Sputum microbiome profiling in COPD: beyond singular pathogen detection

Ditz, Benedikt; Christenson, S.; Rossen, John W. A.; Brightling, Christopher E.; Kerstjens, Huib; Berge, Maarten van den; Faiz, Alen

doi:10.1136/thoraxjnl-2019-214168

Cited by 43 publications

(46 citation statements)

References 89 publications

(131 reference statements)

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“…and respiratory virus infection (40), and our study failed to analyze the effect of glucocorticoids on virus infection. Finally, the bronchoalveolar lavage fluid (BALF) microbiology has great significance when studying the lung microbiome (12,41), and further investigation of the microbiome via analysis of AECOPD patients' BALF is needed.…”

Section: Discussionmentioning

confidence: 99%

Compare the Effect of Inhaled Corticosteroids and Systemic Corticosteroids on Sputum Microbiome of AECOPD

Liang

Bai

et al. 2021

Front. Med.

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Background: To observe the effects of inhaled corticosteroids (ICS) and systemic corticosteroids (SCS) on the sputum microbiology of patients with AECOPD.Methods: The 16S rRNA sequencing results for sputum samples from 36 admitted AECOPD patients were analyzed using ICS or SCS on the basis of standard treatment; sputum samples were collected before and after treatment for 1 day, 7, and 14 days.Results: After 7 days of SCS treatment, the bacterial abundance of Sorangium, Acidibacter, and Fretibacterium decreased at the genus level. After 14 days of SCS treatment, the bacterial abundance of Prevotella_2, Bergeyella, Corynebacterium_1, and Ruminococcaceae_UCG-014 was decreased at the genus level, and an increase in the bacterial abundance of the Clostridiales_vadinBB60_group was observed at the family level. The linear discriminant analysis effect size (LEfSe) algorithm showed that after treatment for 14 days, Sphingobacterium increased in the SCS group, and Corynebacterium_1 (genus level), Bacillales (order level), and Lactobacillales (order level) decreased in the ICS group. However, the abundance of the above bacteria in each group of samples was <1%, suggesting that the two treatments may have similar effects on bacterial abundance. Alpha diversity analysis results showed that there was no significant difference in the ACE index, Chao1 index, Shannon index, or Simpson index between the ICS group and the SCS group. Beta diversity analysis showed that there was little difference in bacterial diversity among each group. BugBase predicted that although bacteria containing mobile elements in the SCS group decreased significantly compared with those in patients using ICS after treatment for 14 days, these two treatments had similar effects on other phenotype categories assigned to the bacterial contents.Conclusions: Our results show that ICS and SCS have remarkably similar effects on the sputum microbiome of AECOPD patients.

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Section: Discussionmentioning

confidence: 99%

Compare the Effect of Inhaled Corticosteroids and Systemic Corticosteroids on Sputum Microbiome of AECOPD

Liang

Bai

et al. 2021

Front. Med.

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“…metagenomics data suggesting their diverse role in the pathophysiology of human diseases (Man et al, 2017;Ditz et al, 2020;Khatiwada and Subedi, 2020). However, the mechanisms for the pathophysiology of COVID-19 and other respiratory tract diseases (non-COVID) as well as specific host-microbiome interactions supporting the changes in microbiome compositions, are considered below and not well-established.…”

Section: Discussionmentioning

confidence: 99%

Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

Hoque

Rahman

Ahmed

et al. 2020

Preprint

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The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesized that the diversity of microbiome compositions and their genomic features are associated with different pathological conditions of these human respiratory tract diseases (COVID-19 and non-COVID; URTI and COPD). To test this hypothesis, we analyzed 21 whole metagenome sequences (WMS) including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the diversity of microbiomes, their genomic features and relevant metabolic functions. The WMS data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19>COPD>URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively, however, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID sequence data, respectively and of them 34.50% strains shared between the conditions. Functional annotation of metagenomics sequences of thevCOVID-19 and non-COVID groups identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, replication and repair, clustering-based subsystems, virulence, disease and defense, adhesion, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, a predominant higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes were detected in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful for developing the microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.

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“…however, further analysis found that these samples are very similar in terms of the abundance of oral ora [24]. Therefore, we believe that although there are differences in microbial composition between samples from the upper respiratory tract and the lower respiratory tract, sputum does re ect the microbial composition of the lower respiratory tract to some extent, especially in terms of abundance [25].…”

Section: Discussionmentioning

confidence: 94%

Microbiome in Acute Exacerbation and Stable Phase of COPD: A Descriptive and Comparative Study of 16s rRNA Sequencing and Metagenomic Sequencing

Chang¹,

Zou²,

Z³

et al. 2020

Preprint

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Background: The role of bacteria in different courses of chronic obstructive pulmonary disease (COPD) has not been very clarified. In this study, sputum samples from patients at different courses of COPD were collected to analyze the differences of the structure and function of respiratory microbiome in different courses of COPD.Results: Our study involved 38 patients with acute exacerbation of COPD (AECOPD). Among them, 42 sputum samples were collected from the acute exacerbation of COPD (Of these 38 patients, 4 patients also collected sputum from the second acute exacerbation phase), and 12 sputum samples were collected from the stable phase of COPD (6 of the above 38 patients). Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria dominated the phylum in all the COPD samples, while Streptococcus, Neisseria, Haemophilus and Prevotella is the top 4 species in the cohort. Salmonella, Salmonella, Pseudomonas and Proteobacteria achieved higher abundance in AECOPD group. Samples from AECOPD patients showed higher α diversity. Moreover, genes annotated to K07481(the function is related to transposase) and K16087(the function is related to hemoglobin/transferrin/lactoferrin receptor protein) in stable subgroup showed higher abundance than that in acute exacerbation subgroup. Compared to samples from stable COPD, Proteobacteria contributed the most antibiotic resistance genes. Conclusions: There are differences in the function and metabolism of respiratory microbiome in patients with different course of COPD. Word count: 218/350

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Sputum microbiome profiling in COPD: beyond singular pathogen detection

Cited by 43 publications

References 89 publications

Compare the Effect of Inhaled Corticosteroids and Systemic Corticosteroids on Sputum Microbiome of AECOPD

Compare the Effect of Inhaled Corticosteroids and Systemic Corticosteroids on Sputum Microbiome of AECOPD

Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

Microbiome in Acute Exacerbation and Stable Phase of COPD: A Descriptive and Comparative Study of 16s rRNA Sequencing and Metagenomic Sequencing

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