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Alterations of Bacteroides sp., Neisseria sp., Actinomyces sp., and Streptococcus sp. populations in the oropharyngeal microbiome are associated with liver cirrhosis and pneumonia.

Lu H, Qian G, Ren Z, Zhang C, Zhang H, Xu W, Ye P, Yang Y, Li L - BMC Infect. Dis. (2015)

Bottom Line: The microbiomes of humans are associated with liver and lung inflammation.Study components were as follows: (1) determination of the temporal stability of the oropharyngeal microbiome; (2) identification of oropharyngeal microbial variation in 90 subjects; (3) quantitative identification of disease-associated bacteria.Whole genome amplification combined with denaturing gradient gel electrophoresis analysis monitored successfully oropharyngeal microbial variations and showed that the composition of each subject's oropharyngeal microbiome remained relatively stable during the follow-up.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China. seawindlu@126.com.

ABSTRACT

Background: The microbiomes of humans are associated with liver and lung inflammation. We identified and verified alterations of the oropharyngeal microbiome and assessed their association with cirrhosis and pneumonia.

Methods: Study components were as follows: (1) determination of the temporal stability of the oropharyngeal microbiome; (2) identification of oropharyngeal microbial variation in 90 subjects; (3) quantitative identification of disease-associated bacteria. DNAs enriched in bacterial sequences were produced from low-biomass oropharyngeal swabs using whole genome amplification and were analyzed using denaturing gradient gel electrophoresis analysis.

Results: Whole genome amplification combined with denaturing gradient gel electrophoresis analysis monitored successfully oropharyngeal microbial variations and showed that the composition of each subject's oropharyngeal microbiome remained relatively stable during the follow-up. The microbial composition of cirrhotic patients with pneumonia differed from those of others and clustered together in subgroup analysis. Further, species richness and the value of Shannon's diversity and evenness index increased significantly in patients with cirrhosis and pneumonia versus others (p < 0.001, versus healthy controls; p < 0.01, versus cirrhotic patients without pneumonia). Moreover, we identified variants of Bacteroides, Eubacterium, Lachnospiraceae, Neisseria, Actinomyces, and Streptococcus through phylogenetic analysis. Quantitative polymerase chain reaction assays revealed that the populations of Bacteroides, Neisseria, and Actinomycetes increased, while that of Streptococcus decreased in cirrhotic patients with pneumonia versus others (p < 0.001, versus Healthy controls; p < 0.01, versus cirrhotic patients without pneumonia).

Conclusions: Alterations of Bacteroides, Neisseria, Actinomyces, and Streptococcus populations in the oropharyngeal microbiome were associated with liver cirrhosis and pneumonia.

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Cluster and diversity analyses of DGGE profiles of the oropharyngeal mucosal microbiomes of 90 PCR-DGGE profiles. (a) Cluster analysis of DGGE profiles of the oropharyngeal mucosal microbiome; (b) Multidimensional scaling analysis (MDS) based on the predominant oropharyngeal bacterial PCR-DGGE profiles. The plot is an optimized three-dimensional representation of the similarity matrix obtained using BioNumerics software, and the x-, y-, and z-axes represent three different dimensions (Dim 1, Dim 2, and Dim 3, respectively). (c) Principal component analysis (PCA) based on bacterial PCR-DGGE profiles. The plots were reoriented to maximize the variation among lanes along the first three principal components (contributions: 18.2, 8.2, and 7.4 %, respectively) obtained using BioNumerics software. Cubes, group CI; spheres, group CC; cylinders, group HC. (d) Comparison of the diversity index, species richness, and evenness index of oropharyngeal mucosal microbiomes. *p < 0.01; **p < 0.001
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Fig3: Cluster and diversity analyses of DGGE profiles of the oropharyngeal mucosal microbiomes of 90 PCR-DGGE profiles. (a) Cluster analysis of DGGE profiles of the oropharyngeal mucosal microbiome; (b) Multidimensional scaling analysis (MDS) based on the predominant oropharyngeal bacterial PCR-DGGE profiles. The plot is an optimized three-dimensional representation of the similarity matrix obtained using BioNumerics software, and the x-, y-, and z-axes represent three different dimensions (Dim 1, Dim 2, and Dim 3, respectively). (c) Principal component analysis (PCA) based on bacterial PCR-DGGE profiles. The plots were reoriented to maximize the variation among lanes along the first three principal components (contributions: 18.2, 8.2, and 7.4 %, respectively) obtained using BioNumerics software. Cubes, group CI; spheres, group CC; cylinders, group HC. (d) Comparison of the diversity index, species richness, and evenness index of oropharyngeal mucosal microbiomes. *p < 0.01; **p < 0.001

Mentions: Cluster analysis of DGGE profiles indicates that almost all individuals in each group (except for three samples from the group CC) clustered together, suggesting that the microbial composition of each individual in the same group was similar to the others and that the microbial composition of patients in the group CI differed from those of both control groups. Notably, the DGGE profiles of all patients in group CI clustered together at high UPGMA coefficient values ranging from 57.7 to 94.0 % (average, 82.30 ± 9.85, Fig. 3a). These results were confirmed using MDS analysis (Fig. 3b) and PCA (Fig. 3c). For example, note the overlap of symbols representing the microbiomes of patients infected with the same pathogen in the group CI.Fig. 3


Alterations of Bacteroides sp., Neisseria sp., Actinomyces sp., and Streptococcus sp. populations in the oropharyngeal microbiome are associated with liver cirrhosis and pneumonia.

Lu H, Qian G, Ren Z, Zhang C, Zhang H, Xu W, Ye P, Yang Y, Li L - BMC Infect. Dis. (2015)

Cluster and diversity analyses of DGGE profiles of the oropharyngeal mucosal microbiomes of 90 PCR-DGGE profiles. (a) Cluster analysis of DGGE profiles of the oropharyngeal mucosal microbiome; (b) Multidimensional scaling analysis (MDS) based on the predominant oropharyngeal bacterial PCR-DGGE profiles. The plot is an optimized three-dimensional representation of the similarity matrix obtained using BioNumerics software, and the x-, y-, and z-axes represent three different dimensions (Dim 1, Dim 2, and Dim 3, respectively). (c) Principal component analysis (PCA) based on bacterial PCR-DGGE profiles. The plots were reoriented to maximize the variation among lanes along the first three principal components (contributions: 18.2, 8.2, and 7.4 %, respectively) obtained using BioNumerics software. Cubes, group CI; spheres, group CC; cylinders, group HC. (d) Comparison of the diversity index, species richness, and evenness index of oropharyngeal mucosal microbiomes. *p < 0.01; **p < 0.001
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4477430&req=5

Fig3: Cluster and diversity analyses of DGGE profiles of the oropharyngeal mucosal microbiomes of 90 PCR-DGGE profiles. (a) Cluster analysis of DGGE profiles of the oropharyngeal mucosal microbiome; (b) Multidimensional scaling analysis (MDS) based on the predominant oropharyngeal bacterial PCR-DGGE profiles. The plot is an optimized three-dimensional representation of the similarity matrix obtained using BioNumerics software, and the x-, y-, and z-axes represent three different dimensions (Dim 1, Dim 2, and Dim 3, respectively). (c) Principal component analysis (PCA) based on bacterial PCR-DGGE profiles. The plots were reoriented to maximize the variation among lanes along the first three principal components (contributions: 18.2, 8.2, and 7.4 %, respectively) obtained using BioNumerics software. Cubes, group CI; spheres, group CC; cylinders, group HC. (d) Comparison of the diversity index, species richness, and evenness index of oropharyngeal mucosal microbiomes. *p < 0.01; **p < 0.001
Mentions: Cluster analysis of DGGE profiles indicates that almost all individuals in each group (except for three samples from the group CC) clustered together, suggesting that the microbial composition of each individual in the same group was similar to the others and that the microbial composition of patients in the group CI differed from those of both control groups. Notably, the DGGE profiles of all patients in group CI clustered together at high UPGMA coefficient values ranging from 57.7 to 94.0 % (average, 82.30 ± 9.85, Fig. 3a). These results were confirmed using MDS analysis (Fig. 3b) and PCA (Fig. 3c). For example, note the overlap of symbols representing the microbiomes of patients infected with the same pathogen in the group CI.Fig. 3

Bottom Line: The microbiomes of humans are associated with liver and lung inflammation.Study components were as follows: (1) determination of the temporal stability of the oropharyngeal microbiome; (2) identification of oropharyngeal microbial variation in 90 subjects; (3) quantitative identification of disease-associated bacteria.Whole genome amplification combined with denaturing gradient gel electrophoresis analysis monitored successfully oropharyngeal microbial variations and showed that the composition of each subject's oropharyngeal microbiome remained relatively stable during the follow-up.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China. seawindlu@126.com.

ABSTRACT

Background: The microbiomes of humans are associated with liver and lung inflammation. We identified and verified alterations of the oropharyngeal microbiome and assessed their association with cirrhosis and pneumonia.

Methods: Study components were as follows: (1) determination of the temporal stability of the oropharyngeal microbiome; (2) identification of oropharyngeal microbial variation in 90 subjects; (3) quantitative identification of disease-associated bacteria. DNAs enriched in bacterial sequences were produced from low-biomass oropharyngeal swabs using whole genome amplification and were analyzed using denaturing gradient gel electrophoresis analysis.

Results: Whole genome amplification combined with denaturing gradient gel electrophoresis analysis monitored successfully oropharyngeal microbial variations and showed that the composition of each subject's oropharyngeal microbiome remained relatively stable during the follow-up. The microbial composition of cirrhotic patients with pneumonia differed from those of others and clustered together in subgroup analysis. Further, species richness and the value of Shannon's diversity and evenness index increased significantly in patients with cirrhosis and pneumonia versus others (p < 0.001, versus healthy controls; p < 0.01, versus cirrhotic patients without pneumonia). Moreover, we identified variants of Bacteroides, Eubacterium, Lachnospiraceae, Neisseria, Actinomyces, and Streptococcus through phylogenetic analysis. Quantitative polymerase chain reaction assays revealed that the populations of Bacteroides, Neisseria, and Actinomycetes increased, while that of Streptococcus decreased in cirrhotic patients with pneumonia versus others (p < 0.001, versus Healthy controls; p < 0.01, versus cirrhotic patients without pneumonia).

Conclusions: Alterations of Bacteroides, Neisseria, Actinomyces, and Streptococcus populations in the oropharyngeal microbiome were associated with liver cirrhosis and pneumonia.

Show MeSH
Related in: MedlinePlus