Conceptual model to inform Legionella–amoebae control, including the roles of extracellular vesicles in engineered water system infections
Bibliographic record
Abstract
Extracellular vesicles (EVs or exosomes) are well described for bacterial pathogens associated with our gastrointestinal system, and more recently as a novel mechanism for environmental persistence, dissemination and infection for human enteric viruses. However, the roles played by EVs in the ancient arms race that continues between amoebae and one of their prey, Legionella pneumophila, is poorly understood. At best we know of intracellular vesicles of amoebae containing a mix of bacterial prey species, which also provides an enhanced niche for bacteriophage infection/spread. Free-living amoeba-associated pathogens have recently been recognized to have enhanced resistance to disinfection and environmental stressors, adding to previously understood (but for relatively few species of) bacteria sequestered within amoebal cysts. However, the focus of the current work is to review the likely impacts of large numbers of respiratory-sized EVs containing numerous L. pneumophila cells studied in pure and biofilm systems with mixed prey species. These encapsulated pathogens are orders of magnitude more resistant to disinfection than free cells, and our engineered systems with residual disinfectants could promote evolution of resistance (including AMR), enhanced virulence and EV release. All these are key features for evolution within a dead-end human pathogen post lung infection. Traditional single-hit pathogen infection models used to estimate the probability of infection/disease and critical environmental concentrations via quantitative microbial risk assessments may also need to change. In short, recognizing that EV-packaged cells are highly virulent units for transmission of legionellae, which may also modulate/avoid human host immune responses. Key data gaps are raised and a previous conceptual model expanded upon to clarify where biofilm EVs could play a role promoting risk as well as inform a more wholistic management program to proactively control legionellosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".