Only time will tell: lipopolysaccharide glycoform and biofilm-formation kinetics in <i>Salmonella</i> species and <i>Escherichia coli</i>
Bibliographic record
Abstract
ABSTRACT In Gram-negative bacteria, LPS (lipopolysaccharide) has been thoroughly characterized and has been shown to play a major role in pathogenesis and bacterial defense. In Salmonella and Escherichia coli , LPS also influences biofilm development. However, the overall role of LPS glycoform in biofilm formation has not been conclusively settled, as there is a lack of consensus on the topic. Some studies show that LPS mutants produce less biofilm biomass than the wild-type strains, while others show that they produce more. This review summarizes current knowledge of LPS biosynthesis and explores the impact of defective steps on biofilm-related characteristics, such as motility, adhesion, auto-aggregation, and biomass production in Salmonella and E. coli . Overall, motility tends to decrease, while adhesion and auto-aggregation phenotypes tend to increase in most LPS-mutant strains. Interestingly, biofilm biomass of various LPS mutants revealed a clear pattern dependent on biofilm maturation time. Incubation times of less than 24 h resulted in a biofilm-defective phenotype compared to the wild-type, while incubation exceeding 24 h led to significantly higher levels of biofilm production. This explains conflicting results found in reports describing the same LPS mutations. It is therefore critical to consider the effect of biofilm maturation time to ascertain the effects of LPS glycoform on biofilm phenotype. Underlying reasons for such changes in biofilm kinetics may include changes in signalling systems affecting biofilm maturation and composition, and dynamic LPS modifications. A better understanding of the role of LPS in the evolution and modification of biofilms is crucial for developing strategies to disperse 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".