Multi-level analysis of gut microbiome extracellular vesicles-host interaction reveals a connection to gut-brain axis signaling
Bibliographic record
Abstract
ABSTRACT Microbiota-released extracellular vesicles (MEVs) have emerged as a key player in intercellular signaling. However, their involvement in the gut-brain axis has been poorly investigated. We hypothesize that MEVs cross host cellular barriers and deliver their cargoes of bioactive compounds to the brain. In this study, we aimed to investigate the cargo capacity of MEVs for bioactive metabolites and their interactions with the host cellular barriers. First, we conducted a multi-omics profiling of MEVs’ contents from ex vivo and stool samples. Metabolomics analysis identified various neuro-related compounds encapsulated within MEVs, such as arachidonyl-dopamine, gabapentin, glutamate, and N-acylethanolamines. Metaproteomics unveiled an enrichment of enzymes involved in neuronal metabolism, primarily in the glutamine/glutamate/gamma-aminobutyric acid (GABA) pathway. These neuro-related proteins and metabolites were correlated with Bacteroides spp. We isolated 18 Bacteroides strains and assessed their GABA production capacity in extracellular vesicles (EVs) and culture supernatant. A GABA-producing Bacteroides finegoldii , released EVs with a high GABA content (4 µM) compared to Phocaeicola massiliensis . Upon testing the capacity of MEVs to cross host barriers, MEVs exhibited a dose-dependent paracellular transport and were endocytosed by Caco-2 and hCMEC/D3 cells. Exposure of Caco-2 cells to MEVs did not alter expression of genes related to intestinal barrier integrity, while affected immune pathways and cell apoptosis process as revealed by RNA-seq analyses. In vivo , MEVs biodistributed across mice organs, including the brain, liver, stomach, and spleen. Our results highlight the ability of MEVs to cross the intestinal and blood-brain barriers to deliver their cargoes to distant organs, with potential implication for the gut-brain axis. IMPORTANCE Microbiota-released extracellular vesicles (MEVs) have emerged as a key player in intercellular signaling. In this study, a multi-level analysis revealed presence of a diverse array of biologically active molecules encapsulated within MEVs, including neuroactive metabolites, such as arachidonyl-dopamine, gabapentin, glutamate, and N-acylethanolamines, and gamma-aminobutyric acid (GABA). Metaproteomics also unveiled an enrichment of neural-related proteins, mainly the glutamine/glutamate/GABA pathway. MEVs were able to cross epithelial and blood-brain barriers in vitro . RNA-seq analyses showed that MEVs stimulate several immune pathways while suppressing cell apoptosis process. Furthermore, MEVs were able to traverse the intestinal barriers and reach distal organs, including the brain, thereby potentially influencing brain functionality and contributing to mental and behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".