Acute SARS-CoV-2 infection is associated with an increased abundance of bacterial pathogens, including Pseudomonas aeruginosa in the nose

Abstract

Research conducted on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogenesis and coronavirus disease 2019 (COVID-19) generally focuses on the systemic host response, especially that generated by severely ill patients, with few studies investigating the impact of acute SARS-CoV-2 at the site of infection. We show that the nasal microbiome of SARS-CoV-2-positive patients (CoV⁺, n = 68) at the time of diagnosis is unique when compared to CoV^- healthcare workers (n = 45) and CoV^- outpatients (n = 21). This shift is marked by an increased abundance of bacterial pathogens, including Pseudomonas aeruginosa, which is also positively associated with viral RNA load. Additionally, we observe a robust host transcriptional response in the nasal epithelia of CoV⁺ patients, indicative of an antiviral innate immune response and neuronal damage. These data suggest that the inflammatory response caused by SARS-CoV-2 infection is associated with an increased abundance of bacterial pathogens in the nasal cavity that could contribute to increased incidence of secondary bacterial infections.

Keywords: COVID-19; Pseudomonas aeruginosa; RNA-seq; SARS-CoV-2; coinfection; inflammation; nasal microbiome; viral RNA load.

Graphical abstract

Figure 1

The nasal microbiome of SARS-CoV-2-infected patients is distinct (A) Study design. (B) Principal coordinate analysis of nasal microbial communities unweighted UniFrac distance colored by host status. The contribution of host status to the total variance in the unweighted UniFrac dissimilarity was measured using permutational multivariate analysis of variance (PERMANOVA). (C–E) Violin plot of (C) average unweighted UniFrac distances, (D) number of observed ASVs, and (E) Shannon diversity. Significance for (C)–(E) was determined using a Kruskal-Wallis non-parametric ANOVA (p values inset at the bottom of each panel) with Dunn’s multiple comparison. ^∗∗p < 0.01, ^∗∗∗∗ = p < 0.0001. (F) Bubble plots of bacterial genera with >1% average abundance across the entire study population. The size of each circle indicates the average relative abundance for each taxa, and the color of each circle denotes bacterial phyla. See also Figure S1 and Table S1.

Figure 2

The nasal microbiome of SARS-CoV-2-infected patients and healthcare workers are enriched in opportunistic bacterial pathogens (A) Selected differentially abundant genera in the nasal microbiome between CoV⁻, HCWs, and CoV⁺ individuals. Differential abundance was determined using LEfSe (log₁₀ linear discriminant analysis [LDA] score >2). (B–H) Scatterplots of bacterial genera and species of interest identified by LEfSe analysis plotted as log₁₀ relative abundance + 0.01. Horizontal black lines represent the mean and whiskers represent the SEM. Significance for (B)–(H) was determined using a Kruskal-Wallis non-parametric ANOVA, with a Dunn’s multiple comparison test. ^∗p < 0.05, ^∗∗∗p < 0.001. See also Figure S2 and Table S2.

Figure 3

The nasal microbiome of SARS-CoV-2-infected patients is associated with vRNA load (A) Differentially abundant genera in the nasal microbiome between CoV⁺ individuals stratified by vRNA load into high, middle, and low Ct. Differential abundance was determined using LEfSe (log₁₀ LDA score >2). (B–F) Scatterplots of bacterial genera and species of interest identified by LEfSe analysis plotted as log₁₀ relative abundance + 0.01. Horizontal black bars represent the mean and whiskers represent the SEM. Significance for (B)–(H) was determined using a Kruskal-Wallis non-parametric ANOVA, with Dunn’s multiple comparison test. ^∗p < 0.05, ^∗∗∗p < 0.001. See also Figure S3.

Figure 4

Transcriptional profiling of the nasal passages reveals robust immune activation (A) Volcano plot of gene expression changes in CoV⁺ patients relative to CoV⁻ HCWs. Upregulated differentially expressed genes (DEGs) are indicated in red; downregulated genes are indicated in blue. (B) Functional enrichment of upregulated DEGs. Horizontal bars represent the number of genes enriching to each GO term, with color intensity representing the negative log of the false discovery rate (FDR)-adjusted p value (−log[q value]). (C) Heatmap of upregulated DEGs. Columns of all heatmaps represent the RPKM (reads per kilobase transcript per million mapped reads) of one individual. Range of colors per each heatmap is based on scaled and centered RPKM values of the represented DEGs (red indicates upregulated; blue indicates downregulated). (D) Functional enrichment of downregulated DEGs as described in (C). (E) Heatmap of downregulated DEGs. See (D) for additional details.