Multisite phosphorylation regulates phenotypic variability in antibiotic tolerance
Bibliographic record
Abstract
Abstract Isogenic populations of cells exhibit phenotypic variability that has specific physiological consequences. For example, individual bacteria within a population can differ in their sensitivity to an antibiotic, but whether this variability can be regulated or is generally an unavoidable consequence of stochastic fluctuations is unclear. We observed that a bacterial stress response gene, the (p)ppGpp synthetase sasA , exhibits high levels of extrinsic noise in expression, suggestive of a regulatory process. We traced this variability to the convergence of two signaling systems that together control the multisite phosphorylation of a transcription factor, an event largely unexplored in bacteria, This regulatory intersection between a Ser/Thr kinase and a prototypical two component system is crucial for controlling the appearance of outliers, rare cells with unusually high levels of sasA expression. Additionally, by examining the full distributions of gene expression we calculated the contribution of the additional Ser/Thr kinase-dependent phosphorylation in setting the relative abundance of cells with a given a level of SasA. We then created a predictive model for the probability of a given cell surviving antibiotic treatment as a function of sasA expression. Therefore, our data show that multisite phosphorylation can be used to strongly regulate variations in phenotypes across a bacterial population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".