Differential transcriptomic host responses in the early phase of viral and bacterial infections in human lung tissue explants ex vivo

Abstract

Background: The first 24 h of infection represent a critical time window in interactions between pathogens and host tissue. However, it is not possible to study such early events in human lung during natural infection due to lack of clinical access to tissue this early in infection. We, therefore, applied RNA sequencing to ex vivo cultured human lung tissue explants (HLTE) from patients with emphysema to study global changes in small noncoding RNA, mRNA, and long noncoding RNA (lncRNA, lincRNA) populations during the first 24 h of infection with influenza A virus (IAV), Mycobacterium bovis Bacille Calmette-Guerin (BCG), and Pseudomonas aeruginosa.

Results: Pseudomonas aeruginosa caused the strongest expression changes and was the only pathogen that notably affected expression of microRNA and PIWI-associated RNA. The major classes of long RNAs (> 100 nt) were represented similarly among the RNAs that were differentially expressed upon infection with the three pathogens (mRNA 77-82%; lncRNA 15-17%; pseudogenes 4-5%), but lnc-DDX60-1, RP11-202G18.1, and lnc-THOC3-2 were part of an RNA signature (additionally containing SNX10 and SLC8A1) specifically associated with IAV infection. IAV infection induced brisk interferon responses, CCL8 being the most strongly upregulated mRNA. Single-cell RNA sequencing identified airway epithelial cells and macrophages as the predominant IAV host cells, but inflammatory responses were also detected in cell types expressing few or no IAV transcripts. Combined analysis of bulk and single-cell RNAseq data identified a set of 6 mRNAs (IFI6, IFI44L, IRF7, ISG15, MX1, MX2) as the core transcriptomic response to IAV infection. The two bacterial pathogens induced qualitatively very similar changes in mRNA expression and predicted signaling pathways, but the magnitude of change was greater in P. aeruginosa infection. Upregulation of GJB2, VNN1, DUSP4, SerpinB7, and IL10, and downregulation of PKMYT1, S100A4, GGTA1P, and SLC22A31 were most strongly associated with bacterial infection.

Conclusions: Human lung tissue mounted substantially different transcriptomic responses to infection by IAV than by BCG and P. aeruginosa, whereas responses to these two divergent bacterial pathogens were surprisingly similar. This HLTE model should prove useful for RNA-directed pathogenesis research and tissue biomarker discovery during the early phase of infections, both at the tissue and single-cell level.

Keywords: Biomarker; Chronic obstructive pulmonary disease; Emphysema; Gene expression; Infection; Long noncoding RNA; Lung tissue; PIWI-associated RNA; Pneumonia; Prokineticin 2; Transcription; miRNA.

Fig. 1

Experimental design and functional validation of the human lung tissue explants (HLTE) model. A Outline of the experimental procedure. After removal from the explanted lung, tissue was divided into pieces of approx. 30 mg and then incubated in medium overnight. HLTE pieces were infected with 2.0 × 10⁵ FFU/ml of influenza virus strain A/Giessen /6/2009 H1N1 (in short IAV), 5 × 10⁶ CFU/ml M. bovis strain H37Rv (BCG), or 1 × 10⁸/ml P. aeruginosa strain PA14. All analyses were performed 24 h post infection (p.i.) unless indicated otherwise. B Comparison of two methods of tissue preservation before RNA extraction. HLTE pieces were either snap frozen in liquid nitrogen (n = 20) or incubated in RNAlater at 4 °C overnight (n = 38) before storage at − 80 °C, followed by RNA extraction. Y-axis = RNA integrity number (RIN). C Time course of LDH release from uninfected or IAV-infected HLTE pieces (n = 3 per group). LDH was measured in tissue culture supernatants and is expressed as % of LDH extracted from lysed tissue control. D Time course of IAV hemagglutinin (HA) mRNA levels (RT-qPCR), indicating transcription of viral RNA (n = 6). E Differences in cytokine/chemokine induction by infection with IAV, BCG, and P. aeruginosa. Upper row: protein concentrations measured by EIA. Bottom row: mRNA levels measured by RT-qPCR relative to mock-infected HLTE, using GAPDH as internal reference. n = 9. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; B–D = unpaired parametric T test; E = Mann–Whitney U test. Data represent means ± SEM

Fig. 2

Modest reprogramming in sncRNA expression in HLTE in viral and bacterial infections. sncRNA populations were determined by sncRNAseq 24 h after infection with IAV, BCG, and P. aeruginosa, or after mock infection (n = 5 per condition). Results of the DE analysis with DEseq2 are found in Table S1. A Detection efficiency (no. of reads) of all sncRNAs vs. sncRNAs mapping to human genome hg38. B Abundance of the major sncRNA subpopulations miRNA, piRNA, snoRNA, and snRNA. C Abundance of sncRNA subtypes detected at a total of ≥ 20 reads in all samples. D Abundance of differentially expressed sncRNA subtypes (pAdj. ≤ 0.1). E PCA showing poor separation of the four groups based on sncRNA. F–H Volcano plots showing DE sncRNAs (pAdj ≤ 0.1, fold change [FC], ≥|2|). F IAV vs. uninfected tissue. G BCG vs. uninfected tissue. H P. aeruginosa vs. uninfected tissue. I, Hallmark GSEA analysis of predicted targets of DE miRNA in P. aeruginosa infection

Fig. 3

Differential reprogramming of RNA populations by viral and bacterial infection. Using RNA from the same total RNA samples as used for small RNA sequencing (Fig. 2), long RNA populations were determined by RNAseq 24 h after infection or mock treatment (n = 5 per condition). A PCA indicating somewhat better separation than with sncRNAseq (compare Fig. 2E). B Venn diagram showing the number of DE mRNAs (pAdj ≤ 0.1, FC ≥|2| with respect to uninfected tissue) unique to each pathogen, shared between any two pathogens, or common to all three. C–H Volcano plots identifying DE mRNA and lncRNA species with respect to uninfected tissue in infection with IAV (C–E), BCG (F), and P. aeruginosa (G), and comparing P. aeruginosa vs. BCG infected HLTEs (H). Cutoffs were set at FC =|2| (i.e. log₂ =|1|) and p-Adj 0.1 (i.e. − log₁₀ = 1)

Fig. 4

Viral and bacterial infections leave different RNA signatures in HLTEs. Unsupervised hierarchical biclustering analysis based on the 75 most significantly up- or downregulated mRNA and lncRNA (p < 7.8E−04, ANOVA across all four groups). Red bracket: RNAs comprising a 5-transcript “viral signature”. The color scheme in the 3 columns on the left shows p-Adj values for DE due to infection with each pathogen compared with uninfected tissue

Fig. 5

Gene set enrichment analysis (GSEA) based on Hallmark pathways identifies common and distinct signaling pathways in bacterial and viral infection of explanted human lung tissue. mRNA expression data were extracted from the bulk RNAseq data set used for Fig. 3, and a GSEA was performed using the prerank gene list from Deseq2 analysis against the Hallmark gene set collection. Significantly enriched or depleted Hallmark pathways are indicated by the triangles as detailed in the legend next to the figure. A GSEA based on Gene Ontology Biological Process (GO:BP) is shown in Figure S6

Fig. 6

Single-cell RNA sequencing identifies strongest induction of antiviral host responses in CD4, CD8 + T, NK cells, mast cells, and macrophages. Single cell transcriptomes were determined in pooled single cell suspensions derived from HLTEs from one donor with emphysema due to COPD 24 h after IAV infection or mock infection. A Identification of 11 cell subpopulations by uniform manifold approximation and projection (UMAP). B CD69 mRNA expression (see Figure S7 for CD103 expression). C Volcano plots identifying DE genes in the major immune cell types and vascular ECs. The dotted lines identify cutoffs FC =|2| (i.e. log₂ =|1|) and p-Adj 0.1 (i.e. − log₁₀ = 1). DE in the other cell types is shown in Figure S9

Fig. 7

Comparison of DE Genes and functional pathways in IAV-infected HLTEs detected at the tissue or the single cell level 9. A, B Venn diagrams illustrating the relationships between DE genes identified by bulk RNAseq and by scRNAseq in different cell types. A Bulk RNAseq vs. scRNAseq (pool of all cell types); B bulk RNAseq vs. scRNAseq (individual cell types indicated in the diagram). The arrow points to 6 mRNAs making up the core tissue response identified by bulk and scRNAseq. C GSEA of IAV infection based on the bulk RNAseq data or scRNAseq data. Hallmark pathways [41] are listed on the vertical axis in reverse alphabetical order (top to bottom). Significantly (FDR ≤ 0.05) enriched or depleted pathways are marked with triangles as indicated in the legend on the right