Microbes Are Associated with Host Innate Immune Response in Idiopathic Pulmonary Fibrosis

Abstract

Rationale: Differences in the lung microbial community influence idiopathic pulmonary fibrosis (IPF) progression. Whether the lung microbiome influences IPF host defense remains unknown.

Objectives: To explore the host immune response and microbial interaction in IPF as they relate to progression-free survival (PFS), fibroblast function, and leukocyte phenotypes.

Methods: Paired microarray gene expression data derived from peripheral blood mononuclear cells as well as 16S ribosomal RNA sequencing data from bronchoalveolar lavage obtained as part of the COMET-IPF (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) study were used to conduct association pathway analyses. The responsiveness of paired lung fibroblasts to Toll-like receptor 9 (TLR9) stimulation by CpG-oligodeoxynucleotide (CpG-ODN) was integrated into microbiome-gene expression association analyses for a subset of individuals. The relationship between associated pathways and circulating leukocyte phenotypes was explored by flow cytometry.

Measurements and main results: Down-regulation of immune response pathways, including nucleotide-binding oligomerization domain (NOD)-, Toll-, and RIG1-like receptor pathways, was associated with worse PFS. Ten of the 11 PFS-associated pathways correlated with microbial diversity and individual genus, with species accumulation curve richness as a hub. Higher species accumulation curve richness was significantly associated with inhibition of NODs and TLRs, whereas increased abundance of Streptococcus correlated with increased NOD-like receptor signaling. In a network analysis, expression of up-regulated signaling pathways was strongly associated with decreased abundance of operational taxonomic unit 1341 (OTU1341; Prevotella) among individuals with fibroblasts responsive to CpG-ODN stimulation. The expression of TLR signaling pathways was also linked to CpG-ODN responsive fibroblasts, OTU1341 (Prevotella), and Shannon index of microbial diversity in a network analysis. Lymphocytes expressing C-X-C chemokine receptor 3 CD8 significantly correlated with OTU1348 (Staphylococcus).

Conclusions: These findings suggest that host-microbiome interactions influence PFS and fibroblast responsiveness.

Keywords: CpG-oligodeoxynucleotide response; bronchoalveolar lavage microbiome; host immune response and microbial interaction; pattern recognition receptors; peripheral blood mononuclear cell transcriptome.

Figure 1.

Interaction network between progression-free survival (PFS)–associated canonical pathways and microbial diversity indices and operational taxonomic units (OTUs). This network illustrates the correlational interaction of PFS-associated host canonical pathways (listed in Table 2), with gray hexagons for pathways with a Wald P value less than 0.05 or gray hexagons with blue outlines for pathways with a Wald P value less than 0.01 in PFS analysis, respectively, and microbial community features designated by gold circles for microbial diversity indices and green circles for OTU abundance. The diameter of the green circles is proportional to the correlation coefficient. The red edges represent positive correlation, and the green edges represent negative correlation. The thickness of the edges is determined by 1 − (P value). All PFS-associated host canonical pathways except the Kyoto Encyclopedia of Genes and Genomes (KEGG) chemokine signaling pathway in Table 2 correlated with microbial community features. Microbial richness (species accumulation curve index) is the hub node in this network, connecting to 7 of 10 pathways, with significant negative correlation demonstrated by green edges. NOD = nucleotide-binding oligomerization domain; RIG I = retinoic acid–inducible gene I.

Figure 2.

Correlation of gene coexpression modules with clinical traits and microbial community features. Gene coexpression modules were constructed using the WGCNA R software package. The number of genes is labeled within each module. Correlation of the module eigengene with each clinical trait and microbial community features was determined by Pearson’s correlation algorithm and is displayed in the corresponding box (coefficient of r value on top and P value in parentheses). The color of each box represents the directionality of the correlation (red = positive correlation; green = negative correlation). The bar on the right scales the degree of correlation. Pearson’s correlation was used to determine the significance of correlation (P < 0.05) between the eigengenes of individual gene modules with clinical traits, including race, sex, age, FVC, Dl_CO, CPI, and prognosis, as well as microbial diversity and OTU abundance. CPI = composite physiologic index; Dl_CO = diffusing capacity of the lung for carbon monoxide; idx = index; OTU = operational taxonomic unit; sac = species accumulation curves.

Figure 3.

Network of canonical signaling pathways that are individually associated with CpG-ODN responsiveness, host gene coexpression modules, and microbial community features. An integrated approach was used to identify common canonical pathways (light blue circles) enriched from the following three paired datasets obtained from patients with IPF in the COMET-IPF study: (1) fibroblast response to CpG-ODN, (2) microbial diversity indices and OTUs, and (3) WGCNA host gene coexpression modules. Up-regulated genes between cases with CpG-ODN response and CpG-ODN nonresponse were subjected to Ingenuity Pathway Analysis with a criterion of FDR less than 0.01 to identify 22 significantly CpG-ODN response–associated canonical pathways. These 22 pathways were then matched to the canonical pathways associated with 10 host gene coexpression modules (gray squares) from PBMC microarray and microbial diversity indices and OTUs (purple diamonds) from bacterial 16S ribosomal RNA sequencing (Tables E2 and E4, respectively). Fourteen pathways (light blue circles) were shared among CpG-ODN response (yellow hexagon), microbial features (purple diamonds), and host gene coexpression modules (gray squares). The red and green edges represent positive and negative associations between nodes, respectively. Gray edges represent both positive and negative associations between nodes. The width of the edge is proportional to the value of −log P value (i.e., thicker edge is more significant). COMET-IPF = Correlating Outcomes with Biochemical Markers to Estimate Time-progression in Idiopathic Pulmonary Fibrosis; FDR = false discovery rate; iNOS = inducible nitric oxide synthase; IPF = idiopathic pulmonary fibrosis; NF-κB = nuclear factor-κB; ODN = oligodeoxynucleotide; OTU = operational taxonomic unit; PBMC = peripheral blood mononuclear cell; PKR = protein kinase R; PPAR = peroxisome proliferator–activated receptor; TNFR2 = tumor necrosis factor receptor 2; WGCNA = weighted gene coexpression network analysis R software package.

Figure 4.

Correlation of circulating leukocyte phenotypes with microbial community. The correlation of microbial diversity indices and OTU abundance with (A) diverse circulating leukocyte phenotypes and (B) CD28 determined by Person’s correlation algorithm are shown. To maintain positive or negative directional correlations, r coefficient instead of r² is displayed on top in each box. Criterion for significance is set at coefficient greater than 0.3 (red box) or less than −0.3 (green box) and at FDR less than 0.1 after multiple testing correction shown in parenthesis below each coefficient r value. CCR = CC chemokine receptor; CXCR = C-X-C chemokine receptor; FDR = false discovery rate; OTU = operational taxonomic unit; sac = species accumulation curve.