Stress survival islet 1 contributes to serotype-specific differences in biofilm formation in <i>Listeria monocytogenes</i>
Bibliographic record
Abstract
Listeria monocytogenes has a significant impact on the food industry by forming biofilms on food-processing equipment. Tandem analysis of whole-genome sequencing data with biofilm data from 166 environmental and food-related L. monocytogenes isolates has revealed serotypic and genetic factors that strongly correlate with adherence and biofilm formation, such as lineage, plasmid harbourage, a three-codon deletion in inlA and the presence of the stress survival islet 1 (SSI-1). Strains from serotype 1/2b, the majority of which contained SSI-1, formed the strongest biofilms, while serotype 4b strains, the majority of which did not contain SSI-1, formed the weakest biofilms. When serotype 1/2a was separated by its SSI-1 genotype, SSI-1-positive 1/2a strains demonstrated significantly higher capacity for biofilm formation after 3 days of growth at 30°C (P < 0·0001). Together, these findings indicate that SSI-1 may contribute to serotype-associated differences in the biofilm-forming capacity in L. monocytogenes. SIGNIFICANCE AND IMPACT OF THE STUDY: Parallel analysis of whole-genome sequences and serotype-specific data was performed to identify genetic markers that correlate with increased adherence and biofilm formation in L. monocytogenes. The analyses revealed the hitherto unrecognized role of SSI-1 in biofilm formation, contributing to deeper understanding of genetic factors that influence behaviour of the species in the food processing environment..
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".