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Peripheral blood microbial signatures in current and former smokers.
Morrow, Jarrett D; Castaldi, Peter J; Chase, Robert P; Yun, Jeong H; Lee, Sool; Liu, Yang-Yu; Hersh, Craig P.
Afiliação
  • Morrow JD; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA. jarrett.morrow@channing.harvard.edu.
  • Castaldi PJ; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
  • Chase RP; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
  • Yun JH; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
  • Lee S; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
  • Liu YY; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
  • Hersh CP; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
Sci Rep ; 11(1): 19875, 2021 10 06.
Article em En | MEDLINE | ID: mdl-34615932
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sepse / Microbiota / Fumantes Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sepse / Microbiota / Fumantes Idioma: En Ano de publicação: 2021 Tipo de documento: Article