Targeting Novel LPXTG Surface Proteins with Monoclonal Antibodies for Immunomagnetic Separation of <i>Listeria monocytogenes</i>
Bibliographic record
Abstract
The Gram-positive bacterium Listeria monocytogenes causes a significantly high percentage of fatalities among human foodborne illnesses. Surface proteins, specifically expressed from a wide range of L. monocytogenes serotypes under selective enrichment culture conditions, can serve as targets for the isolation of this pathogen using antibody-based methods to facilitate molecular detection. In this study, monoclonal antibodies (MAbs), previously raised against the L. monocytogenes LPXTG surface proteins LMOf2365_0639 and LMOf2365_0148, were investigated for their ability to isolate L. monocytogenes from bacterial samples with immunomagnetic separation (IMS). Only 1 out of 35 MAbs against LMOf2365_0639, M3644, was capable of capturing L. monocytogenes. Among all the 24 MAbs examined against LMOf2365_0148, 4 MAbs, M3686, M3697, M3699, and M3700, were capable of capturing L. monocytogenes cells specifically from abbreviated primary selective enrichment cultures in either Palcam or LEB/UVM1 media or from mixed samples containing target and nontarget bacteria. MAb M3686 showed a unique specificity with the capability to capture strains of seven L. monocytogenes serotypes (1/2a, 1/2b, 1/2c, 3a, 4a, 4b, and 4d). These promising MAbs were subsequently characterized by quantitative measurements of antigen-binding affinity using surface plasmon resonance analysis and epitope mapping using overlapping recombinant polypeptides. The usefulness of these MAbs to LMOf2365_0148 in bacterial capture was consistent with their high affinities with K D constants in the nanomolar range and can be explored further for the development of an automated IMS method suitable for routine isolation of L. monocytogenes from food and environmental samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".