Strategy to Detect Genetically Modified Bacteria Carrying Tetracycline Resistance Gene in Fermentation Products
Bibliographic record
Abstract
Abstract Unexpected contaminations of unauthorized genetically modified microorganisms (GMM) harbouring antimicrobial resistance (AMR) genes in food and feed enzymes, additives and flavourings commercialized on the European market have recently alerted the competent authorities regarding the food and feed safety. At the control level, we have therefore proposed a PCR-based strategy as first line screening targeting GMM carrying AMR genes in order to help enforcement laboratories. The potential presence of frequently used AMR genes is first investigated, using real-time PCR. In case of a suspicious matrix, the full-length of the detected AMR genes is then determined, using conventional PCR followed by Sanger sequencing, allowing to support the competent authorities in their evaluation related to potential health risks. In this study, PCR methods targeting an additional key AMR gene, being the tet-L gene (GenBank: D00946.1) conferring a resistance to tetracycline, were developed and successfully assessed in terms of specificity, sensitivity and applicability. In integrating these PCR methods, the proposed PCR-based strategy, initially targeting two key AMR genes conferring a resistance to chloramphenicol (GenBank: NC_002013.1) and kanamycin (GenBank: M19465.1), is consequently strengthened, allowing the coverage of a larger spectrum of potential GMM contaminations in microbial fermentation products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".