{"paper_id":"20e03fed-45ab-4297-b7e7-29de52c44a02","body_text":"Comparative metagenomic analysis of the sputum microbiome in different COPD clinical states | 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 Research Article Comparative metagenomic analysis of the sputum microbiome in different COPD clinical states Lamis Galal, Heba M. Abostate, Maha Eid Omran, Sahar M. R. Radwan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5921039/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Chronic obstructive pulmonary disease (COPD) is a well-known respiratory illness, and COPD patients oscillate between a stable state and an exacerbated state. which can lead to disease deterioration. Studies suggest that respiratory microbiome dysbiosis plays a vital role in COPD exacerbation. However, the exact microbial composition among different clinical states of COPD is still elusive. Objectives To determine and compare the respiratory microbiome composition in different COPD clinical states, namely, the stable state (S-COPD) and the acute exacerbated state (AE-COPD). Methods In this study, 35 sputum samples were collected from COPD patients: S-COPD patients (n = 18), and AE-COPD patients (n = 17). The sputum microbiome was analyzed via 16S rRNA gene sequencing. Bioinformatics analysis was used to determine changes in the microbiota among the comparison groups. Results The most abundant phyla among all the samples were Proteobacteria, Fusobacteria, Firmicutes , and Actinobacteria , with Paracoccus , Streptomyces Leptotrichia Fusobacterium and Ruminococcaceae being the most prevalent genera.A dissimilarity in abundance across the studied COPD states was observed, with significantly greater abundance of Proteobacteria and Fusobacteria in S-COPD patients and greater abundance of Firmicutes in AE-COPD patients at the phylum level. Paracoccus , Fusobacterium, Streptococcus, Haemophilus and Moraxella were significantly different between the two groups and were more prevalent in S-COPD, whereas Cellulosilyticum, Streptomyces, Leptotrichia, Ruminococcaceae_UCG_014 and Atopobium were more prevalent in exacerbated individuals. Alpha diversity revealed greater diversity in stable versus exacerbated patients, and a PCoA plot of Bray‒Curtis and weighted UniFrac distances revealed that stable patients were highly clustered, whereas exacerbated patients were more disseminated. At the genus level, LEfSe analysis revealed the dominance of Cellulosilytic, Liptotrichia and Streptomyces in the AE-COPD group , whereas the S-COPD group microbiome was dominated by the genera Paracoccus , Fusobacterium , Streptococcus Haemophilus and Moraxella ( p < 0.05). Conclusion The results of the present study suggest that COPD patients have unique microbial profiles that differ across different states, with increased abundances of Proteobacteria , chiefly Paracoccus . These findings need more research to clarify the definite role of microbiome dysbiosis in COPD pathogenesis. COPD Exacerbation microbiome proteobacteria paracocus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Chronic obstructive pulmonary disease (COPD) is characterized by chronic airway inflammation leading to impaired lung function and limited airflow ( 1 ). COPD is the fifth leading cause of death worldwide, and by 2030, it is expected to be the fourth leading cause of death ( 2 ); consequently, COPD causes a heavy socioeconomic burden and has become an active research area. COPD patients either have mild symptoms and are in a stable disease state or experience episodes of acute worsening symptoms called acute exacerbations ( 3 ). Exacerbations are triggered by air pollutants, infection or unknown causes ( 4 ). The lung airways contain diverse microbiome compositions that affect the susceptibility or pathogenesis of respiratory diseases ( 5 ). Although bacteria have been detected in sputum cultures from COPD patients during stable states, particularly during exacerbation states, the changes in bacterial ecology and its relationship with disease pathogenesis and exacerbation are yet unclear ( 6 ). Traditional culture techniques have many limitations, such as poor sensitivity and uncultivable bacteria. Moreover, in some cases, colonizing bacteria grow easily from cultured samples and interfere with pathogenic bacteria, causing exacerbations ( 7 ). Recently, advanced diagnostic approaches and the development of culture-independent techniques such as 16S rRNA gene sequencing have provided an opportunity for in-depth studies of the lung microbiome, as these methods can detect uncultivable bacteria and provide data about the microbial composition, diversity, richness and potential functional role of microbiome members ( 8 ). Several noninvasive and invasive procedures are used for studying the lung microbiome; sputum is the most commonly used method because it is noninvasive and easy to access ( 9 ). In this study, 16S rRNA sequencing and metagenomics analysis were used to study the constitution of the sputum microbiome in COPD patients and compare different COPD states (S-COPD and AE-COPD). 2. Methods and statistics 2.1. Ethical Statement In this study, all the procedures performed were reviewed and approved by the Ethical Committee of Al-Azhar University, Egypt. All participants provided written informed consent. 2.2. Participant Selection The participants in the study were hospitalized patients diagnosed with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria (10). The participants underwent examinations, and sociodemographic and clinical information, including smoking history and the existence of coexisting diseases, respiratory symptoms, exacerbation frequency and therapies, was recorded. The exclusion criteria were age less than forty years; a history of smoking; a clinical diagnosis of other pulmonary diseases, such as bronchiectasis, cystic fibrosis, pulmonary tuberculosis, pulmonary edema, etc.; and cooccurring conditions, such as neoplasia; cardiac, hepatic or kidney disease; and HIV infection. The exclusion criteria included patients who received antibiotic therapy (for at least three months without the use of antibiotics for any other reason), immunosuppressive drugs or microbial preparations such as probiotics or prebiotics. 2.3. Clinical sample collection In this investigation, induced sputum samples were collected (11). A total of 35 samples were assigned to two clinical states: S-COPD (n=17) and AE-COPD (n=18). The samples were taken early on the first day. Aliquots (0.5 ml) of sputum samples were taken and stored at −80 °C for DNA extraction, and the remaining samples were subjected to routine culture. 2.4. DNA Extraction With some modifications, such as the addition of lysozyme and dithiothreitol solutions, genomic DNA was extracted from each sputum sample via the commercial QIAamp® DNA Mini Kit (Qiagen, Germany) as directed by the manufacturer. The quality and quantity of the extracted DNA were evaluated via a NanoDrop system (NanoDrop Technology, USA). It was then visualized via 2% agarose gel electrophoresis and stored at -80°C for later analysis. 2.5. PCR amplification and 16S sequencing 16S rRNA gene amplification was applied to the sequencing and DNA amplification samples. The Human Microbiome Project Consortium (HMP) (http://www.hmpdacc.org/tools_protocols/tools_protocols.php) was used for the construction of primers and barcodes. Using the extracted DNA as a template and universal 16S rRNA primers, PCR was performed to amplify the hypervariable V3–V4 regions of the 16S rRNA gene. Using Illumina adapters, forward-primer 27F (5-GCC TAC GGG AGG CAG CAG T) and reverse-primer 1462R (5-GGACTACHVGGGTATCTAATCC) were altered in accordance with a previously published process (12). The PCR conditions were set as follows: a 30-second denaturation stage at 95°C, a 30-second annealing step at 60°C, a 30-second extension step at 72°C, and a final elongation step at 72°C for 5 minutes. The initial hot-start incubation was conducted at 94°C for 3 minutes. Ethidium bromide staining and 2% agarose gel electrophoresis were used to analyze the amplicons. Using the Illumina Nextera XT Index Kit (Illumina, CA, USA), the libraries were assembled by fastening Illumina adapters to the amplifiers. Agencourt AMPure XP beads were used to clean the PCR amplicons in accordance with the manufacturer's instructions (Beckman Coulter, Inc., CA, USA). Purified amplicon libraries were examined via an Agilent Bioanalyzer 2100 with an Agilent DNA 1000 Kit (Agilent, Palo Alto, CA, USA) to guarantee the elimination of primers and any nonspecific amplicons (13). Using paired-end Illumina MiSeq sequencing on an Illumina MiSeq instrument (Illumina Inc., San Diego, CA, USA), the 16S rRNA was sequenced at the IGA Technology Services Company (Udine, Italy) (14). 2.6. Sequence library analysis Amplicon sequences were demultiplexed via MiSeq Reporter v2.3 (Illumina) as a first quality step. 16S rRNA gene sequencing produced raw paired-end sequences in FASTQ format. As a second stage, sequence processing and quality filtering were performed via the Quantitative Insights into Microbial Ecology\" (QIIME2R version 0.99.21) (15) pipeline to extract taxonomic information. The \"join paired ends.py\" argument was used to fuse overlapping paired-end 16S rRNA gene sequences. Sequences with ambiguous reads (N), low-quality sequence ends with mismatched forward or reverse primers, failed sequence reads, barcodes, and primers were eliminated for quality control. Sequences under 200 bp were also trimmed via the \"QIIME script split_libraries.py calls\" parameter (quality score < 25). Next, chimeras were detected in these clean sequences via the \"identify_chimeric_seqs.py\" option. Using an identity criterion of 97% (16), sequences were grouped into operational taxonomic unit (OTU) clusters and aligned via the SILVA alignment database (http://www.arb-silva.de/). (17) 2.7. Statistical analysis All graphical representations and statistical analyses were performed with https://www.microbiomeanalyst.ca. The Kruskal‒Wallis (KW) test and the nonparametric Mann‒Whitney test were used to identify species with significant differences between two or more groups, respectively. Four measures were used to assess bacterial α diversity: observed OTUs, the Chao1 richness measure, the Shannon index and the Simpson index diversity measure. To investigate the differences in the bacterial communities among COPD patients at various phases of species complexity, beta diversity analysis was utilized. On the basis of clinical variables and the relative abundance of taxa at the phylum, genus, and OTU levels, PERMANOVA was used to determine the statistical significance of the groupings. The results are displayed via PCoA plots. After the differentially abundant taxa (p ≤ 0.05) that best explained the differences between the two participant groups were identified, a linear discriminant analysis (LDA) effect size (LEfSe) approach was applied to obtain an LDA-based effect size score (18). The R package pheatmap was used to identify differential expression of OTUs among samples. Finally, Spearman rank correlation analysis was performed to determine the bacterial associations. 3. Results 3.1. Patient demographics: A total of 35 participants were enrolled in the study; all of them were males aged 50--80 years. The samples were assigned to two clinical states: S-COPD (n=18) and AE-COPD (n=17). 3.2. Sequence data profile The sequence data for the raw data were deposited with the accession number PRJNA1021628 in the NCBI Bioproject (http://www.ncbi.nlm.nih.gov/bioproject). There were 495 operational taxonomic units (OTUs) recognized across the 35 samples for the microbiota. The rarefaction curves confirmed that our samples covered the dominant members of the bacterial communities (Fig. 1). The demultiplexing step of the paired-end sequences resulted in a total of 9 351 510 reads, with a minimum of 127 and a maximum of 428 097. The truncation length parameters of DADA2 were p-trunc-len-f 280 and p-trunc-lenr 220. 3.3. The sputum microbiome taxonomic profile The entire taxonomic profile of the two studied groups included 13 phyla, 21 classes, 29 orders, 41 families, and 60 genera. 3.3.1 Relative abundance of microbial communities in S-COPD and AE-COPD The microbial composition in sputum was similar in patients in both groups, with slight differences. At the phylum level, the downstream analysis revealed that Proteobacteria was the most abundant phylum in most samples within the two groups and was significantly more prevalent in the S-COPD group (S-COPD: 75%, AE-COPD: 66%) (p= 0.0027). Additionally, Fusobacteria was more abundant in the S-COPD group (S-COPD: 8%, AE-COPD: 6%). with a significant difference (p= 0.023). On the other hand, Firmicutes and Actinobacteria were more abundant in the AE-COPD group than in the S-COPD group, where Firmicutes (S-COPD: 8%, AE-COPD: 16%) and Actinobacteria (S-COPD: 7%, AE-COPD: 11%) were predominant. The abundance of Actinobacteria was not significantly different in the AE-COPD group, whereas that of Firmicutes was significantly different (p= 0.029). (Fig 2) At the genus level, 60 genera were identified among the two groups. The genera identified from the most to least abundant were Paracoccus , Cellulosilyticum , Streptomyces , Leptotrichia , Fusobacterium , Ruminococcaceae_UCG_014 , Atopobium , Streptococcus , Haemophilus and Moraxella . These results revealed differences in the composition of the respiratory microbiota between the S-COPD and AE-COPD groups. Paracoccus was the most common genus in both groups, but its percentage was greater in stable individuals (S-COPD: 71.6%, AE-COPD: 62.6%). Additionally, Fusobacterium, Streptococcus, Haemophilus and Moraxella were significantly different between the two groups and were more prevalent in the stable group (2.4, 1.5, 1.3 and 1.1 in SCOPD and 0.9, 1, 0.92 and 0.8 in AE-COPD, respectively). On the other hand, some genera, such as Cellulosilyticum, Streptomyces, Leptotrichia, Ruminococcaceae_UCG_014 and Atopobium, were more prevalent in exacerbated individuals (1,6.2, 4.8,0.7 and 0,5 in AE-COPD 9,8.8.5.3, 2.2, and 1.9 in SCOPD, respectively). 3.3.2. Core microbiome The core genera were 2 genera ( Streptomyces and Streptococcus 8%) common in both groups while there were 6 other genera present in all sample in S-COPD group ( Paracoccus, Fusobacterium, Leptotrichia, Moraxella, Oribacterium and Shingomonas ) as shown in figure 4. Most of detected genera were distributed genera that may be present in some but not all the samples. 3.3.3. Bacterial diversity analysis Alpha diversity Different alpha diversity indices were calculated and revealed greater diversity for stable patients than for exacerbated patients in terms of the Chao1, Simpson, observed and Shannon diversity indices ( Fig 5) . The results revealed a significant difference between the S-COPD group and the AE-COPD group according to the Chao, Shannon’s and observed indices (P values equal to 0.00029, 0.04 and 0.0002, respectively), whereas Simpson’s indices were not significantly different (*, p=0.03*), as revealed by the ANOVA samples t test and Mann‒Whitney test, respectively. Beta diversity Beta diversity was used to compare community diversity and determine community ordination and clustering. The PCoA visualization plot of the weighted UniFrac distance revealed high clustering of the stable group, whereas exacerbated samples appeared more scattered (p = 0.354), as shown in Figure (6) . The PCoA visualization plot of the unweighted UniFrac distance revealed high clustering for both the stable and exacerbated states (p=0.009), as shown in Figure (7) . Moreover, the Bray‒Curtis PCoA plot revealed that stable patients were highly clustered, whereas exacerbated patients were more disseminated (p=0.02, as shown in Figure 8) . 3.4.4. LefSe analysis LEfSe on an LDA score of ±3 at a p value cutoff of 0.05 and false discovery rate (FDR) adjustment revealed significant differences between the microbial populations of S-COPD and AE-COPD patients in terms of the abundance of Paracoccus (LDA score = 3.14), Fusobacterium (LDA score = 1.81), Streptococcus (LDA score =1.53) , Haemophilus (LDA score = 1.51), Moraxella (LDA score =1.36) and Streptococcus (LDA score =1.35) genera in the S-COPD group, which were greater than those in the AE-COPD group. (Fig 9) 3.3.5. Correlation analysis 3.4.5.2. A correlation between S-COPD and AE-COPD community members . The Spearman correlation coefficient at the phylum level revealed a significant correlation between OTUs within the phylum Actinobacteria and those within the phylum Proteobacteria (r= 0.49). There was a significant positive correlation between the number of members of the phylum Firmicutes and the number of members of the phylum Proteobacteria (r= 0.42). There was also a significant positive correlation between the number of members of the phylum Fusobacteria and the number of members of the phylum Proteobacteria (r= 0.47), as shown in Figure 10. At the genus level, significant positive correlations were detected between OTUs assigned to the genera Atopobium, Haemophilus, Paracoccus and Streptococcus and Streptomyces (r= 0.45, 0.33, 0.52 and 0.4, respectively), and significant positive correlations were detected between OTUs belonging to the genera Fusobacteria, Haemophilus, Leptotrichia, Moraxella and Ruminococcaceae_UCG_014 and the genus Streptococcus (r= 0.36, 0.42, 0.41, 0.35 and 0.34, respectively). A significant positive correlation was detected between OTUs belonging to the genera Leptotrichia and Paracoccus (r= 0.5), Haemophilus and Leptotrichia (r= 0.38) and Fusobacteria and Haemophilus (r= 0.57), and a negative correlation was detected between Neisseria and Paracoccus (r= -0.30) (Fig. 11). 4. Discussion Research on the COPD microbiome has revealed its role in both disease development and exacerbation, and the disordered microbiome is believed to be involved in the pathogenesis of COPD by modulating inflammatory and immune responses (19). In our study, regarding the overall phyla composition, the most prevalent phyla in the two studied groups were Proteobacteria, Fusobacteria, Firmicutes, and Actinobacteria . Proteobacteria was the most abundant phylum in most samples within the two groups and was significantly more prevalent in the S-COPD group (p= 0.0027). Generally, different COPD microbiome studies have identified Proteobacteria, Firmicutes, Bacteroides and Actinobacteria as the most abundant respiratory microbiome phyla; however, one of them is the most dominant phylum. In accordance with our results, Garcia-Nunez et al . revealed the predominance of the Proteobacteria , Firmicutes, and Actinobacteria phyla (20). Another study reported that the Proteobacteria phylum constituted half of the microbiome of COPD subjects, where Firmicutes were dominant in the healthy microbiome (21). Many previous studies were in line with our findings about the predominance of the Proteobacteria phylum (22.23.24). In contrast to our observations, other studies have reported a greater abundance of Firmicutes in COPD patients (25.26); for example, a study performed by Goolman et al. revealed the dominance of four phyla, Firmicutes , Proteobacteria , Bacteroides and Actinobacteria , with an increased relative abundance of Firmicutes . Wang et al. revealed that Firmicutes was the most dominant phylum in the sputum microbiome of COPD patients, followed by Actinobacteria and Proteobacteria . The dissimilarity in the predominant phyla between different studies could be attributed to many factors, including different sample sizes; different patient sample groups (e.g., smokers, smokers, and types of treatment); different sample types (e.g., sputum, bronchoalveolar lavage or tracheal aspirate); different techniques; and other general factors, such as geographical location, weather, air pollution, and other environmental conditions. At the genus level, an increase in the relative abundance of a certain genus may be conceived as a cause of COPD exacerbation, but it may differ from the culture results, as colonizing microorganisms may be cultivated easily from bronchial samples, as reported for patients with exacerbation showing chronic Pseudomonas aeruginosa colonization (27. 28). The genera most frequently reported to be predominant in different previous COPD studies were Haemophilus, Veillonella, Prevotella, Rothia, Lactobacillus, Granulicatella Staphylococcus and Streptococcus (21.25,26). Notably, our study revealed the predominance of some of these genera, although the predominance of Paracocus , which represented the most dominant genus in our study with increased relative abundance stable states, was very unique. Common members of a microbial community often perform moderate functions of the host–microbe symbiotic system. We estimated a low degree of similarity between the core microbiome for each of the two stages of disease (stable and exacerbation). Two ASVs were present in all the subjects; these belong to the genera Streptococcus, and Streptomyces . Gupta et al. reported that ten ASVs were shared among two stages of disease (stable and exacerbation), belonging to the genera Oribactrum , Streptococcus, and Sphingomonas (27) . Einarsson et al. estimated that the co-occurrence of bacterial taxa and the observation of a putative ‘core’ community within the lower airways, including Prevotella spp., Veillonella spp. and Actinomyces spp., were also apparent in their study. Furthermore, assigning specific OTUs detected in >90% of samples and accounting for more than 1% of the total sequence read abundance to the ‘core’ microbiota demonstrated that it consisted of those same anaerobic taxa, in addition to members of the Streptococcus and Rothia genera (28) . Cameron et al. reported that eight bacterial genera were present in all sputum samples: Haemophilus, Oribactrum and Streptococcus (29) . Our results revealed that microbiome diversity was lower in AE-COPD patients than in S-COPD patients. This aspect is distinct among different studies, and some studies have shown an overall reduction in microbial diversity during COPD exacerbations compared with stable patients (6, 30 and 31). These results are consistent with those of the present study, thus suggesting that microbial diversity may be a biological indicator of AE-COPD. Garcia-Nuñez et al. reported decreased alpha diversity in advanced COPD patients compared with moderate-to-severe disease patients (20). These findings support the occurrence of severity-related changes in the bronchial microbiome in COPD patients. Our results do not correlate with previous data from Pragman and colleagues, who did not find severity-related differences in microbial diversity in BALF samples from COPD patients (22). Hazra et al. reported that the alpha diversity of moderate COPD patients was lower than that of patients with severe COPD (32). However, Goolam et al. did not observe a significant difference in microbial diversity between stable COPD and exacerbated COPD groups (25). Jubinville et al. reported a difference in alpha diversity when paired samples were compared, i.e., the diversity in the paired samples differed across the disease state, with most exacerbated samples showing greater diversity (33). This inconsistency among studies could be due to distinct sampling methods, COPD states and exacerbations, different geographic regions and diversity measures used, whereas Jubinville et al. used the Simpson index. Unlike the Shannon index, the Simpson index is more affected by the relative abundances (i.e., evenness) of the species in a sample; this suggests that during the exacerbated state of disease, the abundances of species/OTUs change but not the number of species/OTUs (richness) ( 34 ) . Additionally, the type of treatment used in COPD patients is another factor affecting microbiome composition. Data show that the use of antibiotics and corticosteroids in exacerbated COPD patients influences the respiratory microbiome (35). Inhaled corticosteroids, the main treatment for COPD, significantly increase bacterial burden and diversity, with increased pathogenic strains (36, 37, 38). Antibiotics are usually used in COPD patients with frequent exacerbations to decrease the recurrence of these episodes. This type of therapy was found to decrease microbial diversity, which lasts for months and persists during stable periods between episodes (39,40). Principal coordinate analysis (PCoA) revealed significant differences in the microbial community structure between the AE-COPD and S-COPD patients (p = 0.02). This finding was in accordance with the findings of Su et al., who reported that principal coordinate analysis (PCoA) revealed significant differences in microbial community structure between the AECOPD group and stable group (p = 0.02) and between the AECOPD group and healthy control group (p = 0.035) (31) . Goolam et al. and colleagues reported that beta diversity measures showed no clustering for any of the variables via PCoA or weighted UniFrac (for microbiome) measures (25). In contrast to our results, Gupta et al., PCoA revealed extensive overlap in membership between the bacterial communities of the ECOPD and stable COPD disease groups. (27) . This inconsistency among studies could be due to the use of different distance methods. LEfSe analysis at the genus level revealed that the sputum microbiome of the AE-COPD group was characterized by a dominance of Cellulosilyticun, Liptotrichia and Streptomyces , whereas the microbiome in the stable COPD group was dominated by the genera Paracoccus , Fusobacterium , Streptococcus Haemophilus and Moraxella ( p < 0.05). Hazra et al. reported that six marker genera, Streptococcus and Rothia , were highly abundant in moderate COPD patients, whereas Leptotrichia and Pseudomonas were highly prevalent in patients with severe COPD (30) . Haldar et al. reported that linear discriminant effect size (LEfSe) analysis revealed that a greater abundance of Proteobacteria and lower proportions of Firmicutes, Bacteroidetes and Actinobacteria were the major contributors to the differentiation of COPD patients from healthy controls (21) . Chang et al. reported that LEfSe analysis revealed that, during the AECOPD period, Pseudomonas was highly abundant. In stable COPD, Haemophilus influenzae and Pasteurellales are more abundant (41) . The Spearman correlation coefficient was used to determine the positive correlation between different bacterial OTUs at different phylum and genus levels. Most of the genera in stable COPD and AECOPD patients were more positively correlated. We also noted that many potentially clinically relevant taxa, such as Streptococcus and Haemophilus , were correlated with other taxa. This finding was consistent with those of other studies (23,27) . In contrast, Yang et al. reported a negative correlation between OTUs assigned to the genus Haemophilus and Streptococcus in nonfrequent exacerbations, and Li et al. reported a negative correlation between OTUs assigned to the genus Fusobacteria and Streptococcus and Fusobacteria and Streptococcus in stable COPD patients (42,43) . However, correlations between taxa are not proof of functional relationships between members of the community. Therefore, further studies are needed to focus on the functional role of such taxa found within these communities. Limitations The noteworthy limitations of our study are as follows: the sample size was small, and the samples were cross-sectional rather than longitudinal; thus, the interference of coexisting factors was unavoidable, and there was a lack of healthy control subjects. Sputum samples were used in this study; although they are the most commonly used samples in airway microbiome studies (noninvasive methods), they have the drawbacks of being a mixture of upper and lower respiratory tract microbiomes. Conclusion In conclusion, we found that dysbiosis in the lung microbiome among different COPD states is characterized by a high abundance of Proteobacteria , especially those associated with the paracocus microbiome . However, the precise role of this dysbiosis in the pathogenesis and severity of COPD is still unclear. More studies using new metagenomics and bioinformatics analyses and metabolomics are necessary to determine the role of the respiratory microbiome and its metabolic profile in COPD progression and open future avenues to apply these findings in clinical practice to introduce new therapeutic approaches and improve clinical outcomes. Declarations Ethics approval and consent to participate In this study, all the procedures performed were reviewed and approved by the Ethical Committee of Al-Azhar University, Egypt. All participants provided written informed consent. There are no individual person’s data in any form in this manuscript. Funding: this work didn’t receive any funding. Availability of data and materials sequence data that support the findings of this study have been deposited in the SRA with the primary accession code PRJNA 1021628 in the NCBI Bioproject (http://www.ncbi.nlm.nih.gov/bioproject). Competing interests I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. the results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration (from you or one of your Contributing Authors) by another publisher. I confirm the corresponding author has read the journal policies and submit this manuscript in accordance with those policies. All of the material is owned by the authors and/or no permissions are required. Author Contributions Statement S R: PI responsible for the idea, aim of work, the whole design, analysis of sequences, supervising all wet and dry lab, writing and reviewing the manuscript. H A: reviewing data analysis, writing and reviewing the manuscript. L G: sample collection, Analysis of the sequences, PCR and writing the methods in the manuscript. M O: helping in writing parts of the manuscript and reviewing Acknowledgement Not applicable References Lei Wang, Ke Hao, Ting Yang, Chen Wan. Role of the Lung Microbiome in the Pathogenesis of Chronic Obstructive Pulmonary Disease Chinese Medical Journal September 5, 2017 Volume 130 ¦ Issue 17 2107 Olortegui-Rodriguez, J.J., Soriano-Moreno, D.R., Benites-Bullón, A. et al . 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Microbiological Analysis of Root Canal Infections Using High Throughput Sequencing on the Illumina MiSeq Platform (University of Leeds (School of Dentistry)). Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7:335–336. http://dx.doi.org/10.1038/nmeth.f.303. 16.Konstantinidis KT, Tiedje JM. Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Current Opinion in Microbiology. 2007; 10: 504–509. doi: 10.1016/j.mib.2007. 08.006 PMID: 17923431 Al-Hebshi NN, Nasher AT, Idris AM, et al. Robust species taxonomy assignment algorithm for 16S rRNA NGS reads: application to oral carcinoma samples. 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Lung microbiome of stable and exacerbated COPD patients in Tshwane, South Africa. Sci Rep 11 , 19758 (2021). https://doi.org/10.1038/s41598-021-99127-w Wang, J., Chai, J., Sun, L., Zhao, J. & Chang, C. The sputum microbiome associated with different subtypes of AECOPD in a Chinese cohort. BMC Infect. Dis. 20 (1), 610 (2020). Gupta S, Shariff M, Chaturvedi G, et al. Comparative analysis of the alveolar microbiome in COPD, ECOPD, Sarcoidosis, and ILD patients to identify respiratory illnesses specific microbial signatures. Sci Rep. 2021 Feb 17;11(1):3963. Einarsson GG, Comer DM, McIlreavey L, et al. Community dynamics and the lower airway microbiota in stable chronic obstructive pulmonary disease, smokers and healthy nonsmokers. Thorax. 2016 Sep;71(9):795-803. Cameron SJ, Lewis KE, Huws SA, Lin W, Hegarty MJ, Lewis PD, Mur LA, Pachebat JA. Metagenomic sequencing of the chronic obstructive pulmonary disease upper bronchial tract microbiome reveals functional changes associated with disease severity. PLoS One. 2016 Feb 12;11(2):e0149095. Sze MA, Dimitriu PA, Hayashi S, et al. The lung tissue microbiome in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;185(10):1073–80 Su, L., Qiao, Y., Luo, J. et al . Characteristics of the sputum microbiome in COPD exacerbations and correlations between clinical indices. J Transl Med 20 , 76 (2022). https://doi.org/10.1186/s12967-022-03278-x Hazra D, Sm F, Chawla K, et al. The altered sputum microbiome profile in patients with moderate and severe COPD exacerbations, compared to the healthy group in the Indian population. F1000Res. 2023 Oct 27;12:528. Jubinville, E. et al . Exacerbation induces a microbiota shift in sputa of COPD patients. PLoS ONE 13 (3), e0194355 (2018). Johnson KV, Burnet PW. Microbiome: Should we diversify from diversity? 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Thorax . 2012; 67 (12):1075–1080. doi: 10.1136/thoraxjnl-2012-201924 [PubMed] [CrossRef] [Google Scholar] Huang YJ, Sethi S, Murphy T, Nariya S, Boushey HA, Lynch SV. Airway microbiome dynamics in exacerbations of chronic obstructive pulmonary disease. J Clin Microbiol . 2014; 52 (8):2813. doi: 10.1128/JCM.00035-14 [PMC free article] [PubMed] [CrossRef] [Google Scholar] Jakobsson HE, Jernberg C, Andersson AF, Sjölung-Karlsoson M, Jansson JK, Engstrand L. Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PLoS One . 2010; 5 (3):e9836. doi: 10.1371/journal.pone.000983 Chang K, Zou X, Li Z, et al. Microbiome in Acute Exacerbation and Stable Phase of COPD: A Descriptive and Comparative Study of 16 s rRNA Sequencing and Metagenomic Sequencing, 10 November 2020, PREPRINT (Version 1) available at Research Square Yang CY, Li SW, Chin CY, et al. Association of exacerbation phenotype with the sputum microbiome in chronic obstructive pulmonary disease patients during the clinically stable state. J Transl Med. 2021 Mar 23;19(1):121 Li W, Wang B, Tan M, et al. Analysis of sputum microbial metagenome in COPD based on exacerbation frequency and lung function: a case control study. Respir Res. 2022 Nov 19;23(1):321. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5921039\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":437917286,\"identity\":\"c06327a9-017f-4790-accf-46cd2a6f0167\",\"order_by\":0,\"name\":\"Lamis Galal\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Girls Al-Azhar University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lamis\",\"middleName\":\"\",\"lastName\":\"Galal\",\"suffix\":\"\"},{\"id\":437917287,\"identity\":\"5fbb84fd-7961-49d3-b704-1b488ee62a53\",\"order_by\":1,\"name\":\"Heba M. 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Radwan\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Girls Al-Azhar University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Sahar\",\"middleName\":\"M. R.\",\"lastName\":\"Radwan\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-01-28 22:08:09\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5921039/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5921039/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":79912840,\"identity\":\"4475692b-e69f-4e24-beb9-85d4f2707375\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":106670,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRarefaction curves of 16S rRNA gene sequences for each sample in both groups (S-COPD, AE-COPD) calculated for OTUs. The vertical axis represents operational taxonomic units, and the horizontal axis represents the number of samples sequenced. OTU = operational taxonomic unit. S-COPD group (blue curve) versus the AE-COPD group (red curve).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/8948a168661ed48377b50f36.png\"},{\"id\":79913340,\"identity\":\"a2f7605e-2066-4196-aac2-e5c33159471b\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:02:28\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":183730,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBar plots of the relative abundances of different phyla within the sputum microbiomes of the S-COPD and AE-COPD groups\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/f4ab65b24db7bdf87e58df9f.png\"},{\"id\":79912841,\"identity\":\"8fd903f4-2dd8-4c36-9752-8367057c9bce\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":40808,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBar plots of the relative abundance of the most prevalent genera in the sputum microbiome of the S-COPD and AE-COPD groups\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/bec61ddeaa64e494ce7f7b27.png\"},{\"id\":79912838,\"identity\":\"8fa7b329-f739-4ab5-b4fc-5d8aebd603b6\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":122421,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eHeatmap of the core microbiome at the genus level of the sputum microbiome of the two diagnostic groups\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/1974a970af20f749418b47fe.png\"},{\"id\":79913343,\"identity\":\"d02c7ec0-24cd-4d08-ac9c-797531649ae3\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:02:29\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":161130,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBoxplot presenting the alpha diversity in terms of A) Chao1, B) Simpson’s, C) observed and D) Shannon’s diversity indices. Differences between S-COPD and AE-COPD were evaluated via the Mann‒Whitney samples t test at the 0.05 level.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/89038f5f54af5875e5a8f879.png\"},{\"id\":79912846,\"identity\":\"754c985a-8b2e-451a-bf14-2f665f55dd66\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":123552,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePCoA 2D, PCoA 3D beta diversity between -COPD and S-COPD based on weighted UniFrac with PREMANOVA as the statistical method, p value=0.354\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/523a8456cbb5b523a8b9f3b7.png\"},{\"id\":79914140,\"identity\":\"a2516574-65d6-4dae-b722-480ef2a0c290\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:10:28\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":126503,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePCoA 2D, PCoA 3D beta diversity between AE-COPD and S-COPD based on unweighted UniFrac with PREMANOVA as the statistical method, p value=0.009\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/215623f4df6e56ed05f600f0.png\"},{\"id\":79912848,\"identity\":\"139f39ca-963f-469b-81e9-e6d4e33a8bd5\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":124288,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePCoA 2D, PCoA 3D beta diversity between AE-COPD and S-COPD based on Bray‒Curtis distance with PREMANOVA as the statistical method, p value=0.02.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/3a6be2a8f433dfbb1976d4c6.png\"},{\"id\":79912844,\"identity\":\"f376bb00-1fd2-411d-af41-74d94c910404\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":87469,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eHistogram of linear discriminant analysis (LDA) effect size (LEfSe) at the genus level in sputum samples from the S-COPD group and the AE-COPD group, with a p value cutoff of0.05. Negative (blue bars) LDA scores represent bacterial groups overrepresented in S-COPD patients, whereas positive (red bars) scores represent bacterial groups overrepresented in AE-COPD patients\\u003c/strong\\u003e.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/9763d1945d44271faf05c3c4.png\"},{\"id\":79913341,\"identity\":\"3bbc29ee-c0fb-443b-a220-8a8a6e7a0fdd\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:02:28\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":96907,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBacterial networkanalysis based on Spearman rank correlation at the phylum level according to diagnostic stage\\u003c/strong\\u003e. \\u003cstrong\\u003eThe average abundance of a genus is represented by the size of the circle, the correlation between two species is represented by a line, the strength of the connection is represented by the line's thickness, and a positive correlation is represented by the line's orange color.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/61e9794bf8ff05e222ba22ce.png\"},{\"id\":79912849,\"identity\":\"b443bc27-00c6-4273-8ef6-560edb71b35d\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 11:54:28\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":200508,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBacterial networkanalysis based on Spearman rank correlation at the genus level according to diagnostic stage\\u003c/strong\\u003e.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/22f0436aa0c8b8c818548775.png\"},{\"id\":79914505,\"identity\":\"8579a715-d05b-4507-9346-1256f5d3e4ae\",\"added_by\":\"auto\",\"created_at\":\"2025-04-04 12:18:29\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2857844,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5921039/v1/5d1c0260-8469-4620-99a1-bd9a16059a69.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Comparative metagenomic analysis of the sputum microbiome in different COPD clinical states\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eChronic obstructive pulmonary disease (COPD) is characterized by chronic airway inflammation leading to impaired lung function and limited airflow (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). COPD is the fifth leading cause of death worldwide, and by 2030, it is expected to be the fourth leading cause of death (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e); consequently, COPD causes a heavy socioeconomic burden and has become an active research area. COPD patients either have mild symptoms and are in a stable disease state or experience episodes of acute worsening symptoms called acute exacerbations (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Exacerbations are triggered by air pollutants, infection or unknown causes (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). The lung airways contain diverse microbiome compositions that affect the susceptibility or pathogenesis of respiratory diseases (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). Although bacteria have been detected in sputum cultures from COPD patients during stable states, particularly during exacerbation states, the changes in bacterial ecology and its relationship with disease pathogenesis and exacerbation are yet unclear (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTraditional culture techniques have many limitations, such as poor sensitivity and uncultivable bacteria. Moreover, in some cases, colonizing bacteria grow easily from cultured samples and interfere with pathogenic bacteria, causing exacerbations (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Recently, advanced diagnostic approaches and the development of culture-independent techniques such as 16S rRNA gene sequencing have provided an opportunity for in-depth studies of the lung microbiome, as these methods can detect uncultivable bacteria and provide data about the microbial composition, diversity, richness and potential functional role of microbiome members (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Several noninvasive and invasive procedures are used for studying the lung microbiome; sputum is the most commonly used method because it is noninvasive and easy to access (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). In this study, 16S rRNA sequencing and metagenomics analysis were used to study the constitution of the sputum microbiome in COPD patients and compare different COPD states (S-COPD and AE-COPD).\\u003c/p\\u003e\"},{\"header\":\"2. Methods and statistics\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1. Ethical Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;In this study, all the procedures performed were reviewed and approved by the Ethical Committee of Al-Azhar University, Egypt. All participants provided written informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2. Participant Selection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe participants in the study were hospitalized patients diagnosed with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria (10). The participants underwent examinations, and sociodemographic and clinical information, including smoking history and the existence of coexisting diseases, respiratory symptoms, exacerbation frequency and therapies, was recorded. The exclusion criteria were age less than forty years; a history of smoking; a clinical diagnosis of other pulmonary diseases, such as bronchiectasis, cystic fibrosis, pulmonary tuberculosis, pulmonary edema, etc.; and cooccurring conditions, such as neoplasia; cardiac, hepatic or kidney disease; and HIV infection. The exclusion criteria included patients who received antibiotic therapy (for at least three months without the use of antibiotics for any other reason), immunosuppressive drugs or microbial preparations such as probiotics or prebiotics.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3. Clinical sample collection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this investigation, induced sputum samples were collected (11). A total of 35 samples were assigned to two clinical states: S-COPD (n=17) and AE-COPD (n=18). The samples were taken early on the first day. Aliquots (0.5 ml) of sputum samples were taken and stored at \\u0026minus;80 \\u0026deg;C for DNA extraction, and the remaining samples were subjected to routine culture.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4. DNA Extraction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWith some modifications, such as the addition of lysozyme and dithiothreitol solutions, genomic DNA was extracted from each sputum sample via the commercial QIAamp\\u0026reg; DNA Mini Kit (Qiagen, Germany) as directed by the manufacturer. The quality and quantity of the extracted DNA were evaluated via a NanoDrop system (NanoDrop Technology, USA). It was then visualized via 2% agarose gel electrophoresis and stored at -80\\u0026deg;C for later analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.5. PCR amplification and 16S sequencing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e16S rRNA gene amplification was applied to the sequencing and DNA amplification samples. The Human Microbiome Project Consortium (HMP) (http://www.hmpdacc.org/tools_protocols/tools_protocols.php) was used for the construction of primers and barcodes. Using the extracted DNA as a template and universal 16S rRNA primers, PCR was performed to amplify the hypervariable V3\\u0026ndash;V4 regions of the 16S rRNA gene. Using Illumina adapters, forward-primer 27F (5-GCC TAC GGG AGG CAG CAG T) and reverse-primer 1462R (5-GGACTACHVGGGTATCTAATCC) were altered in accordance with a previously published process (12). The PCR conditions were set as follows: a 30-second denaturation stage at 95\\u0026deg;C, a 30-second annealing step at 60\\u0026deg;C, a 30-second extension step at 72\\u0026deg;C, and a final elongation step at 72\\u0026deg;C for 5 minutes. The initial hot-start incubation was conducted at 94\\u0026deg;C for 3 minutes. Ethidium bromide staining and 2% agarose gel electrophoresis were used to analyze the amplicons. Using the Illumina Nextera XT Index Kit (Illumina, CA, USA), the libraries were assembled by fastening Illumina adapters to the amplifiers. Agencourt AMPure XP beads were used to clean the PCR amplicons in accordance with the manufacturer\\u0026apos;s instructions (Beckman Coulter, Inc., CA, USA). Purified amplicon libraries were examined via an Agilent Bioanalyzer 2100 with an Agilent DNA 1000 Kit (Agilent, Palo Alto, CA, USA) to guarantee the elimination of primers and any nonspecific amplicons (13). Using paired-end Illumina MiSeq sequencing on an Illumina MiSeq instrument (Illumina Inc., San Diego, CA, USA), the 16S rRNA was sequenced at the IGA Technology Services Company (Udine, Italy) (14).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.6. Sequence library analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAmplicon sequences were demultiplexed via MiSeq Reporter v2.3 (Illumina) as a first quality step. 16S rRNA gene sequencing produced raw paired-end sequences in FASTQ format. As a second stage, sequence processing and quality filtering were performed via the Quantitative Insights into Microbial Ecology\\u0026quot; (QIIME2R version 0.99.21) (15) pipeline to extract taxonomic information. The \\u0026quot;join paired ends.py\\u0026quot; argument was used to fuse overlapping paired-end 16S rRNA gene sequences. Sequences with ambiguous reads (N), low-quality sequence ends with mismatched forward or reverse primers, failed sequence reads, barcodes, and primers were eliminated for quality control. Sequences under 200 bp were also trimmed via the \\u0026quot;QIIME script split_libraries.py calls\\u0026quot; parameter (quality score \\u0026lt; 25). Next, chimeras were detected in these clean sequences via the \\u0026quot;identify_chimeric_seqs.py\\u0026quot; option. Using an identity criterion of 97% (16), sequences were grouped into operational taxonomic unit (OTU) clusters and aligned via the SILVA alignment database (http://www.arb-silva.de/). (17)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.7. Statistical analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll graphical representations and statistical analyses were performed with https://www.microbiomeanalyst.ca. The Kruskal‒Wallis (KW) test and the nonparametric Mann‒Whitney test were used to identify species with significant differences between two or more groups, respectively. Four measures were used to assess bacterial \\u0026alpha; diversity: observed OTUs, the Chao1 richness measure, the Shannon index and the Simpson index diversity measure. To investigate the differences in the bacterial communities among COPD patients at various phases of species complexity, beta diversity analysis was utilized. On the basis of clinical variables and the relative abundance of taxa at the phylum, genus, and OTU levels, PERMANOVA was used to determine the statistical significance of the groupings. The results are displayed via PCoA plots. After the differentially abundant taxa (p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05) that best explained the differences between the two participant groups were identified, a linear discriminant analysis (LDA) effect size (LEfSe) approach was applied to obtain an LDA-based effect size score (18). The R package pheatmap was used to identify differential expression of OTUs among samples. Finally, Spearman rank correlation analysis was performed to determine the bacterial associations.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e3.1. Patient demographics:\\u0026nbsp;\\u003c/strong\\u003e A total of 35 participants were enrolled in the study; all of them were males aged 50--80 years. The samples were assigned to two clinical states: S-COPD (n=18) and AE-COPD (n=17).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.2. Sequence data profile\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe sequence data for the raw data were deposited with the accession number PRJNA1021628 in the NCBI Bioproject (http://www.ncbi.nlm.nih.gov/bioproject). There were 495 operational taxonomic units (OTUs) recognized across the 35 samples for the microbiota. The rarefaction curves confirmed that our samples covered the dominant members of the bacterial communities (Fig. 1). The demultiplexing step of the paired-end sequences resulted in a total of 9 351 510 reads, with a minimum of 127 and a maximum of 428 097. The truncation length parameters of DADA2 were p-trunc-len-f 280 and p-trunc-lenr 220.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3. The sputum microbiome taxonomic profile\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe entire taxonomic profile of the two studied groups included 13 phyla, 21 classes, 29 orders, 41 families, and 60 genera.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3.1 Relative abundance of microbial communities\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003ein\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;S-COPD and AE-COPD\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe microbial composition in sputum was similar in patients in both groups, with slight differences. At the phylum level, the downstream analysis revealed that \\u003cem\\u003eProteobacteria\\u003c/em\\u003e was the most abundant phylum in most samples within the two groups and was significantly more prevalent in the S-COPD group (S-COPD: 75%, AE-COPD: 66%) (p= 0.0027). Additionally,\\u003cem\\u003e\\u0026nbsp;Fusobacteria\\u003c/em\\u003e was more abundant in the S-COPD group (S-COPD: 8%, AE-COPD: 6%). with a significant difference (p= 0.023). On the other hand, \\u003cem\\u003eFirmicutes\\u003c/em\\u003e and \\u003cem\\u003eActinobacteria\\u003c/em\\u003e were more abundant in the AE-COPD group than in the S-COPD group, where \\u003cem\\u003eFirmicutes\\u0026nbsp;\\u003c/em\\u003e(S-COPD: 8%, AE-COPD: 16%) and \\u003cem\\u003eActinobacteria\\u003c/em\\u003e (S-COPD: 7%, AE-COPD: 11%) were predominant. The abundance of Actinobacteria was not significantly different in the AE-COPD group, whereas that of Firmicutes was significantly different (p= 0.029). (Fig 2)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;At the genus level, 60 genera were identified among the two groups. The genera identified from the most to least abundant were\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cem\\u003eParacoccus\\u003c/em\\u003e, \\u003cem\\u003eCellulosilyticum\\u003c/em\\u003e, \\u003cem\\u003eStreptomyces\\u003c/em\\u003e, \\u003cem\\u003eLeptotrichia\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, \\u003cem\\u003eRuminococcaceae_UCG_014\\u003c/em\\u003e, \\u003cem\\u003eAtopobium\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus\\u003c/em\\u003e, \\u003cem\\u003eHaemophilus\\u003c/em\\u003e and \\u003cem\\u003eMoraxella\\u003c/em\\u003e. These results revealed differences in the composition of the respiratory microbiota between the S-COPD and AE-COPD groups. \\u003cem\\u003eParacoccus\\u003c/em\\u003e was the most common genus in both groups, but its percentage was greater in stable individuals (S-COPD: 71.6%, AE-COPD: 62.6%). Additionally, \\u003cem\\u003eFusobacterium, Streptococcus, Haemophilus\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eMoraxella\\u003c/em\\u003e were significantly different between the two groups and were more prevalent in the stable group (2.4, 1.5, 1.3 and 1.1 in SCOPD and 0.9, 1, 0.92 and 0.8 in AE-COPD, respectively). On the other hand, some genera, such as \\u003cem\\u003eCellulosilyticum, Streptomyces, Leptotrichia,\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eRuminococcaceae_UCG_014 and\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eAtopobium,\\u003c/em\\u003e were more prevalent in exacerbated individuals (1,6.2, 4.8,0.7 and 0,5 in AE-COPD 9,8.8.5.3, 2.2, and 1.9 in SCOPD, respectively).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3.2. Core microbiome\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe core genera were 2 genera (\\u003cem\\u003eStreptomyces\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eStreptococcus\\u003c/em\\u003e 8%) common in both groups while there were 6 other genera present in all sample in S-COPD group (\\u003cem\\u003eParacoccus, Fusobacterium, Leptotrichia, Moraxella, Oribacterium\\u003c/em\\u003e and \\u003cem\\u003eShingomonas\\u003c/em\\u003e) as shown in figure 4. Most of detected genera were distributed genera that may be present in some but not all the samples.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3.3. Bacterial diversity analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAlpha diversity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDifferent alpha diversity indices were calculated and revealed greater diversity for stable patients than for exacerbated patients in terms of the Chao1, Simpson, observed and Shannon diversity indices (\\u003cstrong\\u003eFig 5)\\u003c/strong\\u003e. The results revealed a significant difference between the S-COPD group and the AE-COPD group according to the Chao, Shannon\\u0026rsquo;s and observed indices (P values equal to 0.00029, 0.04 and 0.0002, respectively), whereas Simpson\\u0026rsquo;s indices were not significantly different (*, p=0.03*), as revealed by the ANOVA samples t test and Mann‒Whitney test, respectively.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBeta diversity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBeta diversity was used to compare community diversity and determine community ordination and clustering. The PCoA visualization plot of the weighted UniFrac distance revealed high clustering of the stable group, whereas exacerbated samples appeared more scattered (p = 0.354), as shown in \\u003cstrong\\u003eFigure\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;(6)\\u003c/strong\\u003e. The PCoA visualization plot of the unweighted UniFrac distance revealed high clustering for both the stable and exacerbated states (p=0.009), as shown in \\u003cstrong\\u003eFigure\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;(7)\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, the Bray‒Curtis PCoA plot revealed that stable patients were highly clustered, whereas exacerbated patients were more disseminated (p=0.02, as shown in \\u003cstrong\\u003eFigure\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e8)\\u003c/strong\\u003e\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.4.4. LefSe analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLEfSe on an LDA score of \\u0026plusmn;3 at a p value cutoff of 0.05 and false discovery rate (FDR) adjustment revealed significant differences between the microbial populations of S-COPD and AE-COPD patients in terms of the abundance of \\u003cem\\u003eParacoccus\\u003c/em\\u003e (LDA score = 3.14), \\u003cem\\u003eFusobacterium\\u003c/em\\u003e (LDA score = 1.81), \\u003cem\\u003eStreptococcus\\u003c/em\\u003e (LDA score =1.53)\\u003cem\\u003e, Haemophilus\\u003c/em\\u003e (LDA score = 1.51), \\u003cem\\u003eMoraxella\\u003c/em\\u003e (LDA score =1.36) and \\u003cem\\u003eStreptococcus\\u003c/em\\u003e (LDA score =1.35) genera in the S-COPD group, which were greater than those in the AE-COPD group. (Fig 9)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3.5. Correlation analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.4.5.2. A correlation between S-COPD and AE-COPD community members\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Spearman correlation coefficient at the phylum level revealed a significant correlation between OTUs within the phylum \\u003cem\\u003eActinobacteria\\u003c/em\\u003e and those within the phylum \\u003cem\\u003eProteobacteria\\u003c/em\\u003e (r= 0.49). There was a significant positive correlation between the number of members of the phylum \\u003cem\\u003eFirmicutes\\u0026nbsp;\\u003c/em\\u003eand the number of members of the phylum \\u003cem\\u003eProteobacteria\\u003c/em\\u003e (r= 0.42). There was also a significant positive correlation between the number of members of the phylum \\u003cem\\u003eFusobacteria\\u003c/em\\u003e and the number of members of the phylum \\u003cem\\u003eProteobacteria\\u003c/em\\u003e (r= 0.47), as shown in Figure 10.\\u003c/p\\u003e\\n\\u003cp\\u003eAt the genus level, significant positive correlations were detected between OTUs assigned to the genera \\u003cem\\u003eAtopobium, Haemophilus,\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eParacoccus\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eStreptococcus\\u003c/em\\u003e and \\u003cem\\u003eStreptomyces\\u003c/em\\u003e (r= 0.45, 0.33, 0.52 and 0.4, respectively), and significant positive correlations were detected between OTUs belonging to the genera \\u003cem\\u003eFusobacteria, Haemophilus, Leptotrichia, Moraxella\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eRuminococcaceae_UCG_014\\u0026nbsp;\\u003c/em\\u003eand the genus \\u003cem\\u003eStreptococcus\\u003c/em\\u003e (r= 0.36, 0.42, 0.41, 0.35 and 0.34, respectively). A significant positive correlation was detected between OTUs belonging to the genera \\u003cem\\u003eLeptotrichia\\u003c/em\\u003e and \\u003cem\\u003eParacoccus\\u003c/em\\u003e (r= 0.5), \\u003cem\\u003eHaemophilus\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eLeptotrichia\\u003c/em\\u003e (r= 0.38) and \\u003cem\\u003eFusobacteria\\u003c/em\\u003e and \\u003cem\\u003eHaemophilus\\u0026nbsp;\\u003c/em\\u003e(r= 0.57), and a negative correlation was detected between \\u003cem\\u003eNeisseria\\u003c/em\\u003e and \\u003cem\\u003eParacoccus\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e(r= -0.30) (Fig. 11).\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eResearch\\u0026nbsp;on\\u0026nbsp;the COPD\\u0026nbsp;microbiome has revealed its role in both disease development and exacerbation, and the disordered microbiome is believed to be involved in the pathogenesis of COPD by modulating inflammatory and immune responses (19). In our study, regarding the overall phyla composition, the most prevalent phyla in the two studied groups were\\u003cem\\u003e\\u0026nbsp;Proteobacteria, Fusobacteria, Firmicutes,\\u003c/em\\u003e and \\u003cem\\u003eActinobacteria\\u003c/em\\u003e. \\u003cem\\u003eProteobacteria\\u003c/em\\u003e was the most abundant phylum in most samples within the two groups\\u0026nbsp;and was significantly\\u0026nbsp;more prevalent in the S-COPD group (p= 0.0027).\\u003c/p\\u003e\\n\\u003cp\\u003eGenerally, different COPD microbiome studies\\u0026nbsp;have identified\\u0026nbsp;\\u003cem\\u003eProteobacteria, Firmicutes, Bacteroides and Actinobacteria\\u0026nbsp;\\u003c/em\\u003eas the most abundant respiratory microbiome phyla;\\u0026nbsp;however,\\u0026nbsp;one of them is the most dominant phylum.\\u0026nbsp;In accordance with our results, \\u003cem\\u003eGarcia-Nunez\\u0026nbsp;\\u003c/em\\u003eet al\\u003cem\\u003e.\\u0026nbsp;\\u003c/em\\u003erevealed the predominance of\\u0026nbsp;\\u003cem\\u003ethe Proteobacteria\\u003c/em\\u003e\\u003cem\\u003e, Firmicutes,\\u0026nbsp;\\u003c/em\\u003eand\\u003cem\\u003e\\u0026nbsp;\\u003cem\\u003eActinobacteria\\u0026nbsp;\\u003c/em\\u003e\\u003c/em\\u003ephyla (20). Another study reported that the \\u003cem\\u003eProteobacteria\\u003c/em\\u003e phylum constituted half of the microbiome of COPD subjects,\\u0026nbsp;where \\u003cem\\u003eFirmicutes\\u003c/em\\u003e were dominant in the healthy microbiome (21). Many previous studies were in line with our findings about the\\u0026nbsp;predominance of the\\u0026nbsp;\\u003cem\\u003eProteobacteria\\u0026nbsp;\\u003c/em\\u003ephylum (22.23.24). In contrast to our observations, other studies have reported a greater\\u0026nbsp;abundance of \\u003cem\\u003eFirmicutes\\u0026nbsp;\\u003c/em\\u003ein COPD patients (25.26);\\u0026nbsp;for example,\\u0026nbsp;a study\\u0026nbsp;performed\\u0026nbsp;by Goolman et al. revealed\\u0026nbsp;the dominance\\u0026nbsp;of four phyla,\\u0026nbsp;\\u003cem\\u003eFirmicutes\\u003c/em\\u003e\\u003cem\\u003e,\\u003c/em\\u003e \\u003cem\\u003eProteobacteria\\u003c/em\\u003e, \\u003cem\\u003eBacteroides\\u003c/em\\u003e and \\u003cem\\u003eActinobacteria\\u003c/em\\u003e\\u003cem\\u003e,\\u003c/em\\u003e with\\u0026nbsp;an\\u0026nbsp;increased relative abundance of \\u003cem\\u003eFirmicutes\\u003c/em\\u003e\\u003cem\\u003e.\\u003c/em\\u003e Wang et al.\\u0026nbsp;revealed that\\u003cem\\u003e\\u0026nbsp;Firmicutes was the most dominant phylum in\\u003c/em\\u003e the\\u0026nbsp;sputum microbiome of COPD\\u0026nbsp;patients, followed by \\u003cem\\u003eActinobacteria\\u003c/em\\u003e and \\u003cem\\u003eProteobacteria\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe dissimilarity in the predominant phyla between different studies could be attributed to many factors, including different sample sizes; different patient sample groups (e.g., smokers, smokers, and types of treatment); different sample types (e.g., sputum, bronchoalveolar lavage or tracheal aspirate); different techniques; and other general factors, such as geographical location, weather, air pollution, and other environmental conditions.\\u003c/p\\u003e\\n\\u003cp\\u003eAt the genus level, an increase in the relative abundance of a certain genus may be conceived as a cause of COPD exacerbation, but it may differ from the culture results, as colonizing microorganisms may be cultivated easily from bronchial samples, as reported for patients with exacerbation showing chronic \\u003cem\\u003ePseudomonas aeruginosa\\u003c/em\\u003e colonization (27. 28). The genera most frequently reported to be predominant in different previous COPD studies were \\u003cem\\u003eHaemophilus, Veillonella, Prevotella, Rothia, Lactobacillus, Granulicatella Staphylococcus\\u0026nbsp;\\u003c/em\\u003eand\\u003cem\\u003eStreptococcus\\u003c/em\\u003e(21.25,26). Notably, our study\\u0026nbsp;revealed\\u0026nbsp;the predominance of some of these genera,\\u0026nbsp;although the predominance of \\u003cem\\u003eParacocus\\u003c/em\\u003e\\u003cem\\u003e,\\u003c/em\\u003e which represented the most dominant genus in our study with increased relative abundance stable states, was very unique.\\u003c/p\\u003e\\n\\u003cp\\u003eCommon members of a microbial community often perform moderate functions of the host–microbe symbiotic system. We estimated a low degree of similarity between the core microbiome for each of the two stages of disease (stable and exacerbation). Two ASVs were present in all the subjects; these belong to the genera\\u003cem\\u003e\\u0026nbsp;Streptococcus,\\u003c/em\\u003e and \\u003cem\\u003eStreptomyces\\u003c/em\\u003e. Gupta et al. reported that ten ASVs were shared among two stages of disease (stable and exacerbation), belonging to the genera \\u003cem\\u003eOribactrum\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus,\\u003c/em\\u003e and\\u0026nbsp;\\u003cem\\u003eSphingomonas\\u003c/em\\u003e \\u003cstrong\\u003e\\u003cem\\u003e(27)\\u003c/em\\u003e\\u003c/strong\\u003e. Einarsson et al. estimated that the co-occurrence of bacterial taxa and the observation of a putative ‘core’ community within the lower airways, including \\u003cem\\u003ePrevotella\\u003c/em\\u003e spp., \\u003cem\\u003eVeillonella\\u003c/em\\u003e spp. and \\u003cem\\u003eActinomyces\\u003c/em\\u003e spp., were also apparent in\\u0026nbsp;their\\u0026nbsp;study. Furthermore, assigning specific OTUs detected in \\u0026gt;90% of samples and accounting for more than 1%\\u0026nbsp;of the\\u0026nbsp;total sequence read abundance to the ‘core’ microbiota demonstrated that it consisted of those same anaerobic taxa, in addition to members of the \\u003cem\\u003eStreptococcus\\u003c/em\\u003e and \\u003cem\\u003eRothia\\u003c/em\\u003e genera\\u0026nbsp;\\u003cstrong\\u003e\\u003cem\\u003e(28)\\u003c/em\\u003e\\u003c/strong\\u003e.\\u0026nbsp;Cameron\\u0026nbsp;et al.\\u0026nbsp;reported that\\u0026nbsp;eight bacterial genera were present in all\\u0026nbsp;sputum samples:\\u0026nbsp;\\u003cem\\u003eHaemophilus, Oribactrum\\u003c/em\\u003e and\\u0026nbsp;\\u003cem\\u003eStreptococcus\\u003c/em\\u003e \\u003cstrong\\u003e\\u003cem\\u003e(29)\\u003c/em\\u003e\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eOur results revealed that microbiome diversity was lower in AE-COPD patients than in S-COPD patients. This aspect is distinct among different studies, and some studies have shown an overall reduction in microbial diversity during COPD exacerbations compared with stable patients (6, 30 and 31). These results are consistent with those of the present study, thus suggesting that microbial diversity may be a biological indicator of AE-COPD. Garcia-Nuñez et al. reported decreased alpha diversity in advanced COPD patients\\u0026nbsp;compared\\u0026nbsp;with\\u0026nbsp;moderate-to-severe disease\\u0026nbsp;patients\\u0026nbsp;(20).\\u0026nbsp;These findings support the occurrence of\\u0026nbsp;severity-related\\u0026nbsp;changes\\u0026nbsp;in the bronchial microbiome in COPD\\u0026nbsp;patients.\\u003c/p\\u003e\\n\\u003cp\\u003eOur results do not correlate with previous data from Pragman and colleagues, who did not find severity-related differences in microbial diversity in BALF samples from\\u0026nbsp;COPD\\u0026nbsp;patients\\u0026nbsp;(22). Hazra et al. reported that the alpha diversity of moderate COPD patients was lower than that of\\u0026nbsp;patients with severe COPD (32). However, Goolam et al. did not observe\\u0026nbsp;a\\u0026nbsp;significant difference in microbial diversity between stable COPD and exacerbated COPD groups (25).\\u0026nbsp;Jubinville et\\u0026nbsp;al.\\u0026nbsp;reported\\u0026nbsp;a difference in alpha diversity when paired samples\\u0026nbsp;were compared,\\u0026nbsp;i.e.,\\u0026nbsp;the diversity in the paired samples differed across the disease state,\\u0026nbsp;with most exacerbated samples showing\\u0026nbsp;greater\\u0026nbsp;diversity (33).\\u003c/p\\u003e\\n\\u003cp\\u003eThis inconsistency among studies could be due to distinct sampling methods, COPD states and exacerbations, different geographic regions and\\u0026nbsp;diversity measures used, whereas Jubinville et al. used the Simpson index. Unlike the Shannon index, the Simpson index is more affected by the relative abundances (i.e., evenness) of the species in a sample; this suggests that during the exacerbated state of disease, the abundances of species/OTUs change but not the number of species/OTUs (richness) \\u003cstrong\\u003e(\\u003c/strong\\u003e34\\u003cstrong\\u003e)\\u003c/strong\\u003e. Additionally, the type of treatment used in COPD patients is another factor affecting microbiome composition. Data show that the use of antibiotics and corticosteroids in exacerbated COPD patients influences the respiratory microbiome (35). Inhaled corticosteroids, the main treatment for COPD, significantly increase bacterial burden and diversity, with increased pathogenic strains (36, 37, 38). Antibiotics are usually used in COPD patients with frequent exacerbations\\u0026nbsp;to decrease the recurrence of\\u0026nbsp;these\\u0026nbsp;episodes. This\\u0026nbsp;type\\u0026nbsp;of therapy was found to decrease microbial diversity,\\u0026nbsp;which\\u0026nbsp;lasts for months and persists during stable periods between episodes (39,40).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Principal coordinate analysis (PCoA) revealed significant\\u0026nbsp;differences in\\u0026nbsp;the\\u0026nbsp;microbial community structure between the AE-COPD and S-COPD\\u0026nbsp;patients\\u0026nbsp;(p = 0.02). This\\u0026nbsp;finding\\u0026nbsp;was in accordance\\u0026nbsp;with the findings of\\u0026nbsp;Su et al.,\\u0026nbsp;who\\u0026nbsp;reported that\\u0026nbsp;principal coordinate\\u0026nbsp;analysis (PCoA)\\u0026nbsp;revealed\\u0026nbsp;significant differences in microbial community structure between the AECOPD\\u0026nbsp;group\\u0026nbsp;and stable\\u0026nbsp;group\\u0026nbsp;(p = 0.02) and\\u0026nbsp;between the\\u0026nbsp;AECOPD\\u0026nbsp;group\\u0026nbsp;and healthy\\u0026nbsp;control group\\u0026nbsp;(p = 0.035)\\u0026nbsp;\\u003cem\\u003e(31)\\u003c/em\\u003e. Goolam et al. and colleagues\\u0026nbsp;reported\\u0026nbsp;that\\u0026nbsp;beta\\u0026nbsp;diversity measures showed no clustering for any of the variables\\u0026nbsp;via\\u0026nbsp;PCoA\\u0026nbsp;or\\u0026nbsp;weighted UniFrac (for microbiome) measures (25).\\u003c/p\\u003e\\n\\u003cp\\u003eIn contrast to our results,\\u0026nbsp;Gupta et al., PCoA revealed extensive overlap in membership between the bacterial communities of the ECOPD and stable COPD disease groups. \\u003cem\\u003e(27)\\u003c/em\\u003e. This inconsistency among studies could be due to the use of\\u0026nbsp;different distance methods.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;LEfSe analysis at the genus level revealed that the sputum microbiome of the AE-COPD group was characterized by a dominance of \\u003cem\\u003eCellulosilyticun, Liptotrichia\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eStreptomyces\\u003c/em\\u003e\\u003cem\\u003e,\\u003c/em\\u003ewhereas the microbiome in the stable COPD group was dominated by the\\u0026nbsp;genera\\u0026nbsp;\\u003cem\\u003eParacoccus\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, Streptococcus \\u003cem\\u003eHaemophilus\\u0026nbsp;\\u003c/em\\u003eand Moraxella\\u003cem\\u003e\\u0026nbsp;(\\u003c/em\\u003ep \\u0026lt; 0.05).\\u003c/p\\u003e\\n\\u003cp\\u003eHazra et al. reported that six marker genera,\\u0026nbsp;Streptococcus and \\u003cem\\u003eRothia\\u003c/em\\u003e\\u003cem\\u003e,\\u003c/em\\u003e were\\u0026nbsp;highly abundant\\u0026nbsp;in moderate COPD\\u0026nbsp;patients, whereas\\u0026nbsp;Leptotrichia and\\u0026nbsp;Pseudomonas were highly prevalent in patients with severe COPD\\u0026nbsp;\\u003cem\\u003e(30)\\u003c/em\\u003e. Haldar et al. reported that\\u0026nbsp;linear\\u0026nbsp;discriminant effect size (LEfSe) analysis revealed\\u0026nbsp;that a greater\\u0026nbsp;abundance of\\u0026nbsp;Proteobacteria\\u0026nbsp;and lower\\u0026nbsp;proportions\\u0026nbsp;of Firmicutes, Bacteroidetes and Actinobacteria\\u0026nbsp;were\\u0026nbsp;the major contributors\\u0026nbsp;to the differentiation of COPD patients from healthy controls\\u0026nbsp;\\u003cem\\u003e(21)\\u003c/em\\u003e. Chang et al. reported that LEfSe analysis\\u0026nbsp;revealed\\u0026nbsp;that, during the\\u0026nbsp;AECOPD\\u0026nbsp;period, \\u003cem\\u003ePseudomonas\\u003c/em\\u003e was highly abundant. In\\u0026nbsp;stable COPD, \\u003cem\\u003eHaemophilus influenzae\\u003c/em\\u003e and\\u0026nbsp;\\u003cem\\u003ePasteurellales\\u003c/em\\u003e are\\u0026nbsp;more abundant \\u003cem\\u003e(41)\\u003c/em\\u003e\\u003cem\\u003e.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eThe\\u0026nbsp;\\u003c/em\\u003eSpearman correlation coefficient was used to determine the positive correlation between different bacterial OTUs at different phylum and genus levels. Most of the genera in stable COPD and AECOPD patients were more positively correlated. We also noted that many potentially clinically relevant taxa, such as \\u003cem\\u003eStreptococcus\\u003c/em\\u003e and \\u003cem\\u003eHaemophilus\\u003c/em\\u003e, were correlated with other taxa. This finding was consistent with those of other studies \\u003cem\\u003e(23,27)\\u003c/em\\u003e. In contrast, Yang et al. reported a negative correlation between OTUs assigned to the genus \\u003cem\\u003eHaemophilus\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eStreptococcus\\u0026nbsp;\\u003c/em\\u003ein nonfrequent exacerbations, and Li et al. reported a negative correlation between OTUs assigned to the genus \\u003cem\\u003eFusobacteria\\u003c/em\\u003e and \\u003cem\\u003eStreptococcus\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eFusobacteria\\u003c/em\\u003e and \\u003cem\\u003eStreptococcus\\u003c/em\\u003e in stable COPD patients\\u0026nbsp;\\u003cem\\u003e(42,43)\\u003c/em\\u003e.\\u0026nbsp;However, correlations between taxa are not proof of functional relationships between members of the community. Therefore, further studies are\\u0026nbsp;needed\\u0026nbsp;to focus on the functional role of such taxa found within these communities.\\u003c/p\\u003e\\n\\u003cp\\u003eLimitations\\u003c/p\\u003e\\n\\u003cp\\u003eThe noteworthy limitations of our study are as follows: the sample size was small, and the samples were cross-sectional rather than longitudinal; thus, the interference of coexisting factors was unavoidable, and there was a lack of healthy control subjects. Sputum samples were used in this study; although they are the most commonly used samples in airway microbiome studies (noninvasive methods), they have the drawbacks of being a mixture of upper and lower respiratory tract microbiomes.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, we found that dysbiosis in the lung microbiome among different COPD states is characterized by a high abundance of \\u003cem\\u003eProteobacteria\\u003c/em\\u003e\\u003cem\\u003e,\\u003c/em\\u003e especially\\u0026nbsp;those associated with the\\u0026nbsp;\\u003cem\\u003eparacocus\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;microbiome\\u003c/em\\u003e. However, the precise role of this dysbiosis in the pathogenesis and severity of COPD is still unclear. More studies using new metagenomics and bioinformatics analyses and metabolomics are necessary to determine the role of the respiratory microbiome and its metabolic profile in COPD progression and open future avenues to apply these findings in clinical practice to introduce new therapeutic approaches and improve clinical outcomes.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, all the procedures performed were reviewed and approved by the Ethical Committee of Al-Azhar University, Egypt. All participants provided written informed consent. There are no individual person’s data in any form in this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e this work didn’t receive any funding. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003esequence data that support the findings of this study have been deposited in the SRA with the primary accession code PRJNA 1021628 in the NCBI Bioproject (http://www.ncbi.nlm.nih.gov/bioproject).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eI declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003ethe results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration (from you or one of your Contributing Authors) by another publisher.\\u003c/p\\u003e\\n\\u003cp\\u003eI confirm the corresponding author has read the journal policies and submit this manuscript in accordance with those policies. All of the material is owned by the authors and/or no permissions are required.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eS R: PI responsible for the idea, aim of work, the whole design, analysis of sequences, supervising all wet and dry lab, writing and reviewing the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eH A: reviewing data analysis, writing and reviewing the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eL G: sample collection, Analysis of the sequences, PCR and writing the methods in the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eM O: helping in writing parts of the manuscript and reviewing\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable \\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eLei Wang, Ke Hao, Ting Yang, Chen Wan. 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Respir Res. 2022 Nov 19;23(1):321.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"COPD, Exacerbation microbiome, proteobacteria, paracocus\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5921039/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5921039/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntroduction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eChronic obstructive pulmonary disease (COPD) is a well-known respiratory illness, and COPD patients oscillate between a stable state and an exacerbated state. which can lead to disease deterioration. Studies suggest that respiratory microbiome dysbiosis plays a vital role in COPD exacerbation. However, the exact microbial composition among different clinical states of COPD is still elusive.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eObjectives\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo determine and compare the respiratory microbiome composition in different COPD clinical states, namely, the stable state (S-COPD) and the acute exacerbated state (AE-COPD).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, 35 sputum samples were collected from COPD patients: S-COPD patients (n = 18), and AE-COPD patients (n = 17). The sputum microbiome was analyzed via 16S rRNA gene sequencing. Bioinformatics analysis was used to determine changes in the microbiota among the comparison groups.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe most abundant phyla among all the samples were \\u003cem\\u003eProteobacteria, Fusobacteria, Firmicutes\\u003c/em\\u003e, and \\u003cem\\u003eActinobacteria\\u003c/em\\u003e, with \\u003cem\\u003eParacoccus\\u003c/em\\u003e, Streptomyces \\u003cem\\u003eLeptotrichia Fusobacterium\\u003c/em\\u003e and \\u003cem\\u003eRuminococcaceae\\u003c/em\\u003e being the most prevalent genera.A dissimilarity in abundance across the studied COPD states was observed, with significantly greater abundance of \\u003cem\\u003eProteobacteria and Fusobacteria in S-COPD\\u003c/em\\u003e patients and greater abundance of \\u003cem\\u003eFirmicutes\\u003c/em\\u003e in AE-COPD patients at the phylum level. \\u003cem\\u003eParacoccus\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium, Streptococcus, Haemophilus\\u003c/em\\u003e and \\u003cem\\u003eMoraxella\\u003c/em\\u003e were significantly different between the two groups and were more prevalent in S-COPD, whereas \\u003cem\\u003eCellulosilyticum, Streptomyces, Leptotrichia, Ruminococcaceae_UCG_014 and Atopobium\\u003c/em\\u003e were more prevalent in exacerbated individuals. Alpha diversity revealed greater diversity in stable versus exacerbated patients, and a PCoA plot of Bray‒Curtis and weighted UniFrac distances revealed that stable patients were highly clustered, whereas exacerbated patients were more disseminated. At the genus level, LEfSe analysis revealed the dominance of \\u003cem\\u003eCellulosilytic, Liptotrichia\\u003c/em\\u003e and \\u003cem\\u003eStreptomyces in the AE-COPD group\\u003c/em\\u003e, whereas the S-COPD group microbiome was dominated by the genera \\u003cem\\u003eParacoccus\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, Streptococcus \\u003cem\\u003eHaemophilus\\u003c/em\\u003e and Moraxella \\u003cem\\u003e(\\u003c/em\\u003ep \\u0026lt; 0.05).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe results of the present study suggest that COPD patients have unique microbial profiles that differ across different states, with increased \\u003cem\\u003eabundances of Proteobacteria\\u003c/em\\u003e, chiefly \\u003cem\\u003eParacoccus\\u003c/em\\u003e. These findings need more research to clarify the definite role of microbiome dysbiosis in COPD pathogenesis.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Comparative metagenomic analysis of the sputum microbiome in different COPD clinical states\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-04 11:54:24\",\"doi\":\"10.21203/rs.3.rs-5921039/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"5d852d1a-d6a4-449d-9a30-432323a37970\",\"owner\":[],\"postedDate\":\"April 4th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-04-04T11:54:24+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-04-04 11:54:24\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5921039\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5921039\",\"identity\":\"rs-5921039\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}