Abstract
Extracellular vesicles (EVs), nanoparticles secreted by both gram-negative and gram-positive bacteria, carry various biomolecules and cross biological barriers. Gut microbiota-derived EVs are currently being investigated as a communication mechanism between the microbiota and the host. Few clinical studies, however, have investigated gut microbiota-derived EVs. Here, we show that machine learning models were able to accurately distinguish gut microbiota and respective microbiota-derived EV samples according to their taxonomic composition both within each data set (area under the curve [AUC] 0.764–1.00) and in a cross-study setting (AUC 0.701–0.997). These results show that gut microbiota-derived EVs form a distinct taxonomic entity from gut microbiota. Thus, conventional gut microbiota composition may not correctly reflect communication between the gut microbiota and the host unless microbiota-derived EVs are reported separately.Abstract
Extracellular vesicles (EVs), nanoparticles secreted by both gram-negative and gram-positive bacteria, carry various biomolecules and cross biological barriers. Gut microbiota-derived EVs are currently being investigated as a communication mechanism between the microbiota and the host. Few clinical studies, however, have investigated gut microbiota-derived EVs. Here, we show that machine learning models were able to accurately distinguish gut microbiota and respective microbiota-derived EV samples according to their taxonomic composition both within each data set (area under the curve [AUC] 0.764–1.00) and in a cross-study setting (AUC 0.701–0.997). These results show that gut microbiota-derived EVs form a distinct taxonomic entity from gut microbiota. Thus, conventional gut microbiota composition may not correctly reflect communication between the gut microbiota and the host unless microbiota-derived EVs are reported separately
