N-Acetyl-glucosamine influences the biofilm formation of Escherichia coli
Bibliographic record
Abstract
The intestinal mucous layer is a physical barrier that limits the contact between bacteria and host epithelial cells. There is growing evidence that microbiota-produced metabolites can also be specifically sensed by gut pathogens as signals to induce or repress virulence genes. Many E. coli , including adherent and invasive (AIEC) strains, can form biofilm. This property can promote their intestinal colonization and resistance to immune mechanisms. We sought to evaluate the impact of mucus-derived sugars on biofilm formation of E. coli . We showed that the mucin sugar N -acetyl-glucosamine (NAG) can reduce biofilm formation of AIEC strain LF82. We demonstrated that the inactivation of the regulatory protein NagC, by addition of NAG or by mutation of nagC gene, reduced the biofilm formation of LF82 in static condition. Interestingly, real-time monitoring of biofilm formation of LF82 using microfluidic system showed that the mutation of nagC impairs the early process of biofilm development of LF82. Thus, NAG sensor NagC is involved in the early steps of biofilm formation of AIEC strain LF82 under both static and dynamic conditions. Its implication is partly due to the activation of type 1 fimbriae. NAG can also influence biofilm formation of other intestinal E. coli strains. This study highlights how catabolism can be involved in biofilm formation of E. coli . Mucus-derived sugars can influence virulence properties of pathogenic E. coli and this study will help us better understand the mechanisms used to prevent colonization of the intestinal mucosa by pathogens.
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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 | 0.000 | 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 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".