Association between exacerbation history and airway microbiota assessed by extended bacterial culture and metagenomic approaches in stable COPD

Association between exacerbation history and airway microbiota assessed by extended bacterial culture and metagenomic approaches in stable COPD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between exacerbation history and airway microbiota assessed by extended bacterial culture and metagenomic approaches in stable COPD Quentin Lecomte-Thenot, Jeanne-Marie Perotin, Geneviève Héry-Arnaud, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6206453/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background. 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 microbiota have been linked to exacerbation frequency, the underlying mechanisms remain unclear. Methods. This study investigated the associations between airway microbiota composition in stable COPD patients and exacerbation history. 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 metagenomics. The combination of these approaches provided complementary insights, enabling a more comprehensive characterization of the microbiota. Results. Microbial composition analysis revealed a loss of α-diversity in HR patients. This group also exhibited increased abundances of Pseudomonadota and Bacteroidota, alongside a marked decrease in the proportions of Lactobacillus and Streptococcus. Notably, significant reductions were observed at the species level for Streptococcus salivarius and Streptococcus mutans. A comparison of the two methods underlined that 16S rRNA metagenomics 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. Conclusion. This study revealed microbiological characteristics linked to exacerbation history in stable COPD patients, highlighting the need for future functional and longitudinal research to validate these airway microbiota features and develop targeted preventive strategies. Biological sciences/Microbiology/Clinical microbiology Biological sciences/Microbiology/Communities/Microbiome Health sciences/Diseases/Respiratory tract diseases/Chronic obstructive pulmonary disease Biological sciences/Microbiology/Bacteria/Metagenomics Biological sciences/Microbiology/Bacteria/Bacterial techniques and applications Health sciences/Biomarkers/Predictive markers COPD - Chronic Obstructive Pulmonary Disease exacerbation risk extended culture metagenomics microbiota sputum stable state Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Metagenomic analyses allowed an exhaustive description of the airway microbiota ( 1 – 4 ) and showed a lower bacterial density compared to the gut microbiota ( 5 ), a high α-diversity ( 6 ), and a predominance of strict anaerobes, especially Bacteroidota, along with Bacillota, Pseudomonadota, and Actinomycetota ( 7 – 9 ). The airway microbiota has been proposed to maintain lung architecture, enhance antibacterial defenses, and modulate immune system functions ( 9 – 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 microbiota ( 13 , 14 ). Patients with COPD may experience acute exacerbations (AE-COPD), which are critical and pejorative events in the course of the disease ( 15 , 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 management ( 17 ). 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 ( 18 , 19 ), and are associated with significant compositional and functional remodeling of the airway microbiota ( 20 ), notably an increase of the phylum Pseudomonadota ( 21 , 22 ). The practical method currently available to predict exacerbation risk relies on the history of exacerbations in the previous year ( 16 ). The airway microbiota may also offer measurable parameters, such as microbial signatures and diversities which could serve as potential diagnostic, therapeutic, and prognostic biomarkers. Since the metagenomic-based approaches provide valuable insights into the entire microbial community ( 22 ), 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 microorganisms ( 23 ). In this study, we employed both extended culture- and metagenomic-based methods to describe the airway microbiota 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 microbiota composition in stable COPD and exacerbation risk and to identify novel microbiological markers linked to this risk. Additionally, we assessed whether 16S rRNA metagenomics provides superior predictive value over extended culture methods in this context. We anticipate that a deeper understanding of the relationships between COPD exacerbations and lung microbiota—regarded as a potentially modifiable factor—will reveal new opportunities for therapeutic strategies in COPD. 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 exacerbation ( 24 ). 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 FEV 1 /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 : FEV 1 ≥ 80% ; GOLD 2 : 50% ≤ FEV 1 < 80% ; GOLD 3 : 30% ≤ FEV 1 < 50% ; GOLD 4 : FEV 1 < 30%) ( 15 ). Emphysema presence and severity were assessed through computed tomography (CT) scan images by two independent investigators (SD, GD) with a final consensus interpretation ( 25 , 26 ). 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 described ( 24 ). Compared with conventional sputum culture used in laboratory routine, extended culture included additional media (notably selective media), more 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 hours for aerobic and 5% CO 2 cultures, and for five days for anaerobic cultures. All morphologically distinct colonies were quantified as colony-forming units (CFU) per milliliter 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 evenness ( 21 ). 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 minutes, 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 341F forward primer, and 1.5 µL of 10 µM 785R reverse primer. The PCR protocol included an initial denaturation at 95°C for 3 minutes, followed by 30 cycles of 95°C for 30 seconds, 59°C for 30 seconds, and 72°C for 30 seconds, with a final extension at 72°C for 5 minutes. 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 taxonomy. 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. Taxonomy was assigned using the SILVA version 138 species classifier implementation for DADA2. Non-bacterial ASVs were removed, and ASVs were collapsed into operational taxonomic units (OTUs) using dbOTU and the clustering algorithm TreeCluster at 98% on a SATé-enabled phylogenetic placement (SEPP) tree. 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 the Fisher's exact test for qualitative variables, and either the t-test or Mann–Whitney 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, 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. 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, and 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, or smoking history. Table 1 Demographic and clinical characteristics of patients stratified by exacerbation risk. Total Low risk of exacerbation (LR) High risk of exacerbation (HR) p- value Number 62 28 34 Age, years 61.5 ± 9.4 64.2 ± 9.2 59.4 ± 9.0 0.021 Male 36 (58.1%) 22 (78.6%) 14 (41.2%) 0.003 BMI, kg/m² 25.7 ± 5.6 26.3 ± 5.1 25.3 ± 6.0 0.235 Smoking history Current smoker 21 (33.9%) 8 (28.6%) 13 (38.2%) 0.299  Former smoker 41 (66.1%) 20 (71.4%) 21 (61.8%) Pack-years 43.6 ± 19.2 45.5 ± 21.2 42.1 ± 17.6 0.246 Maintenance Treatment Long-acting beta agonist 38 (61.3%) 16 (57.1) 22 (64.7%) 0.614 Long-acting muscarinic antagonist 11 (17.7%) 3 (10.7%) 8 (23.5%) 0.102 Inhaled corticosteroid 21 (33.9%) 8 (28.6%) 13 (38.2%) 0.426 Oral corticosteroid 2 (3.2%) 1 (3.6%) 1 (2.9%) 0.643 Long-term macrolides 5 (8.1%) 2 (7.1%) 3 (8.8%) 0.714 Exacerbation history Exacerbation (previous year) 41 (66.1%) 7 (25.0%) 34 (100%) < 0.001 Nb exacerbations (previous year) 2.5 ± 1.5 1 ± 0 2.8 ± 1.5 0.001 Antibiotics (last 6 months) 35 (56.5%) 6 (21.4%) 29 (85.3%) < 0.001 Oral corticosteroids (last 6 months) 21 (33.9%) 4 (14.3%) 17 (50.0%) 0.003 Symptoms Dyspnea mMRC ≥ 2 47 (75.8%) 22 (78.6%) 25 (73.5%) 0.769 Chronic bronchitis 31 (50.0%) 10 (35.7%) 21 (61.8%) 0.037 CAT score 18.0 ± 7.4 16.2 ± 8.0 19.6 ± 6.6 0.045 Lung function FEV 1 , % pred 46.5 ± 18.7 53.1 ± 20.1 41.1 ± 15.8 0.006 FEV 1 /FVC 47.0 ± 10.9 49.5 ± 10.7 44.9 ± 10.7 0.048 RV, % pred 217.4 ± 88.8 183.5 ± 73.7 244.1 ± 91.5 0.004 TLC, % pred 129.8 ± 26.4 118.5 ± 20.3 138.7 ± 27.6 0.001 DLCO, % pred 46.2 ± 23.5 45.9 ± 22.9 46.4 ± 24.3 0.471 GOLD 1–2 26 (41.9%) 17 (60.7%) 9 (26.5%) 0.010 GOLD 3–4 36 (58.1%) 11 (39.3%) 25 (73.5%)  6 minute walking test* (AA) , 52 21 31 Desaturation, n 24 (46.2%) 9 (42.9%) 15 (48.4%) 0.351 Distance, meters 359 ± 117 388 ± 126 340 ± 108 0.028 CT-scan* , 58 25 33 Emphysema, n 52 (89.7%) 21 (75.0%) 31 (93.9%) 0.213 Emphysema score 9.0 ± 4.7 9.2 ± 4.6 8.9 ± 4.9 0.394 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]. 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; FEV 1 : Forced Expiratory Volume in 1 second; 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 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 72 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. 2 A). The total bacterial counts per sample ranged from 2.1x10⁴ CFU/mL to 1.82x10¹⁰ CFU/mL, with a median of 3.2x10⁷ CFU/mL. Similarly, no difference was observed in 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. 2 B). No significant differences were observed between groups for the Simpson (Fig. 2 C) or Chao1 indexes (Fig. 2 D). In both groups, the distribution of bacterial phyla was predominantly Bacillota, followed by Actinomycetota, Pseudomonadota, and a much smaller proportion of Bacteroidota ( 27 ). Notably, the HR group exhibited a significantly higher proportion of Pseudomonadota compared to the LR group (26.9% vs. 15.3% respectively; p = 0.005; Fig. 2 E). Within this phylum, the Gammaproteobacteria class was also present at a significantly higher proportion 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 Αlphaproteobacteria 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. 2 F). Although it was not statistically significant, a lower proportion 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. 2 G). Focusing on Enterobacterales, 22.6% of samples were positive (14/62), including the genus Citrobacter , Enterobacter , Escherichia , Hafnia , Klebsiella , Morganella , Proteus , and Raoultella (Table S1 ). The prevalence 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. 2 G). 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 frequency of Streptococcus mutans in the HR sputa (HR: 0% vs. LR: 14.3% of positive samples; p = 0.037; Fig. 2 H). Excluding S. pneumoniae , which could not yet be reliably distinguished from S. mitis and S. oralis using MALDI-TOF at the start of the study ( 28 ), several potentially pathogenic microorganisms (PPMs) were identified: Staphylococcus aureus (n = 9 isolates; 14.5% of positive samples), H. influenzae (n = 6; 9.7%), M. catarrhalis (n = 6; 9.7%), and Pseudomonas 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). 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. 2 I). Airway microbiota of COPD patients using 16S rRNA metagenomics. 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 16S rRNA metagenomic-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. 3 A). Unlike the culture results, the 16S rRNA metagenomics 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. 3 B-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. 3 E). Phylum distribution was further analyzed as a percentage of total OTUs (Fig. 3 F), to facilitate comparison with bacterial culture data. Overall, the most prevalent 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 proportion 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 proportion 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 percentage 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. 3 G). 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 prevalent genus. We observed significantly lower proportions 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 (56/56; Fig. 5 C). They accounted for more than half of the total OTUs (55.3%), with no significant difference observed between the groups (LR: 54.1% vs. HR: 56.3%; Fig. 3 H). Enterobacterales were detected in only 23.2% of the samples (13/56; Fig. 5 D), including the genera Citrobacter , Enterobacter , Escherichia , Hafnia , Klebsiella , Morganella , and Proteus . The proportion 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%; Fig. 3 H). 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. 3 I). 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), 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). 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) and, consistent with the bacterial culture results, S. mutans (LR: 44.4% vs. HR: 13.8%; p = 0.017). 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 PPMs prevalence was detected between the two groups (LR: 1.7% vs. HR: 1.9% of total OTUs). 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. 3 J). Comparison of extended bacterial culture and 16S rRNA metagenomics for analyzing the airway COPD microbiota. Although we characterized the difference of airway microbiota composition in HR vs . LR COPD patients (Fig. 4 ), we delved into our understanding of the effectiveness of bacterial culture compared to 16S rRNA metagenomics in analyzing the airway COPD microbiota. We conducted a comparative analysis of their efficiency in detecting various bacterial genera from 56 sputa. As anticipated, the 16S rRNA metagenomic-based approach detected bacteria from five additional phyla and 84 additional genera compared to extended bacterial culture (Fig. 5 A and 5 B). Surprisingly, the extended bacterial culture identified seven genera ( Staphylococcus , Enterococcus , Lactococcus , Paracoccus , Cutibacterium , Raoultella , and Rhizobium ) that were not detected by metagenomics. Our analysis revealed significant differences in the effectiveness of the 16S rRNA metagenomics 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 Fig. 6 A, 84.4% were uniquely identified by metagenomics, 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 100% of the samples, irrespective of the method used. Among the genera detected in a high proportion of samples (> 70%), Streptococcus , Rothia , Neisseria , and Veillonella achieved concordance of detection rates exceeding 50% between both methods (100%, 71%, 65%, and 54%, respectively; Fig. 6 A). Semi-quantitative comparison based on the percentage of positive samples underscored the prominent contribution of the metagenomic-based analysis for bacterial genera identification (Fig. 6 B). 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 of noticing that 16S rRNA metagenomics allowed better detection of strict anaerobes. While the culture-based method identified anaerobes in 40 out of 56 samples (71.4%), the metagenomic approach detected them in all the 56 samples (Fig. 5 C). Genera typically regarded as easy to cultivate, such as Corynebacterium and Haemophilus , also exhibited enhanced detection rates with metagenomics. Specifically, Haemophilus was detected in only 17 of 56 samples (30.4%) using culture, compared to 44 samples (78.6%) with sequencing (Fig. 5 E). For several genera the percentage of positive samples was nonetheless equivalent between the two detection approaches (Fig. 6 B). 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. 6 B). 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 metagenomics (2/56 (3.6%) and 3/56 (5.4%), respectively) (Fig. 5 F and 5 H). Additionally, Enterobacterales were detected in 13 samples (23.2%) by both methods, with overlapping detection in 10 samples (17.9%) (Fig. 5 D). Finally, Staphylococcus was the only genus detected significantly more frequently by culture (Fig. 6 B), being found in 11 samples (19.6%), whereas it was detected in none of the samples by 16S rRNA sequencing (Fig. 5 G). Discussion In this study, we presented a comprehensive characterization of the airway microbiota in stable COPD patients by simultaneously integrating results from extended culture- and 16S rRNA metagenomic-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 metagenomics has emerged as a leading method in microbiota research ( 22 ) due to its ability to detect bacterial communities not identifiable by conventional culturing methods, its clinical application is often constrained by factors such as cost, time, and complexity ( 29 , 30 ). It neither allows taxonomic resolution at the species level for all taxa ( 22 , 31 , 32 ) nor distinguishes between viable and non-viable bacteria, which can limit its diagnostic effectiveness ( 29 ). 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 analysis ( 22 , 33 ). By integrating these two approaches, our study aimed to deepen the understanding of the complex airway microbiota 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. 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 exacerbation ( 34 ). Several other studies investigating sputum microbiota in patients with frequent versus infrequent exacerbations during stable periods reported decreased α-diversity among frequent exacerbators ( 35 – 38 ). Although the HR patients in the RINNOPARI cohort received significantly more antibiotics over the past six months, which may impact the microbiota diversity ( 39 ), they were included only if they had been stable without exacerbations for the preceding four weeks. Whether the observed reduction in bacterial culture α-diversity in HR patients during stable periods arises primarily from inherent variations in disease pathophysiology or antibiotic-induced disruptions requires further elucidation through longitudinal and functional studies. Interestingly, our metagenomic analysis revealed no significant difference in α-diversity between patient groups, suggesting that dysbiosis may primarily affect viable and/or non-fastidious cultivable microbiota. Next, we compared whether the patients in the HR and LR groups could be differentiated using distinct airway microbiota features during stable periods. PCAs comparing the overall microbiota composition between the two patient groups revealed strikingly similar microbiota profiles, with no distinct clusters or “pulmotypes” identified. These findings aligned with several recent studies using 16S rRNA sequencing, which reported similar overall sputum microbiota structure in frequent versus infrequent exacerbators ( 35 – 37 ). Collectively, these findings support the hypothesis that the global microbiota structure in COPD patients may experience 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 to analyze the airway COPD microbiota showed a phylum distribution predominantly composed of Bacillota, which notably includes the genera Streptococcus and Lactobacillus as well as Staphylococcus , Veillonella , and Gemella . Our findings aligned with previous studies on stable COPD patients either carried out by extended culture ( 24 , 40 ) or metagenomics ( 37 , 41 ). Using culture-based analysis, we found, firstly, an increased prevalence of Pseudomonadota in the HR group, including a higher level of Gammaproteobacteria. The class Gammaproteobacteria encompasses several major human pathogens, such as the genera Pseudomonas and Haemophilus , and the order Enterobacterales. This observed elevation in the HR group appeared to result from a global enrichment of various members within this class because we did not evidence any significant increase in individual genus or species within the Gammaproteobacteria class. Secondly, the 16S rRNA metagenomic approach allowed us to evidence an increase 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 metagenomics identified a statistically significant lower proportion in the HR group. This result were in line with a previous study on COPD patients with high-risk of exacerbation ( 37 ). We also evidenced by sequencing a lower frequency of the genus Lactobacillus in the HR group. Lactobacillales, an order that includes both Lactobacillus and Streptococcus genera, is associated with low risk of AE-COPD ( 37 ). 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 sputum of AE-COPD patients are poor in the Streptococcus genus ( 34 ). 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 effects ( 22 , 42 , 43 ). 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-COPD ( 44 ). At the species level, 16S rRNA metagenomic analyses showed that S. mutans and S. salivarius were significantly less prevalent in the HR group compared to the LR group, a finding that 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 vitro ( 45 – 49 ). Five anaerobic species more prevalent in the HR group were identified only by the 16S rRNA metagenomic-based analysis, including three species of Prevotella / Alloprevotella . Both Prevotella and Alloprevotella belong to the Bacteroidota phylum and are part of the core airway anaerobiome of patients with CF ( 50 , 51 ) and COPD ( 41 , 52 – 54 ). 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 better elucidate their precise role ( 55 ). For instance, Prevotella melaninogenica has been associated with anti-inflammatory effects in AE-COPD ( 56 ), whereas Prevotella nigrescens strains have been implicated in tissue-destructive activities via protease production ( 57 ). The anaerobic species Selenomonas artemidis and Leptotrichia_otu12783 were also more prevalent in the HR group samples. This finding aligns with previous studies reporting 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, respectively ( 58 , 59 ). 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 metagenomic-based analysis, confirming they represent an important group within the airway microbiota ( 7 ), and (ii) we found no significant differences in terms of global 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. , which identified a COPD patient cluster with sputum enriched in this bacterial order. This cluster was associated with reduced microbiota diversity, predominant cough, and negative impact on mental health ( 24 ). However, our study did not reveal an association between the HR group and 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 patients ( 24 , 60 – 62 ). PPMs collectively accounted for only 6.1% of the total isolates and showed equal prevalence in both groups. Our study confirmed the superior sensitivity of the 16S rRNA metagenomic-based 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 culture-based approach. However, our results highlighted the complementary value of extended bacterial culture. Specifically, culture revealed an increased presence of Pseudomonadota in 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 for the number of positive samples using culture and 16S rRNA sequencing. These results underscored the effectiveness of commercial culture media - Columbia blood, chocolate, Schaedler and cetrimide agar - in isolating clinically relevant pathogens ( 63 ). The primary strength and uniqueness of our study lay in the dual methodological approach employed. Furthermore, our findings emphasized the relevance of bacterial culture, which can be extended for exhaustivity as described here, as standard operational procedures to analyze the airway microbiota of COPD patients. This study had certain limitations, including its monocentric and cross-sectional design with a one-time sampling. The lack of follow-up restricted our ability to assess the stability and variability of the microbial markers associated with exacerbation risk. Although our study was primarily descriptive and focused on taxonomic composition, we hoped that our findings would serve as a foundation for future, more comprehensive functional analyses, ultimately deepening our understanding of microbial community dynamics and, more broadly, COPD pathogenesis. Conclusion In conclusion, we analyzed the airway microbiota of stable COPD patients using a dual approach that combined extended culture and 16S metagenomics. We found a loss of the α-diversity and a decrease in the genera Streptococcus and Lactobacillus in HR patients. Such findings may confirm other studies suggesting a potential protective role for commensal bacteria and probiotics for preventing exacerbations. Importantly, reductions in S. mutans , as well as α-diversity were readily detectable through bacterial culture. Future research should incorporate functional and longitudinal studies to further validate the identified microbiota features and support the development of targeted preventive strategies for COPD exacerbations. Abbreviations AE Acute Exacerbation BMI Body Mass Index CAT COPD Assessment Test CF Cystic Fibrosis COPD Chronic Obstructive Pulmonary Disease CT Computed Tomography CFU Colony-Forming Units DNA DeoxyriboNucleic Acid DLCO Diffusing capacity of the Lung for Carbon Monoxide FEV 1 Forced Expiratory Volume in 1 second FVC Forced Vital Capacity GOLD Global Initiative for Chronic Obstructive Lung Disease HACEK Haemophilus spp. , Aggregatibacter actinomycetemcomitans , Capnocytophaga spp. , Cardiobacterium hominis , Eikenella corrodens , Kingella kingae HR High Risk LR Low Risk MALDI-TOF Matrix-Assisted Laser Desorption/Ionization - Time-Of-Flight mMRC modified Medical Research Council NVS Nutritionally Variant Streptococci OTU Operational Taxonomic Unit PCA Principal Component Analysis PCR Polymerase Chain Reaction PPM Potentially Pathogenic Microorganisms RNA Ribonucleic Acid rRNA ribosomal RiboNucleic Acid RV Residual Volume SATé Simultaneous Alignment and Tree estimation SD Standard Deviation SEPP SATé-Enabled Phylogenetic Placement TLC Total Lung Capacity Declarations Acknowledgements The authors are deeply grateful to all healthcare professionals who contributed to the RINNOPARI cohort. Authors' 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). 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. The raw data of extended cultures that support the findings are included in this published article (Supplementary information files 1). 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. Consent for publication Not applicable 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. 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Culture Enriched Molecular Profiling of the Cystic Fibrosis Airway Microbiome. Planet PJ, éditeur. PLoS ONE. 28 juill 2011;6(7):e22702. Additional Declarations Competing interest reported. 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. Supplementary Files RINNOPARIExaSMRespirRes.docx Supplementary information Supplementary material. Figure S1 provides an overview of the steps involved in data acquisition and quality control analysis. Figure S2 depicts rarefaction curves of 16S rRNA sequencing data after decontamination. Table S1 presents prevalence and quantification of bacteria in low and high risk exacerbation groups assessed by extended bacterial culture. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6206453","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434145924,"identity":"60046444-1cec-4bd4-ad8f-4ce862234b4f","order_by":0,"name":"Quentin Lecomte-Thenot","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":false,"prefix":"","firstName":"Quentin","middleName":"","lastName":"Lecomte-Thenot","suffix":""},{"id":434145927,"identity":"05891ebd-9484-4104-a350-cfa2b7be2397","order_by":1,"name":"Jeanne-Marie Perotin","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":false,"prefix":"","firstName":"Jeanne-Marie","middleName":"","lastName":"Perotin","suffix":""},{"id":434145929,"identity":"02bd16e5-fb01-4ca2-82e3-670eb38fa616","order_by":2,"name":"Geneviève Héry-Arnaud","email":"","orcid":"","institution":"Université de Brest, Inserm, UMR 1078, Unité de Bactériologie, CHRU de Brest","correspondingAuthor":false,"prefix":"","firstName":"Geneviève","middleName":"","lastName":"Héry-Arnaud","suffix":""},{"id":434145931,"identity":"bb7695b0-1172-4223-9abb-b02022627104","order_by":3,"name":"Lourdes Vélo-Suarez","email":"","orcid":"","institution":"Centre Brestois d’Analyse du Microbiote (CBAM), CHU de Brest","correspondingAuthor":false,"prefix":"","firstName":"Lourdes","middleName":"","lastName":"Vélo-Suarez","suffix":""},{"id":434145933,"identity":"685248cc-0c55-4a56-b2e4-28abf76a7e27","order_by":4,"name":"Audrey Brisebarre","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":false,"prefix":"","firstName":"Audrey","middleName":"","lastName":"Brisebarre","suffix":""},{"id":434145934,"identity":"75bdc958-21a8-4176-8f9b-165f54ff9954","order_by":5,"name":"Alice Clarenne","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":false,"prefix":"","firstName":"Alice","middleName":"","lastName":"Clarenne","suffix":""},{"id":434145935,"identity":"c2fc15eb-1bf6-4f88-b4ac-81993625551d","order_by":6,"name":"RINNOPARI Study Group","email":"","orcid":"","institution":"Centre Hospitalier Universitaire de Reims","correspondingAuthor":false,"prefix":"","firstName":"RINNOPARI","middleName":"Study","lastName":"Group","suffix":""},{"id":434145936,"identity":"b5ccb703-6698-4c4f-8364-b259c98ad6cc","order_by":7,"name":"Stéphanie Gouriou","email":"","orcid":"","institution":"Université de Brest, Inserm, UMR 1078, Unité de Bactériologie, CHRU de Brest","correspondingAuthor":false,"prefix":"","firstName":"Stéphanie","middleName":"","lastName":"Gouriou","suffix":""},{"id":434145937,"identity":"eaca884a-11ff-4d70-a52b-ae40a621733b","order_by":8,"name":"Sandra Dury","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, EA7509 IRMAIC","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Dury","suffix":""},{"id":434145938,"identity":"2bc85188-4fb4-4e59-bcc0-8811ccc83db9","order_by":9,"name":"Gaëtan Deslée","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":false,"prefix":"","firstName":"Gaëtan","middleName":"","lastName":"Deslée","suffix":""},{"id":434145939,"identity":"4faed86f-e92e-47bc-bdb1-b64167e870ab","order_by":10,"name":"Anaëlle Muggeo","email":"","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":false,"prefix":"","firstName":"Anaëlle","middleName":"","lastName":"Muggeo","suffix":""},{"id":434145940,"identity":"e7debc50-399e-4e89-8e31-bcf3dc3da9a3","order_by":11,"name":"Thomas Guillard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYJCCAw8KgOTxBjYwD0xW4FHOA9KSYAAkzxxA0nKGgBYGsJYbCWwIYXxa7PnXGAJtscvnu/n42WMehntyfGLHHzAc3IPHFok3BkAtyZYzb6eZG/MwFBuzSecYMBx4hk/LGZAWZgOD2zls0rz/EhLbpHMYmD8cIKil3sDg5hk2aR6GhPo26fQHDAfwaeHvAWk5bGBwgwesJYFNGhgaeLXcYCsAajluIHkmzUxyDkOCIdBhBgfwaWHvP7z5w4eKagO+44efSbxhSJCXn53+8AE+LQwSCVgE8WlgYODHLz0KRsEoGAWjgIEBANE8UKtD77L8AAAAAElFTkSuQmCC","orcid":"","institution":"Université de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Guillard","suffix":""}],"badges":[],"createdAt":"2025-03-11 19:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6206453/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6206453/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-14994-x","type":"published","date":"2025-09-25T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79585909,"identity":"e0216e6e-4360-4b62-b598-bb8fd5c0ae7c","added_by":"auto","created_at":"2025-03-31 12:29:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the extended bacterial culture and 16S rRNA sequencing protocols. \u003c/strong\u003eCOPD: Chronic Obstructive Pulmonary Disease; DNA: DesoxyriboNucleic Acid; FEV\u003csub\u003e1\u003c/sub\u003e: Forced Expiratory Volume in 1 second; 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\u003csup\u003e®\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/e1c02dc0c572b947a85939b6.png"},{"id":79585912,"identity":"38e3baa3-f376-4eed-819e-28ea086fe257","added_by":"auto","created_at":"2025-03-31 12:29:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":380165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended culture-based approach results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eNumber of species per sample. (\u003cstrong\u003eB) \u003c/strong\u003eShannon Index. (\u003cstrong\u003eC) \u003c/strong\u003eSimpson Index. (\u003cstrong\u003eD) \u003c/strong\u003eChao1 index. (\u003cstrong\u003eE) \u003c/strong\u003ePhyla distribution. (\u003cstrong\u003eF) \u003c/strong\u003eGenera distribution. (\u003cstrong\u003eG) \u003c/strong\u003eStrict anaerobes and \u003cem\u003eEnterobacterales\u003c/em\u003e abundance. (\u003cstrong\u003eH) \u003c/strong\u003eSpecies prevalence. Species with a frequency lower than 4% are not listed. – (\u003cstrong\u003eI) \u003c/strong\u003ePrincipal 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: \u003cstrong\u003e*\u003c/strong\u003e for \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cstrong\u003e**\u003c/strong\u003e for \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 and NS for Not Significant. Statistical analyses were performed using the Student's t-test, Mann–Whitney test, or the Fisher's exact test, as appropriate.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/0cb7629f8ac3da0fbef32e56.png"},{"id":79586868,"identity":"b6095296-473a-4ba7-bd32-ba78fa636d9e","added_by":"auto","created_at":"2025-03-31 12:37:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":520760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e16S rRNA metagenomics results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eNumber of OTUs per sample. (\u003cstrong\u003eB)\u003c/strong\u003e Shannon Index. (\u003cstrong\u003eC) \u003c/strong\u003eSimpson Index. (\u003cstrong\u003eD)\u003c/strong\u003e Chao1 index. (\u003cstrong\u003eE)\u003c/strong\u003e Phyla relative abundance per patient. (\u003cstrong\u003eF)\u003c/strong\u003e Phyla distribution. (\u003cstrong\u003eG)\u003c/strong\u003e Genera distribution. (\u003cstrong\u003eH)\u003c/strong\u003e Strict anaerobes and Enterobacterales abundance. \u003cstrong\u003e(I) \u003c/strong\u003eSpecies prevalence. Only the 30 most frequent bacterial species are represented. – (\u003cstrong\u003eJ)\u003c/strong\u003e 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: \u003cstrong\u003e*\u003c/strong\u003e for p \u0026lt; 0.05, \u003cstrong\u003e**\u003c/strong\u003e for p \u0026lt; 0.01 and NS for Not Significant. Statistical analyses were performed using the Student's t-test, Mann–Whitney test, or the Fisher's exact test, as appropriate.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/088b29c9e579dc63e927aefb.png"},{"id":79585914,"identity":"91591f79-b160-4af3-8f53-322e2ddbe242","added_by":"auto","created_at":"2025-03-31 12:29:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAirway microbiota in HR \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003evs\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e LR of AE-COPD patient.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure depicts differences in airway microbiota composition between high-risk (HR) and low-risk (LR) AE-COPD patients, identified using an extended culture-based approach (blue) and 16S rRNA sequencing (red). Created with BioRender\u003csup\u003e®\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/5f98edb18039bea56d7cfebf.png"},{"id":79585916,"identity":"f03e19cf-9a8b-41fb-b18b-7cdd42822dad","added_by":"auto","created_at":"2025-03-31 12:29:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":203251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagrams comparing bacterial detection by culture and metagenomics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Phyla detection. \u003cstrong\u003e(B)\u003c/strong\u003e Genera detection. The values represent the number of different phyla and genera detected from the 56 sputa analyzed using both bacterial culture and metagenomics. The area of each circle is proportional to the count of phyla/genera. – \u003cstrong\u003e(C)\u003c/strong\u003e Strict anaerobes detection. \u003cstrong\u003e(D)\u003c/strong\u003e Enterobacterales detection. \u003cstrong\u003e(E-H)\u003c/strong\u003e Detection of bacterial genera associated with COPD exacerbations. The values represent the count of positive samples from a total of 56 sputa analyzed using both bacterial culture and 16S rRNA metagenomics. The area of each circle is proportional to the count of positive samples. – Yellow circles indicate culture data, while green circles represent 16S rRNA metagenomics data.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/e5a9712798315870293eae71.png"},{"id":79586871,"identity":"ed3f3e22-0476-40c1-b571-a24fe2b0ca3c","added_by":"auto","created_at":"2025-03-31 12:37:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":598945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of genus-level bacterial detection by extended culture and 16S rRNA metagenomics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A\u003c/strong\u003e) Heatmap displaying qualitative detection patterns for the 68 most frequently identified genera across bacterial culture and/or metagenomics. Blue boxes represent genera detected exclusively by metagenomics, red boxes indicate those identified only by culture, and purple boxes denote genera detected by both methods. – \u003cstrong\u003e(B)\u003c/strong\u003e Scatter plot depicting semi-quantitative detection patterns 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 indicate genera with similar detection rates between culture and sequencing methods. Statistical significance (Fisher's exact test) is indicated as follows:\u003cstrong\u003e **\u003c/strong\u003e for p \u0026lt; 0.01, \u003cstrong\u003e***\u003c/strong\u003e for p \u0026lt; 0.001 and \u003cstrong\u003ens\u003c/strong\u003e for not significant.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/bf5b980e5254f7fbdcf2c937.png"},{"id":92430485,"identity":"e75f8bd7-0dbc-4266-aae8-b9f32620eaa1","added_by":"auto","created_at":"2025-09-29 16:05:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3495239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/65251635-c4f2-437b-b7ec-66ead548d76f.pdf"},{"id":79586873,"identity":"43422294-c802-4196-a729-eccc731b029e","added_by":"auto","created_at":"2025-03-31 12:37:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8439537,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary information\u003c/p\u003e\n\u003cp\u003eSupplementary material. \u003cstrong\u003eFigure S1\u003c/strong\u003e provides an overview of the steps involved in data acquisition and quality control analysis. \u003cstrong\u003eFigure S2\u003c/strong\u003e depicts rarefaction curves of 16S rRNA sequencing data after decontamination. \u003cstrong\u003eTable S1\u003c/strong\u003e presents prevalence and quantification of bacteria in low and high risk exacerbation groups assessed by extended bacterial culture.\u003c/p\u003e","description":"","filename":"RINNOPARIExaSMRespirRes.docx","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/044b32af74fa01e3c951dedc.docx"},{"id":79585910,"identity":"94eb5035-4333-44a2-adb9-3480194ac33f","added_by":"auto","created_at":"2025-03-31 12:29:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15372,"visible":true,"origin":"","legend":"","description":"","filename":"Consortium.docx","url":"https://assets-eu.researchsquare.com/files/rs-6206453/v1/1a1f3f86f7a6f55ac0a9b85d.docx"}],"financialInterests":"Competing interest reported. 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.","formattedTitle":"Association between exacerbation history and airway microbiota assessed by extended bacterial culture and metagenomic approaches in stable COPD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetagenomic analyses allowed an exhaustive description of the airway microbiota (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and showed a lower bacterial density compared to the gut microbiota (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), a high α-diversity (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and a predominance of strict anaerobes, especially Bacteroidota, along with Bacillota, Pseudomonadota, and Actinomycetota (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe airway microbiota has been proposed to maintain lung architecture, enhance antibacterial defenses, and modulate immune system functions (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). 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 microbiota (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients with COPD may experience acute exacerbations (AE-COPD), which are critical and pejorative events in the course of the disease (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). 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 management (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). AE-COPD are frequently triggered by viral and/or bacterial airway proliferation, including pathogens such as \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003cem\u003eMoraxella catarrhalis\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and are associated with significant compositional and functional remodeling of the airway microbiota (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), notably an increase of the phylum Pseudomonadota (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The practical method currently available to predict exacerbation risk relies on the history of exacerbations in the previous year (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The airway microbiota may also offer measurable parameters, such as microbial signatures and diversities which could serve as potential diagnostic, therapeutic, and prognostic biomarkers.\u003c/p\u003e \u003cp\u003eSince the metagenomic-based approaches provide valuable insights into the entire microbial community (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), 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 microorganisms (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we employed both extended culture- and metagenomic-based methods to describe the airway microbiota 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe aimed to investigate the associations between airway microbiota composition in stable COPD and exacerbation risk and to identify novel microbiological markers linked to this risk. Additionally, we assessed whether 16S rRNA metagenomics provides superior predictive value over extended culture methods in this context. We anticipate that a deeper understanding of the relationships between COPD exacerbations and lung microbiota\u0026mdash;regarded as a potentially modifiable factor\u0026mdash;will reveal new opportunities for therapeutic strategies in COPD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003ePatients 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 (Comit\u0026eacute; de Protection des Personnes\u0026mdash;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 exacerbation (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). 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 FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70% and GOLD (Global initiative for chronic Obstructive Lung Disease) grades were defined by the severity of airflow obstruction measured by spirometry (GOLD 1 : FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;80% ; GOLD 2 : 50% \u0026le; FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;80% ; GOLD 3 : 30% \u0026le; FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50% ; GOLD 4 : FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;30%) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Emphysema presence and severity were assessed through computed tomography (CT) scan images by two independent investigators (SD, GD) with a final consensus interpretation (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Patients were stratified into two groups based on their exacerbation history over the preceding year: Low Risk of exacerbation (LR) characterized by \u0026le;\u0026thinsp;1 exacerbation with no exacerbation-related hospitalization and High Risk of exacerbation (HR), defined by \u0026ge;\u0026thinsp;2 exacerbations or \u0026ge;\u0026thinsp;1 exacerbation-related hospitalization(s) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eExtended culture of sputum samples and bacterial identification.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor each patient, induced or non-induced sputum was collected at inclusion, and processed by an extended microbiological culture as previously described (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Compared with conventional sputum culture used in laboratory routine, extended culture included additional media (notably selective media), more 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 \u003cem\u003ePseudomonas\u003c/em\u003e-selective cetrimide agar (Thermo Fisher Scientific, USA) at 37\u0026deg;C for 48 hours for aerobic and 5% CO\u003csub\u003e2\u003c/sub\u003e cultures, and for five days for anaerobic cultures. All morphologically distinct colonies were quantified as colony-forming units (CFU) per milliliter and identified using MALDI-TOF mass spectrometry (MALDI Biotyper\u0026reg;, Bruker Daltonics, Bremen, Germany) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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 evenness (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction and 16S rRNA sequencing of sputum samples\u003c/h3\u003e\n\u003cp\u003eSputum samples were stored in cryotubes at -80\u0026deg;C, for further processing using 16S rRNA sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each sputum sample, 150 \u0026micro;L was sonicated for 5 minutes, and bacterial DNA was extracted using the QIAamp\u0026reg; 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 \u0026micro;L of extracted DNA, 25 \u0026micro;L of KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Cape Town, South Africa), 17 \u0026micro;L of water, 1.5 \u0026micro;L of 10 \u0026micro;M 341F forward primer, and 1.5 \u0026micro;L of 10 \u0026micro;M 785R reverse primer. The PCR protocol included an initial denaturation at 95\u0026deg;C for 3 minutes, followed by 30 cycles of 95\u0026deg;C for 30 seconds, 59\u0026deg;C for 30 seconds, and 72\u0026deg;C for 30 seconds, with a final extension at 72\u0026deg;C for 5 minutes.\u003c/p\u003e \u003cp\u003eAmplicon 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.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e 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 taxonomy. 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.\u003c/p\u003e \u003cp\u003eTaxonomy was assigned using the SILVA version 138 species classifier implementation for DADA2. Non-bacterial ASVs were removed, and ASVs were collapsed into operational taxonomic units (OTUs) using dbOTU and the clustering algorithm TreeCluster at 98% on a SAT\u0026eacute;-enabled phylogenetic placement (SEPP) tree. 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.\u003c/p\u003e \u003cp\u003eFollowing data processing, two samples were excluded due to an insufficient number of reads (\u0026lt;\u0026thinsp;2000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (interquartile range), or numbers and percentages, as appropriate. Comparisons were made using the Fisher's exact test for qualitative variables, and either the t-test or Mann\u0026ndash;Whitney test for quantitative variables, as appropriate. A \u003cem\u003ep\u003c/em\u003e-value (p)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eTo 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eSixty-two patients were enrolled in the study, with 28 (45.2%) assigned to the LR group and 34 (54.8%) to the HR group. Detailed patient characteristics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. They were predominantly male (58.1%) with a mean age of 61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;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. COPD was classified as severe or very severe (GOLD 3 or 4) in 58.1% of the patients.\u003c/p\u003e \u003cp\u003eCompared with the LR group, the HR group had a lower proportion of males (41.2% \u003cem\u003evs.\u003c/em\u003e 78.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), a younger age (59.4 \u003cem\u003evs.\u003c/em\u003e 64.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), and, as expected, more frequent symptoms of chronic bronchitis, and 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, or smoking history.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of patients stratified by exacerbation risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow risk of exacerbation (LR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh risk of exacerbation (HR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.299\u003cb\u003e\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePack-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaintenance Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-acting beta agonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-acting muscarinic antagonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInhaled corticosteroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOral corticosteroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-term macrolides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExacerbation history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExacerbation (previous year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNb exacerbations (previous year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAntibiotics (last 6 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (85.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOral corticosteroids (last 6 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDyspnea mMRC\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChronic bronchitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCAT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e, % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRV, % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217.4\u0026thinsp;\u0026plusmn;\u0026thinsp;88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183.5\u0026thinsp;\u0026plusmn;\u0026thinsp;73.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244.1\u0026thinsp;\u0026plusmn;\u0026thinsp;91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTLC, % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.8\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118.5\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.7\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDLCO, % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGOLD 1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGOLD 3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6 minute walking test* (AA)\u003c/b\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDesaturation, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (48.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDistance, meters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359\u0026thinsp;\u0026plusmn;\u0026thinsp;117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e388\u0026thinsp;\u0026plusmn;\u0026thinsp;126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340\u0026thinsp;\u0026plusmn;\u0026thinsp;108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT-scan*\u003c/b\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmphysema, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (89.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmphysema score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnless 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\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and median [25th-75th percentile].\u003c/p\u003e \u003cp\u003eAA: Ambient Air; BMI: Body Mass Index; CAT: COPD Assessment Test; CT-scan : Computed Tomography scan; DLCO: Diffusing capacity of the Lung for Carbon monOxide; FEV\u003csub\u003e1\u003c/sub\u003e: Forced Expiratory Volume in 1 second; 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eViable and culturable airway microbiota of COPD patients using extended culture-based approach.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 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 72 distinct species, distributed among 34 genera and four phyla (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The mean number of species per sample was 6.6 with no significant difference between groups (LR group: 6.5 \u003cem\u003evs.\u003c/em\u003e HR group: 6.7; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The total bacterial counts per sample ranged from 2.1x10⁴ CFU/mL to 1.82x10\u0026sup1;⁰ CFU/mL, with a median of 3.2x10⁷ CFU/mL. Similarly, no difference was observed in bacterial load between the LR and the HR groups (median of 3.5\u0026times;10⁷ \u003cem\u003evs.\u003c/em\u003e 3.2\u0026times;10⁷ CFU/mL respectively). The Shannon index was significantly lower in the HR group compared to the LR group (0.9 \u003cem\u003evs.\u003c/em\u003e 1.2 respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). No significant differences were observed between groups for the Simpson (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) or Chao1 indexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In both groups, the distribution of bacterial phyla was predominantly Bacillota, followed by Actinomycetota, Pseudomonadota, and a much smaller proportion of Bacteroidota (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Notably, the HR group exhibited a significantly higher proportion of Pseudomonadota compared to the LR group (26.9% \u003cem\u003evs.\u003c/em\u003e 15.3% respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Within this phylum, the Gammaproteobacteria class was also present at a significantly higher proportion in the HR group (HR: 15.9% \u003cem\u003evs.\u003c/em\u003e LR: 8.7% of total bacteria; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), whereas no significant differences were observed for the Αlphaproteobacteria and Betaproteobacteria classes.\u003c/p\u003e \u003cp\u003eThe distribution of bacterial genera was found to be similar between the two groups, with \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, and \u003cem\u003eActinomyces\u003c/em\u003e as the most prevalent, collectively accounting for 72.9% of the identified bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Although it was not statistically significant, a lower proportion of \u003cem\u003eStreptococcus\u003c/em\u003e in the HR group was observed (HR: 32.2% \u003cem\u003evs.\u003c/em\u003e LR: 36.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;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% \u003cem\u003evs.\u003c/em\u003e HR: 17.2%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Focusing on Enterobacterales, 22.6% of samples were positive (14/62), including the genus \u003cem\u003eCitrobacter\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eHafnia\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eMorganella\u003c/em\u003e, \u003cem\u003eProteus\u003c/em\u003e, and \u003cem\u003eRaoultella\u003c/em\u003e (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The prevalence 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%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eAt the species level, the most frequently isolated bacteria were \u003cem\u003eStreptococcus oralis/mitis/pneumoniae\u003c/em\u003e, followed by \u003cem\u003eVeillonella parvula/dispar/atypica\u003c/em\u003e and \u003cem\u003eStreptococcus salivarius\u003c/em\u003e, 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 frequency of \u003cem\u003eStreptococcus mutans\u003c/em\u003e in the HR sputa (HR: 0% \u003cem\u003evs.\u003c/em\u003e LR: 14.3% of positive samples; p\u0026thinsp;=\u0026thinsp;0.037; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Excluding \u003cem\u003eS. pneumoniae\u003c/em\u003e, which could not yet be reliably distinguished from \u003cem\u003eS. mitis\u003c/em\u003e and \u003cem\u003eS. oralis\u003c/em\u003e using MALDI-TOF at the start of the study (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), several potentially pathogenic microorganisms (PPMs) were identified: \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;9 isolates; 14.5% of positive samples), \u003cem\u003eH. influenzae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;6; 9.7%), \u003cem\u003eM. catarrhalis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;6; 9.7%), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4; 6.5%), collectively representing only 6.1% of the total bacterial isolates (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The total prevalence of these PPMs did not significantly differ between the LR and HR groups (6.6% \u003cem\u003evs.\u003c/em\u003e 5.7%, respectively).\u003c/p\u003e \u003cp\u003eFinally, 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 \u0026ldquo;pulmotypes\u0026rdquo; could be identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI).\u003c/p\u003e\u003cp\u003e \u003cb\u003eAirway microbiota of COPD patients using 16S rRNA metagenomics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong 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 16S rRNA metagenomic-based approach on 56 samples (LR: 27 (48.2%) \u003cem\u003evs\u003c/em\u003e. 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 \u003cem\u003evs.\u003c/em\u003e 61.6 OTUs, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eUnlike the culture results, the 16S rRNA metagenomics 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003ePhylum distribution was further analyzed as a percentage of total OTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), to facilitate comparison with bacterial culture data. Overall, the most prevalent 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 proportion of Bacteroidota in the HR group compared to the LR group (29.3% \u003cem\u003evs.\u003c/em\u003e 25.9%, respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and a non-significant lower proportion of Bacillota in the HR group (LR: 37.1% \u003cem\u003evs.\u003c/em\u003e HR: 34.3%). In contrast to the results observed with the culture method, no significant difference was observed between the two groups in the percentage of Pseudomonadota (LR: 12.4% \u003cem\u003evs.\u003c/em\u003e HR: 13.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60).\u003c/p\u003e \u003cp\u003eWe next examined the genus-level taxonomy distribution, focusing on the percentage of OTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). \u003cem\u003ePrevotella\u003c/em\u003e (13.8%) emerged as the most dominant genus overall, followed by \u003cem\u003eLeptotrichia\u003c/em\u003e (6.0%) and \u003cem\u003eCapnocytophaga\u003c/em\u003e (4.5%). This ranking was maintained in the LR group; however, in the HR group, \u003cem\u003eStreptococcus\u003c/em\u003e surpassed \u003cem\u003eCapnocytophaga\u003c/em\u003e and was the third most prevalent genus. We observed significantly lower proportions of \u003cem\u003eStreptococcus\u003c/em\u003e (LR: 5.3% \u003cem\u003evs.\u003c/em\u003e HR: 3.8%; p\u0026thinsp;=\u0026thinsp;0.042) and \u003cem\u003eLactobacillus\u003c/em\u003e (LR: 3.3% \u003cem\u003evs.\u003c/em\u003e HR: 1.7%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) in the HR group. Strict anaerobes, encompassing 48 distinct bacterial genera, were identified in all samples (56/56; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). They accounted for more than half of the total OTUs (55.3%), with no significant difference observed between the groups (LR: 54.1% \u003cem\u003evs.\u003c/em\u003e HR: 56.3%; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Enterobacterales were detected in only 23.2% of the samples (13/56; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), including the genera \u003cem\u003eCitrobacter\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eHafnia\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eMorganella\u003c/em\u003e, and \u003cem\u003eProteus\u003c/em\u003e. The proportion of Enterobacterales was notably low, representing only 0.5% of the total OTUs, with no significant difference between the groups (LR: 0.6% \u003cem\u003evs.\u003c/em\u003e HR: 0.3%; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eAt the species level, the five groups of species with the highest prevalence of positive samples were \u003cem\u003eS. mitis/oralis/pneumoniae/parasanguinis\u003c/em\u003e (96.4%), \u003cem\u003eGemella morbillorum/parahaemo-lysans/sanguinis\u003c/em\u003e (92.9%), \u003cem\u003eV. atypica/dispar/parvula/rogosae/tobetsuensis\u003c/em\u003e (92.9%), \u003cem\u003eRothia mucilaginosa\u003c/em\u003e (91.1%), and \u003cem\u003eCapnocytophaga gingivalis/granulosa\u003c/em\u003e (85.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). 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: \u003cem\u003ePrevotella oris\u003c/em\u003e (LR: 51.9% \u003cem\u003evs.\u003c/em\u003e HR: 79.3%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), \u003cem\u003ePrevotella conceptionensis\u003c/em\u003e (LR: 18.5% \u003cem\u003evs.\u003c/em\u003e HR: 44.8%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), \u003cem\u003eAlloprevotella_otu7057\u003c/em\u003e (LR: 25.9% \u003cem\u003evs.\u003c/em\u003e HR: 65.5%; p\u0026thinsp;=\u0026thinsp;0.004), \u003cem\u003eEikenella corrodens\u003c/em\u003e (LR: 25.9% \u003cem\u003evs.\u003c/em\u003e HR: 62.1%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), \u003cem\u003eSelenomonas artemidis\u003c/em\u003e (LR: 11.1% \u003cem\u003evs.\u003c/em\u003e HR: 41.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), and \u003cem\u003eLeptotrichia_otu12783\u003c/em\u003e (LR: 11.1% \u003cem\u003evs.\u003c/em\u003e HR: 37.9%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). Two \u003cem\u003eStreptococcus\u003c/em\u003e species had a lower prevalence of positive samples in the HR group: \u003cem\u003eS. salivarius\u003c/em\u003e (LR: 77.8% \u003cem\u003evs.\u003c/em\u003e HR: 48.3%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) and, consistent with the bacterial culture results, \u003cem\u003eS. mutans\u003c/em\u003e (LR: 44.4% \u003cem\u003evs.\u003c/em\u003e HR: 13.8%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). Excluding \u003cem\u003eS. pneumoniae\u003c/em\u003e, which could not be distinguished from \u003cem\u003eStreptococcus mitis\u003c/em\u003e, \u003cem\u003eS. oralis\u003c/em\u003e, and \u003cem\u003eS. parasanguinis\u003c/em\u003e in this study, and considering PPMs only at the genus level, \u003cem\u003eStaphylococcus\u003c/em\u003e (no positive samples), \u003cem\u003eHaemophilus\u003c/em\u003e (78.6% of positive samples), \u003cem\u003eMoraxella\u003c/em\u003e (3.6% of positive samples), and \u003cem\u003ePseudomonas\u003c/em\u003e (5.4% of positive samples) collectively accounted for just 1.8% of the total OTUs. No significant difference in PPMs prevalence was detected between the two groups (LR: 1.7% \u003cem\u003evs.\u003c/em\u003e HR: 1.9% of total OTUs).\u003c/p\u003e \u003cp\u003eConsistent 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of extended bacterial culture and 16S rRNA metagenomics for analyzing the airway COPD microbiota.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough we characterized the difference of airway microbiota composition in HR \u003cem\u003evs\u003c/em\u003e. LR COPD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), we delved into our understanding of the effectiveness of bacterial culture compared to 16S rRNA metagenomics in analyzing the airway COPD microbiota. We conducted a comparative analysis of their efficiency in detecting various bacterial genera from 56 sputa.\u003c/p\u003e\u003cp\u003eAs anticipated, the 16S rRNA metagenomic-based approach detected bacteria from five additional phyla and 84 additional genera compared to extended bacterial culture (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Surprisingly, the extended bacterial culture identified seven genera (\u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eCutibacterium\u003c/em\u003e, \u003cem\u003eRaoultella\u003c/em\u003e, and \u003cem\u003eRhizobium\u003c/em\u003e) that were not detected by metagenomics.\u003c/p\u003e \u003cp\u003eOur analysis revealed significant differences in the effectiveness of the 16S rRNA metagenomics 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 Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, 84.4% were uniquely identified by metagenomics, 2.9% were exclusive to culture, and 12.8% were detected by both methods. It is noteworthy that only \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e were detected in 100% of the samples, irrespective of the method used. Among the genera detected in a high proportion of samples (\u0026gt;\u0026thinsp;70%), \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, and \u003cem\u003eVeillonella\u003c/em\u003e achieved concordance of detection rates exceeding 50% between both methods (100%, 71%, 65%, and 54%, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eSemi-quantitative comparison based on the percentage of positive samples underscored the prominent contribution of the metagenomic-based analysis for bacterial genera identification (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As expected, these included genera that are not routinely culturable, such as \u003cem\u003eTreponema\u003c/em\u003e, \u003cem\u003eMycoplasma\u003c/em\u003e, and \u003cem\u003eSolobacterium\u003c/em\u003e, as well as those that are fastidious, such as strict anaerobes, HACEK bacteria (\u003cem\u003eHaemophilus spp.\u003c/em\u003e excluding \u003cem\u003eH. influenzae\u003c/em\u003e species, \u003cem\u003eAggregatibacter actinomycetemcomitans\u003c/em\u003e, \u003cem\u003eCapnocytophaga spp.\u003c/em\u003e, \u003cem\u003eCardiobacterium hominis\u003c/em\u003e, \u003cem\u003eEikenella corrodens\u003c/em\u003e, \u003cem\u003eKingella kingae\u003c/em\u003e), and Nutritionally Variant Streptococci (NVS) species (\u003cem\u003eAbiotrophia spp.\u003c/em\u003e and \u003cem\u003eGranulicatella spp.)\u003c/em\u003e. It is worth of noticing that 16S rRNA metagenomics allowed better detection of strict anaerobes. While the culture-based method identified anaerobes in 40 out of 56 samples (71.4%), the metagenomic approach detected them in all the 56 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Genera typically regarded as easy to cultivate, such as \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003eHaemophilus\u003c/em\u003e, also exhibited enhanced detection rates with metagenomics. Specifically, \u003cem\u003eHaemophilus\u003c/em\u003e was detected in only 17 of 56 samples (30.4%) using culture, compared to 44 samples (78.6%) with sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eFor several genera the percentage of positive samples was nonetheless equivalent between the two detection approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Excluding genera detected in less than 5% of samples by either method, we found the two detection approaches equivalent for \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eMicrococcaceae, Pseudomonas, Moraxella\u003c/em\u003e, \u003cem\u003eCitrobacter\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, and the entire Enterobacterales order (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). \u003cem\u003eMoraxella\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, two major PPMs, were better detected \u0026mdash;although not significant\u0026mdash; by culture (6/56 (10.7%) and 5/56 (8.9%), respectively) compared with metagenomics (2/56 (3.6%) and 3/56 (5.4%), respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Additionally, Enterobacterales were detected in 13 samples (23.2%) by both methods, with overlapping detection in 10 samples (17.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFinally, \u003cem\u003eStaphylococcus\u003c/em\u003e was the only genus detected significantly more frequently by culture (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), being found in 11 samples (19.6%), whereas it was detected in none of the samples by 16S rRNA sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we presented a comprehensive characterization of the airway microbiota in stable COPD patients by simultaneously integrating results from extended culture- and 16S rRNA metagenomic-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 metagenomics has emerged as a leading method in microbiota research (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) due to its ability to detect bacterial communities not identifiable by conventional culturing methods, its clinical application is often constrained by factors such as cost, time, and complexity (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). It neither allows taxonomic resolution at the species level for all taxa (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) nor distinguishes between viable and non-viable bacteria, which can limit its diagnostic effectiveness (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). 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 analysis (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). By integrating these two approaches, our study aimed to deepen the understanding of the complex airway microbiota and enhance the identification of readily assessable microbial markers associated with exacerbation history.\u003c/p\u003e \u003cp\u003eThe analysis of extended culture data revealed a significant loss in the α-diversity among HR 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 exacerbation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Several other studies investigating sputum microbiota in patients with frequent versus infrequent exacerbations during stable periods reported decreased α-diversity among frequent exacerbators (\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Although the HR patients in the RINNOPARI cohort received significantly more antibiotics over the past six months, which may impact the microbiota diversity (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), they were included only if they had been stable without exacerbations for the preceding four weeks. Whether the observed reduction in bacterial culture α-diversity in HR patients during stable periods arises primarily from inherent variations in disease pathophysiology or antibiotic-induced disruptions requires further elucidation through longitudinal and functional studies. Interestingly, our metagenomic analysis revealed no significant difference in α-diversity between patient groups, suggesting that dysbiosis may primarily affect viable and/or non-fastidious cultivable microbiota.\u003c/p\u003e \u003cp\u003eNext, we compared whether the patients in the HR and LR groups could be differentiated using distinct airway microbiota features during stable periods. PCAs comparing the overall microbiota composition between the two patient groups revealed strikingly similar microbiota profiles, with no distinct clusters or \u0026ldquo;pulmotypes\u0026rdquo; identified. These findings aligned with several recent studies using 16S rRNA sequencing, which reported similar overall sputum microbiota structure in frequent versus infrequent exacerbators (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Collectively, these findings support the hypothesis that the global microbiota structure in COPD patients may experience 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.\u003c/p\u003e \u003cp\u003eAt the phyla level, both approaches to analyze the airway COPD microbiota showed a phylum distribution predominantly composed of Bacillota, which notably includes the genera \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e as well as \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, and \u003cem\u003eGemella\u003c/em\u003e. Our findings aligned with previous studies on stable COPD patients either carried out by extended culture (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) or metagenomics (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Using culture-based analysis, we found, firstly, an increased prevalence of Pseudomonadota in the HR group, including a higher level of Gammaproteobacteria. The class Gammaproteobacteria encompasses several major human pathogens, such as the genera \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eHaemophilus\u003c/em\u003e, and the order Enterobacterales. This observed elevation in the HR group appeared to result from a global enrichment of various members within this class because we did not evidence any significant increase in individual genus or species within the Gammaproteobacteria class. Secondly, the 16S rRNA metagenomic approach allowed us to evidence an increase in the phylum Bacteroidota, which encompasses a substantial proportion of anaerobes. It may explain the significant increase detected exclusively through sequencing.\u003c/p\u003e \u003cp\u003eAt the genus level, while the culture data indicated only a non-significant trend for \u003cem\u003eStreptococcus\u003c/em\u003e, the 16S rRNA metagenomics identified a statistically significant lower proportion in the HR group. This result were in line with a previous study on COPD patients with high-risk of exacerbation (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). We also evidenced by sequencing a lower frequency of the genus \u003cem\u003eLactobacillus\u003c/em\u003e in the HR group. Lactobacillales, an order that includes both \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e genera, is associated with low risk of AE-COPD (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Altogether, these findings suggested a potential protective role for the \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e genera in the airway microbiota. It is established that a reduction in commensal microflora increases the risk of subsequent exacerbations and that sputum of AE-COPD patients are poor in the \u003cem\u003eStreptococcus\u003c/em\u003e genus (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Our findings on \u003cem\u003eLactobacillus\u003c/em\u003e spp., may have significant implications for future interventions. Indeed, studies have shown that the administration of probiotics containing \u003cem\u003eLactobacillus\u003c/em\u003e species, such as \u003cem\u003eL. rhamnosus\u003c/em\u003e and \u003cem\u003eL. gasseri\u003c/em\u003e, may be beneficial in COPD, primarily due to their anti-inflammatory and immunomodulatory effects (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In addition, a recent multicenter randomized controlled trial reported that long-term oral administration of \u003cem\u003eL. rhamnosus\u003c/em\u003e significantly delayed the onset of moderate-to-severe AE-COPD (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the species level, 16S rRNA metagenomic analyses showed that \u003cem\u003eS. mutans\u003c/em\u003e and \u003cem\u003eS. salivarius\u003c/em\u003e were significantly less prevalent in the HR group compared to the LR group, a finding that was confirmed by culture analysis for \u003cem\u003eS. mutans\u003c/em\u003e. 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 \u003cem\u003eS. salivarius\u003c/em\u003e (i) produced bacteriocins that inhibited \u003cem\u003eS. pneumoniae\u003c/em\u003e growth and reduced its adhesion to airway epithelial cells and (ii) lowered the burden of \u003cem\u003eP. aeruginosa\u003c/em\u003e in a rat infection model and (iii) inhibited the growth of \u003cem\u003eM. catarrhalis\u003c/em\u003e and \u003cem\u003eS. aureus in vitro\u003c/em\u003e (\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Five anaerobic species more prevalent in the HR group were identified only by the 16S rRNA metagenomic-based analysis, including three species of \u003cem\u003ePrevotella\u003c/em\u003e/\u003cem\u003eAlloprevotella\u003c/em\u003e. Both \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAlloprevotella\u003c/em\u003e belong to the Bacteroidota phylum and are part of the core airway anaerobiome of patients with CF (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) and COPD (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Despite their prevalence, the role of \u003cem\u003ePrevotella\u003c/em\u003e in COPD remains ambiguous, due to conflicting evidence regarding their pathogenic versus protective effects, warranting further research to better elucidate their precise role (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). For instance, \u003cem\u003ePrevotella melaninogenica\u003c/em\u003e has been associated with anti-inflammatory effects in AE-COPD (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), whereas \u003cem\u003ePrevotella nigrescens\u003c/em\u003e strains have been implicated in tissue-destructive activities via protease production (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The anaerobic species \u003cem\u003eSelenomonas artemidis\u003c/em\u003e and \u003cem\u003eLeptotrichia_otu12783\u003c/em\u003e were also more prevalent in the HR group samples. This finding aligns with previous studies reporting increased relative abundance of \u003cem\u003eSelenomonas\u003c/em\u003e and \u003cem\u003eLeptotrichia\u003c/em\u003e, along with \u003cem\u003ePseudomonas\u003c/em\u003e, in the sputum of stable COPD patients who experienced frequent exacerbations, and severe COPD patients, respectively (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). 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 metagenomic-based analysis, confirming they represent an important group within the airway microbiota (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), and (ii) we found no significant differences in terms of global 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 \u003cem\u003eet al.\u003c/em\u003e, which identified a COPD patient cluster with sputum enriched in this bacterial order. This cluster was associated with reduced microbiota diversity, predominant cough, and negative impact on mental health (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, our study did not reveal an association between the HR group and 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 \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eH. influenzae\u003c/em\u003e, \u003cem\u003eM. catarrhalis\u003c/em\u003e, and \u003cem\u003eP. aeruginosa\u003c/em\u003e in proportions fairly comparable to those previously observed in stable COPD patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). PPMs collectively accounted for only 6.1% of the total isolates and showed equal prevalence in both groups.\u003c/p\u003e \u003cp\u003eOur study confirmed the superior sensitivity of the 16S rRNA metagenomic-based 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 culture-based approach. However, our results highlighted the complementary value of extended bacterial culture. Specifically, culture revealed an increased presence of Pseudomonadota in HR group\u0026mdash;a finding not captured by 16S rRNA sequencing\u0026mdash;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 for the number of positive samples using culture and 16S rRNA sequencing. These results underscored the effectiveness of commercial culture media - Columbia blood, chocolate, Schaedler and cetrimide agar - in isolating clinically relevant pathogens (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). The primary strength and uniqueness of our study lay in the dual methodological approach employed. Furthermore, our findings emphasized the relevance of bacterial culture, which can be extended for exhaustivity as described here, as standard operational procedures to analyze the airway microbiota of COPD patients. This study had certain limitations, including its monocentric and cross-sectional design with a one-time sampling. The lack of follow-up restricted our ability to assess the stability and variability of the microbial markers associated with exacerbation risk. Although our study was primarily descriptive and focused on taxonomic composition, we hoped that our findings would serve as a foundation for future, more comprehensive functional analyses, ultimately deepening our understanding of microbial community dynamics and, more broadly, COPD pathogenesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we analyzed the airway microbiota of stable COPD patients using a dual approach that combined extended culture and 16S metagenomics. We found a loss of the α-diversity and a decrease in the genera \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e in HR patients. Such findings may confirm other studies suggesting a potential protective role for commensal bacteria and probiotics for preventing exacerbations. Importantly, reductions in \u003cem\u003eS. mutans\u003c/em\u003e, as well as α-diversity were readily detectable through bacterial culture. Future research should incorporate functional and longitudinal studies to further validate the identified microbiota features and support the development of targeted preventive strategies for COPD exacerbations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Exacerbation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCAT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCOPD Assessment Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCystic Fibrosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputed Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCFU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColony-Forming Units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDNA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyriboNucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDLCO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusing capacity of the Lung for Carbon Monoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFEV\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced Expiratory Volume in 1 second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFVC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForced Vital Capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGOLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Initiative for Chronic Obstructive Lung Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHACEK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eHaemophilus spp.\u003c/em\u003e, \u003cem\u003eAggregatibacter actinomycetemcomitans\u003c/em\u003e, \u003cem\u003eCapnocytophaga spp.\u003c/em\u003e, \u003cem\u003eCardiobacterium hominis\u003c/em\u003e, \u003cem\u003eEikenella corrodens\u003c/em\u003e, \u003cem\u003eKingella kingae\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMALDI-TOF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMatrix-Assisted Laser Desorption/Ionization - Time-Of-Flight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003emMRC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodified Medical Research Council\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNVS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNutritionally Variant Streptococci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOTU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOperational Taxonomic Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePotentially Pathogenic Microorganisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRNA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003erRNA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eribosomal RiboNucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResidual Volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSAT\u0026eacute;\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimultaneous Alignment and Tree estimation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSEPP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSAT\u0026eacute;-Enabled Phylogenetic Placement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTLC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Lung Capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are deeply grateful to all healthcare professionals who contributed to the RINNOPARI cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was funded by a grant from the University Hospital of Reims and the University of Reims Champagne-Ardenne (Hospital University Project RINNOPARI).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe 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. The raw data of extended cultures that support the findings are included in this published article (Supplementary information files 1).\u003c/p\u003e\n\u003cp\u003eEthics approval and informed consent\u003c/p\u003e\n\u003cp\u003eThis 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 (Comit\u0026eacute; de Protection des Personnes\u0026mdash;Dijon EST I, no. 2016-A00242-49). Informed consent was obtained from all the patients.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eJ.M. Perotin reports lecture honoraria from AstraZeneca, and support for attending meetings from AstraZeneca and Chiesi, outside the submitted work. G. Desl\u0026eacute;e 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.\u003c/p\u003e\n\u003cp\u003eAuthor details\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eUniversit\u0026eacute; de Reims Champagne-Ardenne, INSERM, CHU de Reims, Laboratoire de Bact\u0026eacute;riologie-Virologie-Hygi\u0026egrave;ne hospitali\u0026egrave;re, P3Cell, U 1250, Reims, France. \u003csup\u003e2\u003c/sup\u003eUniversit\u0026eacute; de Reims Champagne-Ardenne, INSERM, CHU de Reims, Service des Maladies Respiratoires, P3Cell, U 1250, Reims, France. \u003csup\u003e3\u003c/sup\u003eCentre Brestois d\u0026rsquo;Analyse du Microbiote (CBAM), CHU de Brest, Brest, France. \u003csup\u003e4\u003c/sup\u003eUniversit\u0026eacute; de Brest, Inserm, UMR 1078, Unit\u0026eacute; de Bact\u0026eacute;riologie, CHRU de Brest, F-29200 Brest, France. \u003csup\u003e5\u003c/sup\u003eUniversit\u0026eacute; de Reims Champagne-Ardenne, INSERM, P3Cell, U 1250, Reims, France. \u003csup\u003e6\u003c/sup\u003eUniversit\u0026eacute; de Reims Champagne-Ardenne, EA7509 IRMAIC, CHU de Reims, Service des Maladies Respiratoires, Reims, France.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eErb-Downward JR, Thompson DL, Han MK, Freeman CM, McCloskey L, Schmidt LA, et al. 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Severity-Related Changes of Bronchial Microbiome in Chronic Obstructive Pulmonary Disease. Munson E, \u0026eacute;diteur. J Clin Microbiol. d\u0026eacute;c 2014;52(12):4217‑23. \u003c/li\u003e\n\u003cli\u003eSibley CD, Grinwis ME, Field TR, Eshaghurshan CS, Faria MM, Dowd SE, et al. Culture Enriched Molecular Profiling of the Cystic Fibrosis Airway Microbiome. Planet PJ, \u0026eacute;diteur. PLoS ONE. 28 juill 2011;6(7):e22702. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COPD - Chronic Obstructive Pulmonary Disease, exacerbation risk, extended culture, metagenomics, microbiota, sputum, stable state","lastPublishedDoi":"10.21203/rs.3.rs-6206453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6206453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background.\nChronic 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 microbiota have been linked to exacerbation frequency, the underlying mechanisms remain unclear.\n\nMethods. \nThis study investigated the associations between airway microbiota composition in stable COPD patients and exacerbation history. 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 metagenomics. The combination of these approaches provided complementary insights, enabling a more comprehensive characterization of the microbiota.\n\nResults.\nMicrobial composition analysis revealed a loss of α-diversity in HR patients. This group also exhibited increased abundances of Pseudomonadota and Bacteroidota, alongside a marked decrease in the proportions of Lactobacillus and Streptococcus. Notably, significant reductions were observed at the species level for Streptococcus salivarius and Streptococcus mutans. A comparison of the two methods underlined that 16S rRNA metagenomics 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.\n\nConclusion.\nThis study revealed microbiological characteristics linked to exacerbation history in stable COPD patients, highlighting the need for future functional and longitudinal research to validate these airway microbiota features and develop targeted preventive strategies.","manuscriptTitle":"Association between exacerbation history and airway microbiota assessed by extended bacterial culture and metagenomic approaches in stable COPD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 12:29:37","doi":"10.21203/rs.3.rs-6206453/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T06:08:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-30T09:46:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244069188513918307290441309866691867807","date":"2025-04-29T16:28:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141280565049369368805108001126404999370","date":"2025-04-29T09:59:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T10:26:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149549671935092511312231757438777922799","date":"2025-04-03T07:24:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34739160366794466516311598665861121821","date":"2025-03-21T16:21:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-21T16:14:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T15:49:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-17T13:42:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-14T07:52:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-11T19:41:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a65612c6-7db8-4c78-b528-0c43b6350817","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46227630,"name":"Biological sciences/Microbiology/Clinical microbiology"},{"id":46227631,"name":"Biological sciences/Microbiology/Communities/Microbiome"},{"id":46227632,"name":"Health sciences/Diseases/Respiratory tract diseases/Chronic obstructive pulmonary disease"},{"id":46227633,"name":"Biological sciences/Microbiology/Bacteria/Metagenomics"},{"id":46227634,"name":"Biological sciences/Microbiology/Bacteria/Bacterial techniques and applications"},{"id":46227635,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-09-29T16:01:05+00:00","versionOfRecord":{"articleIdentity":"rs-6206453","link":"https://doi.org/10.1038/s41598-025-14994-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-25 15:57:09","publishedOnDateReadable":"September 25th, 2025"},"versionCreatedAt":"2025-03-31 12:29:37","video":"","vorDoi":"10.1038/s41598-025-14994-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-14994-x","workflowStages":[]},"version":"v1","identity":"rs-6206453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6206453","identity":"rs-6206453","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}