Indole cell signaling occurs primarily at low temperatures in <i>Escherichia coli</i>
Bibliographic record
Abstract
We have shown that the quorum-sensing signals acylhomoserine lactones, autoinducer-2 (AI-2) and indole influence the biofilm formation of Escherichia coli. Here, we investigate how the environment, that is, temperature, affects indole and AI-2 signaling in E. coli. We show in biofilms that indole addition leads to more extensive differential gene expression at 30 degrees C (186 genes) than at 37 degrees C (59 genes), that indole reduces biofilm formation (without affecting growth) more significantly at 25 and 30 degrees C than at 37 degrees C and that the effect is associated with the quorum-sensing protein SdiA. The addition of indole at 30 degrees C compared to 37 degrees C most significantly repressed genes involved in uridine monophosphate (UMP) biosynthesis (carAB, pyrLBI, pyrC, pyrD, pyrF and upp) and uracil transport (uraA). These uracil-related genes are also repressed at 30 degrees C by SdiA, which confirms SdiA is involved in indole signaling. Also, compared to 37 degrees C, indole more significantly decreased flagella-related qseB, flhD and fliA promoter activity, enhanced antibiotic resistance and inhibited cell division at 30 degrees C. In contrast to indole and SdiA, the addition of (S)-4,5-dihydroxy-2,3-pentanedione (the AI-2 precursor) leads to more extensive differential gene expression at 37 degrees C (63 genes) than at 30 degrees C (11 genes), and, rather than repressing UMP synthesis genes, AI-2 induces them at 37 degrees C (but not at 30 degrees C). Also, the addition of AI-2 induces the transcription of virulence genes in enterohemorrhagic E. coli O157:H7 at 37 degrees C but not at 30 degrees C. Hence, cell signals cause diverse responses at different temperatures, and indole- and AI-2-based signaling are intertwined.
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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 | 0.001 | 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 | 0.001 | 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 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".