Reconfiguring the Quorum-Sensing Regulator SdiA of<i>Escherichia coli</i>To Control Biofilm Formation via Indole and<i>N</i>-Acylhomoserine Lactones
Bibliographic record
Abstract
SdiA is a homolog of quorum-sensing regulators that detects N-acylhomoserine lactone (AHL) signals from other bacteria. Escherichia coli uses SdiA to reduce its biofilm formation in the presence of both AHLs and its own signal indole. Here we reconfigured SdiA (240 amino acids) to control biofilm formation using protein engineering. Four SdiA variants were obtained with altered biofilm formation, including truncation variants SdiA1E11 (F7L, F59L, Y70C, M94K, and K153X) and SdiA14C3 (W9R, P49T, N87T, frameshift at N96, and L123X), which reduced biofilm formation by 5- to 20-fold compared to wild-type SdiA in the presence of endogenous indole. Whole-transcriptome profiling revealed that wild-type SdiA reduced biofilm formation by repressing genes related to indole synthesis and curli synthesis compared to when no SdiA was expressed, while variant SdiA1E11 induced genes related to indole synthesis in comparison to wild-type SdiA. These results suggested altered indole metabolism, and corroborating the DNA microarray results in regard to indole synthesis, variant SdiA1E11 produced ninefold more indole, which led to reduced swimming motility and cell density. Also, wild-type SdiA decreased curli production and tnaA transcription, while SdiA1E11 increased tnaA transcription (tnaA encodes tryptophanase, which forms indole) compared to wild-type SdiA. Hence, wild-type SdiA decreased biofilm formation by reducing curli production and motility, and SdiA1E11 reduced biofilm formation via indole. Furthermore, an AHL-sensitive variant (SdiA2D10, having four mutations at E31G, Y42F, R116H, and L165Q) increased biofilm formation sevenfold in the presence of N-octanoyl-DL-homoserine lactone and N-(3-oxododecatanoyl)-L-homoserine lactone. Therefore, SdiA can be evolved to increase or decrease biofilm formation, and biofilm formation may be controlled by altering sensors rather than signals.
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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 | 0.001 |
| 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".