Bacterial and Viral Respiratory Tract Microbiota and Host Characteristics in Adults With Lower Respiratory Tract Infections: A Case-Control Study

Abstract

Background: Viruses and bacteria from the nasopharynx are capable of causing community-acquired pneumonia (CAP), which can be difficult to diagnose. We aimed to investigate whether shifts in the composition of these nasopharyngeal microbial communities can be used as diagnostic biomarkers for CAP in adults.

Methods: We collected nasopharyngeal swabs from adult CAP patients and controls without infection in a prospective multicenter case-control study design. We generated bacterial and viral profiles using 16S ribosomal RNA gene sequencing and multiplex polymerase chain reaction (PCR), respectively. Bacterial, viral, and clinical data were subsequently used as inputs for extremely randomized trees classification models aiming to distinguish subjects with CAP from healthy controls.

Results: We enrolled 117 cases and 48 control subjects. Cases displayed significant beta diversity differences in nasopharyngeal microbiota (P = .016, R2 = .01) compared to healthy controls. Our extremely randomized trees classification models accurately discriminated CAP caused by bacteria (area under the curve [AUC] .83), viruses (AUC .95) or mixed origin (AUC .81) from healthy control subjects. We validated this approach using a dataset of nasopharyngeal samples from 140 influenza patients and 38 controls, which yielded highly accurate (AUC .93) separation between cases and controls.

Conclusions: Relative proportions of different bacteria and viruses in the nasopharynx can be leveraged to diagnose CAP and identify etiologic agent(s) in adult patients. Such data can inform the development of a microbiota-based diagnostic panel used to identify CAP patients and causative agents from nasopharyngeal samples, potentially improving diagnostic specificity, efficiency, and antimicrobial stewardship practices.

Keywords: 16S rRNA sequencing; community-acquired pneumonia; influenza; microbiome; nasopharynx.

Figure 1.

Overview of causative pathogens of CAP patients. Cumulative overview of causative pathogens (A) and the proportion of bacterial, viral, and mixed cases within the cohort (B). *Other pathogens constitute Rothia dentocariosa, Stenotrophomonas maltophilia, Moraxella osloensis, and Streptococcus salivarius. Abbreviations: CAP, community-acquired pneumonia; hMPV: human metapneumovirus; RSV: respiratory syncytial virus.

Figure 2.

Alpha and beta diversity of cases (n = 117) and control subjects (n = 48). Inverse Simpson index (A) and the observed taxa (B) index were used to calculate the alpha diversity community and richness within each individual microbiota sample. Data are presented as box plot overlaid by a dot plot with a line at the median. P values were calculated using the Wilcoxon rank sum test. Beta diversity is depicted by unweighted (C) and weighted (D) UniFrac index in a PCoA representation. P values were calculated using permutational multivariate analysis of variance (PERMANOVA). Abbreviation: PCoA, principal coordinates analysis.

Figure 3.

High nasopharyngeal abundance of Streptococcus species (top) and Haemophilus species (bottom) in patients with microbiological diagnosis of these pathogens as obtained via culture or urine antigen test. Relative abundances (proportion of total 16s rRNA reads) within each individual microbiota sample are presented as box plot overlaid by a dot plot with a line at the median. P values were calculated using the Kruskal-Wallis test.

Figure 4.

ROC curves for extremely randomized trees classifying models aimed to discriminate cases from controls using nasopharyngeal bacterial abundance, viral presence, and host characteristics. Depiction of mean area under response curve (AUC) in the entire CAP cohort (blue line), patients with bacterial CAP (gold line), and viral CAP (green line) cohort (A). Depiction of the 15 discriminatory variables with the highest feature importance in all CAP patients (B), patients with bacterial CAP only (C), and patients with viral CAP only (D). Relative feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node. Node probability is depicted as a percentage, which can be calculated by the number of samples that reach the node, divided by the total number of samples. The higher the value, the more important the feature. Abbreviations: AUC, area under the curve; CAP, community-acquired pneumonia; COPD, chronic obstructive pulmonary disease; ROC, receiver operating characteristic.