Gut microbiota-derived extracellular vesicles form a distinct entity from gut microbiota

Area of Science:

• Microbiology
• Bioinformatics
• Immunology

Background:

• Extracellular vesicles (EVs) are nanoparticles secreted by bacteria, involved in intercellular communication.
• Gut microbiota-derived EVs are proposed as a key mechanism for microbiota-host interaction.
• Limited clinical data exist on the specific composition of gut microbiota-derived EVs.

Purpose of the Study:

• To investigate and compare the taxonomic composition of gut microbiota-derived EVs with that of the gut microbiota.
• To determine if EVs represent a distinct taxonomic entity separate from the host microbiota.
• To assess the utility of machine learning in classifying these distinct entities.

Main Methods:

• Utilized machine learning models to classify 16S rRNA gene sequencing data.
• Analyzed seven clinical datasets containing both gut microbiota and gut microbiota-derived EV samples.
• Evaluated classification accuracy using the area under the curve (AUC) metric.

Main Results:

• Machine learning models accurately distinguished between gut microbiota and microbiota-derived EV samples (AUC 0.764-1.00).
• Classification remained accurate in cross-study analyses (AUC 0.701-0.997).
• Results demonstrate that gut microbiota-derived EVs form a distinct taxonomic entity.

Conclusions:

• Gut microbiota-derived EVs possess a unique taxonomic profile separate from the gut microbiota.
• Current assessments of gut microbiota may not fully represent host communication without considering EVs.
• Including microbiota-derived EV analysis in clinical studies is essential for comprehensive gut-host communication insights.