Probiotic bacteria-released extracellular vesicles enhance macrophage phagocytosis in polymicrobial sepsis by activating the FPR1/2 pathway
Bibliographic record
Abstract
Abstract Background Sepsis-induced organ failure and high mortality are largely ascribed to the failure of bacterial clearance from the infected tissues. Recently, probiotic bacteria-released extracellular vesicles (BEVs) have been implicated as critical mediators of intercellular communication which are widely involved in the regulation of the inflammatory response. However, their functional role in macrophage phagocytosis during sepsis has never been explored. Methods BEVs were collected from three different strains of probiotics including Lactiplantibacillus plantarum WCFS1 (LP WCFS1), Lactobacillus rhamnosus Gorbach-Goldin (LGG), and Escherichia coli Nissle 1917 (EcN), or from LGG cultured under three pH conditions (pH5-acid, pH6.5-standard, pH8-akaline) through differential centrifugation, filtration, and ultracentrifugation of their culture supernatants. In vitro phagocytosis was measured in Raw264.7 cells and bone marrow-derived macrophages using pHrodo red E. coli BioParticles. The in vivo therapeutic effects of BEVs were tested using a feces-injection-in-peritoneum (FIP) model of polymicrobial sepsis. Results LGG-derived EVs (BEV LGG ) were the best among these three probiotics BEVs in stimulating macrophages to take up bacteria. Furthermore, BEV LGG collected from pH8 culture condition (BEV pH8 ) exhibited the strongest capacity of phagocytosis, compared with BEV pH5 and BEV pH6.5 . Treatment of septic mice with BEV pH8 significantly prolonged animal survival; increased bacterial clearance from the blood, peritoneal lavage fluid, and multiple organs; and decreased serum levels of pro-inflammatory cytokines/chemokines, as well as reduced multiple organ injuries, in comparison with control-treated septic mice. Mechanistically, RNA-seq and bioinformatic analysis identified that the FPR1/2 signaling was remarkably activated, along with its downstream pathways (PI3K-Akt-MARCO and NADPH-ROS) in BEV pH8 -treated macrophages, compared with control cells. Accordingly, pre-addition of Boc2, a specific antagonist of FPR1/FPR2, to macrophages significantly attenuated BEV pH8 -mediated phagocytosis, compared to controls. Conclusions This study demonstrates that LGG-derived BEVs may have therapeutic effects against sepsis-induced organ injury and mortality through enhancing FPR1/2-mediated macrophage phagocytosis.
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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 | 0.000 |
| 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".