3R in overexpressing IP3R HBE cells after ESS treatment. C, The variation in ROS formation in IP3R+ HBE cells after ESS treatment. D‐G, The level of MDA and the activities of CAT, SOD and GSH‐Px in IP3R+ HBE cells after exposure to ESS. H‐K, The results of RT‐qPCR analysis of TNF‐α, IL‐1β, IL‐4 and IL‐6 gene expression in IP3R+ HBE cells pre‐treated with ESS. One single experimental band was selected to represent the results. Experiments were performed at least three times, and the data are shown as the mean ± SD. *P < .05, **P < .01 compared with the control group; #P < .05, ##P < .01 compared with the single ESS‐treated HBE cells

Aberrant expression of IP3R changed autophagy level in ESS‐treated HBE cells

To further explore the role of IP3R in autophagy, we analysed the protein expression of Atg5, LC3, P62 and Beclin1 in ESS‐treated HBE cells. Relative expression of the Atg5 and LC3 was markedly decreased in the si‐IP3R group but increased in the IP3R+ group compared with the control group (P < .05 or P < .01; Figure 5A,B). In contrast, the relative protein expression of p62 was significantly increased in the si‐IP3R group and decreased in the IP3R+ group compared with the control group (P < .05 or P < .01; Figure 5C). Moreover, we found that the protein expression of Beclin1 was decreased in the si‐IP3R group and increased in the IP3R+ group; however, no significant differences were detected (Figure 5D).

FIGURE 5

Expression of autophagy‐related proteins in ESS‐pre‐treated HBE cells. A‐D, The expression levels of the Atg5, LC3, P62 and Beclin1 proteins after overexpression or silencing of IP3R in ESS‐pre‐treated HBE cells. All experiments were performed at least three times, and similar results were obtained; the data are shown as the mean ± SD. *P < .05, **P < .01 compared with the control group

Protective effects of IP3R on ESS‐treated HBE cells were through regulation of autophagy

To further determine whether IP3R had protective effects in ESS‐treated HBE cells by regulating autophagy, the cells were pre‐treated with the autophagy inhibitor 3‐MA. As shown in the figures, administration of 3‐MA significantly increased the ROS (Figure 6A), lowered cell viability (Figure 6B) and elevated the expression of inflammatory cytokines (Figure 6C‐F) under the condition of IP3R overexpression and ESS exposure (P < .05 or P < .01). These data suggested that the protective effects of IP3R on ESS‐treated HBE cells might be through regulation of autophagy. A schematic diagram of the study is demonstrated in Figure 7.

FIGURE 6

Variation in the oxidative stress level and inflammatory response in ESS‐pre‐treated IP3R+ HBE cells after the addition of 3‐MA. A, The variation of ROS formation in IP3R+ HBE cells after ESS treatment. B, The variation of cell viability after exposure to ESS. C‐F, The results of RT‐qPCR analysis of TNF‐α, IL‐1β, IL‐4 and IL‐6 gene expression in IP3R+ HBE cells pre‐treated with ESS. Experiments were performed at least three times, and the data are shown as the mean ± SD. *P < .05, **P < .01 compared with the no‐ESS–treated group. #P < .05, ##P < .01 compared with the no‐3‐MA–treated group

FIGURE 7

A schematic diagram of the study

DISCUSSION

Numerous studies have reported the possible related mechanisms of COPD. The pathogenic mechanisms of tobacco smoke were previously reported to be closely associated with the inflammatory response, oxidative damage and autophagy in the airway. , , However, the most critical gene and protein in tobacco smoke–induced COPD remain poorly understood. Therefore, this study aimed to investigate variations in this hub gene and protein in tobacco smoke–induced COPD and further explore the role of this key target.

In recent years, bioinformatics analysis has been widely used to determine increasingly reliable and crucial biomarkers of diseases. , , We analysed gene expression data from GEO data sets, to identify the DEGs common to tobacco smoke‐induced COPD patient and normal smoker samples, both compared with that in volunteer non‐smoker controls. The use of GEO2R and Venn software revealed 30 DEGs common to tobacco smoke–induced COPD patient and normal smoker samples. Fundamentally, the samples from the normal smoker volunteers still belonged to the normal group, and it is conceivable that there are not a large number of DEGs common to the two groups. In patients with COPD, these common DEGs were considered smoke‐sensitive genes. Subsequently, we further enriched gene‐ and pathway‐related information via GO and KEGG analyses. Based on the results of BP, MF, and CC and KEGG pathway analyses, we screened several crucial genes (IP, PLA2G2A and NOTCH3). Previous studies have proposed that the autophagy acts as a novel strategy to control COPD‐emphysema pathogenesis. , , In addition, oxidative stress and inflammation, which are closely related to the progresses of COPD, have been demonstrated their roles in promoting autophagy‐flux impairment. These three factors could work together to further disequilibrate the clearance of incorrectly folded proteins and bacterial/viral pathogens, leading to cell death. Furthermore, it has been revealed that IP3R is associated with autophagy. Inhibition of IP3R with a particular antagonist, such as xestospongin B, or knockdown of IP3R with small interfering RNAs (siRNAs) is a strong stimulus of autophagy. In consideration of the association between autophagy and oxidative stress and inflammation and association between autophagy and IP3R, we speculated that IP3R‐mediated autophagy might be participated in the occurrence and development of COPD by regulation of oxidative stress and inflammation. To confirm our speculation, we firstly determined the expression of IP3R in patients with COPD by analysing IP gene expression in the GES103174 data set and found that the mRNA expression level of IP was decreased in tobacco smoke–induced COPD patients compared with the non‐smoker controls. Similar results were observed when peripheral blood samples from COPD patients (who had smoked for more than 15 years) and volunteer non‐smokers were compared. In addition, we also observed that both the mRNA expression and protein expression of IP3R were markedly decreased in ESS‐treated HBE cells compared with control cells.

To further explore the functional role of IP3R in COPD, the effects of IP3R silencing and overexpressing on oxidative stress and the inflammatory response were observed in ESS‐treated HBE cells. As demonstrated in our data, silencing of IP3R could significantly elevate the ROS formation and activities of MDA and CAT, but decrease the activities of SOD and GSH‐Px, whereas exposure to ESS could further promote the silencing of IP3R‐induced oxidative stress injury. However, we found that overexpressing of IP3R reversed the above effects. This information indicated that IP3R had a protective function in ESS‐induced HBE cell injury through decreasing oxidative stress. These results might be associated with variation in Ca2+ release induced by IP3R, which can influence the generation of mitochondrial ROS. Our results were similar with previous studies, in which the authors have confirmed that the redox sensitivity of IP3R augments oxidative injury by producing positive feedback to accelerate the generation of mitochondrial ROS and to escalate ER Ca2+ release and mitochondrial Ca2+ loading. , However, the exact underlying mechanism(s) of the functions of IP3R on oxidative stress need to be further explored. Furthermore, in this study, we showed that the loss of IP3R increased the level of inflammation‐related cytokines, including TNF‐α, IL‐1β, IL‐4 and IL‐6, compared with those in the control group. Nevertheless, this consequence was reversed in ESS‐treated IP3R+ HBE cells. These results indicated that IP3R might negatively regulate the ESS‐induced inflammatory response in HBE cells. Interestingly, the effects of IP3R on inflammatory response were in line with a previous study demonstrating IP3R inhibitor 2APB attenuated inflammation in CD36‐overexpressing preadipocytes.

Autophagy is a widespread activity in the inflammatory response of COPD, , and autophagy was shown to be induced by the relatively excessive accumulation of ROS. In the present study, we observed variations in a series of autophagy‐related proteins in ESS‐treated si‐IP3R HBE cells and IP3R+ HBE cells compared with control cells. However, the regulatory mechanism of IP3R on autophagy is still not obvious. A previous research has confirmed that IP3 binds to IP3R on the ER and induces the release of intracellular Ca2+ stores. The high level of intracellular Ca2+ activates calmodulin, thereby blocking autophagy. In addition, it has reported that endoplasmic reticulum stress (ERS)–induced IP3R can lead to the occurrence of autophagy through different signal transduction pathways, but the mechanism of ERS that can cause autophagy through IP3R remains not obvious. Therefore, we suspected that the effects of IP3R on ESS‐treated HBE cells may be also due to the involvement of autophagy. To further verify our speculation, we investigated autophagy‐related proteins. An increase in Beclin1, LC3 and Atg5 indicates active cell autophagy. Furthermore, p62, an autophagy adaptor protein, could mediate Keap1 inactivation and induce the accumulation of Nrf2, finally causing inflammation. In the present study, as shown by variations in autophagy‐related protein expression, the autophagy level was decreased in si‐IP3R HBE cells but increased in IP3R+ HBE cells compared with control cells after treatment with ESS. To further investigate whether the protective function of IP3R in HBE cells was achieved through autophagy, autophagy was inhibited in ESS‐treated IP3R+ HBE cells with an inhibitor. As shown in our data, the protective function of IP3R was decreased in the inhibitor group. Based on these results, we suspected that IP3R might play a protective role in ESS‐induced inflammation and oxidative stress in HBE cells and that the function of IP3R might be achieved associated with the autophagy‐related proteins. However, a more detailed upstream mechanism and the molecular targets associated with the regulation of IP3R remain not obvious. In addition, functional in vivo animal model, such as knockout animal experiments, should be performed to verify the antioxidant and anti‐inflammatory properties of IP3R, as well as the regulatory mechanism of IP3R on autophagy.

In summary, the study demonstrated that IP3R had protective effects in ESS‐treated HBE cells and suggests IP3R as a potential target to prevent tobacco smoke–induced COPD.

CONFLICT OF INTEREST

The authors declare that they have no competing interests.

AUTHOR CONTRIBUTIONS

Qiang Zhang: Data curation (lead); Investigation (equal); Methodology (equal); Supervision (equal); Visualization (equal); Writing‐original draft (equal). Wei Li: Investigation (equal); Software (equal). Nahemuguli Ayidaerhan: Data curation (equal); Investigation (equal); Resources (equal); Software (equal). Wuxin Han: Formal analysis (equal); Project administration (equal); Visualization (equal). Yingying Chen: Data curation (equal); Methodology (equal); Software (equal). Wei Song: Investigation (equal); Methodology (equal); Validation (equal). Yuanyi Yue: Conceptualization (lead); Investigation (equal); Supervision (equal); Writing‐original draft (equal); Writing‐review & editing (equal).

Figure S1

Click here for additional data file.