Bacterial membrane vesicles and phages in blood after consumption of <i>Lacticaseibacillus rhamnosus</i> JB-1
Bibliographic record
Abstract
Gut microbiota have myriad roles in host physiology, development, and immunity. Though confined to the intestinal lumen by the epithelia, microbes influence distal systems via poorly characterized mechanisms. Recent work has considered the role of extracellular vesicles in interspecies communication, but whether they are involved in systemic microbe-host interaction is unclear. Here, we show that distinctive nanoparticles can be isolated from mouse blood within 2.5 h of consuming Lacticaseibacillus rhamnosus JB-1. In contrast to blood nanoparticles from saline-fed mice, they reproduced lipoteichoic acid-mediated immune functions of the original bacteria, including activation of TLR2 and increased IL-10 expression by dendritic cells. Like the fed bacteria, they also reduced IL-8 induced by TNF in an intestinal epithelial cell line. Though enriched for host neuronal proteins, these isolated nanoparticles also contained proteins and viral (phage) DNA of fed bacterial origin. Our data strongly suggest that oral consumption of live bacteria rapidly leads to circulation of their membrane vesicles and phages and demonstrate a nanoparticulate pathway whereby beneficial bacteria and probiotics may systemically affect their hosts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".