Chronic obstructive pulmonary disease (COPD) is the main cause of human morbidity and mortality worldwide, resulting in a huge economic and social burden to the healthcare system. COPD is characterized by a sustained decrease in airflow and poorly reversible airflow limitation, which are usually associated with a progressive inflammatory response in the airways caused by tobacco exposure. Airway remodeling is well known as one of the chief pathologies in COPD patients. Nonetheless, the mechanisms underlying this process are complicated and remain unelucidated. Airway remodeling reportedly involves significant pathological variations, such as abnormalities of the endothelial cells, fibrosis of the airway wall, and hypertrophy and hyperplasia of the smooth muscle. In particular, peribronchial and sub-epithelial fibrosis are considered as the key changes in airflow limitation. Therefore, preventing airway fibrosis may be beneficial in treating COPD.[5,6]
Clusterin (CLU), also known as apolipoprotein J, is an extremely conserved and multifunctional glycoprotein, which is ubiquitously expressed in many tissues. The CLU gene has two alternatively spliced isoforms that encode for either a nuclear (50 kDa) or a secreted protein (75–80 kDa). The nuclear form plays a significant role in regulating deoxyribonucleic acid (DNA) repair, whereas the secreted form has various biological functions, including the modulation of protein homeostasis/proteostasis and the pro-survival signalling and cell death pathways. Furthermore, CLU has been confirmed to contribute in the development of many diseases, such as Alzheimer's disease, renal fibrosis, diabetes, and cancer. Recently, emerging evidence suggests that CLU is also responsible for several types of lung injury and disease. For example, secretory CLU is reportedly upregulated in a rat model of pulmonary hypertension and subsequently stimulated vascular remodeling. In addition, CLU was found to be associated with oxidative stress in asthma[16,17] and idiopathic pulmonary fibrosis. Furthermore, the increased serum concentration of CLU has been suggested to be a potential biomarker of cognitive dysfunction in COPD patients, whereas CLU has been demonstrated to protect airway fibroblasts from cigarette smoke extract (CSE)-induced oxidative stress injury in vitro. However, the specific role of CLU in COPD is not well understood.
In the present study, we performed isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics and bioinformatics analyses to examine the differentially expressed proteins (DEPs) between patients with tobacco-induced COPD and normal smokers. We detected the serum concentration and expression levels of the identified biomarker candidate, CLU, in COPD patients at different stages. We further investigated the effects of CSE treatment and CLU silencing on lung fibroblasts to determine the function of CLU in COPD progression. Therefore, our findings may provide novel therapeutic targets for the treatment of COPD.
Our study was approved by the Ethics Committee of Shengjing Hospital of China Medical University (No. 2020PS025K) and performed in accordance with the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from each participant.
We enrolled two subject groups from the Shengjing Hospital of China Medical University, namely, the discovery group with six male subjects (n = 3, smokers without COPD; and n= 3, smokers with COPD) and the verification group with 68 male subjects (n = 26, smokers without COPD; n= 8 Global Initiative for Obstructive Lung Disease [GOLD] I; n= 12 GOLD II; n= 14 GOLD III; and n= 8 GOLD IV, smokers with COPD). The first group was recruited from April to July 2019 for the detection of potential serum biomarker candidates via iTRAQ-based proteomics, whereas the second group was recruited from February to August 2020 for the validation of serum biomarker candidates by enzyme-linked immu-nosorbent assay (ELISA).
The inclusion criteria for COPD patients consisted of the following conditions: (1) male patients aged 50 to 70 years; (2) patients were diagnosed according to the GOLD guidelines and must be in a stable stage; and (3) a smoking history of ten or more pack-years and a smoking cessation duration of five or more years. The exclusion criteria comprised the following: (1) patients presenting with unstable cardiovascular and cerebrovascular diseases, significant impairment of liver and kidney function, and mental diseases; (2) patients diagnosed with other lung diseases within 2 months, such as asthma, pulmonary tuberculosis, and pneumonia; and (3) patients taking prescribed immuno-suppressive medications, including hormonal drugs (eg, methylprednisolone), cytotoxic drugs (eg, azathioprine), calmodulin inhibitors (eg, cyclosporine), biologics (eg, antilymphocyte globulin), and new generation monoclonal antibodies. In addition, male smokers without COPD aged 50 to 70 years were enrolled as a control group. Basic information of the discovery and verification groups is presented in Tables 1 and 2, respectively.
Table 1 -
Baseline data of the enrolled subjects in the discovery group (n
||Control (n = 3)
||COPD (n = 3)
||63 ± 4
||62 ± 5
||25 ± 3
||22 ± 3
|Smoking history (pack-years)
||35 ± 4
||36 ± 5
|Smoking cessation duration (years)
||7 ± 2
||9 ± 1
||83 ± 15
||25 ± 3
||65 ± 12
||272 ± 49
||28 ± 7
||66 ± 13
||83 ± 6
||57 ± 6
|CT emphysema (%)
||1 ± 1
||12 ± 8
Data are presented as means ± SDs. BMI: Body mass index; COPD: Chronic obstructive pulmonary disease; CT: Computerized tomography; FEV1: Forced expiratory volume in 1 s; FVC: Forced vital capacity; %pred: Percent predicted; RV: Residual volume; SD: Standard deviation; TLC: Total lung capacity.
Table 2 -
Baseline data of the enrolled subjects in the verification group (n
||Control (n = 26)
||GOLD I (n = 8)
||GOLD II (n = 12)
||GOLD III (n = 14)
||GOLD IV (n = 8)
||62 ± 3
||64 ± 2
||65 ± 4
||64 ± 3
||64 ± 3
||24 ± 4
||25 ± 2
||23 ± 5
||25 ± 3
||23 ± 4
|Smoking history (pack-years)
||33 ± 5
||37 ± 7
||36 ± 8
||40 ± 7
||39 ± 8
|Smoking cessation duration (years)
||8 ± 3
||9 ± 3
||10 ± 4
||11 ± 5
||11 ± 4
||85 ± 13
||83 ± 3
||70 ± 2∗
||41 ± 4∗
||23 ± 5∗
||66 ± 11
||67 ± 38
||116 ± 36∗
||168 ± 26∗
||266 ± 42∗
||26 ± 8
||32 ± 12
||43 ± 11∗
||54 ± 8∗
||74 ± 5∗
||82 ± 7
||63 ± 6
||58 ± 5∗
||52 ± 4∗
||46 ± 7∗
|CT emphysema (%)
||1 ± 1
||4 ± 2
||5 ± 4
||13 ± 4∗
||30 ± 10∗
Data are presented as means ± SDs. ∗P < 0.0001 vs. control. BMI: Body mass index; CT: Computerized tomography; FEV1: Forced expiratory volume in 1 s; FVC: Forced vital capacity; GOLD: Global Initiative for Obstructive Lung Disease; %pred: Percent predicted; RV: Residual volume; SD: Standard deviation; TLC: Total lung capacity.
Plasma samples were collected from each subject after overnight fasting. The samples were centrifuged for 30 minutes at room temperature and temporarily stored at −80°C until use.
iTRAQ-based proteomic analysis
Equal amounts of plasma were mixed into two samples per group. The highly abundant proteins were removed using ProteoMiner kits (Bio-Rad Laboratories, Hercules, CA, USA), following the manufacturer's protocol. After quantification via the Bradford method, the protein (100 μg) was centrifuged at 14,000×g for 40 minutes at 4°C, added with 200 μL 50% triethyl ammonium bicarbonate (TEAB; Sigma-Aldrich, St. Louis, MO, USA), and then centrifuged again. The proteins were digested with 1 μg/μL trypsin (Promega Corp., Madison, WI, USA) at 37°C for 24 hours. The peptides were processed using iTRAQ® Reagent-8Plex Multiplex Kit (AB Sciex, Foster City, CA, USA) and subsequently labeled with different iTRAQ tags, incubated at room temperature for 1 hour, added with 8 μ L5% hydroxylamine, and incubated again at room temperature for 15 minutes. Next, the labeled samples were pooled and dried by vacuum centrifugation. The peptides were separated via strong cation exchange chromatography (SCX) using Luna SCX column (250 mm × 4.60 mm, 100 Å; Phenomenex, Torrance, CA, USA) and ion-pair reversed-phase high-performance liquid chromatography, followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) using Q-Exactive (Thermo Fisher Scientific, Waltham, MA, USA). Finally, the obtained proteins were searched against the Homo sapiens database using Proteome Discoverer 1.4 (Thermo Fisher Scientific) and Mascot version 2.3 (Matrix Science, London, UK). All proteomics and bioinformatics analyses were conducted by Beijing Protein Innovation (Beijing, China).
Functional characterization of the identified DEPs was performed by Gene Ontology (GO) annotation[21,22] using UniProt-GO database (http://www.ebi.ac.uk/GOA/). The DEPs were assigned to biological pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG)[23,24] database (http://www.genome.jp/kegg/). Based on the enriched GO terms, the DEPs were classified into three functional categories, namely, biological process (BP), cellular component (CC), and molecular function (MF).
Human bronchial epithelial (HBE) cells, obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA), were maintained in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco, Thermo Fisher Scientific) with 10% fetal bovine serum (FBS; Gibco) at 37°C with 5% CO2. Normal human lung fibroblasts (NHLFs) were purchased from Cambrex Bio Science Walkersville (Walkersville, MD, USA) and cultured in Dulbecco's Modified Eagle Medium (Gibco) containing 10% FBS, 100 U/mL streptomycin, and 100 μg/mL penicillin (Gibco).
CSE preparation and cell treatment
The CSE was prepared as previously described. In brief, the cigarettes were burned, and the mainstream of the cigarette smoke was continuously aspirated through a suction filter at a constant flow rate of 1.050 L/min to remove the tar and nicotine. The remaining cigarette smoke gas phase was soaked into 15 mL phosphate buffered saline. The filter was air-dried for 12 hours at room temperature, and the amount of tar was calculated as the increase in the dry weight of the filter. The 100% CSE solution, which was considered as the smoke from one cigarette, was slowly bubbled through 10 mL RPMI 1640 medium and then sterilized, aliquoted, and stored at −80°C until use. The HBE cells were incubated with 12% CSE at different durations (0 h, 12 h, 24 h, 36 h, and 48 h), whereas the NHLFs were exposed to 8% CSE at indicated time points (0 h, 2 h, 4 h, 6 h, and 8 h) in the absence of serum.
The cells were transfected using Lipofectamine® 3000 reagent (Invitrogen, Carlsbad, CA, USA), following the manufacturer's protocol. Briefly, the NHLFs (1 × 104 cells/ well) were plated in 96-well plates and allowed to reach 70% to 80% confluency. The cells were subsequently transfected with 50 nmol/L small interfering (si)-CLU (5′-GCAGCAGAGUCUUCAUCAU-3′) or negative controls (si-NCs) (GenePharma, Shanghai, China).
Cell proliferation assay
After the CSE treatment or cell transfection, the NHLFs were seeded into 96-well plates (3 × 103 cells/well) and allowed to grow for 24 hours before treatment with Hyclone RPMI 1640 medium (Invitrogen) supplemented with 10% FBS. The proliferation ability of NHLFs was detected using Cell Counting Kit-8 (CCK-8; Dojindo Molecular Technologies, Kumamoto, Japan), according to the manufacturer's instructions.
Real-time quantitative reverse-transcription PCR (qRT-PCR)
Total RNA was extracted from HBE cells or NHLFs using TRIzol® reagent (Invitrogen), following the manufacturer's protocol, and reverse transcribed into complementary DNA (cDNA) using Omniscript Reverse Transcription Kit (Qiagen, Hilden, Germany). The expression level of CLU mRNA was measured using Takara TP960 PCR Thermal Cycler Dice Real Time System (Takara Bio Inc., Otsu, Japan), with glyceraldehyde-3-phosphate dehydro-genase (GAPDH) as an internal control. The primer sequences of CLU and GAPDH are listed in Table 3. The thermal cycling conditions were as follows: an initial denaturation at 94°C for 30 s, 40 cycles at 95°C for 15 s, 60°C for 30 s, and 68°C for 20 s, and a final extension at 72°C for 90 s. The relative gene expression levels were quantified using the comparative cycle threshold (Ct) (2−ΔΔCt) method.
Table 3 -
Information of the primers used for qRT-PCR.
||Forward (5′→ 3′)
CLU: Clusterin; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase; qRT-PCR: Quantitative reverse-transcription PCR.
Western blot analysis
Total protein was extracted from HBE cells or NHLFs using Radio-Immunoprecipitation Assay lysis buffer (Beyotime, Shanghai, China) supplemented with protease inhibitors (Beyotime), following the manufacturer's manual. The protein concentrations were measured using Bicinchoninic Acid Assay protein assay kit (Beyotime). The proteins were separated using 10% to 15% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, transferred to nitrocellulose filter membranes, and blocked with 5% nonfat milk in Tris-buffered saline–Tween 20. Then, the membranes were incubated overnight at 4°C with primary antibodies against CLU (ab185957; Abcam, Cambridge, MA, USA); α-smooth muscle actin (α-SMA, ab5694; Abcam); fibronectin (FN, 15613-1-AP; Proteintech, Chicago, IL, USA); collagen I (ab34710; Abcam); collagen III (ab184993; Abcam); matrix metalloproteinase 2 (MMP-2, SAB4501891; Sigma-Aldrich); MMP-9 (AV33090; Sigma-Aldrich); B-cell lymphoma-2 (Bcl-2, PRS3335; Sigma-Aldrich); Bcl-2-associated X (Bax, sc7480; Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA); pro-caspase-3 (ab32499; Abcam); cleaved-caspase-3 (ab2302; Abcam); p53 (#2527; Cell Signaling Technology [CST], Inc., Danvers, MA, USA); p21 (#2947; CST); and GAPDH (ab8227; Abcam). The blots were subsequently incubated with secondary antibodies for 1 hour at room temperature and visualized using electrogenerate chemiluminescence Western blotting KIT (Solarbio, Beijing, China). Protein quantification was performed using Image-Pro Plus 6.0 Software (Media Cybernetics, Silver Spring, MD, USA).
Enzyme-linked immunosorbent assay
The serum CLU levels were evaluated using ELISA kits (ab174447; Abcam), following the manufacturer's instructions. The levels of interleukin (IL)-6 (ab178013; Abcam), IL-8 (ab214030; Abcam), and tumor necrosis factor-α (TNF-α, ab181421; Abcam) in the NHLFs were also determined using commercial ELISA kits.
The continuous data with normal distribution are presented as means ± standard deviations. Normal distribution was confirmed by Shapiro-Wilk test. Student's t-test and one-way analysis of variance with a Tukey's post hoc test were performed to analyze the differences between two groups and among three or more groups using GraphPad Prism 8.0 statistical software (GraphPad, San Diego, CA, USA). Pearson correlation analysis was used to assess the clinical data relative to the CLU serum levels. Data with P values < 0.05 were considered statistically significant.
CLU is identified as a serum biomarker candidate for COPD
To identify the potential biomarker candidates for COPD, we analyzed the discovery group (n = 3 for male smokers with or without COPD, respectively) and discovered no significant differences in the ages (t = 0.2705, P= 0.8002), body mass index (BMI, t= 1.2247, P= 0.2879), smoking histories (t = −0.2705, P= 0.8002), and smoking cessation durations (t = −1.5492, P= 0.1963) between smokers with and without COPD [Table 1]. In contrast, significant differences were observed in the forced expiratory volume in 1 second (FEV1, t = 6.5672, P = 0.0028), residual volume (RV, t= −7.1070, P= 0.0021), RV/total lung capacity (TLC, t= −4.4578, P= 0.0112), FEV1/forced vital capacity (FVC, t = 5.3072, P = 0.0061), and CT emphysema (t = 2.1060, P = 0.0029) values between the two subgroups.
The results of the iTRAQ-based proteomic analysis are presented in Figure 1. We successfully identified 144 DEPs, including 80 upregulated and 64 downregulated proteins, between the COPD patients and controls [Figure 2A]. Functional characterization of the identified DEPs via bioinformatics analysis revealed their corresponding GO annotations and KEGG pathway associations [Figure 2B–D]. The GO annotation showed that the most highly enriched GO terms were extracellular region, “immune response,” and “molecular function regulator” under the CC, BP, and MF categories, respectively. For the KEGG pathway analysis, the identified DEPs were found to be closely associated with “complement and coagulation cascades” [Supplementary Figure 1, https://links.lww.com/CM9/A983]. In addition, a total of 15 proteins, including prothrombin, fibrinogen α chain, coagulation factor XIII A chain, α-1-antitrypsin, complement component C6, complement component C8 γ chain, CLU, cDNA FLJ60818 (highly similar to complement C3), α-2-macroglobulin, complement C5, heparin cofactor 2, C4b-binding protein β chain, fibrinogen β chain, complement C4A, and complement factor H, were highly enriched in this specific pathway. Notably, the CLU protein was observed to be highly enriched under the CC and BP categories and was closely related to the identified KEGG pathway; hence, CLU was selected for further analysis.
CLU is significantly upregulated in COPD patients and correlated with clinical variables
To detect the expression profile of CLU in COPD patients, we analyzed the verification group (n = 68 smokers with and without COPD) and found that RV and RV/TLC values were significantly higher and FEV1 and FEV1/FVC were significantly lower in the GOLD II, III, and IV COPD patients than in the controls (P < 0.0001), whereas CT emphysema was significantly higher in the GOLD III and IV patients than in the controls (P < 0.0001) [Table 2]. The ELISA results confirmed that the levels of CLU in the blood sera were significantly increased in the GOLD II, III, and IV COPD patients compared to the controls (P < 0.0001) [Figure 3A]. Furthermore, the serum levels of CLU were significantly higher in GOLD III (P = 0.0216) and IV (P = 0.0127) COPD patients than in GOLD I COPD patients. To determine the correlations between CLU levels and selected clinical variables, we performed Pearson correlation analysis and discovered that the CLU serum concentration was negatively correlated with FEV1 (percent predicted, %pred) (R = −0.7705, P< 0.0001) and positively correlated with RV (%pred) (R = 0.6281, P< 0.0001), RV/TLC (%) (R = 0.5454, P< 0.0001), and CT emphysema (%) (R = 0.7878, P< 0.0001) [Figure 3B–E]. These findings imply that the elevated levels of CLU in the blood sera are significantly associated with disease severity in COPD patients.
CLU is upregulated in CSE-treated HBE cells
In addition, the mRNA and protein levels of CLU in CSE-treated HBE cells at different time points were analyzed. The qRT-PCR results demonstrated that the relative expression levels of CLU mRNA were significantly upregulated in CSE-treated HBE cells at 24 hours (P = 0.005), 36 hours (P < 0.0001), and 48 hours (P < 0.0001) [Figure 4A]. Similarly, the expression levels of CLU protein in CSE-treated HBE cells at 12 hours (P = 0.002), 24 hours (P < 0.0001), 36 hours (P < 0.0001), and 48 hours (P < 0.0001) were significantly increased [Figure 4B]. These results indicate that CLU may be involved in the development of COPD.
CSE treatment significantly promotes human lung fibroblast activation
We established an in vitro COPD model via administration of CSE to NHLFs at different time points and investigated the effects of CSE treatment on cell proliferation, inflammatory response, apoptosis, differentiation, and collagen deposition of NHLFs. The results showed that the cell viability of NHLFs was significantly decreased after CSE treatment at 4 hours (P = 0.0061), 6 hours (P = 0.0017), and 8 hours (P < 0.0001) [Figure 5A]. We also observed that the expression of proapoptotic proteins, including Bax (P = 0.0143 at 2 h, P= 0.0112 at 4 h, P= 0.0007 at 6 h, and P< 0.0001 at 8 h), cleaved/pro-caspase 3 (P = 0.0020 at 2 h, P= 0.0015 at 4 h, P= 0.0004 at 6 h, and P= 0.0001 at 8 h), p53 (P = 0.0098 at 2 h, P= 0.0093 at 4 h, P= 0.0087 at 6 h, and P= 0. 0081 at 8 h), and p21 (P = 0.0392 at 2 h, P= 0.0325 at 4 h, P= 0.0029 at 6 h, and P= 0.0011 at 8 h), was significantly upregulated in a time-dependent manner, whereas the expression of anti-apoptotic protein Bcl-2 (P = 0.0054 at 6 h, and P= 0.0037 at 8 h) was markedly downregulated after CSE administration [Figure 5B]. Similarly, the CSE treatment significantly increased the expression of inflammatory factors IL-6 (P = 0.0292 at 4 h, P= 0.0168 at 6 h, and P= 0.0075 at 8 h), IL-8 (P = 0.0128 at 2 h, P= 0.0092 at 4 h, P= 0.0079 at 6 h, and P= 0.0008 at 8 h), and TNF-α (P = 0.0469 at 2 h, P= 0.0311 at 4 h, P= 0.0224 at 6 h, and P< 0.0001 at 8 h) in a time-dependent manner [Figure 5C]. Furthermore, the expression of α-SMA (P = 0.0387 at 4 h, P= 0.0334 at 6 h, and P= 0.0243 at 8 h) and FN (P = 0.0027 at 6 h, and P= 0.0022 at 8 h), which are characteristic markers during the differentiation of lung fibroblasts into myofibroblasts, was notably enhanced after CSE treatment [Figure 5D]. We also observed that the expression levels of collagen I (P= 0.0001 at 2 h, P< 0.0001 at 4 h, 6 h, and 8 h), collagen III (P = 0.0060 at 2 h, P< 0.0001 at 4 h, 6 h, and 8 h), MMP-2 (P < 0.0001 at 4 h, 6 h, and 8 h), and MMP-9 (P = 0.0063 at 2 h, P< 0.0001 at 4 h, 6 h, and 8 h) were greatly elevated after CSE treatment [Figure 5E and 5F]. From these data, we confirmed that the maximum effect was achieved at 8 hours; therefore, we selected 8 hours as the duration of CSE treatment for subsequent analyses. Collectively, these findings suggest that CSE treatment simultaneously inhibited the proliferation, promoted the inflammatory response, differentiation, and collagen deposition, and induced the apoptosis of NHLFs.
si-CLU is successfully transfected into NHLFs
To further explore the function of CLU in COPD, the CLU gene was silenced, and the si-CLU was transfected into NHLFs. The transfection efficiency of CLU silencing was detected by qRT-PCR and Western blot experiments. Our results exhibited that both the mRNA and protein levels of CLU were significantly reduced in CSE-treated NHLFs (P < 0.0001) [Figure 6A and 6B], indicating that si-CLU was successfully transfected into the NHLFs.
CLU silencing significantly inhibits human lung fibroblast activation
Subsequently, we analyzed the effects of CLU silencing on the proliferation, inflammatory response, apoptosis, differentiation, and collagen deposition of NHLFs. The data showed that the combination of CSE treatment and CLU silencing (CSE + si-CLU) significantly increased the cell proliferation ability of NHLFs (P = 0.0001) [Figure 7A] and reduced the expression of proapoptotic proteins (Bax, P= 0.0004; c/p-caspase3, P= 0.0005; p21, P= 0.0023) [Figure 7B], proinflammatory factors (IL-6, P = 0.0004; IL-8, P = 0.0042; TNF-α, P = 0.0023) [Figure 7C], markers of differentiation (α-SMA, P < 0.0001; FN, P= 0.0062) [Figure 7D], markers of collagen deposition (collagen I, P< 0.0001; collagen III, P< 0.0001) [Figure 7E], and marker of matrix deposition (MMP-9, P= 0.0114) [Figure 7F], but significantly upregulated the expression of antiapoptotic protein Bcl-2 (P < 0.0001). However, no significant difference was observed in the expression of p53 and MMP-2. These data indicate that CLU silencing promoted the proliferation and inhibited the inflammatory response, differentiation, collagen matrix deposition, and apoptosis of CSE-treated lung fibroblasts.
In the present study, we aimed to identify the key proteins involved in the development of tobacco smoke-induced COPD and to investigate the function of a selected biomarker candidate. We discovered that the serum concentration of CLU was significantly elevated in smoker patients with COPD and correlated with key clinical variables, including FEV1, RV, RV/TLC, and CT emphysema. In addition, our results demonstrated that CLU expression was significantly upregulated in CSE-treated HBE cells, whereas CLU silencing inhibited lung fibroblast activation, specifically by enhancing the proliferation ability and inhibiting the inflammatory response, differentiation, collagen matrix deposition, and apoptosis of NHLFs. Taken together, our results suggest that CLU is potentially involved in the development of airway fibrosis in COPD patients.
Biomarkers are critical for the diagnosis and treatment of complex diseases. For example, fetuin-B is a potential plasma biomarker associated with the severity of lung function abnormalities in COPD. In addition, plasma fibrinogen is reportedly a relatively useful and prognostic marker for classifying the risks of future exacerbations and for identifying COPD patients with high mortality. Furthermore, the club cell protein-16 can demonstrate the severity of COPD in former smokers. Fibrinogen, C reactive protein, and white cell count were also associated with a higher risk of mortality in COPD patients. Despite these reports, it is still difficult to ascertain COPD progression using available biomarkers. Hence, there is an urgent need to discover new biomarkers for the pulmonary function deterioration in COPD. Unbiased and high-throughput techniques have been well acknowledged and well utilized for identifying biomarkers. Among these, proteomics is a powerful and promising tool for detecting novel prognostic proteins that has been extensively used for disease biomarker discovery. Recently, an increasing number of studies have utilized proteomics for COPD biomarker discovery.[36–39] However, to our knowledge, this technique has not been applied to examine the serum biomarkers in smokers with and without COPD. Thus, we performed an iTRAQ-based proteomic analysis to identify the serum biomarkers in smoker patients with COPD and normal smokers. We discovered a total of 144 DEPs, including 80 upregulated and 64 downregulated proteins, between tobacco-induced COPD patients and normal smokers. Fundamentally, these common DEPs were considered as smoke-sensitive proteins in COPD patients.
We identified CLU as a biomarker candidate through functional characterization of the identified DEPs via GO annotation and KEGG pathway analysis and validated its serum concentration using the blood plasma from the verification group by ELISA. The results were consistent with the proteomic analysis data, which demonstrated the highly accurate quantification of the proteomics technique used. Furthermore, we confirmed that the serum levels of CLU were negatively correlated with FEV1 and positively correlated with RV, RV/TLC, and CT emphysema. Therefore, CLU may be used as an auxiliary biomarker in the diagnosis and treatment of COPD. Our data also revealed that the CLU levels in the GOLD II, III, and IV COPD patients were significantly higher than in the controls and GOLD I COPD patients, suggesting that CLU may be useful in detecting the more severe grades of COPD and in screening the COPD progression in GOLD I patients. Our in vitro experiments confirmed that the mRNA and protein levels of CLU were significantly elevated in CSE-treated HBE cells in a time-dependent manner, which were in line with the serum concentrations of CLU in COPD patients. However, the exact role of CLU in COPD was not clearly understood. Therefore, we evaluated the effects of CLU silencing on lung fibroblast activation.
Lung fibroblasts, which exist within the pulmonary airway and parenchyma, are involved in the development of airway fibrosis and abnormal deposition of extracellular matrix in COPD. Hence, the activation of lung fibroblasts is a key process during airway fibrosis in COPD. It can occur under diverse pathological circumstances, such as during collagen matrix deposition and during differentiation of fibroblasts to myofibroblasts. Therefore, we used CSE-treated NHLFs to mimic the COPD fibrosis model and investigated the effects of CSE treatment on the proliferation, inflammatory response, apoptosis, differentiation, and collagen matrix deposition of NHLFs. Similar to the results of previous studies, we confirmed that CSE treatment markedly inhibited the proliferation and induced the apoptosis,[41,42] differentiation, inflammatory response, and collagen matrix deposition of lung fibroblasts.[45,46] These results indicated that CSE successfully induced the activation of lung fibroblasts. Notably, CLU silencing partly reversed the observed effects of CSE treatment.
The reported biological functions of CLU are still unclear. For example, CLU can reportedly improve the proliferation of primary astrocytes, whereas lentivirus-mediated short hairpin (sh)-CLU can inhibit the proliferation of ovarian cancer cells. Additionally, CLU can suppress apoptosis in cardiomyocytes and human retinal pigment epithelial cells. By contrast, overexpression of CLU can reduce the cell proliferation ability of simian virus (SV40)-immortalized human prostate epithelial cells. In the present study, we discovered that CLU silencing significantly improved the proliferation but suppressed the apoptosis of lung fibroblasts. The differences in cell/tissue types or sources may account for the observed discrepancies in the data. Interestingly, CLU has been confirmed as a multi-faceted protein that functions in inflammation and autoimmunity. For example, plasma CLU level is a potential biomarker of the inflammatory process in Alzheimer's disease and a surrogate marker of obesity-associated systemic inflammation.  In addition, CLU can regulate allergic airway inflammation by suppressing the recruitment of C-C motif chemokine ligand 20-mediated dendritic cells. However, we found that CLU silencing suppressed the inflammatory response by reducing the expression of inflammatory factors IL-6, IL-8, and TNF-α in lung fibroblasts. Furthermore, CLU silencing inhibited differentiation by decreasing the expression of α-SMA and FN and hindered collagen matrix deposition by down-regulating the expression of collagens I and III and MMP-9 in lung fibroblasts. However, these results contradict those of a previous study reporting that CLU knockdown enhanced renal inflammation and aggravated tissue fibrosis after ischemia-reperfusion injury in the kidney. Hence, these discrepancies should be explored and further research must be performed to fully elucidate the role of CLU during the development of airway fibrosis in COPD.
In conclusion, our findings suggest that elevated serum CLU levels are associated with the disease severity in COPD patients. Furthermore, CLU silencing inhibited lung fibroblast activation, demonstrating that CLU may be responsible for the development of airway fibrosis in COPD.
This study was supported by the grants from Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Ministry of Education (No. 17-134-8-00), Department of Science and Technology of Liaoning Province (No. 2018225006), Shenyang Science and Technology Plan Project (No. 21-173-9-43) and 345 Talent Project of Shengjing Hospital.
Conflicts of interest
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