Between March 2015 and January 2018, a total of 85 patients with COPD, who also suffered from pulmonary infections, and a total of 96 COPD patients, without pulmonary infection, were enrolled from the First Affiliated Hospital of Chongqing Medical University, and were assigned to either the pulmonary infection or control group. Baseline characteristics, such as, age, gender, forced expiratory volume in the first second (FEV1), and FEV1/forced vital capacity (FVC) ratio were collected for comparisons. All participants signed informed consent before entry into the study, and all clinical practices were consistent with our institution’s code of ethics. This study conformed with the 2013 revised Helsinki Declaration and was approved by the Medical Ethics Committee of The First Affiliated Hospital of Chongqing Medical University.

Inclusion criteria: (1) COPD: Diagnostic criteria consistent with the Global Strategy for the Diagnosis, Management, and Prevention of COPD (2016 revised edition): Patients who had dyspnea, chronic cough or sputum production, and/or a history of exposure to risk factors, such as, smoking. The diagnosis was further confirmed via post-bronchodilator spirometry (FEV1/FVC ratio < 0.7)[13]; (2) AECOPD: Patients with COPD, who experienced a sustained increase in cough, shortness of breath, sputum production or purulence of sputum, and/or dyspnea; and (3) Pulmonary infections: COPD patients who experienced fever, produced abnormal sounds like crackles or rhonchi in lungs, and exhibited pulmonary infiltrates on chest X-ray. Pathogenic bacteria were isolated from the cultures of their sputum samples.

Exclusion criteria: (1) COPD patients who received antibiotics, hormones, or immunosuppressive drugs within 1 mo prior to admission; (2) COPD patients who suffered from other infections, such as abdominal, skin, soft tissue, bone, and cartilage infections; (3) COPD patients with pulmonary infection, but pathogenic bacteria could not be isolated from sputum culture; (4) COPD patients who also suffered from other respiratory diseases like asthma, bronchiectasis, pneumothorax, hemothorax, or comorbidities; (5) COPD patients who also suffered from tumors, autoimmune diseases, cardiovascular and cerebrovascular diseases, or renal and hepatic dysfunction; (6) COPD patients with other diseases that could lead to acute exacerbations, such as, heart failure, spontaneous pneumothorax, pulmonary embolism, or pleural effusion; and (7) COPD patients who died or suffered worsening of condition due to un-related diseases during hospitalization.

The sputum samples were collected from the lower airways of patients in the pulmonary infection group and were cultivated for bacterial identification before these patients were given antibiotics. Patients rinsed their mouths with normal saline before sputum collection to avoid contamination by oral flora. In case of difficulty in sputum collection, due to coughing, samples were taken via fiberoptic bronchoscopy. Gram staining was initially performed on the sputum samples. Sputum samples containing < 10 squamous epithelial cells and > 25 Leukocytes per low-power field (squamous epithelial cells/leukocytes < 1:2.5) were considered qualified, otherwise the sputum sample was re-collected. The qualified samples were then inoculated on blood agar and MacConkey agar plates, and cultured at 37 °C for 24 h before the fully automated VITEK 2 Compact bacterial identification system (BioMérieux) was applied for the identification of pathogenic bacteria. All samples were processed according to the National Guide to Clinical Laboratory Procedures[14].

Upon hospital admission, we collected 2 mL venous blood from the median cubital vein in the antecubital fossa of all patients and treated each sample with the anticoagulant agent, ethylenediaminetetraacetic acid. Subsequently, to each 50 μL of anti-coagulated blood, 5 μL of anti-CD64-PE (Invitrogen, MA5-16436) and 5 μL of anti-CD45-PerCP (Invitrogen, MHCD4531) were added, mixed thoroughly, and the mixture was incubated at room temperature in the dark for 30 min. This was followed by red cell lysis with 500 μL of hemolytic agent (Beijing Tongsheng Shidai Biotech Co., Ltd., Z6910001S) incubated at room temperature in the dark for an additional 15min. The test samples were then centrifuged at 3000 r/min for 5 min. Subsequently, the supernatants were removed and the cell pellets were resuspended in 300 μL PBS for flow cytometry analysis using instrument from Becton-Dickinson, FACS Calibur. Monocytes, lymphocytes, and neutrophils were identified by an established gate, based on the forward and side scatters, as well as CD45-PerCP. For each test sample, the fluorescence signals of 10000 cells were collected, and the average fluorescence intensities of sub-populations were measured. The lymphocyte CD64 Levels were used as the internal negative control (< 1.0), whereas the monocyte CD64 Levels were set as the internal positive control (> 8.0). The CD64 index was calculated as follows: CD64 index = (CD64 average fluorescence intensity on the neutrophil/CD64 average fluorescence intensity on the lymphocyte)/(CD64 average fluorescence intensity on the monocyte/CD64 average fluorescence intensity on the neutrophil).

Upon hospital admission, we collected 2 mL of venous blood from all patients. Following a 20 min incubation at room temperature, the blood samples were centrifuged at 6000 r/min for 15 min. Subsequently, the supernatants were collected and analyzed for serum activin A levels using the Elisa kit (Shanghai Renjie Biological Technology Co., Ltd. RJ12742), following manufacturer’s instructions.

Upon hospital admission, we collected 2 mL of heparinized venous blood from all patients. The blood samples were then centrifuged at 2000 r/min for 10 min to separate the blood components into three layers: Blood plasma, a buffy coat containing platelet cells, and red blood cells. The blood plasma was collected in sterile centrifuge tubes containing 1 mL of 1.090 g/mL Percoll solution (Solarbio, P8370) and an additional 1mL of 1.077 g/mL Percoll solution were successively added to the tubes. The buffy coat layer of centrifuged samples was next pipetted into a Percoll density gradient solution, and was followed by another centrifugation at 2000 r/min for 15 min, which separated the components into four layers: Blood plasma, Percoll solution, neutrophils, and Percoll solution. The neutrophils were then pipetted into a new centrifuge tube, mixed with blood plasma and centrifuged again at 1000 r/min for 10 min. Following this, the cells were rinsed three times, and re-suspended in appropriate amount of plasma. After the cell count, 10⁶ cells were collected, completely lysed by adding 100 μL of lysis buffer (Beyotime Biotech, China, P0013), and centrifuged at 12000 r/min for 5 min. The supernatants were then collected for BCA protein quantification (Beyotime Biotech, China, P0012). For each test sample, 20 μg of total protein was obtained for polyacrylamide gel electrophoresis. The proteins were then transferred onto PVDF membranes, and blocked in 5% non-fat dry milk at room temperature for 2 h. After the blocking process, the membranes were incubated with primary antibodies for either anti-activator A (1:500, Abcam, ab89307), anti-Smad3 (1:500, Abcam, ab40854), anti-TLR4 (1:500, Abcam, ab13556), anti-MyD88 (1:500, Abcam, ab2064), anti-NFκB (1:1000, Abcam, ab32360), or anti-GAPDH (1:1000, Abcam, ab8245) at 4 °C overnight. The membranes were then rinsed three times and incubated with HRP-conjugated goat anti-rabbit IgG (Boster, BA1056, 1:2000) at room temperature for 1 h. The membranes were then rinsed three times, followed by incubation with the ECL substrate solution (Beyotime Biotech, China, P0018), and exposure and imaging using gel doc (Bio-Rad, GelDoc XR⁺). The protein band gray values were determined with the Image Pro Plus 6.0 software, and the intensity of the target protein band divided by the intensity of GAPDH in the control group was adjusted as 1[15].

All data were statistically processed using the SPSS 20.0 software. Data are expressed as mean ± SD. Inter-group comparisons were made with the independent-sample t-test. Enumeration data are expressed as cases/percentage (n/%). Inter-group comparisons were performed using the chi-squared test. P < 0.05 was considered statistically significant.

Baseline patient characteristics are summarized in Table 1, including gender, age, course of disease, smoking history, FEV1, and FEV1/FVC. A total of 181 cases met our inclusion criteria. Among these cases, 46 patients had pulmonary infection, and 96 patients were included in the control group. The gender, patient age, disease, and smoking history between the two groups were comparable (all P > 0.05). Patients in the pulmonary infection group presented with a lower level of FEV1 (%), compared to the control group (41.30 ± 9.91 vs 47.65 ± 10.07, P < 0.001). Similarly, the level of FEV1/FVC (%) was lower in the pulmonary infection group, compared to the control group (50.48 ± 9.13 vs 58.24 ± 8.62, P < 0.001).

The bacterial spectrum of patients in the pulmonary infection group is shown in Table 2. Among the 85 strains, 49 (57.65%) were gram-negative bacteria and 35 (41.18%) were gram-positive bacteria. The most prevalent gram-negative species were Pseudomonas aeruginosa (16, 18.82%), followed by Klebsiella pneumoniae (14, 16.47%), Haemophilus influenzae (7, 8.24%), and Haemophilus parainfluenzae (7, 8.24%). Among the gram-positive bacteria, 24 (28.24%) were streptococcus pneumoniae, and 11 (12.94%) were staphylococcus epidermidis. Apart from these, fungi were identified in 1.17% sputum samples.

The CD64 index was 0.91 ± 0.38 in the pulmonary infection group and 0.23 ± 0.14 in the control group. Based on our statistical analyses, the pulmonary infection group had significantly lower CD64 index than the control group (P < 0.001) (Figure 1A).

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Figure 1 Comparison of CD64 index and serum activin A levels between chronic obstructive pulmonary disease patients with pulmonary infections and those without. A: CD64 index; B: Serum activin A. ^dP < 0.001.

Figure 1B illustrates the activin A levels in the pulmonary infection and control groups. The activin A levels were 43.50 ± 5.22 ng/mL in the pulmonary infection group, and 34.82 ± 4.16 ng/mL in the control group (P < 0.001). Hence, the pulmonary infection group had significantly higher activin A levels than the control group (P < 0.001).

The activin A and neutrophil Smad3 protein expressions and their corresponding statistical analyses are shown in Figure 2. The activin A expression was 3.22 ± 0.67 in the pulmonary infection group, and 1.00 ± 0.28 in the control group. The expressions of neutrophil Smad3 in the pulmonary infection group was 2.35 ± 0.49, and in the control group was 1.00 ± 0.19. Hence, patients in the pulmonary infection group presented with higher levels of activin A and neutrophil Smad3, compared to the control group (P < 0.001).

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Figure 2 Pulmonary infections promote activation of the neutrophil activin A/Smad3 signaling pathway. A: Western blot analysis of activin A and Smad3 levels in neutrophils; B: Comparison of the relative gray values of activin A between the chronic obstructive pulmonary disease (COPD) patients with pulmonary infections and those without; C: Comparison of the relative gray values of Smad3 between COPD patients with pulmonary infections and those without. ^dP < 0.001.

The relative expressions of neutrophil TLR4, MyD88, and NFκB were confirmed, using western blot, and are presented in Figure 3. The TLR4 expression was 2.96 ± 0.55 in the pulmonary infection group, and 1.00 ± 0.28 in the control group. The MyD88 expression was 1.98 ± 0.37 in the pulmonary infection group, and 1.00 ± 0.21 in the control group. Lastly, the NFκB expression was 2.02 ± 0.37 in the pulmonary infection group, and 1.00 ± 0.25 in the control group. Based on our statistical analyses, the levels of neutrophil TLR4, MyD88, and NFκB were significantly higher in the pulmonary infection group than the control group (all P < 0.001).

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Figure 3 Comparisons of expressions of TLR4, MyD88 and NFκB in neutrophils between two groups. A: Western blot bands of TLR4, MyD88 and NFκB in neutrophils; B: Comparison of relative gray values of TLR4 between two groups; C: Comparison of relative gray values of MyD88 between two groups; D: Comparison of relative gray values of NFκB between two groups. ^dP < 0.001.

A sharp exacerbation of chronic obstructive pulmonary disease (COPD) is often triggered by a lung infection and often has a poor prognosis.

Since COPD induces complex inflammatory events, Activin A and CD64 may collectively contribute to the development and progression of this disorder.

To analyze the bacterial profile of COPD patients with pulmonary infections and to assess activin A levels, CD64 index, and the underlying mechanisms involved in disease development.

The whole data set consisted of 85 COPD patients with pulmonary infection and 96 COPD patients without pulmonary infection. Sputum samples were obtained from patients with pulmonary infections for further bacterial culture. The levels of CD64 index, activin A, Smad3, TLR4, MyD88, and NFκB proteins were assessed and compared between 85 COPD patients with pulmonary infections and 96 COPD patients without pulmonary infections.

In the pulmonary infection group sputum samples, the Gram-negative bacteria, Gram-positive bacteria, and Fungi were 57.65%, 41.18%, and 1.17%, respectively. In addition, the relative CD64 index, and levels of activin A, Smad3, TLR4, MyD88, and NFκB proteins were all significantly higher in the pulmonary infection group, compared to the control group (all P < 0.001).

Pulmonary infections in COPD patients may be caused by a variety of pathogens. In COPD patients, the CD64 index and serum activin A levels were significantly increased in patients with lung infection, compared to those without. This may have a positive regulatory effect on the downstream activin A/Smad3 and TLR4/MyD88/NFκB signaling pathways.

Together, our findings provide a novel mechanism underlying pulmonary infection in COPD patients, and offer a potential therapeutic target for an enhanced and effective therapy against COPD.