The acquisition of additional control over quorum sensing regulation reduces the variability of final cell density in Burkholderia
Bibliographic record
Abstract
Bacteria usually possess more than one quorum sensing (QS) regulatory modules that sometimes form complex regulatory networks. These configurations have evolved through the integration of novel transcription factors into the native regulatory systems. However, the selective advantages provided by these alternative configurations on QS-related phenotypes is poorly predictable only based on their underlying network structure. Here, we show that the acquisition of extra regulatory modules of QS has important consequences on the overall regulation of microbial growth by significantly reducing the variability in the final cell density in Burkholderia. By mapping the distribution of horizontally transferred QS modules in extant bacterial genomes, we found that these tend to add up to already-present modules in the majority of cases. We then selected a strain harboring two intertwined QS modules and, using mathematical modeling, we predicted an intrinsic ability of the newly acquired module to buffer the variability in the final cell density. We validated this prediction choosing one strain possessing both systems, deleting one of the two and measuring parameters such as cell density and QS synthase promoter activity. Finally, using transcriptomics, we show that the de-regulation of metabolism likely plays a key role in differentiating the two configurations.
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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 | 0.001 |
| 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 | 0.001 | 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".