16S rRNA sequencing-based analysis of sputum microbiome in patients with acute exacerbations of chronic obstructive pulmonary disease: retrospective cohort study

Introduction

Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by persistent airflow limitation, primarily caused by abnormalities in the airways or alveoli (1,2). The global prevalence of COPD in 2019 was 10.3%, with higher rates in low- and middle-income countries (3). In China, the incidence of COPD is relatively high in Sichuan, Gansu, and Shaanxi regions (4). Acute exacerbation of COPD (AECOPD) is characterised by worsening cough, shortness of breath or wheezing, and an increase in the amount of purulent sputum, which may be accompanied by fever and other significant symptomatic exacerbations. Most COPD hospitalisations are due to acute exacerbations, which can lead to deterioration of lung function, decline in quality of life, and increase in mortality (5). The high prevalence and mortality rate of COPD pose a significant challenge to the health-care systems.

Respiratory pathogenic bacterial infections are the main cause of COPD disease progression and lung function decline (6). Increased bacterial load and species changes in the airways are associated with exacerbation of airway inflammation and decreased lung function (7). Studies have shown that Pseudomonas aeruginosa, Klebsiella pneumoniae, and Haemophilus influenzae provide strong evidence that respiratory pathogens are involved in the progression of COPD (8,9). However, conventional bacterial cultures can only identify known pathogens and are prone to false-negative results. Therefore, it is not possible to explain the dynamics and potential role of the respiratory microbial community during acute exacerbation and stabilization of COPD. Currently, 16S ribosomal RNA (rRNA) sequencing can be used to identify species or groups of closely related species, allowing a more comprehensive assessment of the different microbial communities present in the lungs of patients with COPD, as well as differences in the microbes in the respiratory tracts of patients with varying disease severity (10).

Therefore, in this study, we analysed respiratory pathogens in AECOPD patients based on 16S rRNA sequencing to further explore the knowledge of acute exacerbation risk and the differences in respiratory pathogens between high- and low-risk patients. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-995/rc).

Methods

Subject enrolment

Sputum samples were collected from 48 patients with AECOPD who were hospitalized in the Respiratory Department of the Xijing 986 Hospital. Twenty healthy volunteers who underwent physical examinations at the same time were randomly selected as the control group.

Inclusion criteria for the AECOPD group: (I) diagnosed as AECOPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) standards (11); (II) age greater than 40 years; (III) no use of antibiotics within 1 month prior to enrolment; (IV) all patients are in stable condition, without any signs of unconsciousness, intensive care, or tracheal intubation; and (V) patients in the recovery group were defined as those who met the discharge criteria 2 weeks after treatment and had stopped using antibiotics.

Exclusion criteria are as follows: (I) patients with a history of bronchial asthma, allergic rhinitis, and hereditary allergy; (II) pulmonary fibrosis, atelectasis, pulmonary embolism, and other cases; (III) serious cardiovascular and cerebrovascular diseases or kidney failure; and (IV) use of supplementary oxygen.

The inclusion criteria for the healthy group are as follows: no chronic respiratory diseases (such as COPD, bronchiectasis, lung tumours, bronchial asthma, etc.); no autoimmune diseases. The exclusion criteria for the healthy group are as follows: (I) those with other oral diseases or lung diseases; (II) those with severe dysfunction of other organs and other malignant tumours in other parts; and (III) those who have used antibiotics, immunosuppressants, etc., within 4 weeks.

All patients with AECOPD received antibiotic and inhaled corticosteroid treatment in accordance with the guidelines (12).

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Ethics Committee of Xijing 986 Hospital (No. KYLL2024-986-113), and informed consent was obtained from all participating patients.

Samples collection and DNA extraction

Before collecting the sputum samples, all subjects were required to rinse their mouths with normal saline. During the sample collection process, strict operational standards were followed to minimize contamination. Sputum samples were obtained from COPD patients by natural expectoration into a sterile container. Healthy controls underwent induced sputum collection according to the protocol of Pin et al. (13), which utilized a 3–5% gradient hypertonic saline mist for 10–15 minutes. All sputum samples were examined under a microscope for sputum smear tests. In the low-power field, sputum samples with fewer than 10 squamous cells and more than 25 white blood cells (WBCs) were considered qualified sputum samples. At least 2 mL of qualified sputum samples were collected. A total of 68 samples were collected and divided into three groups: AECOPD group (n=35); recovery group (n=13); Healthy control group (n=20). All sputum samples were immediately stored at −80 ℃ for DNA extraction. Total microbiome DNA was extracted from sputum samples using the cetyltrimethylammonium bromide (CTAB) method; its quality was assessed by agarose gel electrophoresis and quantity was determined by ultraviolet (UV) spectrophotometry.

PCR amplification and sequencing

The gene fragments were amplified using primers specific to the V3–V4 region (341F 5'-CCTACGGGNGGCWGCAG-3' and 805R 5'-GACTACHVGGGTATCTAATCC-3'). polymerase chain reaction (PCR) amplification of the prokaryotic 16S rRNA gene fragments was carried out in a 25 µL reaction system containing 50 ng of template DNA, 12.5 µL of PCR premix, and 2.5 µL of each primer, under the following conditions: initial denaturation at 98 ℃ for 30 s; 35 cycles of denaturation at 98 ℃ for 10 s, annealing at 54 ℃ for 30 s, and extension at 72 ℃ for 45 s; followed by a final extension at 72 ℃ for 10 min. The PCR products were purified by AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified using Qubit (Invitrogen, Waltham, MA, USA). The size and quantity of PCR products were evaluated using the Agilent 2100 bioanalyzer (Agilent, Santa Clara, CA, USA) and Illumina’s library quantification kits (KapaBiosystems, Pleasanton, CA, USA). The library was sequenced on the NovaSeq PE250 platform (Illumina, San Diego, CA, USA).

Bioinformatics analysis

Raw reads were subjected to quality control using fqtrim to generate high-quality clean tags. Subsequent bioinformatic processing included chimera removal with Vsearch (v2.3.4) and dereplication using DADA2, yielding an amplicon sequence variant (ASV) feature table and feature sequences. Both alpha and beta diversity metrics were calculated from this final ASV table.

Statistical analyses

Measurements were expressed as mean ± standard deviation (SD), and counts were expressed as rate (%) using the χ² test. SPSS 20.0 software was used to analyse the data. Alpha and beta diversity were calculated from the final ASV table and sequences. In the alpha diversity analysis, the Wilcox rank-sum test was used to calculate indices such as Chao1, Simpson, and Shannon for the analysis of differences between groups. The beta diversity analysis was conducted using the Bray-Curtis’s distance matrix. Intergroup differential species analysis was conducted using linear discriminant analysis (LDA) effect size (LEfSe). The threshold for LEfSe analysis in this project was set at LDA value >4. Spearman’s correlation method was used to calculate the correlation between sputum flora and clinical indicators.

Results

Basic information

A total of 48 sputum samples from COPD patients and 20 samples from healthy controls were collected. The clinical characteristics of the patients, including age, gender, body mass index (BMI), smoking status, comorbidities, medications, and inflammatory markers, were summarized and are presented in Table 1. No statistically significant differences were observed among the groups in terms of age, gender, BMI, comorbidities, and medications (P>0.05). The smoking index was lower in the healthy population. Compared with the healthy controls, the lung function [forced expiratory volume in 1 s (FEV₁)% pred and FEV₁/forced vital capacity (FVC)%] of COPD patients was significantly reduced (P<0.001). There were statistically significant differences in both routine blood parameters and inflammatory markers between the groups. Specifically, the WBC count (P=0.002) and neutrophil (NEUT) count (P<0.001) showed significant variations. Regarding inflammatory markers, significant differences were observed in procalcitonin (PCT) (P=0.03), C-reactive protein (CRP) (P<0.001), and interleukin-6 (IL-6) (P=0.03).

Sequencing result

In this study, the sputum microbiome was evaluated using 16S sequencing. A total of 3,902,959 high-quality sequences were identified, with a median of 63,152 (ranging from 40,304 to 77,151). The Venn diagram distinguishes three groups of common and endemic microbial sequences. Among the AECOPD group, the recovery group, and the healthy control group, there were a total of 6,225 features. AECOPD and the healthy control group shared 5,004 features, of which 879 (17.57%) were common to both groups; AECOPD and the recovery group shared 5,133 features, of which 1,337 (26.04%) were common; the recovery group and the healthy control group shared 4,090 features, of which 777 (18.99%) were common to both groups (Figure S1).

Analysis of microbial diversity in sputum

The alpha diversity and beta diversity of the microbiota in sputum samples from the AECOPD group, the recovery group, and the control group were compared to assess the characteristics of the sputum microbiota. Chao1, Simpson, and Shannon indices estimated the species abundance and species diversity, respectively. Based on the Chao1 index, microbial abundance was significantly lower in the AECOPD group than in the control group (P=0.01) (Figure 1A). The Shannon index revealed significantly lower species diversity in the AECOPD group compared to both the control (P<0.001) and recovery (P=0.03) groups. The recovery group also showed significantly reduced diversity relative to the control group (P=0.048) (Figure 1B). According to the Simpson index, species diversity was significantly lower in the AECOPD group than in both the control (P<0.001) and recovery groups (P=0.03) (Figure 1C).

Figure 1 Bacterial community structure in healthy controls and COPD patients (AECOPD and recovery). The Chao1 index (A) reflects microbial richness, the Shannon index (B) and Simpson index (C) indices represent microbial diversity. (D) The PCoA analysis based on Bray-Curtis’s distance between the groups at the species level. PCoA showed that the two dimensions, PCoA1 (13.49%) and PCoA2 (10.45%), together represented 23.94% of the total variance in sputum microbial β-diversity among samples. *, P<0.05; **, P<0.01; ****, P<0.001. AECOPD, acute exacerbation of COPD; COPD, chronic obstructive pulmonary disease; PCoA, principal coordinates analysis.

Beta diversity assesses the magnitude of similarity in flora composition between samples. Adonis analysis was used to test whether between-group differences were significantly greater than within-group differences. Principal coordinates analysis (PCoA) analysis based on Bray-Curtis’s distance showed that the separation of microbial communities in these three groups was significant (P=0.001). Significant differences in microbial community structure were observed between the AECOPD and healthy control groups, as well as between the healthy control and recovery groups (P=0.001) (Figure 1D).

Species analysis of the microbial community in sputum

Figure 2 shows the structure of the respiratory flora between the different groups. At the phylum level, the dominant phylum in the AECOPD group was Firmicutes (47.54%), followed by Proteobacteria (15.88%) and Actinobacteriota (15.74%). In the recovery group, Firmicutes (48.56%), Proteobacteria (17.67%), and Actinobacteriota (16.18%) were dominant. In the control group, Firmicutes (44.23%), Actinobacteriota (22.76%), and Proteobacteria (13.57%) dominated (Figure 2A). At the genus level, the dominant genera in the AECOPD group were in the order Streptococcus (30.23%), Neisseria (7.62%), Rothia (7.06%), Prevotella_7 (6.20%), and Veillonella (5.69%). In the recovery group, Streptococcus (23.09%), Neisseria (8.82%), Rothia (7.77%), Actinomyces (6.11%), and Veillonella (5.99%) were dominant. Among the healthy controls, the most representative genus was Streptococcus (23.30%) followed by Actinomyces (11.51%), Rothia (8.44%), Neisseria (6.58%), and Veillonella (5.00%) (Figure 2B).

Figure 2 Relative abundance of the most prevalent bacterial phylum (A) and genus (B) in healthy controls and COPD patients (AECOPD and recovery). AECOPD, acute exacerbation of COPD; COPD, chronic obstructive pulmonary disease.

Changes in sputum microbiome at the phylum and genus level in COPD patients

At the phylum level, compared with the recovery group and the healthy control group, the relative abundance of Patescibacteria in AECOPD patients was significantly lower. Compared with the healthy control group, the Actinobacteria, Fusobacteriota, Spirochaetota, and Campylobacterota were decreased in the AECOPD group (Figure 3A). At the genus level, compared with the control group, the proportions of Neisseria, in AECOPD patients were significantly increased, while the proportions of Actinomyces and Porphyromonas decreased. Compared with the AECOPD group, the Actinomyces in the recovery group was also increased. Compared with the healthy control group, the Gemella in the recovery group was increased (Figure 3B).

Figure 3 Boxplot showing the statistic differences of relative abundance of taxa among healthy controls and COPD patients (AECOPD and recovery) at phylum (A) and genus (B) levels. Y-axis displays log₁₀ transformed values of normalized relative abundance counts. *, P<0.05; ***, P=0.001; ****, P<0.001. AECOPD, acute exacerbation of COPD; COPD, chronic obstructive pulmonary disease.

Bacterial taxonomic differences among the groups

LEfSe was used to analyse species with significant differences in abundance among the groups at the genus level. We selected LDA greater than 4.0 for the distinct species to represent the bacterial groups that were statistically different between groups (Figure 4). The relative abundance of Corynebacterium and Haemophilus was elevated in the AECOPD group, whereas the control group was dominated by Actinomyces and Lautropia.

Figure 4 The LEfSe analysis revealed the microbial species that were significantly different between AECOPD, the recovery group, and the control group. (A) LEfSe. The differences in abundance between the COPD (AECOPD and recovery) and healthy controls groups. (B) Cladogram indicating the phylogenetic distribution of microbiota correlated with the COPD (AECOPD and recovery) and healthy controls groups. AECOPD, acute exacerbation of COPD; COPD, chronic obstructive pulmonary disease; LDA, linear discriminant analysis; LEfSe, LDA integrated with effect size.

Relationship between sputum microbiome and clinical indices in AECOPD

Sputum microbiome of AECOPD patients showed a strong correlation with clinical parameters by Spearman’s correlation analysis (corrected |r|>0.4, P<0.05). WBCs and NEUTs were negatively correlated with Rothia, whereas the relative abundance of Staphylococcus was significantly positively correlated with CRP (Figure 5).

Figure 5 A heatmap of Spearman correlation between major sputum microbiome and clinical indices in COPD patients. *, P<0.05; **, P<0.01. COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; FEV₁, forced expiratory volume in 1 s; FVC, forced vital capacity; IL-6, interleukin-6; BMI, body mass index; NEUT, neutrophil; PCT, procalcitonin; WBC, white blood cell.

Discussion

This study conducted a cross-sectional observation and employed the 16S rRNA sequencing method to analyse the characteristics of the microbial composition in the sputum of AECOPD patients, COPD recovery stage, and healthy controls. The results revealed a lower bacterial abundance and a significant difference in microbial composition of the lung microbiome in AECOPD patients compared with the healthy control group and the recovery group. In addition, we analysed the relationship between sputum microecological diversity and relevant clinical indicators in AECOPD patients. Overall, our findings suggest that airway microecology is disturbed in AECOPD patients and that microbial ecological dysregulation may contribute to AECOPD.

In this study, 16S rRNA sequencing was used to analyse the changes in sputum flora diversity and abundance of AECOPD patients, COPD recovery stage, and healthy controls. The results showed that sputum microbiome species abundance and diversity were lower in AECOPD patients than in the healthy controls. The study demonstrated with that the diversity of the AECOPD flora was significantly lower compared to the stable COPD group (14). Our results are consistent with them, suggesting that altered microbial diversity in the respiratory tract may be a biological indicator of AECOPD pathogenesis. As the severity of advanced COPD increases, the diversity of the microbiota decreases (15). Thus, microbial community diversity may be critical for maintaining respiratory health and plays an important role in immune regulation.

As the severity of COPD increases, the airway microbiota is continuously altered (16). In our study, Firmicutes were the dominant bacteria in the AECOPD group. The results of this study are generally consistent with those of previous studies (17,18). Studies have shown that differences in pH, oxygen saturation, and temperature in the pulmonary environment of COPD patients may be responsible for the proliferation of Firmicutes (19). In our study, members such as Streptococcus, Neisseria, and Rothia have dominated the lung microbiome in the AECOPD group at the genus level. The results of Su are generally consistent with our findings (20). In other studies, Streptococcus was the predominant genus in COPD (21-23). Streptococcus belongs to the phylum Firmicutes and is often considered part of the core pulmonary microbiome (24). Streptococcus was associated with lower FEV₁ and chronic bronchitis (25). Increased Streptococcus was associated with the severity of impaired lung function, severity of disease, and quality of life (26). Our study results indicate that the proportion of Neisseria in the sputum of patients with AECOPD has significantly increased. Increased abundance of Neisseria may induce inflammatory responses in patients with severe COPD (27). Neisseria shows a tendency to be relevant to bronchiectasis in COPD and may be an early sign of airway dilatation in COPD patients (28). LEfSe was employed to identify differentially abundant taxa in the airway microbiota between healthy individuals and AECOPD patients. The level of Haemophilus and Corynebacterium in the AECOPD group was significantly higher than that in the healthy control group. Studies have shown that Corynebacterium was the most abundant taxon in COPD (29), and is associated with cases of disease progression (27). Previous study has shown that the worsening of COPD is associated with an increase in Haemophilus influenzae in the airways (30). Haemophilus were associated with increased levels of neutrophilic inflammation and elevated levels of interleukin-1β (IL-1β) and tumour necrosis factor-α (TNF-α) in the sputum of patients with COPD (14). These results may indicate that changes in the distribution of the flora may lead to increased risk of inflammation enhancement and worsening.

Our study revealed a significant correlation of the sputum microbiota with clinical indicators. It was found that WBC and NEUT were negatively correlated with Rothia, and CRP was positively correlated with Staphylococcus. Rothia is more abundant in healthy populations (31). Rothia in the respiratory tract of COPD patients was negatively correlated with pro-inflammatory factors (32). Staphylococcus aureus can cause lung infections that predispose to increased drug resistance, increased frequency of hospitalisation, and increased mortality (8,33). Airway Staphylococcus aureus promotes decreased lung function via homocysteine and causes NEUT extracellular trap formation, which may affect inflammation in COPD (34). In summary, Rothia and Staphylococcus may affect lung function through local inflammatory response and participate in the pathogenesis of COPD. Patients with AECOPD develop pulmonary microecological imbalances that increase inflammatory responses and airflow restriction, ultimately increasing the risk of disease progression.

Our study has several limitations. Firstly, some of the samples were from cross-sectional studies, collected from different patients. Many factors of the patients (such as gender, age, smoking history, disease severity, and antibiotic use) interfered with the research on the respiratory microbiome of COPD. Therefore, longitudinal studies can eliminate individual variations, reduce the interference of confounding factors, and more accurately analyse the microbial markers related to treatment (such as antibiotics and glucocorticoids). Secondly, the sample size of COPD patients included in this study was relatively small, especially for the recovery period samples, which might have prevented the detection of certain low-abundance bacterial communities. Future research requires a larger sample size to enhance statistical reliability. After treating patients with COPD (such as using antibiotics and bronchodilators), the symptoms of coughing and expectoration have improved, but the natural expulsion of sputum has become more difficult. Some patients failed to provide samples due to poor tolerance or low compliance. Due to the limited sample size, future studies need to validate the discovered microbiome characteristics in larger cohorts to confirm their association with the pathogenesis or prognosis of COPD. Finally, in this study, induced sputum from healthy controls was compared with spontaneous sputum from COPD patients. Although standardized processing was applied, the differences in sampling methods might still introduce potential biases. Future studies will adopt a unified sampling method to further verify this finding.

Conclusions

In conclusion, this study indicated that as COPD progresses, the diversity and composition of the microbial community in patients’ sputum change. Moreover, the microbial community in sputum is correlated with common clinical indicators. These findings suggest that the dysregulation of the microbial community may promote the development of the disease, providing microbial biomarkers for the diagnosis of AECOPD.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-995/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-995/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-995/prf

Funding: This work was supported by the Key Research and Development Program of Shaanxi (No. 2022SF-378).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-995/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Ethics Committee of Xijing 986 Hospital (No. KYLL2024-986-113), and informed consent was obtained from all participating patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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