Comprehensive multi-omics characterization of gut microbiome extracellular vesicles reveals a connection to gut-brain axis signaling
Bibliographic record
Abstract
Abstract Microbiota-gut-brain axis is an evident pathway of host-microbiota crosstalk that is linked to multiple brain disorders. Microbiota released extracellular vesicles (MEVs) has emerged as a key player in intercellular signaling in host microbiome communications. However, their role in gutbrain axis signaling is poorly investigated. Here, we performed a deep multi-omics profiling of MEVs content generated ex vivo and from stool samples in order to get some insights on their role in gut-brain-axis signaling. Metabolomics profiling identified a wide array of metabolites embedded in MEVs, including lipids, carbohydrates, amino acids, vitamins, and organic acids. Interestingly, many neurotransmitter-related compounds were detected inside MEVs, including arachidonyl-dopamine (NADA), gabapentin, glutamate and N-acylethanolamines. Next, we aimed to identify commensal microbes with psychobiotic activity. We isolated 58 Bacteroides strains assigned to four genera, 11 species, and 4 new species based on 16S rDNA sequencing. We performed whole genome sequencing of 18 representative isolates, followed by a comparative analysis of the structure of polysaccharide utilization loci (PUL) and glutamate decarboxylase (GAD), a genetic system involved in GABA production. Quantifying GABA was done using competitive ELISA, wherein three isolates ( B. finegoldii, B. faecis , and B. caccae ) showed high GABA production (4.5-7 mM range) in supernatant whereas 2.2 to 4 uM GABA concentration was detected inside microvesicles extracted using ultracentrifugation. To test the biodistribution of MEVs from the gut to other parts of the body, CACO-2, RIN-14 B, and hCMEC/D3 cells showed a capacity to internalize labeled MEVs through an endocytic mechanism. Additionally, MEVs exhibited a dose dependent paracellular transport through CACO-2 intestinal cells and hCMEC/D3 brain endothelial cells. In vivo results showed biodistribution of MEVs to liver, stomach and spleen. Overall, our results reveal the capabilities of MEVs to cross the intestinal and blood brain barriers to deliver their cargoes of neuroactive molecules to the brain as a new signaling mechanism in microbiota-gut-brain axis communications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".