Lower respiratory tract co-infection of Streptococcus pneumoniae and respiratory syncytial virus shapes microbial landscape and clinical outcomes in children

Abstract

Background: Lower respiratory tract infections (LRTI), which are most commonly caused by Streptococcus pneumoniae (SP) and respiratory syncytial virus (RSV), pose a substantial global health burden in children. However, the causal pathways of bacterial-viral co-infections, particularly the mechanisms by which commensal microbiota could modulate SP-RSV-associated LRTI outcomes remain to be elucidated.

Methods: A population-based cross-sectional study was conducted on children aged 0-18 years who were admitted to Beijing Children's Hospital and Baoding Children's Hospital in China from September 2021 to August 2022. Children with LRTI who underwent respiratory pathogen testing were divided into SP single infection and SP-RSV co-infection groups, with sex- and time-matched non-LRTI children as controls. Sputum and LRT secretion samples were collected for microbiota analysis using 16S rRNA sequencing, and child characteristics were obtained from medical records and pharmacy data.

Results: A total of 125 children with LRTI (84 with SP infection and 41 with SP-RSV co-infection) and 87 children without LRTI were recruited for this study. We found that LRT microbiota composition was strongly related to age, with a more pronounced increase in Shannon index within the first 5 years of life. Children with SP and RSV infection exhibited significantly altered microbiota composition in comparison to children without LRTI, particularly a higher abundance of Streptococcus. The competitive interactions among respiratory bacteria were found to be more complex in the SP single-infection group and simpler in the SP-RSV co-infection group.

Conclusion: Our findings show that RSV co-infection exacerbates SP-induced LRTI microbiota disorder and disease severity. This study may help us to better understand the characteristics of SP-RSV interaction and provide direction for the pathogen diagnosis of LRTI.

Keywords: Streptococcus pneumoniae; children; lower respiratory tract infections; microbiota; respiratory syncytial virus.

Figure 1

Experimental design of the study, analysis, and workflow schematic.

Figure 2

The lower respiratory tract microbiota across ages in healthy children. (A) Mosaic plot of the proportion of participants in each respiratory secretions microbial cluster per age and sex category. The varying width of each age group represents the proportion of the study population within that specific age group. (B) ASV-level Shannon diversity across age in years in different age. Through piecewise regression analysis and the Davies test, significant breakpoints (denoted by red dashed lines) were identified, indicating shifts in Shannon diversity at specific age points. (C) Shannon diversity across different age groups. P values were calculated using Wilcoxon rank sum test. (D) Bray-Curtis dissimilarities between samples per niche in individuals aged under 18 years. Boxplots represent the 25th and 75th percentiles (lower and upper boundaries of boxes, respectively), the median (middle horizontal line), and measurements that fall within 1.5 times the interquartile range (IQR; distance between the 25th and 75th percentiles; whiskers). P values were calculated using Wilcoxon rank sum test. (E) Principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarities of different age groups. Explained variance (R2) and P values were calculated using PERMANOVA tests. (F) Average relative abundance of the dominant phylum found in the microbiota of the respiratory represented by stacked bar plots. The right line chart showed the abundance change of phylum along different age groups. (G) Average relative abundance of the ten most frequent genera found in the microbiota of the respiratory represented by stacked bar plots. (H) Analysis of the spearman correlations between the twenty most frequent genera found in the microbiota of the respiratory and age in children. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 3

Respiratory infections are correlated to specific lower respiratory tract microbiota disorders. (A) Shannon and Simpson diversities among three groups. P values were calculated using Wilcoxon rank sum test. (B) Principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarities among three groups. (C) Relative abundance of phyla and genera among three groups. (D) Relative abundance at top 10 genera level among three groups (compared with non-LRTI group). ***p < 0.001; NS p>0.05.

Figure 4

Variations in the composition of lower respiratory tract microbiota in children with Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection. (A) Four beta-diversity indicates dissimilarities between Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection groups. (B) Relative abundance of genera in each individual. (C) Venn plot showed the number of distinctive and common amplicon sequence variants (ASVs) between two groups. Volcano plots highlight the significant differences in lower respiratory microbiota of children in the Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection groups. Metastats was used to compare differences in the relative abundance of species between two groups. Bacteria with a BH-adjusted P values <0.05 (adjusted per contrast) and a log2-fold change of 1.0 were deemed significantly different. (D) Interaction networks of taxa at genera level in Streptococcus pneumoniae single infection children. (E) Interaction networks of taxa at genera level in Streptococcus pneumoniae-respiratory syncytial virus co-infection children. Only significant (adjusted P values <0.05) correlations are displayed. Node size scales with the total number of interaction links per genus (degree centrality). The red lines indicate positive genera interactions, and the blue lines indicate negative interactions. SP si, Streptococcus pneumoniae single infection; SP-RSV co, Streptococcus pneumoniae-respiratory syncytial virus co-infection. *P <0.05; ***P <0.001.

Figure 5

Association of clinical symptoms and lower respiratory tract microbiota in children with Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection. (A) Forest plot for the risk factors associated with Streptococcus pneumoniae-respiratory syncytial virus co-infection when compared with the Streptococcus pneumoniae single infection group. The odds ratio (OR) was based on the logistic regression model and adjusted for age. E/P=Events/Patients; SP si=Streptococcus pneumoniae; SP-RSV co=Streptococcus pneumoniae-respiratory syncytial virus co-infection. (B) Variance in the lower respiratory microbiota composition explained by potential factors was assessed through permutational multivariate analysis of variance (PERMANOVA) analysis. The P value was determined through 999 permutations. (C) The proportion of clinical symptoms in different relative abundance of Streptococcus pneumoniae. High abundance was classified as >20% Streptococcus pneumoniae reads (based on distribution in Figure 4C ). (D) Correlation between the relative abundance of top 10 lower respiratory bacteria genera and the clinical blood routine indices. WBC, White blood cell count; PLA, Platelets count; NEUT, Neutrophil percentage; LY, Lymphocyte percentage; MONO, Monocyte percentage; EO, Eosinophil percentage; BASO, Basophil percentage; ANEUT, Absolute neutrophil cout; ALY, Absolute lymphocytes count; AMONO, Absolute monocytes count; AEO, Absolute eosinophils count; ABASO, Absolute basophil count; CRP, C-reactive protein. Significance levels are indicated as follows: *P <0.05; **P <0.01; ***P <0.001; ns, P >0.05.

Figure 6

Random forest models for the diagnosis of respiratory Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection. (A) Schematic diagram of random forest model development. (B) The model constructed from the training set was validated in the Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection, and the AUC value was calculated. (C) The model constructed from the testing set was validated in the Streptococcus pneumoniae Streptococcus pneumoniae single infection and Streptococcus pneumoniae-respiratory syncytial virus co-infection, and the AUC value was calculated. (D) Mean decrease in the accuracy of the 50 respiratory bacteria markers. AUC=Area under of receiver operating characteristic curve. SP si, Streptococcus pneumoniae single infection; SP-RSV co, Streptococcus pneumoniae-respiratory syncytial virus co-infection.