Outer membrane vesicles as a platform for the discovery of antibodies to bacterial pathogens
Bibliographic record
Abstract
Bacterial outer membrane vesicles (OMVs) are nanosized spheroidal particles shed by gram-negative bacteria that contain biomolecules derived from the periplasmic space, the bacterial outer membrane, and possibly other compartments. OMVs can be purified from bacterial culture supernatants, and by genetically manipulating the bacterial cells that produce them, they can be engineered to harbor cargoes and/or display molecules of interest on their surfaces including antigens that are immunogenic in mammals. Since OMV bilayer-embedded components presumably maintain their native structures, OMVs may represent highly useful tools for generating antibodies to bacterial outer membrane targets. OMVs have historically been utilized as vaccines or vaccine constituents. Antibodies that target bacterial surfaces are increasingly being explored as antimicrobial agents either in unmodified form or as targeting moieties for bactericidal compounds. Here, we review the properties of OMVs, their use as immunogens, and their ability to elicit antibody responses against bacterial antigens. We highlight antigens from bacterial pathogens that have been successfully targeted using antibodies derived from OMV-based immunization and describe opportunities and limitations for OMVs as a platform for antimicrobial antibody development. KEY POINTS: • Outer membrane vesicles (OMVs) of gram-negative bacteria bear cell-surface molecules • OMV immunization allows rapid antibody (Ab) isolation to bacterial membrane targets • Review and analysis of OMV-based immunogens for antimicrobial Ab development.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".