Immunomodulatory properties of Bacillus subtilis extracellular vesicles on rainbow trout intestinal cells and splenic leukocytes
Bibliographic record
Abstract
Extracellular vesicles (EVs) are cell-derived membrane-surrounded vesicles that carry bioactive molecules. Among EVs, outer membrane vesicles (OMVs), specifically produced by Gram-negative bacteria, have been extensively characterized and their potential as vaccines, adjuvants or immunotherapeutic agents, broadly explored in mammals. Nonetheless, Gram-positive bacteria can also produce bilayered spherical structures from 20 to 400 nm involved in pathogenesis, antibiotic resistance, nutrient uptake and nucleic acid transfer. However, information regarding their immunomodulatory potential is very scarce, both in mammals and fish. In the current study, we have produced EVs from the Gram-positive probiotic Bacillus subtilis and evaluated their immunomodulatory capacities using a rainbow trout intestinal epithelial cell line (RTgutGC) and splenic leukocytes. B. subtilis EVs significantly up-regulated the transcription of several pro-inflammatory and antimicrobial genes in both RTgutGC cells and splenocytes, while also up-regulating many genes associated with B cell differentiation in the later. In concordance, B. subtilis EVs increased the number of IgM-secreting cells in splenocyte cultures, while at the same time increased the MHC II surface levels and antigen-processing capacities of splenic IgM + B cells. Interestingly, some of these experiments were repeated comparing the effects of B. subtilis EVs to EVs obtained from another Bacillus species, Bacillus megaterium , identifying important differences. The data presented provides evidence of the immunomodulatory capacities of Gram-positive EVs, pointing to the potential of B. subtilis EVs as adjuvants or immunostimulants for aquaculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".