<i>Escherichia coli</i>transcription factor YncC (McbR) regulates colanic acid and biofilm formation by repressing expression of periplasmic protein YbiM (McbA)
Bibliographic record
Abstract
Quorum-sensing signal autoinducer 2 (AI-2) stimulates Escherichia coli biofilm formation through the motility regulator MqsR that induces expression of the putative transcription factor encoded by yncC. Here, we show that YncC increases biofilm formation by repressing overproduction of the exopolysaccharide identified as colanic acid (corroborated by decreasing mucoidy and increased sensitivity to bacteriophage P1 infection). Differential gene expression and gel shift assays demonstrated that YncC is a repressor of the predicted periplasmic protein-encoding gene, ybiM, which was corroborated by the isogenic yncC ybiM double mutation that repressed the yncC phenotypes (biofilm formation, colanic acid overproduction, mucoidy and bacteriophage resistance). Through nickel-enrichment DNA microarrays and additional gel shift assays, we found that the putative transcription factor B3023 (directly upstream of mqsR) binds the yncC promoter. Overexpressing MqsR, AI-2 import regulators LsrR/LsrK and AI-2 exporter TqsA induced yncC transcription, whereas the AI-2 synthase LuxS and B3023 repressed yncC. MqsR has a toxic effect on E. coli bacterial growth, which is partially reduced by the b3023 mutation. Therefore, AI-2 quorum-sensing control of biofilm formation is mediated through regulator MqsR that induces expression of the transcription factor YncC. YncC inhibits the expression of periplasmic YbiM, which prevents overproduction of colanic acid (excess colanic acid causes mucoidy) and prevents YbiM from inhibiting biofilm formation.
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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) | 0.001 | 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) | 0.002 | 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".