Early nasopharyngeal microbial signature associated with severe influenza in children: a retrospective pilot study

Abstract

A few studies have highlighted the importance of the respiratory microbiome in modulating the frequency and outcome of viral respiratory infections. However, there are insufficient data on the use of microbial signatures as prognostic biomarkers to predict respiratory disease outcomes. In this study, we aimed to evaluate whether specific bacterial community compositions in the nasopharynx of children at the time of hospitalization are associated with different influenza clinical outcomes. We utilized retrospective nasopharyngeal (NP) samples (n=36) collected at the time of hospital arrival from children who were infected with influenza virus and had been symptomatic for less than 2 days. Based on their clinical course, children were classified into two groups: patients with mild influenza, and patients with severe respiratory or neurological complications. We implemented custom 16S rRNA gene sequencing, metagenomic sequencing and computational analysis workflows to classify the bacteria present in NP specimens at the species level. We found that increased bacterial diversity in the nasopharynx of children was strongly associated with influenza severity. In addition, patients with severe influenza had decreased relative abundance of Staphylococcus aureus and increased abundance of Prevotella (including P. melaninogenica), Streptobacillus, Porphyromonas, Granulicatella (including G. elegans), Veillonella (including V. dispar), Fusobacterium and Haemophilus in their nasopharynx. This pilot study provides proof-of-concept data for the use of microbial signatures as prognostic biomarkers of influenza outcomes. Further large prospective cohort studies are needed to refine and validate the performance of such microbial signatures in clinical settings.

Keywords: biomarker; children; emergency unit; influenza; intensive care; microbiome.

Fig. 1.

NP microbiota composition associated with each respiratory sample. (a) heat map displaying the relative abundance of the 300 OTUs detected in each NP sample. Relative abundance is shown with a black to white gradient scale, with OTUs that were not quantified in white, and OTUs with a relative abundance of 1 in black. The samples were ordered based on patient group and sample number. OTUs were clustered based on their relative abundance values across samples using Euclidian distances and a complete linkage function. (b) Bar plot showing relative abundance of the five most abundant OTUs in each sample, coloured by OTUs. The samples were ordered as in (a).

Fig. 2.

The NP microbiota composition differentiates influenza severity. Non-metric multidimensional scaling (NMDS) plot comparing the global NP microbial profiles with distances calculated using Unifrac distance. Each dot represents a sample: samples with similar microbial profiles are close together in the NMDS plot, while increasing distances in the plot between samples suggest divergent microbial profiles. Patients’ groups are depicted with different colours, and the sample natures (NP aspirate or swab) with different shapes. Stress=0.1102

Fig. 3.

Patients developing severe influenza have more diverse NP bacterial communities than patients with mild influenza. Box plots showing different alpha diversity measures for the three groups of patients (mild, neurological complications and respiratory complications).

Fig. 4.

Bacterial species that were differentially abundant between patients with mild and severe influenza (a) and between patients with respiratory and neurological complications. (b) The heat map shows the relative abundance of 12 OTUs that were found to be differentially abundant between patients with mild or severe disease after adjustment for covariates (P-value <0.05). Relative abundance is shown with a black to white gradient scale, with OTUs that were not quantified in white, and OTUs with a relative abundance of 1 in black. The samples were ordered based on patient group and sample number. OTUs were clustered based on their relative abundance values across samples using Euclidian distances and a complete linkage function.