Upper/Lower Respiratory Tract Microbiome Changes in AECOPD and COPD

doi:10.21203/rs.3.rs-139979/v1

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Upper/Lower Respiratory Tract Microbiome Changes in AECOPD and COPD

https://doi.org/10.21203/rs.3.rs-139979/v1

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Background: Microbiome residing in the respiratory tract has emerged as an important player in the etiology and progression of COPD, but results are conflicting regarding the features of respiratory tract microbiome in COPD and at exacerbations and it is unknown whether these features differ by ethnicity and geography.

Method: To address these questions, we enrolled healthy individuals and patients with COPD, including healthy-COPD pairs from same households, from four geographical regions of Yunan province, representative of different ethnicities and/or environmental exposures. Sputum and oropharyngeal swabs were collected from these healthy individuals and from COPD patients at stable state (COPD) or exacerbations (AECOPD) and subjected to 16S microbiome profiling.

Results: Both COPD disease status and region had an impact on alpha-diversity of sputum and oropharyngeal microbiomes, with AECOPD having the lowest microbiome diversity. Shifts in the relative abundance (≥ 1.5 fold, adj. p < 0.05) of microbes at healthy, exacerbation and stable COPD. Microbes enriched at exacerbation COPD were primarily Proteobacteria and Firmicutes phylum in upper respiratory tract. In the lower respiratory tract, population-based study did not find any statistical differential abundance of microbe, however, paired-based study showed phylum of Proteobacteria, Chloroflexi, Bacteroidota, Acidobacteriota, Desulfobacterota, Firmicutes and Verrucomicrobiota enriched in exacerbation COPD.

Conclusions: COPD is associated with specific changes in upper and lower respiratory tract microbiomes, whereas exacerbation events confer additional alterations in respiratory tract microbiome compositions. These specific microbiome changes may inform future study on the pathogenesis or management of COPD.

Health Economics & Outcomes Research

General Microbiology

exacerbation

COPD

microbiome

Bacterial communities

Chronic obstructive pulmonary disease (COPD) is a heterogeneous complex, progressive respiratory disease featured by persistent symptoms, occasional exacerbation episodes, and chronic airway inflammation, the latter of which may lead to pathological changes in the respiratory tract including parenchymal destruction and airflow limitation. The mechanisms underlying overreactive immunity in the lungs of COPD patients are as yet unclear, which are considered to be an interplay between environmental and exogenous factors. Ethnicity may be a factor associated with the risk and outcome of COPD. Studies have found that the prevalence of COPD differs among the black, white, and Asian populations, and the black group seemed to have more severe exacerbations leading to hospitalization and are at increased risk of mortality[1, 2]. In addition, geographic region may affect individual’s predisposition for COPD, evidenced by the geographic disparity in the prevalence of COPD in China.

Microorganisms are increasingly recognised as an important player in the pathogenesis and progression of COPD. Childhood infection in the respiratory tract has been linked to increased risk for COPD in adulthood[3]. A variety of pathogenic bacteria were isolated in the lower respiratory tract of patients with stable COPD. Even more, pathogen infection was identified to account for 50% of exacerbation events. However, this pathogenic view is being reconsidered as it is now appreciated that even the healthy lower respiratory tract is not sterile but colonized with numerous commensal microbes. Instead of being caused by specific pathogens, it is hypothesized that dysbiosis of respiratory tract microbiome may be involved in COPD development and exacerbation events. Respiratory tract microbiome in COPD has been the subject of several studies, but results were conflicting regarding whether there were marked changes in the respiratory tract microbiome of COPD patients at stable or exacerbation state [4–7]. this regard and considering the variability of COPD risks by ethnicity and geography, a question that remains to be answered is whether a common respiratory tract microbiome signature featuring COPD exists.

In this study, we aimed to identify the features of respiratory tract microbiome in COPD and reveal the impact of ethnicity and geography on the respiratory tract microbiome. To this aim, we enrolled patients in the core family as the smallest unit in Yunan province of China. The core families have similar genetic backgrounds and living habits, so as to maximize the control of the influence of these factors on the respiratory tract microbiome. The complex topography in Yunan province gives rise to varied climates, ranging from frigid, temperate, to tropic zone climate. There is considerable ethnical diversity in this province, where people of one ethnicity usually reside in one region and have similar lifestyle. As such, it is anticipated that respiratory tract microbiomes of individuals from different regions of Yunan have been shaped by different genetic and/or environmental factors. In addition, we enrolled healthy-COPD pairs from the same households to control non-disease related influences on the microbiome.

Participant Enrollment

This study was approved by the ethic committees of the 2016YY140; all enrolled participants gave written informed consent. Genetics or lifestyle shapes our microbiome, order to minimize the impact of those factors on upper and lower respiratory tract microbiome, this study enrolled patients in the core family as the smallest unit from four cities in Yunnan province, from 2018 to 2020, including LiJiang, DaLi, Xishuangbanna(BanNa) and KunMing. Total 233 core families (486 samples, dropped 2samples) were collected, including BanNa (101), DaLi (92), KunMing (204) and LiJiang (87). The healthy family members were defined as healthy control. All COPD patients were doctor-diagnosed, according to their clinical history and pulmonary function tests. The COPD patient was defined as stable if there was no exacerbation for the last month and the patient was not currently have an exacerbation. The AECOPD patient was defined as an acute exacerbation of COPD patient who was suffering a sustained (e.g. 24–48 h) increase in cough, sputum production, and/or dyspnea. In this study, we totally collected 280 AECOPD samples, 72 COPD samples and 132 Healthy samples. The detailed study area and sampling location and type have been shown in Supplementary Fig. 1. The metadata of the patients were tabulated in Supplementary Table 1.

Study Design

In this study, we took nasopharyngeal swabs or sputum samples from enrolled samples or took both sample types from the same enrolled sample; Sampling was carried out in the same way under the condition of COPD; during the entire enrollment and sampling process, we faithfully carried out enrollment based on the normal clinical procedural. We divided all samples into groups according to their physical condition and sampling type. Each sample type is divided into three groups for pairwise comparison; at the same time, according to the core family, for the same sample type from the same family members, form a paired sample, then performed pairwise comparison. Among the sputum samples, there were only 6 core families including one patient with two COPD stages and corresponding healthy family members. Among the swab samples, such core families were only 10. Therefore, this study did not conduct a three-group pairwise comparison analysis. We incorporated these samples into specific pairwise comparative studies based on their characteristics. The detailed study design and sample enrollment have been shown in Table 1.

DNA extraction and microbiome sequencing

Samples were pre-treated, depending on the clinical sample type. Total DNA was extracted from swab and digested sputum samples by use of QIAamp DNA blood kit (Qiagen, Germany) according to the manufacturer’s instructions. DNA was eluted from the QIAamp column with 50 µl sterile distilled water, quantified by Nanodrop spectrophotometer (Thermo Fisher Scientific, USA), and qualified by agarose gel electrophoresis.

Sequencing data analysis

The V3-4 hypervariable regions of the 16S rDNA amplicon sequencing was undertaken at KingMed Diagnostics (Guangzhou, China), based on a published standard protocol[8]. The V3-4 region the 16S rDNA was amplified with the universal primer pair 338F/806R. The pooled amplicon was applied to a MiSeq Genome Sequencer (Illumina, USA).

The raw fastq files were trimmed adapter before starting the subsequent analysis. The operational taxonomic units (OTUs) classification analysis was done using published 16S microbial analysis with mothur pipeline[9, 10]. The raw sequences data were merged into contigs, followed by filtering and trimming of reads based on quality score and several other metrics. The filtered sequences data were aligned to the reference Silva v138[11]. The chimera sequences were removed using the vsearch algorithm[12]. Finally, the cleaned sequences data were assigned to taxonomy database for classification, here we used Bayesian classifier and a mothur pre-formatted Silva v138 taxonomy database as reference taxonomy[11]. The corresponding OTU and species annotations in this study can be found in the supplementary table 2.

Statistical Analyses

Statistical analyses were undertaken using R (v4.0.0). P value less than 0.05 was considered statistically significant. Core microbiota was defined as those found in more than 10 samples and the relative abundance was more than 0.05%. The final outputs from mothur were exported as count table and related taxonomy table, then import these files and clinical meta table into Phyloseq (v1.32.0) package to generate S4 object for further analysis[13].

Alpha diversity was calculated on the basis of the taxonomy profile for each sample based on Shannon. Alpha diversity estimates were computed using Microbiome (v2.1.26, http://microbiome.github.com/microbiome) package’s alpha function. Pair-wised mean comparison was performed.

Principal Coordinates Analysis (PCoA) was used to assess the beta diversity and overall upper and lower respiratory tract microbiomes composition. PCoA plots were generated using the first two principal coordinates according to physical status (Healthy vs AECOPD vs COPD), as well as according to location (BanNa vs DaLi vs KunMing vs LiJiang). The Phyloseq (v1.32.0) package’s ordinate function[13] was used to calculate beta diversity and plotted by plot-ordination function. PCoA analysis based on weighted UniFrac distance matrices, PERMANOVA was tested on the same UniFrac distance matrices by Vegan packages’ Adonis[14] function with default parameters.

Core microbiota of each group as counts table was input into SPIEC-EASI (v0.1.4, Sparse InversE Covariance estimation for Ecological Association and Statistical Inference)[14] for the microbial ecological association networks analysis with meinshausen-buhlmann's neighborhood selection method, the networks were plotted using ggnet2 (v0.1.0) with setting the minimum degree (nodes) value to 1.

The Phyloseq object data was converted to the relevant DESeqDataSet object with phyloseq_to_deseq2 function. The pair wised OTU differential abundance analysis was done using the DESeq2 (v1.28.1) [15] negative binomial Wald test.

Overview of sputum and oropharyngeal microbiome

A total of 249 sputum samples and 235 oropharyngeal swab samples were collected. For group comparisons, 58 vs 40, 58 vs 151, and 40 vs 151 sputum samples, and 74 vs 32, 74 vs 129, and 32 vs 129 oropharyngeal swabs were included for comparing heathy controls vs COPD, healthy control vs AECOPD, and COPD vs AECOPD, respectively. For within-household pairwise comparisons, paired groups included numbers of samples ranging from 12 to 48 per group (Table 1). Enrolled individuals were long-term residents in Xishuangbanna, Dali, Kunming, and Lijiang (see Supplementary Fig. 1 for sample distribution and Supplementary Table 1 for individual demographic and clinical characteristics). Medications for COPD maintenance and exacerbations were prescribed as per routine clinical practice.

Using 16S rRNA amplicon sequencing, 1137 and 970 OTUs were identified across all sputum and oropharyngeal swab samples. Prevotellaceae, Steptococcaceae, Neisseriaceae, Veilonellaceae, Fusobacteriaceae, Lachnospiraceae, Leptotrichiaceae, and Pasteurellaceae were the predominant bacterial families in both sputum and oropharyngeal microbiomes, whereas Porphyromonadaceae and Micrococcaceae were also common in sputum and oropharyngeal microbiomes, respectively (Fig. 1).

Alteration of sputum and oropharyngeal microbiome by COPD and resident regions

Although sputum microbiome vs oropharyngeal swab microbiome were not compared within individuals, between-group comparison showed that sputum samples had slightly higher alpha-diversity than oropharyngeal swabs (Fig. 2a, c). The alpha diversity of oropharyngeal swab microbiome was lowest in AECOPD (AECOPD vs COPD, p < 0.0001; AECOPD vs healthy control, p < 0.01; COPD vs healthy control, p < 0.01), and a similar trend was observed in sputum microbiome (healthy control vs COPD, p < 0.05; AECOPD vs healthy control, p < 0.001). Compared with healthy controls, alpha diversity was increased in oropharyngeal swab microbiome of COPD patients but decreased in their sputum microbiome. Differences in alpha-diversity of microbiome were observed across different geographical locations, with individuals from Lijiang consistently had the least diverse microbiome in both sputum and oropharyngeal swabs (Fig. 2b, d).

The distinction in overall microbiome composition was evaluated by principal coordinate analysis (PCoA) based on weighted UniFrac distance metrices (Fig. 3). The first and second coordinates captured about 50.3% and 48.3% of variations in the composition of oropharyngeal microbiome and sputum microbiome, respectively. However, both oropharyngeal microbiome and sputum microbiome could not be discriminated by disease group or region. Nevertheless, PERMANOVA showed that both COPD disease status and resident region had an impact on microbiome compositions, suggesting the existence of differences in relative abundances of certain microbial taxa (Supplementary Table 3).

Respiratory tract microbiome taxa associated with COPD

In order to identify which bacterial clades in the oropharyngeal and sputum microbiome were different in relative abundances between healthy vs COPD stable state vs exacerbations, we performed negative binomial Wald test using DESeq2. A total of 35 OTUs were significantly different in relative abundances with a fold change ≥ .1.5 and an adjusted p-value ≤ 0.05 in either group comparisons or within-household pair-wise comparisons (Table 2, Supplementary Table 4). We considered differences revealed by pair-wise comparisons more likely to reflect disease effects on microbiome. Compared with healthy controls, oropharyngeal microbiome under COPD stable state had relative higher relative abundances of Streptococcus, Actinomyces, Actinobacteria spp., Rothia, and Veillonella. During COPD exacerbations compared with healthy controls, oropharyngeal microbiome consisted of higher relative abundances of Raoultella, Actinobacillus, Enterobacteriaceae spp., and Haemophilus, and sputum microbiome comprised higher relative abundances of Anaerolineaceae spp., Desulfobacterota spp., Anaerolineae spp., Pedosphaeraceae spp., Rhodobacteraceae spp., gammaproteobacterial spp., and alphaproteobacteria spp.. No taxa showed significant differences in relative abundances between COPD stable state and exacerbations in pair-wise comparisons using above mentioned cutoffs.

Genus level core microbial community structure profile

The core microbial community structure of association networks was generated from the oropharyngeal and sputum microbiome dataset in Healthy, COPD and AECOPD groups, respectively (Fig. 4 and Fig. 5, to better annotated the relationship between OTU-OTU, only OTUs with degree > 1 were shown in the figure.).

In heathy controls, oropharyngeal microbiome contained one large network made up by 40 OTUs. However, under COPD stable state, members of oropharyngeal microbiome became less correlated, showing no large networks with more than 7 OUTs. However, at exacerbations, the complexity of oropharyngeal microbiome networks increased compared to that at the stable state, where the largest two networks contained 16 and 11 OUTs, respectively.

A similar pattern was found with sputum microbiome. In healthy controls, the two biggest networks contained 14 OTUs and 7 OTUs, respectively. However, under COPD stable state, the biggest network was reduced in size containing 10 OTUs, while the others contained less than five OUTs. At exacerbations, microbiome members inter-correlations increased compared with the stable state, with the largest network containing 29 OTUs (49.2%) and the second and third largest ones containing 11 and 10 OTUs.

Under the same physical status in upper/lower respiratory tract, the composition of the core microbial community was similar (Fig. 6, A, B and C). Among the core microbe 61 OTUs with degree > 1 in the Healthy group, 27 OTUs (44.3%) were found in upper/lower respiratory tract, 23 OTUs (37.7% were only found in upper respiratory tract, and 11 OTUs (18%) were found only in lower respiratory tract (Fig. 6.A, The detailed list is in Supplementary Table 5). In Fig. 6.B (Supplementary Table 6), showed 18 common OTUs (56.3%) were detected in upper/lower respiratory tract, and 7 OTUs (21.9%) were only found in the upper respiratory tract with COPD, and also 7 OTUs (21.9%) were only found in the lower respiratory tract with COPD. Among the 72 OTUs were detected in the AECOPD groups, 35 OTUs (48.6%) were found in upper/lower respiratory tract, and 13 OTUs (18.1%) were only detected in upper respiratory tract, and 24 OTUs (33.3%) were only in lower respiratory tract (Fig. 6.C, Supplementary Table 7).

Bacterial composition in upper respiratory tract in healthy, COPD and AECOPD groups, among the core microbe 57 OTUs detected in the upper respiratory tract, 22 OTUs (38.6%) were detected in all three groups. Only 1 OTU (1.8%) were found in COPD group, and 6 OTUs (10.5%) were only in Healthy, and 6 OTUs (10.5%) were found only in AECOPD, Two OTUs (3.6%) were detected in Healthy and COPD groups, and 20 OTUs (35.1%) were detected in Healthy and AECOPD groups, and there was no common OTU only detected in COPD and AECOPD (Fig. 6.D, Supplementary Table 8). In lower respiratory tract, there were 62 core microbes in all three groups, 17 OTUs (27.4%) were identical in all three groups. Only 1 OTU (1.6%) were found in COPD group, and 2 OTUs (3.2%) were only in Healthy, and 16 OTUs (25.8%) were found in AECOPD. There was no identical OTU only detected in COPD and Healthy, 19 OTUs (30.6%) were identified both in Healthy and AECOPD groups, and 7 OTUs were found in COPD and AECOPD (Fig. 6.E, Supplementary Table 9).

Differences in network complexity suggested that bacterium-bacterium interactions within respiratory tract microbiome was changed in COPD and influenced by exacerbations.

Based on respiratory tract microbiome profiles of a cohort of healthy individuals and COPD patients from different geographical regions, representative of diverse ethnicities and environmental exposures, we found that both COPD disease status and geography had an impact on the respiratory tract microbiome. The alterations in respiratory tract microbiome are potentially related to the pathological state or pathogenesis of COPD.

The predominant microbial families in sputum and oropharyngeal swabs are largely the same, indicating a continuum of microbiome composition along the respiratory tract. Previous studies showed that in healthy individuals Bacteroidetes and Firmicutes are two predominant phyla, whereas at the genus level, Prevotella, Veillonella, and Streptococcus are highly abundant in sputum microbiome[16–18]. The sputum microbiome is more diverse than the oropharyngeal swab, consistent with the fact that sputum microbiome is a mixture of oral and upper/lower respiratory tract microbiomes.

Reduced diversity of sputum microbiome under COPD stable state compared with healthy state has been reported in previous studies[6]. Moreover, the diversity of sputum microbiome has been found to be further decreased at exacerbations[6]. We found a consistent decreasing pattern in sputum microbiome; however, the oropharyngeal swab microbiome showed an increase in COPD compared with healthy control, which needs to be confirmed in further studies with balanced sample distribution between groups.

Pair-wise comparison of patients and healthy controls who shared the same household help reduce confounding factors, such as diet and genetic factors, and may reveal true microbiome alterations associated with diseases. Identified enriched or depleted taxa within COPD respiratory microbiome may underlie COPD pathogenesis or be a reflection of the pathological status. Some of the taxa found to be increased under COPD stable state have been reported to be associated with COPD before, such as Actinomyces, Veillonella, and Rothia[19]. In addition, Actinobacillus was also markedly enriched under COPD stable state, which was discovered as an aggressive pathogen related to periodontitis. Previous studies have shown that the COPD respiratory tract microbiome became more abundant in Proteobacteria bacteria such as Pseudomonas spp. and Haemophilus spp.[20–22]. In our study, many bacterial clades under Proteobacteria were observed to be increased in abundance in COPD patients during exacerbations compared with healthy individuals, including Enterobacteriaceae spp., Gammaproteobacteria spp., Comamonadaceae spp., Alphaproteobacteria spp., Rhodobacteraceae spp., Raoultella, and Haemophilus. Raoultella is considered as opportunistic pathogens that may result in pneumonia in individuals with abnormal immunity[23]. Haemophilus contains the well-known opportunistic pathogen Haemophilus influenzae, and respiratory infection by this bacterium causes airway inflammation and is linked to respiratory diseases including COPD[24]. Though still a conjecture, these clades may contribute to episodic exacerbations in COPD patients.

This study was initially based on the results of the population study, compared with the results generated from the core-family study, and the follow-up microbe’s biological function verification study can be focused on the identical microbes. However, there are still many limitations. First, sample sizes across different groups were imbalanced, due to difficulty in collecting respiratory microbiome samples. Second, the sample sizes for intra-household pairwise comparisons of healthy control vs COPD and healthy control vs AECOPD were quite limited, which still help eliminate confounding factors related to microbiome compositions. Third, few individuals had both sputum and oropharyngeal swab samples available, not allowing for comparing upper and lower microbiome within individuals. Fourth, due to small sample size at one geographical location, whether patients of different ethnics/locations in healthy or COPD respiratory tract microbiome between cannot be investigated.

This study was the first attempt to combine the population-based and core-family-based to investigate the microbiome changes in the upper and lower respiratory tract in COPD patients. COPD is associated with specific changes in upper and lower respiratory tract microbiomes, whereas exacerbation events confer additional alterations in respiratory tract microbiome compositions. These specific microbiome changes may inform future study on the pathogenesis or management of COPD.

COPD

Chronic Obstructive Pulmonary Disease

AECOPD

Exacerbations Chronic Obstructive Pulmonary Disease

Xishuangbanna

BanNa

OTU

Operational Taxonomic Units

PCoA

Principal Coordinates Analysis

PERMANOVA

Permutational multivariate analysis of variance

SPIEC-EASI

Sparse InversE Covariance estimation for Ecological Association and Statistical Inference

Ethics statement:

The studies involving human participants were reviewed and approved by The human research ethical board from institutional review board of the First People's Hospital of Yunnan Province approved the study (2016YY140). The patients/participants provided their written informed consent to participate in this study.

Data availability statement:

The dataset used for this study can be download in NCBI Sequence Read Archive (SRA), accession PRJNA687932 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA687932/ ).

Funding:

This work was supported by Regional Science Fund of National Natural Science Foundation of China (NSFC−81660012), Foundation for Medical Discipline Leader in Health and Family Planning Commission of Yunnan Province (D-2017050) and Yunnan respiratory disease clinical medical center project (ZX20190103，2019LCZXKF-HX02).

Competing interests:

The authors declare there are no competing interests.

Author contributions:

Bing Yuan & Yunhui Zhang designed the study;

Yanyan Xu & Nailiang Liu interpreted the data and wrote the paper;

Dailing Yan, Kun Tian, Xingqiu Duan, Xiaohui Yang, Zhenming Huang, Bo Cai, Jianyuan Su and Quan Pan gathered clinical data;

Shiyi Zhou, Jingxin Zhang, Jian Qin and Gang Yang conducted data analysis.

Acknowledgements:

Not applicable.

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Table 1. The detailed study design and sample enrollment

Type	Group1	AECOPD	COPD	Healthy	Total	Pop2	Paired3
Sputum
	AECOPD~COPD~Healthy4	6	6	6		-	-
	AECOPD~COPD	6	6	-		151 vs 40	12 vs 12
	AECOPD~Healthy	27	-	27		151 vs 58	33 vs 33
	COPD~Healthy	-	13	13		40 vs 58	19 vs 19
	Single5	112	15	12*		-	-
Subtotal		151	40	58	249	-	-
Swab
	AECOPD~COPD~Healthy4	10	10	10		-	-
	AECOPD~COPD	4	4	-		129 vs 32	14 vs 14
	AECOPD~Healthy	38	-	38		129 vs 74	48 vs 48
	COPD~Healthy	-	12	12		32 vs 74	22 vs 22
	Single5	77	6	14*		-	-
Subtotal		129	32	74	235	-	-
	Dropped6				2
Total					486

1.In this study, we enrolled patients according to their clinical diagnostic report, then regrouped into several contrasts, including AECOPD ~ COPD ~ Healthy, AECOPD ~ COPD, AECOPD ~ Healthy and COPD ~ Healthy.

2.Pop is the abbreviation of ”Population”, we clustered the same medical report from different patients into same group, for example, “AECOPD~COPD” means 151 AECOPD sputum samples compared to 40 COPD samples. 151 AECOPD is the sum of 6 (AECOPD ~ COPD ~ Healthy), 6 (AECOPD ~ COPD), 27( AECOPD ~ Healthy), and 112 (Single).

3.Paired means the comparison is carried out in paired samples, for example, “AECOPD ~ COPD” means 12 AECOPD sputum samples compared to 12 COPD samples. 12 AECOPD is the sum of 6 (AECOPD ~ COPD ~ Healthy) and 6 (AECOPD ~ COPD) which are from the same patients at two different disorder stage.

4.AECOPD ~ COPD ~ Healthy means the set of samples collected from the same family members, and AECOPD and COPD samples were collected from the same patient, and Healthy was collect from healthy family mender.

5.Single means the sample was collect from the patient only once in specific type and condition. For example, 112 AECOPD samples in sputum were collected from 112 patients, and some of these patients may also collected throat swab samples.

6.Dropped means excluded due to sample quality or missing clinical data.

*. * means some patients underwent one form of sample collection, but their healthy relatives were taken another form of sample. Such as 12 healthy sputum samples.

Table 2. Population-based and Paired-based microbiome differential abundance analysis with foldchange ≥.1.5 and p.adj ≤ 0.05.

Paired_Group	Type	Study_Type	Taxonomy	Genus	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	Enriched
HC	Swab	paired	OTU-49	Actinobacteria_unclassified	6.9914	2.0701	0.5430	3.8123	0.0001	0.0230	COPD
HC	Swab	paired	OTU-50	Actinomyces	40.1187	2.2748	0.4819	4.7205	0.0000	0.0016	COPD
HA	Swab	paired	OTU-1068	Enterobacteriaceae_unclassified	90.3075	4.6202	1.2440	3.7138	0.0002	0.0407	AECOPD
HA	Swab	paired	OTU-1073	Raoultella	170.4903	10.2211	1.4033	7.2834	0.0000	0.0000	AECOPD
HC	Swab	paired	OTU-548	Streptococcus	210.6977	2.3663	0.6142	3.8528	0.0001	0.0230	COPD
HA	Swab	paired	OTU-1121	Actinobacillus	26.1118	5.4973	1.0556	5.2077	0.0000	0.0001	AECOPD
HA	Swab	paired	OTU-1124	Haemophilus	18.4993	2.9040	0.7551	3.8456	0.0001	0.0319	AECOPD
HC	Swab	paired	OTU-662	Veillonella	122.2021	1.7688	0.4576	3.8655	0.0001	0.0230	COPD
AH	Swab	Population	OTU-999	Alysiella	2.7272	2.2137	0.4750	4.6608	0.0000	0.0031	Healthy
AC	Swab	Population	OTU-258	Capnocytophaga	25.1070	1.5513	0.5620	2.7601	0.0058	0.0472	COPD
AC	Swab	Population	OTU-541	Lactobacillus	7.2243	1.7344	0.5807	2.9866	0.0028	0.0346	COPD
AC	Swab	Population	OTU-108	Rothia	38.5429	1.8354	0.6520	2.8150	0.0049	0.0472	COPD
AC	Swab	Population	OTU-1121	Actinobacillus	17.5709	4.7427	1.0151	4.6720	0.0000	0.0001	COPD
AC	Swab	Population	OTU-661	Megasphaera	8.9167	-2.3310	0.5743	-4.0588	0.0000	0.0012	AECOPD
AC	Swab	Population	OTU-663	Veillonellaceae_unclassified	5.4088	-2.2470	0.6071	-3.7009	0.0002	0.0035	AECOPD
HA	Sputum	paired	OTU-786	Alphaproteobacteria_unclassified	6.2442	2.6260	0.7069	3.7150	0.0002	0.0031	AECOPD
HA	Sputum	paired	OTU-341	Anaerolineae_unclassified	6.6133	4.4966	0.7094	6.3383	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-344	Anaerolineaceae_unclassified	13.0673	5.6317	0.5817	9.6821	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-350	uncultured	8.8103	4.7789	0.5696	8.3899	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-177	Bacteria_unclassified	104.4581	3.1572	0.3133	10.0770	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-209	Bacteroidia_unclassified	15.9308	2.0951	0.3158	6.6348	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-13	Bryobacter	2.1317	2.5064	0.7260	3.4525	0.0006	0.0063	AECOPD
HA	Sputum	paired	OTU-953	Burkholderiales_unclassified	25.5903	2.1402	0.5870	3.6459	0.0003	0.0038	AECOPD
HA	Sputum	paired	OTU-961	Comamonadaceae_unclassified	6.9015	1.8326	0.4359	4.2038	0.0000	0.0005	AECOPD
HA	Sputum	paired	OTU-239	uncultured	3.0942	1.9475	0.5879	3.3124	0.0009	0.0098	AECOPD
HA	Sputum	paired	OTU-435	Desulfobacterota_unclassified	4.2099	4.8359	0.8933	5.4137	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-649	Firmicutes_unclassified	9.0465	1.7126	0.4604	3.7199	0.0002	0.0031	AECOPD
HA	Sputum	paired	OTU-1092	Gammaproteobacteria_unclassified	19.5853	2.7583	0.4878	5.6549	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-368	KD4-96_ge	1.4139	3.0792	0.8768	3.5118	0.0004	0.0054	AECOPD
HA	Sputum	paired	OTU-536	Enterococcaceae_unclassified	2.1134	2.5220	0.8333	3.0264	0.0025	0.0229	AECOPD
HA	Sputum	paired	OTU-1196	Pedosphaeraceae_unclassified	3.1190	3.7237	0.6796	5.4791	0.0000	0.0000	AECOPD
HA	Sputum	paired	OTU-1197	uncultured	2.2229	2.4332	0.8122	2.9960	0.0027	0.0231	AECOPD
HA	Sputum	paired	OTU-1165	Proteobacteria_unclassified	1.9770	2.4939	0.7690	3.2430	0.0012	0.0118	AECOPD
HA	Sputum	paired	OTU-875	Rhodobacteraceae_unclassified	2.7146	3.1139	0.8602	3.6202	0.0003	0.0038	AECOPD
HA	Sputum	paired	OTU-662	Veillonella	87.2735	1.6663	0.5687	2.9298	0.0034	0.0273	AECOPD

SupplementaryFigure1.pptx
Supplementary Figure 1: Study area and sampling location and type. To Study the difference between the upper (swab) and lower (sputum) respiratory tract flora in COPD patients and healthy people. The study collected 484 samples from 4 cities, including BanNa (101), DaLi (92), KunMing (204) and LiJiang (87). This study mainly used patients from the core family and healthy family members as healthy controls.
SupplementaryFigure2.pptx
Supplementary Figure 2. Upper respiratory tract microbial community structure in Healthy group, which was conclusively associated with certain clinical status.
SupplementaryFigure3.pptx
Supplementary Figure 3. Upper respiratory tract microbial community structure in COPD group, which was conclusively associated with certain clinical status.
SupplementaryFigure4.pptx
Supplementary Figure 4. Upper respiratory tract microbial community structure in AECOPD group, which was conclusively associated with certain clinical status.
SupplementaryFigure5.pptx
Supplementary Figure 5. Lower respiratory tract microbial community structure in Healthy group, which was conclusively associated with certain clinical status.
SupplementaryFigure6.pptx
Supplementary Figure 6. Lower respiratory tract microbial community structure in COPD group, which was conclusively associated with certain clinical status.
SupplementaryFigure7.pptx
Supplementary Figure 7. Lower respiratory tract microbial community structure in AECOPD group, which was conclusively associated with certain clinical status.
SupplementaryTable1.xlsx
Supplementary Table 1: Clinical Characteristics of the Sample
SupplementaryTable2.xlsx
Supplementary Table 2: Taxonomy reference codebook used in this study.
SupplementaryTable3.xlsx
Supplementary Table 3: Permutation test for homogeneity of Groups/Locations dispersions
SupplementaryTable4.xlsx
Supplementary Table 4: Population-based & Paired-based microbiome differential abundance analysis with p-value ≤ 0.05.
SupplementaryTable5.xlsx
Supplementary Table 5: The healthy group of the identical and unique bacteria in the upper and lower respiratory tract.
SupplementaryTable6.xlsx
Supplementary Table 6: The COPD group of the identical and unique bacteria in the upper and lower respiratory tract.
SupplementaryTable7.xlsx
Supplementary Table 7: The AECOPD group of the identical and unique bacteria in the upper and lower respiratory tract.
SupplementaryTable8.xlsx
Supplementary Table 8: The identical and unique bacteria in the upper respiratory tract among different groups.
SupplementaryTable9.xlsx
Supplementary Table 9: The identical and unique bacteria in the lower respiratory tract among different groups.

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Upper/Lower Respiratory Tract Microbiome Changes in AECOPD and COPD

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Abstract

Figures

Background

Methods

Participant Enrollment

Study Design

DNA extraction and microbiome sequencing

Sequencing data analysis

Statistical Analyses

Results

Overview of sputum and oropharyngeal microbiome

Alteration of sputum and oropharyngeal microbiome by COPD and resident regions

Respiratory tract microbiome taxa associated with COPD

Genus level core microbial community structure profile

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Supplementary Files

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