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. 2016 Jan;10(1):97-108.
doi: 10.1038/ismej.2015.99. Epub 2015 Jul 7.

Dysbiosis of upper respiratory tract microbiota in elderly pneumonia patients

Wouter A A de Steenhuijsen Piters  1 , Elisabeth G W Huijskens  2   3 , Anne L Wyllie  1 , Giske Biesbroek  1 , Menno R van den Bergh  1   4 , Reinier H Veenhoven  4 , Xinhui Wang  1 , Krzysztof Trzciński  1 , Marc J Bonten  5 , John W A Rossen  2   6 , Elisabeth A M Sanders  1 , Debby Bogaert  1
Affiliations

Dysbiosis of upper respiratory tract microbiota in elderly pneumonia patients

Wouter A A de Steenhuijsen Piters et al. ISME J. 2016 Jan.

Abstract

Bacterial pneumonia is a major cause of morbidity and mortality in elderly. We hypothesize that dysbiosis between regular residents of the upper respiratory tract (URT) microbiome, that is balance between commensals and potential pathogens, is involved in pathogen overgrowth and consequently disease. We compared oropharyngeal microbiota of elderly pneumonia patients (n=100) with healthy elderly (n=91) by 16S-rRNA-based sequencing and verified our findings in young adult pneumonia patients (n=27) and young healthy adults (n=187). Microbiota profiles differed significantly between elderly pneumonia patients and healthy elderly (PERMANOVA, P<0.0005). Highly similar differences were observed between microbiota profiles of young adult pneumonia patients and their healthy controls. Clustering resulted in 11 (sub)clusters including 95% (386/405) of samples. We observed three microbiota profiles strongly associated with pneumonia (P<0.05) and either dominated by lactobacilli (n=11), Rothia (n=51) or Streptococcus (pseudo)pneumoniae (n=42). In contrast, three other microbiota clusters (in total n=183) were correlated with health (P<0.05) and were all characterized by more diverse profiles containing higher abundances of especially Prevotella melaninogenica, Veillonella and Leptotrichia. For the remaining clusters (n=99), the association with health or disease was less clear. A decision tree model based on the relative abundance of five bacterial community members in URT microbiota showed high specificity of 95% and sensitivity of 84% (89% and 73%, respectively, after cross-validation) for differentiating pneumonia patients from healthy individuals. These results suggest that pneumonia in elderly and young adults is associated with dysbiosis of the URT microbiome with bacterial overgrowth of single species and absence of distinct anaerobic bacteria. Whether the observed microbiome changes are a cause or a consequence of the development of pneumonia or merely coincide with disease status remains a question for future research.

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Conflict of interest statement

EAMS declares to have received unrestricted research support from Pfizer, grant support for vaccine studies from Pfizer and GlaxoSmithKline and fees paid to the institution for advisory boards or participation in independent data monitoring committees for Pfizer and GSK. RHV reported receiving grant support from GlaxoSmithKline and Wyeth/Pfizer for vaccine studies and consulting fees from GlaxoSmithKline. KT received grant support and consulting fees from Pfizer. DB received consulting fees from Pfizer. These grants and fees were not received for the research described in this paper. The remaining authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Two-dimensional nonmetric multidimensional scaling (nMDS) plot of the oropharyngeal microbiome composition in adult and elderly pneumonia patients and healthy controls based on the Weighted Unifrac distance measure. Each dot represents the total oropharyngeal microbiome composition of a single individual and was colored according to group; healthy elderly in blue, elderly pneumonia patients in yellow, healthy adults in green and adult pneumonia patients in red. Ellipsoids represent the standard deviation per cohort. Data points are positioned in such a manner that the two-dimensional plot represents the multidimensional data structure the best way possible (that is, (dis)similarities in microbiome structure between individuals are conserved). The microbiome structure was well captured in this nMDS visualization (stress=0.15).
Figure 2
Figure 2
Patterns in relative abundance of frequently occurring OTUs in elderly and adult controls and pneumonia patients. Relative abundance (percentage) is depicted per individual for Gemellales, Streptococcus (pseudo)pneumoniae, Neisseria, Rothia, Veillonella, Prevotella melaninogenica, Haemophilus, Leptotrichia and Lactobacillus. Statistically significant differences between groups (healthy elderly vs elderly pneumonia (circles), healthy adults vs adult pneumonia (triangles) and healthy adults vs healthy elderly) were calculated using significance analysis of microarrays with false discovery rate correction for multiple tests. *q⩽0.05; **q⩽0.01; ***q⩽0.001; ****q⩽0.0001; NS, not significant. Bars represent geomean and 95% CI of geomean per group. OTUs are color coded based on the phylum level; Firmicutes, red; Actinobacteria, yellow; Bacteroidetes, green; Proteobacteria, blue; and Fusobacteria, purple.
Figure 3
Figure 3
Bacterial density in the oropharynx of elderly and adult controls and pneumonia patients. For each group, the bacterial density per sample is depicted in pg μl−1. Bars represent geomean and 95% CI of geomean per group. Statistically significant differences between groups (healthy elderly vs elderly pneumonia (circles), healthy adults vs adult pneumonia (triangles) and healthy adults vs healthy elderly) were calculated using Mann–Whitney U-tests; *P⩽0.05; **P⩽0.01; ***P⩽0.001; ****P⩽0.0001. Healthy subjects are depicted in light gray, patients are shown in dark gray.
Figure 4
Figure 4
Weighted Unifrac (phylogenetic) average linkage hierarchical clustering analysis in elderly and adult healthy controls and pneumonia patients. The weighted Unifrac distance matrix was used to construct a circle dendrogram including all 405 individuals; healthy elderly (blue branches), elderly pneumonia patients (yellow), healthy adults (green) and adult pneumonia patients (red). Adjacent to the dendrogram branch ends, viral data and information on disease severity are visualized and stacked bar charts show the relative abundance of the 15 highest ranked OTUs. Color coding: presence of influenza virus and non-influenza virus is shown in purple and orange respectively; dark purple/orange, virus present in moderate-high amount (CT-value <35 cycles); moderate light purple/orange, virus present in low amount (CT-value 35–45 cycles); light purple/orange, viral qPCR performed and tested negative (CT-value >45 cycles); white, no viral qPCR performed. Clusters (defined as more than four samples; bright) are designated by dotted gray lines originating from the center of the dendrogram. Groups of samples comprising less than four samples are shown opaque. Three clusters were significantly associated with health; these clusters showed predominance of Prevotella melaninogenica (IIIa and IIIb) and Leptotrichia (IIIb) or showed a balanced microbial community (VIII). Clusters that were correlated with disease showed high relative abundance of either lactobacilli (IV), Rothia (Va) or Streptococcus (pseudo)pneumoniae (VII). Cluster IV was most strongly associated with the presence of influenza virus (50%). Five clusters showed no association with either health or disease, these clusters showed high relative abundance of Neisseria (Ia and Ib), Actinomyces (II), Gemellales (VI) or Rothia (Vb). Per cluster, the mean relative abundance of the 15 highest ranked oropharyngeal OTUs and the residual bacteria separated by phylum is given in circle graphs that were size-scaled according to the number of individuals within each cluster. PSI, pneumonia severity score.
Figure 5
Figure 5
Decision tree model to distinguish healthy individuals from pneumonia patients based on URT microbiota composition. On the basis of random forest analysis, seven variables (Prevotella melaninogenica OTU rank 5, Leptotrichia, Streptococcus OTU rank 8, Gemellales, Parascardovia, Prevotella OTU rank 20 and Veillonella dispar) were found to be most important in the discrimination between healthy controls and pneumonia patients. Measures of impurity (Gini- and information indices) were used to identify the most discriminative OTUs based on the random forest analysis. Subsequently, these OTUs were used to construct a decision tree model, which was based on the relative abundance of Prevotella melaninogenica, Leptotrichia, Streptococcus, Gemellales and Parascardovia. From the root of the tree, each branch represents a division of the initial group based on the relative abundance (shown in small font) of an OTU. The numbers (regular font size) neighboring the branches depict the number of individuals before each split. Adjacent to the branch ends are circle graphs illustrating the number of individuals (represented by the surface of the circles, colored by the study group they originate from) that are stratified by the prior split. Light green, healthy adults; dark green, healthy elderly; light red, adult pneumonia and dark red, elderly pneumonia. The numbers below the graphs represent the exact proportions. The combined presence and abundance of Prevotella and Leptotrichia appears to be the strongest determinant for respiratory health.
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