A proposed workflow to robustly analyze bacterial transcripts in RNAseq data from extracellular vesicles

Area of Science:

• Microbiology
• Molecular Biology
• Neuroscience

Background:

• The gut microbiota influences host health, but mechanisms linking it to diseases are unclear.
• Bacterial extracellular vesicles (bEVs) are implicated in microbiota-host communication.
• Detecting bEVs in host samples is technically challenging.

Purpose of the Study:

• To develop and validate a novel computational workflow for identifying bacterial RNA within circulating extracellular vesicles.
• To apply this workflow to analyze extracellular vesicle RNA sequencing data from individuals with multiple sclerosis (MS).

Main Methods:

• Analysis of total extracellular vesicle RNA sequencing data from healthy controls and MS patients (Relapsing-Remitting and Secondary Progressive phases).
• Multi-reference mapping against host and bacterial genomes, followed by consensus taxonomic profiling.
• Inclusion of biological and technical controls to ensure workflow specificity and accuracy.

Main Results:

• Demonstrated successful detection of bacterial RNA in total extracellular vesicle RNA sequencing data.
• The workflow proved effective for reanalyzing existing extracellular vesicle RNA sequencing datasets.
• Identified specific bacterial RNA signatures differentially expressed between multiple sclerosis disease phases.

Conclusions:

• The developed workflow enables robust identification of bacterial RNA in extracellular vesicles, offering insights into bEVs' role in microbiota-host interactions.
• This method is translatable to other total RNA studies, mitigating false positive microbial detection.
• The findings highlight potential bacterial involvement in multiple sclerosis pathogenesis.