Rigorous Plasma Microbiome Analysis Method Enables Disease Association Discovery in Clinic
Zhenwu Luo1, Alexander V Alekseyenko2,3, Elizabeth Ogunrinde1
1Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, United States.
Frontiers in Microbiology
|January 25, 2021
Summary
This study introduces a new quality-filtering method to analyze plasma microbiome, identifying specific bacteria linked to diseases like HIV and SLE. This advance aids in understanding microbial roles in disease and diagnosing infections.
Area of Science:
- Microbiology
- Immunology
- Genetics
Background:
- Investigating the blood microbiome is crucial for understanding microbial-host interactions and immune system responses.
- Challenges in blood microbiome research include low microbial biomass and background noise from contaminants and artifacts.
Purpose of the Study:
- To develop and validate a robust quality-filtering strategy for analyzing plasma microbiome using 16S DNA sequencing.
- To identify specific microbial signatures associated with diseases in human cohorts.
Main Methods:
- Applied 16S DNA sequencing to analyze plasma microbiome in three cohorts: tobacco-smokers, HIV-infected individuals, and individuals with systemic lupus erythematosus (SLE), plus controls.
- Developed and implemented a stringent quality-filtering strategy to remove low-biomass sequences, contaminations, and artifacts, processing over 97% of the sequence data.
Main Results:
- Identified specific pathobiont bacterial amplicon sequence variants (ASVs) enriched in the plasma of individuals with tobacco use, HIV infection, and SLE, but not in controls.
- Demonstrated associations between these enriched ASVs and disease pathogenesis, with some bacterial activities verified in vitro.
Conclusions:
- Presents a validated quality-filtering strategy for accurate plasma microbiome analysis.
- This approach enables the study of pathogenesis-associated plasma microbiomes, aiding in the diagnosis of subclinical infections and understanding microbiome-host interactions in disease.
