The Microbiome and the Lung

Cui, Lijia; Morris, Alison; Huang, Laurence; Beck, James M.; Twigg, Homer L.; Mutius, Erika von; Ghedin, Elodie

doi:10.1513/annalsats.201402-052pl

Cited by 81 publications

(68 citation statements)

References 42 publications

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“…However, Massilia will need more study to unravel its potential involvement in pneumonia inducement and progression, because there were just several reports about Massilia contribution to eye and skin illness (20). Previous reports indicated that S. pneumoniae, S. aureus, P. aeruginosa and H. influenzae were also the main pathogenic agents for and accumulated in children with respiratory diseases (2)(3)(4). In this study, Pseudomonas and Haemophilus showed no discrepancy among three groups.…”

Section: Previously Identified Microbial Colonizers Of Lung Includingmentioning

confidence: 52%

“…In C group, Pseudomonas and Arthrobacter were in the core of interaction network and built positive relationship with other bacterial residents. Pseudomonas carriage was documented to be normal in prior reports (4), and this correlation also implicated homeostasis of microbiota at BALF, even if children from C group had tracheomalacia. When children were diagnosed pneumonia infected by S. pneumoniae, linkage of Pseudomonas and Arthrobacter to other bacterial colonizers were implicated to be broken up.…”

Section: Previously Identified Microbial Colonizers Of Lung Includingmentioning

confidence: 62%

“…Increasing reports documented that respiratory tract is home to various microbes, which were associated with respiratory health (4). Sputum microbiota was demonstrated to be not concordant to microbes in the lung (13,14), and lung tissue could be collected only when lung transplantation will be conducted (3).…”

Section: Discussionmentioning

confidence: 99%

“…Besides above-mentioned pathogens, lung was home to many kinds of bacteria (3,4). However, most of microbes in lung microbiota could not be cultured through conventional clinical detection.…”

Section: Introductionmentioning

confidence: 99%

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Mycoplasma pneumoniae and Streptococcus pneumoniae caused different microbial structure and correlation network in lung microbiota

Wang

Dai²,

Qiu

et al. 2016

J. Thorac. Dis.

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Pneumonia is one of the most serious diseases for children, with which lung microbiota are proved to be associated. We performed 16S rDNA analysis on broncho-alveolar lavage fluid (BALF) for 32 children with tracheomalacia (C group), pneumonia infected with Streptococcus pneumoniae (S. pneumoniae) (D1 group) or Mycoplasma pneumoniae (M. pneumoniae) (D2 group). Children with tracheomalacia held lower microbial diversity and accumulated Lactococcus (mean ± SD, 45.21%±5.07%, P value <0.05), Porphyromonas (0.12%±0.31%, P value <0.05). D1 and D2 group were enriched by Streptococcus (7.57%±11.61%, P value <0.01 when compared with D2 group) and Mycoplasma (0.67%±1.25%, P value <0.01) respectively.Bacterial correlation in C group was mainly intermediated by Pseudomonas and Arthrobacter. Whilst, D1 group harbored simplest microbial correlation in three groups, and D2 group held the most complicated network, involving enriched Staphylococcus (0.26%±0.71%), Massilia (0.81%±2.42%). This will be of significance for understanding pneumonia incidence and progression more comprehensively, and discerning between bacterial infection and carriage.

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Section: Previously Identified Microbial Colonizers Of Lung Includingmentioning

confidence: 52%

Section: Previously Identified Microbial Colonizers Of Lung Includingmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mycoplasma pneumoniae and Streptococcus pneumoniae caused different microbial structure and correlation network in lung microbiota

Wang

Dai²,

Qiu

et al. 2016

J. Thorac. Dis.

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“…Airway microbiome dynamics and microbe-tomicrobe interactions are increasingly recognized as critical factors in lung diseases (Cui et al, 2014). Particularly for chronic lung diseases with frequent bacterial infections, such as cystic fibrosis (CF) and chronic obstructive pulmonary disease, airway microbiome dynamics have been associated with disease progression (Hunter et al, 2012, Pragman et al, 2012Huang et al, 2014).…”

mentioning

confidence: 99%

The fermentation product 2,3-butanediol alters P. aeruginosa clearance, cytokine response and the lung microbiome

Nguyen

Sharma

et al. 2016

The ISME Journal

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Diseases that favor colonization of the respiratory tract with Pseudomonas aeruginosa are characterized by an altered airway microbiome. Virulence of P. aeruginosa respiratory tract infection is likely influenced by interactions with other lung microbiota or their products. The bacterial fermentation product 2,3-butanediol enhances virulence and biofilm formation of P. aeruginosa in vitro. This study assessed the effects of 2,3-butanediol on P. aeruginosa persistence, inflammatory response, and the lung microbiome in vivo. Here, P. aeruginosa grown in the presence of 2,3-butanediol and encapsulated in agar beads persisted longer in the murine respiratory tract, induced enhanced TNF-α and IL-6 responses and resulted in increased colonization in the lung tissue by environmental microbes. These results led to the following hypothesis that now needs to be tested with a larger study: fermentation products from the lung microbiota not only have a role in P. aeruginosa virulence and abundance, but also on the increased colonization of the respiratory tract with environmental microbes, resulting in dynamic shifts in microbiota diversity and disease susceptibility.

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Exact variance component tests for longitudinal microbiome studies

Zhai

Knox

Twigg

et al. 2019

Genetic Epidemiology

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In metagenomic studies, testing the association between microbiome composition and clinical outcomes translates to testing the nullity of variance components. Motivated by a lung human immunodeficiency virus (HIV) microbiome project, we study longitudinal microbiome data by using variance component models with more than two variance components. Current testing strategies only apply to models with exactly two variance components and when sample sizes are large. Therefore, they are not applicable to longitudinal microbiome studies. In this paper, we propose exact tests (score test, likelihood ratio test, and restricted likelihood ratio test) to (a) test the association of the overall microbiome composition in a longitudinal design and (b) detect the association of one specific microbiome cluster while adjusting for the effects from related clusters. Our approach combines the exact tests for null hypothesis with a single variance component with a strategy of reducing multiple variance components to a single one. Simulation studies demonstrate that our method has a correct type I error rate and superior power compared to existing methods at small sample sizes and weak signals. Finally, we apply our method to a longitudinal pulmonary microbiome study of HIV‐infected patients and reveal two interesting genera Prevotella and Veillonella associated with forced vital capacity. Our findings shed light on the impact of the lung microbiome on HIV complexities. The method is implemented in the open‐source, high‐performance computing language Julia and is freely available at https://github.com/JingZhai63/VCmicrobiome.

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The Microbiome and the Lung

Cited by 81 publications

References 42 publications

Mycoplasma pneumoniae and Streptococcus pneumoniae caused different microbial structure and correlation network in lung microbiota

Mycoplasma pneumoniae and Streptococcus pneumoniae caused different microbial structure and correlation network in lung microbiota

The fermentation product 2,3-butanediol alters P. aeruginosa clearance, cytokine response and the lung microbiome

Exact variance component tests for longitudinal microbiome studies

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