Exosomes and other microvesicles in infection biology: organelles with unanticipated phenotypes
Bibliographic record
Abstract
The release of exosomes and other microvesicles by diverse prokaryotic and eukaryotic cells and organisms was first appreciated early in the 20th century. The functional properties of these organelles, however, have only recently been the focus of rigorous investigation. In this review, we discuss the release of microvesicles of varying complexity by diverse microbial pathogens. This includes vesicle secretion by Gram-negative bacteria, eukaryotic parasites of the kinetoplast lineage and opportunistic fungal pathogens of both the ascomycetes and basidiomycetes lineages. We also discuss vesicle release from mammalian cells brought about as a result of infection with bacteria, viruses and prions. In addition, we review the evidence showing that in their specific microenvironments, release of these organelles from diverse pathogens contributes to pathogenesis. Germane to this and based upon recent findings with Leishmania, we propose a model whereby exosome release by an intracellular pathogen serves as a general mechanism for effector molecule delivery from eukaryotic pathogen to host cell cytosol. These new findings linking exosomes and other microvesicles to infection biology have important implications for understanding the immune response to infection and for the design of research strategies aimed at the development of novel therapeutics and vaccines.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".