# Association between exacerbation history and airway bacterial community assessed by extended bacterial culture and sequencing approaches in stable COPD

Quentin Lecomte-Thenot, Jeanne-Marie Perotin, Geneviève Héry-Arnaud, Lourdes Vélo-Suarez, Audrey Brisebarre, Alice Clarenne, Stéphanie Gouriou, Sandra Dury, Gaëtan Deslée, Anaëlle Muggeo, et al.

► To cite this version:

Quentin Lecomte-Thenot, Jeanne-Marie Perotin, Geneviève Héry-Arnaud, Lourdes Vélo-Suarez, Audrey Brisebarre, et al. Association between exacerbation history and airway bacterial community assessed by extended bacterial culture and sequencing approaches in stable COPD. Scientific Reports, 2025, 15 (1), pp.32767. 10.1038/s41598-025-14994-x. hal-05291309

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OPEN

# Association between exacerbation history and airway bacterial community assessed by extended bacterial culture and sequencing approaches in stable COPD

Quentin Lecomte-Thenot¹, Jeanne-Marie Perotin², Geneviève Héry-Arnaud³,⁴,
Lourdes Vélo-Suarez⁵,⁶, Audrey Brisebarre⁷, Alice Clarenne¹, Stéphanie Gouriou⁴,
Sandra Dury⁸, Gaëtan Deslée², Anaëlle Muggeo¹, Thomas Guillard¹& The RINNOPARI
Study Group includes

Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition characterized by chronic respiratory symptoms due to abnormalities of the airways and/or alveoli that cause airflow obstruction. It is a leading cause of death worldwide. While alterations in airway bacterial community have been linked to exacerbation frequency, the underlying mechanisms remain unclear. We aimed to characterize associations between airway bacterial community structure and exacerbation history in stable COPD patients and to identify candidate microbial markers that could assist in risk assessment for the clinical management of COPD patients. Sixty-two stable COPD patients were enrolled and categorized into two groups based on their exacerbation history: low risk (LR) and high risk (HR) of exacerbation. Sputum samples were collected and analyzed using both bacterial extended culture and 16S rRNA gene sequencing. The combination of these approaches provided complementary insights, enabling a more comprehensive characterization of the bacterial community. To our knowledge, this is the first study to combine these two approaches in this context and to evaluate their relative performance in detecting microbiological markers associated with exacerbation risk. Microbial composition analysis revealed a loss of α-diversity in HR patients based on extended culture data, a finding not corroborated by sequencing. This discrepancy suggests that the observed impoverishment of diversity may primarily affect the viable fraction of the airway microbial community. The HR group also exhibited increased relative abundances of *Pseudomonadota* and *Bacteroidota*, alongside a marked decrease in relative abundances of *Lactobacillus* and *Streptococcus*. Notably, significant reductions in the proportion of positive samples were observed at the species level for *Streptococcus salivarius* and *Streptococcus mutans*. A comparison of the two methods underlined that 16S rRNA gene sequencing identified five additional phyla and 84 genera not detected by culture, notably strict anaerobes. However, extended culture demonstrated robust sensitivity in detecting Enterobacterales and the pathogenic *Moraxella* and *Pseudomonas*. This study revealed microbiological features linked to exacerbation history in stable COPD patients, highlighting the need for future functional and longitudinal research to validate these airway bacterial community signatures and develop targeted preventive strategies.

**Keywords** COPD - chronic obstructive pulmonary disease, Exacerbation risk, Extended culture, 16S rRNA gene sequencing, Microbiota, Bacterial community, Sputum, Stable state

**Abbreviations**

AE Acute exacerbation

BMI Body Mass Index

CAT COPD assessment test

CF Cystic fibrosis

COPD Chronic obstructive pulmonary disease
<table><tr><td>CT</td><td>Computed tomography</td></tr><tr><td>CFU</td><td>Colony-forming units</td></tr><tr><td>DNA</td><td>DeoxyriboNucleic acid</td></tr><tr><td>DLCO</td><td>Diffusing capacity of the Lung for Carbon Monoxide</td></tr><tr><td>FEV<sub>1</sub></td><td>Forced Expiratory Volume in 1 second</td></tr><tr><td>FVC</td><td>Forced Vital Capacity</td></tr><tr><td>GOLD</td><td>Global Initiative for Chronic Obstructive Lung Disease</td></tr><tr><td>HACEK</td><td>Haemophilus spp., Aggregatibacter actinomycetemcomitans, Capnocytophaga spp., Cardiovacibacter hominis, Eikenella corrodens, Kingella kingae</td></tr><tr><td>HR</td><td>High risk</td></tr><tr><td>LR</td><td>Low risk</td></tr><tr><td>MALDI-TOF</td><td>Matrix-Assisted Laser Desorption/Ionization - Time-Of-Flight</td></tr><tr><td>mMRC</td><td>modified Medical Research Council</td></tr><tr><td>NVS</td><td>Nutritionally Variant Streptococci</td></tr><tr><td>OTU</td><td>Operational taxonomic unit</td></tr><tr><td>PCA</td><td>Principal component analysis</td></tr><tr><td>PCR</td><td>Polymerase Chain Reaction</td></tr><tr><td>PPM</td><td>Potentially oathogenic microorganisms</td></tr><tr><td>RNA</td><td>Ribonucleic Acid</td></tr><tr><td>rRNA</td><td>ribosomal RiboNucleic Acid</td></tr><tr><td>RV</td><td>Residual volume</td></tr><tr><td>SAT<sup>é</sup></td><td>Simultaneous alignment and tree estimation</td></tr><tr><td>SD</td><td>Standard deviation</td></tr><tr><td>SEPP</td><td>SAT<sup>é</sup>-Enabled Phylogenetic Placement</td></tr><tr><td>TLC</td><td>Total Lung Capacity</td></tr></table>

¹Université de Reims Champagne-Ardenne, INSERM, CHU de Reims, Laboratoire de Bactériologie-Virologie-Hygienne Hospitalière, P3Cell, U 1250, Reims, France. ²Université de Reims Champagne-Ardenne, INSERM, CHU de Reims, Service des Maladies Respiratoires, P3Cell, U 1250, Reims, France. ³Centre Brestois d'Analyse du Microbiote (CBAM), CHRU de Brest, Brest, France. ⁴Université de Brest, Inserm, UMR 1078, Unité de Bactériologie, CHRU de Brest, 29200 Brest, France. ⁵Univ Brest, Inserm, EFS, UMR 1078, GGB, 29200 Brest, France. ⁶CHRU Brest, 29200 Brest, France. ⁷Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250, Reims, France. ⁸Service des Maladies Respiratoires, Université de Reims Champagne-Ardenne, EA7509 IRMAIC, CHU de Reims, Reims, France.

✉️email: tguillard@chu-reims.fr

Sequencing-based approaches have allowed for an exhaustive description of the airway microbiota¹⁻⁴. They have revealed a lower bacterial density compared to the gut microbiota⁵, a high α-diversity⁶, and a predominance of strict anaerobes, especially Bacteroidota, along with Bacillota, Pseudomonadota, and Actinomycetota⁷⁻⁹.

The airway microbiota has been proposed to maintain lung architecture, enhance antibacterial defenses, and modulate immune system functions⁹⁻¹². Its importance is particularly evident in chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD), characterized by lung dysbiosis with alterations in the composition and distribution of the microbiota¹³,¹⁴.

Patients with COPD may experience acute exacerbations (AE-COPD), which are critical and pejorative events in the course of the disease¹⁵,¹⁶. Understanding the mechanisms that lead to exacerbations has become a primary focus, particularly in steady-state patients, intending to improve prevention strategies, which are a critical aspect of COPD management¹⁷. AE-COPD are frequently triggered by viral and/or bacterial airway proliferation, including pathogens such as *Haemophilus influenzae*, *Streptococcus pneumoniae*, *Moraxella catarrhalis*, *Pseudomonas aeruginosa*, and *Staphylococcus aureus*. These bacterial species are commonly classified as potentially pathogenic microorganisms (PPMs) in the context of COPD¹⁸,¹⁹. AE-COPD are associated with significant compositional and functional remodeling of the airway bacterial community²⁰, notably an increase abundance of the phylum *Pseudomonadota*²¹⁻²³. Currently, the primary practical method for predicting exacerbation risk relies on the history of exacerbations in the previous year¹⁶. Nevertheless, the airway bacterial community may also offer measurable parameters, such as microbial signatures and diversity metrics which could serve as potential diagnostic, therapeutic, and prognostic biomarkers.

Since the sequencing-based approaches provide valuable insights into the entire microbial community²², conventional culture-based techniques are frequently disregarded due to their perceived limitations. However, they provide distinct advantages, as they represent standard operating procedures for sputum analysis and focus on viable and culturable microorganisms²⁴.

In this study, we employed both extended culture- and 16S rRNA gene sequencing-based methods to describe the airway bacterial community in sputum samples from stable COPD patients with low risk (LR) and high risk (HR) of exacerbation based on exacerbation history in the previous year (Fig. 1).

We aimed to investigate the associations between airway bacterial community composition in stable COPD and exacerbation risk and to identify novel microbiological markers linked to this risk. Additionally, we assessed whether 16S rRNA gene sequencing provides superior predictive value over extended culture methods in this context. A deeper understanding of the relationships between COPD exacerbations and lung bacterial community—regarded as a potentially modifiable factor—will reveal new opportunities for therapeutic strategies in COPD.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Overview of the extended bacterial culture and 16S rRNA gene sequencing protocols. COPD: Chronic Obstructive Pulmonary Disease; DNA: DesoxyriboNucleic Acid; FEV₁: Forced Expiratory Volume in 1 s; FVC: Forced Vital Capacity; MALDI-TOF: Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight; PCR: Polymerase Chain Reaction; rRNA: ribosomal RiboNucleic Acid. Created with BioRender®.</figcaption></figure>

# Methods

## Study population

Patients with COPD were prospectively included in the Recherche et INNOvation en PAthologie Respiratoire Inflammatoire (RINNOPARI) cohort (University Hospital of Reims, France; NCT02924818; registered on October 4, 2016). The study was approved by the regional ethics committee (Comité de Protection des Personnes—Dijon EST I, no. 2016-A00242-49) and all patients provided informed consent. Exclusion criteria were patients with asthma, cystic fibrosis (CF), bronchiectasis, or pulmonary fibrosis. Enrollment occurred during stable state periods, defined as at least four weeks after the last exacerbation²⁵. Baseline data collection encompassed demographic data, smoking history, treatment, respiratory symptoms [modified Medical Research Council dyspnea scale (mMRC), chronic bronchitis, COPD assessment test (CAT score) assessing the global impact of COPD on health status, exacerbation history in the previous year], arterial blood gas analysis, 6-min walking distance, and pulmonary function test results. COPD diagnosis was defined by postbronchodilator FEV₁/FVC < 70% and GOLD (Global initiative for chronic Obstructive Lung Disease) grades were defined by the severity of airflow obstruction measured by spirometry (GOLD 1: FEV₁ ≥ 80%; GOLD 2: 50% ≤ FEV₁ < 80%; GOLD 3: 30% ≤ FEV₁ < 50%; GOLD 4: FEV₁ < 30%)¹⁵. Emphysema presence and severity were assessed through computed tomography (CT) scan images by two independent investigators (SD, GD) with a final consensus interpretation²⁶,²⁷. Patients were stratified into two groups based on their exacerbation history over the preceding year: Low Risk of exacerbation (LR) characterized by ≤ 1 exacerbation with no exacerbation-related hospitalization and High Risk of exacerbation (HR), defined by ≥ 2 exacerbations or ≥ 1 exacerbation-related hospitalization(s)¹⁵.

## Extended culture of sputum samples and bacterial identification

For each patient, induced or non-induced sputum was collected at inclusion, and processed by an extended microbiological culture as previously described²⁵. Compared with conventional sputum culture used in laboratory routine, extended culture included additional media (notably selective media), multiple atmospheres (including anaerobic culture), and multiple dilutions to detect low-abundance bacteria. Serial dilutions (1/1,000, 1/10,000, and 1/100,000) of the liquefied sputum, processed with N-acetylcysteine, were cultured on Columbia blood agar, chocolate agar, Schaedler agar, and Pseudomonas-selective cetrimide agar (Thermo Fisher Scientific, USA) at 37 °C for 48 h for aerobic and 5% CO₂ cultures, and for five days for anaerobic cultures. All morphologically distinct colonies—without any predefined limit on the number analyzed—were quantified as colony-forming units (CFU) per milliliter (ranging from 10² to 10¹⁰ CFU/mL) and identified using MALDI-TOF mass spectrometry (MALDI Biotyper®, Bruker Daltonics, Bremen, Germany) (Fig. 1). The α-diversity of
the viable and culturable respiratory microbiota was assessed using the Shannon, Simpson, and Chao1 indices. α-diversity represents a measure of species diversity within a specific location and is composed of richness and evenness²¹.

## DNA extraction and 16S rRNA sequencing of sputum samples

Sputum samples were stored in cryotubes at -80 °C, for further processing using 16S rRNA sequencing (Fig. 1). For each sputum sample, 150 µL was sonicated for 5 min, and bacterial DNA was extracted using the QIAamp DNA Mini Kit (Qiagen). Environmental DNA contamination was monitored by processing a negative control for each extraction series. PCR amplification of the V3-V4 regions of the 16S rRNA bacterial gene was performed with a mix of 5 µL of extracted DNA, 25 µL of KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Cape Town, South Africa), 17 µL of water, 1.5 µL of 10 µM 341 F forward primer, and 1.5 µL of 10 µM 785R reverse primer. The PCR protocol included an initial denaturation step at 95 °C for 3 min, followed by 30 cycles of denaturation at 95 °C for 30 s, annealing at 59 °C for 30 s, and extension at 72 °C for 30 s, with a final extension at 72 °C for 5 min.

Amplicon libraries were normalized and sequenced on an Illumina MiSeq (Illumina, San Diego, California, USA), generating 300 bp paired-end reads using PE300, 600-cycle kits (Genomer platform, Roscoff, France). Extraction negative controls and a positive control of known microbial composition (ZYMO D6305, ZymoBIOMICS) were processed and sequenced in parallel with each pool of study samples.

Figure S1 provides an overview of the steps involved in data acquisition and quality control analysis. Sequence data were demultiplexed and separated into forward and reverse FASTQ files. The quality of the demultiplexed raw sequence reads was assessed using the FastQC and MultiQC tools. Primers were removed and sequence quality scores consistently higher than 20 were maintained using CutAdapt and BBDuk. DADA2 was used to infer amplicon sequence variants (ASVs) and assign taxonomy²⁸. Sequencing reads were dereplicated, pooled, and ASVs were inferred for each sample using the DADA2 sample inference algorithm and the estimated error model. Denoised sequences were generated by merging forward and reverse reads. Chimeric sequences were identified by reconstructing them from the left and right segments of more abundant sequences and then removed from the ASVs table.

ASVs were first clustered into operational taxonomic units (OTUs) using dbOTU prior to taxonomic assignment. Taxonomy was then assigned using the SILVA version 138 species classifier implementation for DADA2 (assignTaxonomy() and assignSpecies()). Following taxonomic assignment, additional clustering was performed using the TreeCluster algorithm at 98% similarity on a SATé-enabled phylogenetic placement (SEPP) tree. Non-bacterial and spurious ASVs (i.e., those with fewer than 5 reads across all sequenced biological specimens and no-template controls) were removed.

Nucleic acid extraction and sequencing efficiency were assessed by comparing the mock bacterial community extraction and sequencing controls to the manufacturer's profiles. Sequence data from biological specimens and extraction-negative controls were used to identify potential contaminants by applying the microDecon package.

Following data processing, two samples were excluded due to an insufficient number of reads (< 2000) (Fig. 1).

## Statistical analysis

Data are presented as mean values ± standard deviation, median (interquartile range), or numbers and percentages, as appropriate. Comparisons were made using Fisher's exact test for qualitative variables, and either Student's t-test or the Mann-Whitney U test for quantitative variables, as appropriate. A *p*-value (*p*) < 0.05 was considered statistically significant.

To summarize and visualize the dissimilarities in bacterial communities between groups in a low-dimensional Euclidean space, an unsupervised principal component analysis (PCA) was performed and plotted along the first two principal components which explain most of the variance.

## Results

### Patients

Sixty-two patients were enrolled in the study between November 2016 and April 2022, with 28 (45.2%) assigned to the LR group and 34 (54.8%) to the HR group. Detailed patient characteristics are provided in Table 1. They were predominantly male (58.1%) with a mean age of 61.5 ± 9.4 years. Most were former smokers (66.1%) and had cardiovascular comorbidities (59.7%). Fifty-one patients (82.2%) received inhaled treatment, including bronchodilators (61.3% long-acting beta-agonists and 17.7% long-acting muscarinic antagonists) and/or inhaled corticosteroids (33.9%). Sixteen patients (25.8%) were on triple inhaled therapy, with no significant differences between groups. Most patients experienced at least one exacerbation in the previous year (66.1%), with a mean of 2.5 exacerbations, and 56.5% had received antibiotics in the past six months. Of note, 2 patients (3.2%) received antibiotics for a reason other than a COPD exacerbation in the past month. COPD was classified as severe or very severe (GOLD 3 or 4) in 58.1% of the patients.

Compared with the LR group, the HR group had a lower proportion of males (41.2% vs. 78.6%, *p* = 0.003), a younger age (59.4 vs. 64.2, *p* = 0.02), and, as expected, more frequent symptoms of chronic bronchitis, as well as a higher CAT score. The HR group was also characterized by more impaired lung function, more severe airway obstruction, and more frequent use of antibiotics and oral corticosteroids in the last six months. Notably, no significant differences were observed between groups in terms of COPD maintenance treatment, CT emphysema severity, comorbidities, smoking history and antibiotic use in the month prior to inclusion.
<table><thead><tr><th></th><th>Total</th><th>Low risk of exacerbation (LR)</th><th>High risk of exacerbation (HR)</th><th>p-value</th></tr></thead><tbody><tr><td>Number</td><td>62</td><td>28</td><td>34</td><td></td></tr><tr><td>Age, years</td><td>61.5 ± 9.4</td><td>64.2 ± 9.2</td><td>59.4 ± 9.0</td><td><strong>0.021</strong></td></tr><tr><td>Male</td><td>36 (58.1%)</td><td>22 (78.6%)</td><td>14 (41.2%)</td><td><strong>0.003</strong></td></tr><tr><td>BMI, kg/m²</td><td>25.7 ± 5.6</td><td>26.3 ± 5.1</td><td>25.3 ± 6.0</td><td>0.235</td></tr><tr><td><em>Sputum samples</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Spontaneous</td><td>57</td><td>26 (92.9%)</td><td>31 (91.2%)</td><td>&gt; 0.999</td></tr><tr><td>Induced</td><td>5</td><td>2 (7.1%)</td><td>3 (8.8%)</td><td></td></tr><tr><td><em>Smoking history</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Current smoker</td><td>21 (33.9%)</td><td>8 (28.6%)</td><td>13 (38.2%)</td><td>0.299</td></tr><tr><td>Former smoker</td><td>41 (66.1%)</td><td>20 (71.4%)</td><td>21 (61.8%)</td><td></td></tr><tr><td>Pack-years</td><td>43.6 ± 19.2</td><td>45.5 ± 21.2</td><td>42.1 ± 17.6</td><td>0.246</td></tr><tr><td><em>Maintenance treatment</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Long-acting beta agonist</td><td>38 (61.3%)</td><td>16 (57.1)</td><td>22 (64.7%)</td><td>0.614</td></tr><tr><td>Long-acting muscarinic antagonist</td><td>11 (17.7%)</td><td>3 (10.7%)</td><td>8 (23.5%)</td><td>0.102</td></tr><tr><td>Inhaled corticosteroid</td><td>21 (33.9%)</td><td>8 (28.6%)</td><td>13 (38.2%)</td><td>0.426</td></tr><tr><td>Oral corticosteroid</td><td>2 (3.2%)</td><td>1 (3.6%)</td><td>1 (2.9%)</td><td>0.643</td></tr><tr><td>Long-term macrolides</td><td>5 (8.1%)</td><td>2 (7.1%)</td><td>3 (8.8%)</td><td>0.714</td></tr><tr><td><em>Recent antibiotic and corticosteroid use</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Antibiotics (last 6 months)</td><td>35 (56.5%)</td><td>6 (21.4%)</td><td>29 (85.3%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Antibiotics (last month)<sup>a</sup></td><td>2 (3.2%)</td><td>1 (3.6%)</td><td>1 (2.9%)</td><td>&gt; 0.999</td></tr><tr><td>Oral corticosteroids (last 6 months)</td><td>21 (33.9%)</td><td>4 (14.3%)</td><td>17 (50.0%)</td><td><strong>0.003</strong></td></tr><tr><td><em>Exacerbation history</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Exacerbation (previous year)</td><td>41 (66.1%)</td><td>7 (25.0%)</td><td>34 (100%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Nb exacerbations (previous year)</td><td>2.5 ± 1.5</td><td>1 ± 0</td><td>2.8 ± 1.5</td><td><strong>0.001</strong></td></tr><tr><td><em>Symptoms</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Dyspnea mMRC ≥ 2</td><td>47 (75.8%)</td><td>22 (78.6%)</td><td>25 (73.5%)</td><td>0.769</td></tr><tr><td>Chronic bronchitis</td><td>31 (50.0%)</td><td>10 (35.7%)</td><td>21 (61.8%)</td><td><strong>0.037</strong></td></tr><tr><td>CAT score</td><td>18.0 ± 7.4</td><td>16.2 ± 8.0</td><td>19.6 ± 6.6</td><td><strong>0.045</strong></td></tr><tr><td><em>Lung function</em></td><td></td><td></td><td></td><td></td></tr><tr><td>FEV<sub>1</sub>, % pred</td><td>46.5 ± 18.7</td><td>53.1 ± 20.1</td><td>41.1 ± 15.8</td><td><strong>0.006</strong></td></tr><tr><td>FEV<sub>1</sub>/FVC</td><td>47.0 ± 10.9</td><td>49.5 ± 10.7</td><td>44.9 ± 10.7</td><td><strong>0.048</strong></td></tr><tr><td>RV, % pred</td><td>217.4 ± 88.8</td><td>183.5 ± 73.7</td><td>244.1 ± 91.5</td><td><strong>0.004</strong></td></tr><tr><td>TLC, % pred</td><td>129.8 ± 26.4</td><td>118.5 ± 20.3</td><td>138.7 ± 27.6</td><td><strong>0.001</strong></td></tr><tr><td>DLCO, % pred</td><td>46.2 ± 23.5</td><td>45.9 ± 22.9</td><td>46.4 ± 24.3</td><td>0.471</td></tr><tr><td>GOLD 1-2</td><td>26 (41.9%)</td><td>17 (60.7%)</td><td>9 (26.5%)</td><td><strong>0.010</strong></td></tr><tr><td>GOLD 3-4</td><td>36 (58.1%)</td><td>11 (39.3%)</td><td>25 (73.5%)</td><td></td></tr><tr><td>6-minute walking test* (AA),</td><td>52</td><td>21</td><td>31</td><td></td></tr><tr><td>Desaturation, n</td><td>24 (46.2%)</td><td>9 (42.9%)</td><td>15 (48.4%)</td><td>0.351</td></tr><tr><td>Distance, meters</td><td>359 ± 117</td><td>388 ± 126</td><td>340 ± 108</td><td><strong>0.028</strong></td></tr><tr><td>CT-scan*,</td><td>58</td><td>25</td><td>33</td><td></td></tr><tr><td>Emphysema, n</td><td>52 (89.7%)</td><td>21 (75.0%)</td><td>31 (93.9%)</td><td>0.213</td></tr><tr><td>Emphysema score</td><td>9.0 ± 4.7</td><td>9.2 ± 4.6</td><td>8.9 ± 4.9</td><td>0.394</td></tr></tbody></table>

**Table 1.** Demographic and clinical characteristics of patients stratified by exacerbation risk. Unless otherwise stated (*), data are available for all patients. Characteristics that are statistically significant between LR and HR groups are indicated in bold. Values are presented as n (%), mean ± SD and median [25th-75th percentile].

ªAntibiotic treatments for other causes than acute exacerbation. AA: Ambient Air; BMI: Body Mass Index; CAT: COPD Assessment Test; CT-scan: Computed Tomography scan; DLCO: Diffusing capacity of the Lung for Carbon monoxide; FEV₁; Forced Expiratory Volume in 1 s; FVC: Forced Vital Capacity; GOLD: Global initiative for chronic Obstructive Lung Disease; mMRC: modified Medical Research Council dyspnea scale; n: number; RV: Residual Volume; TLC: Total Lung Capacity.

## Viable and culturable airway microbiota of COPD patients using an extended culture-based approach

The viable and culturable airway microbiota of the 62 sputum samples (one per patient) was analyzed. A total of 410 bacterial isolates were identified across all samples, representing 71 distinct species, distributed among 34 genera and four phyla (Table S1). The mean number of species per sample was 6.6 with no significant difference
between groups (LR group: 6.5 vs. HR group: 6.7; Fig. 2A). The total bacterial counts per sample ranged from 2.1 × 10⁴ CFU/mL to 1.82 × 10¹⁰ CFU/mL, with a median of 3.2 × 10⁷ CFU/mL. Similarly, no difference was observed in overall bacterial load between the LR and the HR groups (median of 3.5 × 10⁷ vs. 3.2 × 10⁷ CFU/mL respectively). The Shannon index was significantly lower in the HR group compared to the LR group (0.9 vs. 1.2 respectively; *p* = 0.015; Fig. 2B). No significant differences were observed between groups for the Simpson (Fig. 2C) or Chao1 indexes (Fig. 2D). In both groups, the distribution of bacterial phyla was predominantly Bacillota, followed by Actinomycetota, Pseudomonadota, and a much smaller proportion of Bacteroidota²⁹. Notably, the HR group exhibited a significantly higher relative abundance of Pseudomonadota compared to the LR group (26.9% vs. 15.3% respectively; *p* = 0.005; Fig. 2E). Within this phylum, the Gammaproteobacteria class was also present at a significantly higher relative abundance in the HR group (HR: 15.9% vs. LR: 8.7% of total bacteria; *p* = 0.036), whereas no significant differences were observed for the Alphaproteobacteria and Betaproteobacteria classes.

The distribution of bacterial genera was found to be similar between the two groups, with *Streptococcus*, *Rothia*, *Veillonella*, *Neisseria*, and *Actinomyces* as the most prevalent, collectively accounting for 72.9% of the identified bacteria (Fig. 2F). Although it was not statistically significant, a lower relative abundance of *Streptococcus* in the HR group was observed (HR: 32.2% vs. LR: 36.6%; *p* = 0.35). Strict anaerobes were identified in 74.2% of samples (46/62), accounting for 18.3% of the total isolates, with no significant difference observed between groups (LR: 19.7% vs. HR: 17.2%; Fig. 2G). Focusing on Enterobacteriales, 22.6% of samples were positive (14/62), including the genus *Citrobacter*, *Enterobacter*, *Escherichia*, *Hafnia*, *Klebsiella*, *Morganella*, *Proteus*, and *Raoultella* (Table S1). The relative abundance of Enterobacterales represented only 4.1% of the total isolates, with no statistically significant difference between the HR and LR groups (HR: 5.3% vs. LR: 2.7%; *p* = 0.22; Fig. 2G).

At the species level, the most frequently isolated bacteria were *Streptococcus oralis/mitis/pneumoniae*, followed by *Veillonella parvula/dispar/atypica* and *Streptococcus salivarius*, detected in 90.3%, 54.8%, and 48.4% of samples, respectively. Analysis of the species data revealed a significant difference between groups, with a lower prevalence of *Streptococcus mutans* in the HR sputa (HR: 0% vs. LR: 14.3% of positive samples; *p* = 0.037; Fig. 2H). Bacterial load of *S. mutans* was also significantly lower in the HR group (*p* = 0.037; Table S1). Excluding *S. pneumoniae*, which could not yet be reliably distinguished from *S. mitis* and *S. oralis* using MALDI-TOF at the start of the study³⁰, several PPMs were identified: *S. aureus* (*n* = 9 isolates; 14.5% of positive samples), *H. influenzae* (*n* = 6; 9.7%), *M. catarrhalis* (*n* = 6; 9.7%), and *P. aeruginosa* (*n* = 4; 6.5%), collectively representing only 6.1% of the total bacterial isolates (Table S1). The total prevalence of these PPMs did not significantly differ between the LR and HR groups (6.6% vs. 5.7%, respectively).

Differences in airway bacterial community composition between HR vs. LR patients, as determined by culture results, are summarized on the left side (blue) of Figs. 3 and 4.

Finally, a principal component analysis (PCA) was conducted to assess the similarities in viable and culturable airway microbiota between COPD patients. This analysis revealed no significant differences in the overall microbial composition between the two groups, and no distinct clusters or “pulmotypes” could be identified (Fig. 2I).

## Airway microbiota of COPD patients using 16S rRNA gene sequencing

Among the 62 sputum samples, four did not meet the required volume for microbiota analyses, and following quality filtering, 2 did not pass quality control. Consequently, the airway microbiota was investigated using a 16 S rRNA gene sequencing-based approach on 56 samples (LR: 27 (48.2%) vs. HR: 29 (51.8%); one sample per patient). The rarefaction curves reached a plateau, indicating that the sequencing depth was sufficient to capture most of the bacterial diversity present in the samples (Fig. S2). A total of 1,631,976 high-quality reads were retained, enabling the identification of 3,307 OTUs (364 distinct), distributed across 111 genera and 9 phyla. On average, each sample contained 59 OTUs, ranging from 14 to 119 OTUs. There was no significant difference in the average number of OTUs per sample between the LR and HR groups (mean of 56.3 vs. 61.6 OTUs, respectively; Fig. 3A).

Unlike the culture results, the 16 S rRNA gene sequencing data revealed no differences in the α-diversity between the overall microbiota of the LR and HR groups, as none of the Shannon, Chao1, and Simpson indices exhibited significant variation (Fig. 3B-D). The relative abundance of bacterial phyla was evaluated using the percentage of reads for each sample, providing a quantitative overview of phyla distribution across patients. The most abundant phyla were Bacillota, Pseudomonadota, Bacteroidota, and Actinomycetota, with global mean relative abundances of 54.2%, 16.7%, 14.2%, and 10.2%, respectively. Despite considerable inter-sample variability, this pattern was consistent in both the LR and HR groups (Fig. 3E).

Phylum distribution was further analyzed as a percentage of total OTUs (Fig. 3F), to facilitate comparison with bacterial culture data. Overall, the most abundant phyla were Bacillota (35.6%), Bacteroidota (27.8%), and Pseudomonadota (12.7%), with the same hierarchy observed in both the LR and HR groups. Interestingly, we found a significantly higher relative abundance of Bacteroidota in the HR group compared to the LR group (29.3% vs. 25.9%, respectively; *p* = 0.032) and a non-significant lower relative abundance of Bacillota in the HR group (LR: 37.1% vs. HR: 34.3%). In contrast to the results observed with the culture method, no significant difference was observed between the two groups in the relative abundance of Pseudomonadota (LR: 12.4% vs. HR: 13.0%; *p* = 0.60).

We next examined the genus-level taxonomy distribution, focusing on the percentage of OTUs (Fig. 3G). *Prevotella* (13.8%) emerged as the most dominant genus overall, followed by *Leptotrichia* (6.0%) and *Capnocytophaga* (4.5%). This ranking was maintained in the LR group; however, in the HR group, *Streptococcus* surpassed *Capnocytophaga* and was the third most abundant genus. We observed significantly lower relative abundance of *Streptococcus* (LR: 5.3% vs. HR: 3.8%; *p* = 0.042) and *Lactobacillus* (LR: 3.3% vs. HR: 1.7%; *p* = 0.005) in the HR group. Strict anaerobes, encompassing 48 distinct bacterial genera, were identified in all samples
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Extended culture-based approach results. (A) Number of species per sample. (B) Shannon Index. (C) Simpson Index. (D) Chao1 index. (E) Phyla distribution (relative abundance). (F) Genera distribution (relative abundance). (G) Strict anaerobes and Enterobacterales relative abundance. (H) Species prevalence. Species with a prevalence lower than 4% are not listed. – (I) Principal Component Analysis (PCA). The two principal components (PC1 and PC2) explaining 11% and 9% of the variance, respectively, were used for visualization. Statistical significance is indicated as follows: * for *p* < 0.05, ** for *p* < 0.01 and NS for Not Significant. Statistical analyses were performed using Student's t-test, the Mann–Whitney U test, or Fisher's exact test, as appropriate.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** 16 S rRNA gene sequencing results. (A) Number of OTUs per sample. (B) Shannon Index. (C) Simpson Index. (D) Chao1 index. (E) Phyla relative abundance per patient. (F) Phyla distribution (relative abundance). (G) Genera distribution (relative abundance). (H) Species prevalence. Only the 30 most prevalent bacterial species are represented. – (I) Principal Component Analysis (PCA). The two principal components (PC1 and PC2) explaining 59% and 15% of the variance, respectively, were used for visualization. Statistical significance is indicated as follows: * for *p* < 0.05, ** for *p* < 0.01 and NS for Not Significant. Statistical analyses were performed using Student's t-test, the Mann–Whitney U test, or Fisher's exact test, as appropriate.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Airway bacterial community differences between HR vs. LR AE-COPD patients detected by extended bacterial culture and 16 S rRNA gene sequencing.</figcaption></figure>

(56/56; Fig. 5C). They accounted for more than half of the total OTUs (55.3%), with no significant difference in relative abundance observed between the groups (LR: 54.1% vs. HR: 56.3%; data not shown). Enterobacterales were detected in only 23.2% of the samples (13/56; Fig. 5D), including the genera *Citrobacter*, *Enterobacter*, *Escherichia*, *Hafnia*, *Klebsiella*, *Morganella*, and *Proteus*. The relative abundance of Enterobacterales was notably low, representing only 0.5% of the total OTUs, with no significant difference between the groups (LR: 0.6% vs. HR: 0.3%; data not shown).

At the species level, the five groups of species with the highest prevalence of positive samples were *S. mitis/oralis/pneumoniae/parasanguinis* (96.4%), *Gemella morbillorum/parahaemo-lysans/sanguinis* (92.9%), *V. atypica/dispar/parvula/rogosae/tobetsuensis* (92.9%), *Rothia mucilaginosa* (91.1%), and *Capnocytophaga gingivalis/granulosa* (85.7%) (Fig. 3H). Interestingly, we observed significant differences in the prevalence of positive samples between HR and LR groups for eight species. Six species showed a higher prevalence of positive samples in the HR group: *Prevotella oris* (LR: 51.9% vs. HR: 79.3%; *p* = 0.048) (Fig. 3H), *Prevotella conceptionensis* (LR: 18.5% vs. HR: 44.8%; *p* = 0.047), *Alloprevotella otu7057* (LR: 25.9% vs. HR: 65.5%; *p* = 0.004), *Eikenella corrodens* (LR: 25.9% vs. HR: 62.1%; *p* = 0.008), *Selenomonas artemidis* (LR: 11.1% vs. HR: 41.4%; *p* = 0.015), and *Leptotrichia otu12783* (LR: 11.1% vs. HR: 37.9%; *p* = 0.030) (data not shown). Two *Streptococcus* species had a lower prevalence of positive samples in the HR group: *S. salivarius* (LR: 77.8% vs. HR: 48.3%; *p* = 0.029) (Fig. 3H) and, consistent with the bacterial culture results, *S. mutans* (LR: 44.4% vs. HR: 13.8%; *p* = 0.017) (data
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Venn diagrams comparing bacterial detection by culture and 16S rRNA gene sequencing. (A) Phyla detection. (B) Genera detection. The values represent the number of different phyla and genera detected from the 56 sputa analyzed using both bacterial culture and 16S rRNA gene sequencing. The area of each circle is proportional to the count of phyla/genera. (C) Strict anaerobes detection (prevalence). (D) Enterobacterales detection (prevalence). (E–H) Detection of bacterial genera associated with COPD exacerbations (prevalence). The values represent the count of positive samples from a total of 56 sputa analyzed using both bacterial culture and 16S rRNA gene sequencing. The area of each circle is proportional to the count of positive samples. Yellow circles indicate culture data, while green circles represent 16 S rRNA gene sequencing data.</figcaption></figure>
not shown). Excluding *S. pneumoniae*, which could not be distinguished from *Streptococcus mitis*, *S. oralis*, and *S. parasanguinis* in this study, and considering PPMs only at the genus level, *Staphylococcus* (no positive samples), *Haemophilus* (78.6% of positive samples), *Moraxella* (3.6% of positive samples), and *Pseudomonas* (5.4% of positive samples) collectively accounted for just 1.8% of the total OTUs. No significant difference in the relative abundance of PPMs was detected between the two groups (LR: 1.7% vs. HR: 1.9% of total OTUs) (data not shown).

Differences in airway bacterial community composition between HR vs. LR patients, as determined by 16S rRNA gene sequencing, are summarized on the right side (red) of Fig. 4.

Consistent with bacterial culture results, PCA based on 16S rRNA sequencing data revealed no significant difference in overall microbial composition between the LR and HR groups, and no distinct clusters or “pulmotypes” were identified (Fig. 3I).

To assess microbial co-occurrence patterns, we inferred an interaction network from global 16 S rRNA gene sequencing data. Eight distinct modules were identified, each comprising taxa with significant co-occurrence relationships (Fig. S3-A). These modules varied in connectivity: some, such as M1 and M5, formed densely connected networks indicative of tightly interacting microbial consortia, whereas others, notably M6 and M8, were sparsely connected, potentially reflecting specialized or peripheral taxa. This network structure provides insight into community organization and highlights potential keystone taxa involved in ecosystem-level interactions. As we observed a decreased relative abundance of the *Lactobacillus* genus and a lower prevalence of *S. mutans* in high-risk patients, it is noteworthy that *S. mutans* clustered within the same module (M2) as two *Lactobacillus* species (*Lactobacillus_otu13571* and *Lactobacillus salivarius*).

Several microbial modules showed significant associations with clinical parameters (Fig. S3-B). Module M4 was positively associated with female gender, while M6 correlated with higher epithelial cell counts in sputum. In contrast, M8 was negatively associated with both epithelial cell and leukocyte counts. Notably, no module showed a significant association with exacerbation frequency or antibiotic use in the month preceding sampling.

This figure summarizes the main differences in airway bacterial community composition between patients at high risk (HR) vs. low risk (LR) of AE-COPD. Taxa are indicated as increased (↑) or decreased (↓) in the HR group. Results from extended culture-based analysis are presented on the left (blue), and those from 16S rRNA gene sequencing on the right (red). Created with BioRender™.

## Comparison of extended bacterial culture and 16S rRNA gene sequencing for analyzing the airway COPD bacterial community

Although we characterized differences in airway bacterial community composition between HR vs. LR COPD patients (Fig. 4), we further explored the effectiveness of bacterial culture compared to 16S rRNA gene sequencing for analyzing the airway bacterial community in COPD. We conducted a comparative analysis of their efficiency in detecting various bacterial genera from 56 sputa.

As anticipated, the 16S rRNA gene sequencing-based approach detected bacteria from five additional phyla and 84 additional genera compared to extended bacterial culture (Fig. 5A and B). Surprisingly, the extended bacterial culture identified seven genera (*Staphylococcus*, *Enterococcus*, *Lactococcus*, *Paracoccus*, *Cutibacterium*, *Raoultella*, and *Rhizobium*) that were not detected by 16S rRNA gene sequencing.

Our analysis revealed significant differences in the effectiveness of the 16S rRNA gene sequencing analysis compared to the extended culture-based approach for detecting major bacterial genera in the 56 sputum samples. Of the 1,727 bacterial detections depicted in Figs. 6 and 84.4% were uniquely identified by 16S rRNA gene sequencing, 2.9% were exclusive to culture, and 12.8% were detected by both methods. It is noteworthy that only *Streptococcus* and *Prevotella* were detected in all samples, either by metagenomics alone or by both metagenomics and culture. Among the genera detected in a high proportion of samples (> 70%), *Streptococcus*, *Rothia*, *Neisseria*, and *Veillonella* achieved concordance of detection rates exceeding 50% between the two methods (100%, 71%, 65%, and 54%, respectively; Fig. 6).

Semi-quantitative comparison based on the percentage of positive samples underscored the prominent contribution of the 16 S rRNA gene sequencing-based analysis for bacterial genera identification (Fig. 7). As expected, these included genera that are not routinely culturable, such as *Treponema*, *Mycoplasma*, and *Solobacterium*, as well as those that are fastidious, such as strict anaerobes, HACEK bacteria (*Haemophilus spp.* excluding *H. influenzae* species, *Aggregatibacter actinomycetemcomitans*, *Capnocytophaga spp.*, *Cardiobacterium hominis*, *Eikenella corrodens*, *Kingella kingae*), and Nutritionally Variant Streptococci (NVS) species (*Abiotrophia spp.* and *Granulicatella spp.*). It is worth noting that 16 S rRNA gene sequencing allowed better detection of strict anaerobes. While the culture-based method identified anaerobes in 40 out of 56 samples (71.4%), the 16S rRNA gene sequencing approach detected them in all the 56 samples (Fig. 5C). Genera typically regarded as easy to cultivate, such as *Corynebacterium* and *Haemophilus*, also exhibited enhanced detection rates with 16S rRNA gene sequencing. Specifically, *Haemophilus* was detected in only 17 of 56 samples (30.4%) using culture, compared to 44 samples (78.6%) with sequencing (Fig. 5E).

For several genera the percentage of positive samples was nonetheless equivalent between the two detection approaches (Fig. 7). Excluding genera detected in less than 5% of samples by either method, we found the two detection approaches equivalent for *Streptococcus*, *Neisseria*, *Micrococcaceae*, *Pseudomonas*, *Moraxella*, *Citrobacter*, *Escherichia*, and the entire *Enterobacterales* order (Fig. 7). *Moraxella* and *Pseudomonas*, two major PPMs, were better detected —although not significant— by culture (6/56 (10.7%) and 5/56 (8.9%), respectively) compared with 16S rRNA gene sequencing (2/56 (3.6%) and 3/56 (5.4%), respectively) (Fig. 5F and H). Additionally, *Enterobacterales* were detected in 13 samples (23.2%) by both methods, with overlapping detection in 10 samples (17.9%) (Fig. 5D).

Finally, *Staphylococcus* was the only genus detected significantly more frequently by culture (Fig. 7), being found in 11 samples (19.6%), whereas it was detected in none of the samples by 16 S rRNA sequencing (Fig. 5G).
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Qualitative comparison of genus-level bacterial detection by extended culture and 16S rRNA gene sequencing.</figcaption></figure>

Heatmap displaying qualitative detection patterns (prevalence) for the 68 most frequently identified genera across bacterial culture and/or 16S rRNA gene sequencing. Blue boxes represent genera detected exclusively by 16S rRNA gene sequencing, red boxes indicate those identified only by culture, and purple boxes denote genera detected by both methods.

Scatter plot depicting semi-quantitative detection patterns (prevalence) relative to each detection method for the 68 most frequent bacterial genera identified. Each point represents a bacterial genus, clearly labeled for reference. Points near the diagonal line, within the purple area, indicate genera with similar detection rates between culture and 16S rRNA gene sequencing methods. Points in the green zone indicate genera detected in significantly more samples by 16S rRNA gene sequencing, while points in the red zone indicate genera detected in significantly more samples by extended bacterial culture. Statistical significance (Fisher's exact test) is indicated as follows: ** for $p < 0.01$, *** for $p < 0.001$, and ns for not significant.

## Discussion

Our results delineate distinct features of the airway microbiota associated with an increased risk of COPD exacerbation. HR patients exhibited reduced viable bacterial α-diversity and a marked enrichment of Pseudomonadota in culture-based analyses. Sequencing further revealed an increased relative abundance of Bacteroidota, coupled with a depletion of the *Streptococcus* and *Lactobacillus* genera, and a lower prevalence
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Semi-quantitative comparison of genus-level bacterial detection by extended culture and 16S rRNA gene sequencing.</figcaption></figure>

of *S. salivarius*. Notably, *S. mutans* was the only microbial marker to show a consistent pattern across both methodologies, with a reduced prevalence in HR patients. These results are summarized in Fig. 4.

In this study, we presented a comprehensive characterization of the airway bacterial community in stable COPD patients by simultaneously integrating results from extended culture- and 16S rRNA gene sequencing-based approaches. To our knowledge, this is the first study to combine these methods in this context and to assess their relative capabilities in detecting microbiological markers associated with the risk of COPD exacerbation. While 16S rRNA gene sequencing and metagenomics have emerged as leading methods in microbiota research²² due to their ability to detect bacterial communities not identifiable by conventional culturing methods, their clinical application is often constrained by factors such as cost, time, and complexity³¹,³². It neither allows taxonomic resolution at the species level for all taxa²²,³³,³⁴ nor distinguishes between viable and non-viable bacteria, which can limit its diagnostic effectiveness³¹. This latter point has been underlined by demonstrating that microbiota sputum composition identified by 16S rRNA sequencing did not correlate with viable microorganisms, as revealed by RNA-based metatranscriptomic analysis²²,³⁵. By integrating these two approaches, our study aimed to deepen the understanding of the complex airway bacterial community and enhance the identification of readily assessable microbial markers associated with exacerbation history.

The analysis of extended culture data revealed a significant loss in the α-diversity among HR patients. To our knowledge, no previous study has substantially assessed microbial diversity using extended culture-based methods regarding exacerbation risk in stable COPD patients. This decline suggests a less stable and less robust viable and culturable airway microbiota, with relative dysbiosis persisting even under stable conditions for this patient group. Such microbial imbalance could promote the colonization and/or proliferation of PPMs and
contribute to an increased risk of exacerbation³⁶. Although HR patients in the RINNOPARI cohort received significantly more antibiotics over the past six months, which may have adversely affected viable bacterial α-diversity³⁷, they were included only if they were stable without exacerbations for at least four weeks prior to sampling. Furthermore, a detailed review of clinical data revealed that only two patients (one per group) had received antibiotics within the month preceding inclusion (for a reason other than a COPD exacerbation), minimizing the likelihood of a substantial impact on overall bacterial diversity. While antibiotic-induced perturbations alone are unlikely to fully explain the observed reduction in α-diversity, this possibility cannot be entirely excluded. Whether this perturbation reflects intrinsic disease pathophysiology or antibiotic effects remains uncertain and warrants further investigation through longitudinal and functional studies to disentangle their respective contributions. Notably, our 16S rRNA sequencing analysis revealed no significant difference in α-diversity between patient groups, suggesting that dysbiosis may primarily affect viable and/or non-fastidious cultivable microbiota. This discrepancy highlights the added value of culture-based approaches in capturing relevant shifts in microbial diversity that may be overlooked by sequencing alone. Of note, most studies using sequencing approaches have reported decreased α-diversity in the sputum microbiota of frequent compared to infrequent exacerbators during stable periods³⁸⁻⁴¹. However, this finding has not been consistently replicated⁴². These discrepancies likely reflect differences in study design, such as inclusion and stratification criteria or variations in sampling methodology.

Next, we compared whether the patients in the HR and LR groups could be differentiated using distinct airway bacterial community features during stable periods. PCAs comparing the overall bacterial community composition between the two patient groups revealed strikingly similar profiles, with no distinct clusters or “pulmotypes” identified. These findings align with several recent studies using 16S rRNA sequencing, which reported similar overall sputum microbiota structures in frequent versus infrequent exacerbators²³,³⁸⁻⁴⁰. Collectively, these findings support the hypothesis that the global bacterial community structure in COPD patients undergoes a “homeostatic shift” between exacerbations, reflecting a significant capacity for recovery. Consequently, identifying precise microbiological markers for exacerbation risk may require more detailed analyses at various taxonomic levels.

At the phyla level, both approaches used to analyze the airway bacterial community in COPD showed a phylum distribution predominantly composed of *Bacillota*, which notably includes the genera *Streptococcus*, *Lactobacillus*, *Staphylococcus*, *Veillonella*, and *Gemella*. Our findings align with previous studies on stable COPD patients either conducted using extended culture²⁵,⁴³ or 16S rRNA gene sequencing⁴⁰,⁴⁴. With culture-based analysis, we found, firstly, an increased relative abundance of Pseudomonadota in the HR group, including a higher abundance of Gammaproteobacteria. The class Gammaproteobacteria encompasses several major human pathogens, including the genera *Pseudomonas* and *Haemophilus*, as well as the order Enterobacteriales. This observed elevation in the HR group appeared to result from a global enrichment of various members within this class, as no significant increase was observed in any individual genus or species within the Gammaproteobacteria class. Secondly, the 16 S rRNA gene sequencing approach revealed an increased relative abundance in the phylum Bacteroidota, which encompasses a substantial proportion of anaerobes. It may explain the significant increase detected exclusively through sequencing.

At the genus level, while the culture data indicated only a non-significant trend for *Streptococcus*, the 16S rRNA gene sequencing identified a statistically significant lower relative abundance in the HR group. These results were in line with a previous study on COPD patients with a high risk of exacerbation⁴⁰. We also evidenced, through sequencing, a lower relative abundance of the genus *Lactobacillus* in the HR group. Lactobacillales, an order that includes both *Lactobacillus* and *Streptococcus* genera, has previously been associated with a low risk of AE-COPD⁴⁰. Altogether, these findings suggested a potential protective role for the *Streptococcus* and *Lactobacillus* genera in the airway microbiota. It is established that a reduction in commensal microflora increases the risk of subsequent exacerbations and that the sputum of AE-COPD patients are poor in the *Streptococcus* genus³⁶. Our findings on *Lactobacillus* spp. may have significant implications for future interventions. Indeed, studies have shown that the administration of probiotics containing *Lactobacillus* species, such as *L. rhamnosus* and *L. gasseri*, may be beneficial in COPD, primarily due to their anti-inflammatory and immunomodulatory effects²²,⁴⁵,⁴⁶. In addition, a recent multicenter randomized controlled trial reported that long-term oral administration of *L. rhamnosus* significantly delayed the onset of moderate-to-severe AE-COPD⁴⁷.

At the species level, 16S rRNA gene sequencing analyses showed that *S. mutans* and *S. salivarius* were significantly less prevalent in the HR group compared to the LR group. This finding was confirmed by culture analysis for *S. mutans*. Such findings were consistent with our observation at the genus level and support the hypothesis of a potential protective effect against dysbiosis and exacerbation. Interestingly, this is sustained by three pathophysiological reports showing that *S. salivarius* (i) produced bacteriocins that inhibited *S. pneumoniae* growth and reduced its adhesion to airway epithelial cells, and (ii) lowered the burden of *P. aeruginosa* in a rat infection model, and (iii) inhibited the growth of *M. catarrhalis* and *S. aureus* in vitro⁴⁸⁻⁵². Five anaerobic species more prevalent in the HR group were identified only by the 16S rRNA gene sequencing analysis, including three species of *Prevotella/Aloprevotella*. Both *Prevotella* and *Aloprevotella* belong to the Bacteroidota phylum and are part of the core airway anaerobiome of patients with CF⁵³,⁵⁴ and COPD⁴⁴,⁵⁵⁻⁵⁷. Despite their prevalence, the role of *Prevotella* in COPD remains ambiguous, due to conflicting evidence regarding their pathogenic versus protective effects, warranting further research to elucidate their precise role⁵⁸. For instance, *Prevotella melaninogenica* has been associated with anti-inflammatory effects in AE-COPD⁵⁹, whereas *Prevotella nigrescens* strains have been implicated in tissue-destructive activities via protease production⁶⁰. The anaerobic species *Selenomonas artemidis* and *Leptotrichia otu12783* were also more prevalent in the HR group samples. This finding aligns with previous studies reporting an increased relative abundance of *Selenomonas* and *Leptotrichia*, along with *Pseudomonas*, in the sputum of stable COPD patients who experienced frequent exacerbations, and severe COPD patients, respectively⁴²,⁶¹. Despite these results regarding specific anaerobes, it should be noted
that (i) the overall anaerobes accounted for 18% of the total isolates in extended-culture and more than 55% of the total OTUs in our 16S rRNA gene sequencing analysis, confirming they represent an important group within the airway bacterial community⁷, and (ii) we found no significant differences in terms of global relative abundance or prevalence of positive samples between HR and LR patients. We also assessed the distribution of Enterobacterales across LR and HR groups, based on findings by Muggeo et al., who identified a COPD patient cluster with sputum enriched in this bacterial order using an extended bacterial culture approach. This cluster was associated with reduced bacterial diversity, predominant cough, and negative impact on mental health²⁵. However, our study did not reveal an association between the HR group and the relative abundance or prevalence of Enterobacterales, regardless of whether the analysis was performed by culture or sequencing. It is noteworthy that Enterobacterales constituted only 4.1% of the culture isolates and 0.45% of the total detected OTUs. Finally, culture analysis identified several PPMs, including *S. aureus*, *H. influenzae*, *M. catarrhalis*, and *P. aeruginosa* in proportions fairly comparable to those previously observed in stable COPD patients²⁵,⁶²⁻⁶⁴. PPMs collectively accounted for only 6.1% of the total isolates and showed equal relative abundance or prevalence in both groups.

Interestingly, network analysis based on 16S rRNA sequencing data revealed that *S. mutans* clustered within the same module as two *Lactobacillus* species. This co-clustering may reflect a shared ecological pattern or functional association between these taxa. However, this module did not exhibit significant associations with the clinical parameters analyzed.

Our study confirmed the superior sensitivity of the 16S rRNA gene sequencing approach, particularly for detecting non-culturable or fastidious bacteria. It undeniably identified a greater number of potential airway microbiota features associated with exacerbation risk at both the genus and species level compared to the culture-based approach. However, our results highlighted the complementary value of extended bacterial culture. Specifically, culture revealed an increased relative abundance of Pseudomonadota in the HR group—a finding not captured by 16S rRNA sequencing—suggesting a higher abundance of viable and cultivable bacteria within this phylum. In addition, several pathogenic species, particularly respiratory PPMs, as well as members of Enterobacterales, were detected with equal performance regarding the number of positive samples using culture and 16S rRNA sequencing. These results underscore the effectiveness of commercial culture media—Columbia blood, chocolate, Schaedler, and cetrimide agar—in isolating clinically relevant pathogens⁶⁵. An unexpected outcome of our comparative analysis was that the *Staphylococcus* genus was detected solely by culture-based methods, with no corresponding signal in 16S rRNA gene sequencing. We hypothesize that this discrepancy may result from a combination of factors, including the low relative abundance of *Staphylococcus* among cultured isolates (2.93%), its modest bacterial load (median: 5.50 × 10⁵ CFU/ml), and the selective nature of culture-based methods, which are particularly favorable for the growth of PPMs⁶⁵. Primer and extraction biases appear unlikely, as multiple *Staphylococcus* OTUs were successfully identified using the same DNA extraction and 16S rRNA sequencing protocol in other concurrent studies of the lung microbiota conducted by our group. Moreover, no specific limitations of 16S rRNA sequencing for this genus have been reported in the literature.

The primary strength and uniqueness of our study lay in the dual methodological approach employed. Furthermore, our findings emphasize the relevance of bacterial culture, which can be extended for exhaustivity as described here, as a standard operational procedure to analyze the airway bacterial community of COPD patients.

This study has several limitations. First, its monocentric, cross-sectional design, based on single time-point sampling, and the relatively modest sample size compared with that of some previous studies may have limited the statistical power to detect subtle associations. The absence of longitudinal follow-up precluded assessment of the temporal stability and variability of microbial markers associated with exacerbation risk. We employed 16S rRNA gene sequencing rather than whole-metagenome sequencing, which introduces several constraints. Most notably, the limited taxonomic resolution of 16S rRNA sequencing, due to the short length of the amplified region, often prevents confident species-level identification³³. Primer selection can also bias detection toward specific bacterial taxa, and the compositional nature of sequencing data complicates interpretation of relative abundance changes⁶⁶. The use of sputum samples, rather than more invasive approaches such as bronchoalveolar lavage (BAL), presents both advantages and limitations. While sputum collection carries a risk of contamination from the upper airway and may not accurately represent the microbiota of the lower respiratory tract as precisely as BAL²², it is non-invasive, widely accessible, and routinely used in clinical microbiology laboratories, thereby enhancing the translational potential of our findings. Although primarily descriptive and taxonomic in nature, this study was designed to serve as a foundation for future investigations that incorporate functional and longitudinal analyses, advancing our understanding of microbial community dynamics and their contribution to COPD pathogenesis.

## Conclusion

In conclusion, we analyzed the airway bacterial community of stable COPD patients using a dual approach that combined extended bacterial culture and 16S rRNA gene sequencing.

While culture-based analysis revealed a loss of viable α-diversity in high-risk patients, overall diversity appeared similar across groups when assessed by sequencing. In addition, sequencing revealed a decrease in the relative abundance of the genera *Streptococcus* and *Lactobacillus* in high-risk individuals. Such findings may confirm other studies suggesting a potential protective role for commensal bacteria and probiotics for preventing exacerbations. Importantly, reductions in *S. mutans* prevalence, as well as in α-diversity, were readily detectable through bacterial culture. Future research should incorporate functional and longitudinal studies to further validate the identified bacterial community features and support the development of targeted preventive strategies for COPD exacerbations.
## Data availability

The datasets of 16S rRNA sequencing generated and/or analyzed during the current study have been deposited in the European Nucleotide Archive (ENA) under the project reference PRJEB85758. Supplementary figures and tables are provided in Supplementary File 1. Raw data from extended cultures supporting the findings are available in this published article (Supplementary File 2), along with control data (Supplementary File 3) and the completed STORMS checklist (Supplementary File 4). The analysis code is accessible at: https://github.com/lvelosuarez/amplicon.

Received: 11 March 2025; Accepted: 5 August 2025

Published online: 25 September 2025

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## Acknowledgements

The authors are deeply grateful to all healthcare professionals who contributed to the RINNOPARI cohort.

## Author contributions

The study was designed by AM, JMP, GD and TG. The microbiological data acquisition was performed by QLT, AC and AM. The patients were included, and their clinical data were acquired by JMP, SD and GD. NGS experiments were designed by GHA and performed by SG. Bioinformatics analyses were conducted by LVS, AB, and QLT. The original draft was written by QLT and AM. Editing of the manuscript was performed by JMP, GD, GHA and TG. All the authors contributed to the final data interpretation and manuscript writing. All the authors approved the final version of the manuscript.

## Funding

This work was funded by a grant from the University Hospital of Reims and the University of Reims Champagne-Ardenne (Hospital University Project RINNOPARI).
## Declarations

### Competing interests

J.M. Perotin reports lecture honoraria from AstraZeneca, and support for attending meetings from AstraZeneca and Chiesi, outside the submitted work. G. Deslée reports lecture honoraria from Chiesi, AstraZeneca and GlaxoSmithKline; outside the submitted work. S. Dury reports fees from Boehringer-Ingelheim and Sanofi-Adventis, outside the submitted work. Rest of the authors have no conflict of interest.

### Consent for publication

Not applicable.

### Ethics approval and informed consent

This research was conducted in accordance with the Declaration of Helsinki, followed the rules applicable to medical research in France, and received the authorization needed. The study was approved by the regional ethics committee (Comité de Protection des Personnes—Dijon EST I, no. 2016-A00242-49). Informed consent was obtained from all the patients.

### Additional information

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-14994-x.

Correspondence and requests for materials should be addressed to T.G.

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© The Author(s) 2025

## The RINNOPARI Study Group includes

Michel Abely<fnref n="9" />,¹⁰, Julien Ancel², Philippe Birembaut¹⁰, Audrey Brisebarre⁷, Audrey Brisebarre<fnref n="11" />, Alice Clarenne¹, Christophe Champs¹, Gonzague Delépine¹⁰, Gaëtan Deslée², Valérian Dormoy<fnref n="9" />, Sandra Dury¹², Thomas Guillard¹, Claire Launois², Richard Naour¹³, François Lebargy¹⁰, Quentin Lecomte-Thenot¹, Pierre Mauran¹⁴, Jean-Claude Mérol<fnref n="15" />, Anaëlle Muggeo¹, Pauline Mulette², Jeanne-Marie Perotin² & Myriam Polette<fnref n="11" />

<footnote n="9">⁹Université de Reims Champagne-Ardenne, INSERM, CHU de Reims, Service de Pédiatrie, P3Cell, U 1250, Reims, France. ¹⁰Centre de ressources et de compétences de la mucoviscidose mixte, CHU de Reims, Reims, France.</footnote>

<footnote n="11">¹¹Université de Reims Champagne-Ardenne, INSERM, CHU de Reims, Laboratoire de biopathologie, P3Cell, U 1250, Reims, France. ¹²Université de Reims Champagne-Ardenne, INSERM, CHU de Reims, Service de chirurgie cardio-vasculaire et thoracique, P3Cell U 1250, Reims, France. ¹³Université de Reims Champagne-Ardenne, EA7509 IRMAIC, Reims, France. ¹⁴Unité de Cardiologie Pédiatrique et Congénitale, CHU de Reims, Reims, France.</footnote>

<footnote n="15">¹⁵Université de Reims Champagne-Ardenne, INSERM, CHU de Reims, Service de Chirurgie ORL, P3Cell, U 1250, Reims, France.</footnote>