Membrane Vesicles: an Overlooked Component of the Matrices of Biofilms
Bibliographic record
Abstract
The matrix helps define the architecture and infrastructure of biofilms and also contributes to their resilient nature. Although many studies continue to define the properties of both gram-positive and gram-negative bacterial biofilms, there is still much to learn, especially about how structural characteristics help bridge the gap between the chemistry and physical aspects of the matrix. Here, we show that membrane vesicles (MVs), structures derived from the outer membrane of gram-negative bacteria, are a common particulate feature of the matrix of Pseudomonas aeruginosa biofilms. Biofilms grown using different model systems and growth conditions were shown to contain MVs when thin sectioned for transmission electron microscopy, and mechanically disrupted biofilms revealed MVs in association with intercellular material. MVs were also isolated from biofilms by employing techniques for matrix isolation and a modified MV isolation protocol. Together these observations verified the presence and frequency of MVs and indicated that MVs were a definite component of the matrix. Characterization of planktonic and biofilm-derived MVs revealed quantitative and qualitative differences between the two and indicated functional roles, such as proteolytic activity and binding of antibiotics. The ubiquity of MVs was supported by observations of biofilms from a variety of natural environments outside the laboratory and established MVs as common biofilm constituents. MVs appear to be important and relatively unacknowledged particulate components of the matrix of gram-negative or mixed bacterial biofilms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".