Biofilm formation in Escherichia coli and regulatory gene expression via quorum sensing systems
Bibliographic record
Abstract
Bacterial biofilms are microbial communities that adhere to abiotic or biotic surfaces. Biofilm formation (BF) studies in E. coli have primarily concentrated on uropathogenic E. coli, commensal K-12 and enterohemorrhagic E. coli O157:H7. This does not include the vast diversity of environmental strains. Quorum sensing (QS) is a means by which bacteria can communication with one another through the production of signalling molecules. The autoinducer 2 (AI-2) QS system is utilized by E. coli and several other bacterial species for controlling gene expression. The role of AI-2 in E. coli BF varies among different strains. For example in the K-12 strain, AI-2 regulates motility, and thus can affect BF; whereas in O157:H7, AI-2 has a more metabolic role. Interestingly, in strain O157:H7, motility is controlled by a newly discovered QS system regulated by the autoinducer 3 (AI-3) molecule plus the mammalian hormones epinephrine (Epi) and norepinephrine (Ne). The purpose of this study was to investigate the ability of a panel of environmental E. coli strains to form biofilms and to determine whether QS is involved in the process. A new pathotype of E. coli, adherent invasive E. coli (AIEC) which is associated with Crohn’s disease was included in the investigation. Study 1 sought to determine whether BF under different media conditions correlated with the presence of genes involved in the AI-2 QS system or adhesin factors. Media conditions were the principal variable affecting the BF. Study 2 examined the role of the AI-2 and AI-3/Epi/Ne QS systems in motility and BF by the AIEC strain. It was discovered that the AI-3 system is involved in motility; whereas the AI-2 system had no effect on BF or motility. In Study 3, microarray gene expression analysis and invasion assays were performed using qseB or qseC mutants. These genes encode the two-component regulatory system recognizing AI-3 or its cognate, epinephrine. Our findings indicate that alternative pathways likely account for the BF observed for the qseB and qseC mutants. It was concluded that the AI-3/Epi/Ne QS system partially controls AIEC motility and the invasion of epithelial cells.
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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 | 0.001 | 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".