Abstract
The human microbiome has a role in the development of multiple diseases. Individual microbiome profiles are highly personalized, though many species are shared. Understanding the relationship between the human microbiome and disease may inform future individualized treatments. We hypothesize the blood microbiome signature may be a surrogate for some lung microbial characteristics. We sought associations between the blood microbiome signature and lung-relevant host factors. Based on reads not mapped to the human genome, we detected microbial nucleic acids through secondary use of peripheral blood RNA-sequencing from 2,590 current and former smokers with and without chronic obstructive pulmonary disease (COPD) from the COPDGene study. We used the Genome Analysis Toolkit (GATK) microbial pipeline PathSeq to infer microbial profiles. We tested associations between the inferred profiles and lung disease relevant phenotypes and examined links to host gene expression pathways. We replicated our analyses using a second independent set of blood RNA-seq data from 1,065 COPDGene study subjects and performed a meta-analysis across the two studies. The four phyla with highest abundance across all subjects were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. In our meta-analysis, we observed associations (q-value < 0.05) between Acinetobacter, Serratia, Streptococcus and Bacillus inferred abundances and Modified Medical Research Council (mMRC) dyspnea score. Current smoking status was associated (q < 0.05) with Acinetobacter, Serratia and Cutibacterium abundance. All 12 taxa investigated were associated with at least one white blood cell distribution variable. Abundance for nine of the 12 taxa was associated with sex, and seven of the 12 taxa were associated with race. Host-microbiome interaction analysis revealed clustering of genera associated with mMRC dyspnea score and smoking status, through shared links to several host pathways. This study is the first to identify a bacterial microbiome signature in the peripheral blood of current and former smokers. Understanding the relationships between systemic microbial signatures and lung-related phenotypes may inform novel interventions and aid understanding of the systemic effects of smoking.
Highlights
Pi10 SRWA-Pi10; square root wall area of a hypothetical airway with 10 mm internal perimeter PRISm Preserved Ratio Impaired Spirometry 6-min walk distance (6 MW) 6-Minute Walk distance TMM Trimmed Mean of M values WBC White Blood Cell
We detected microbial signatures through secondary use of whole blood RNA-sequencing data from large subsets of the COPDGene (Genetic Epidemiology of chronic obstructive pulmonary disease (COPD)) study, repurposing sequencing reads not mapped to the human g enome[27,30,36]
RNA-seq data were available for 2,647 samples from current and former smokers from the COPDGene five-year follow-up visit
Summary
Pi10 SRWA-Pi10; square root wall area of a hypothetical airway with 10 mm internal perimeter PRISm Preserved Ratio Impaired Spirometry 6 MW 6-Minute Walk distance TMM Trimmed Mean of M values WBC White Blood Cell. Through use of culture-independent sequencing methods, evidence has emerged regarding a possible healthy human blood m icrobiome[26,27,28,29,30]. Culture-independent methods in microbiome studies do not provide evidence of whether a blood microbial signature is from transient nucleic acids or from live b acteria[31]. As with the lung microbiome, low biomass is an issue for peripheral blood microbiome s tudies[35] Sequencing of both R NA27,30 and the 16S rRNA gene[28,32,33] has been used to study the peripheral blood microbial signature. An overarching challenge in population-based microbiome studies relates to statistical power, as testing for associations between the detected microbial profiles and variables of interest places demands on sample size. A blood microbiome signature has the potential to serve as a biomarker of disease severity and progression and may inform personalized diagnostic or treatment efforts
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