Three (and more) component regulatory systems – auxiliary regulators of bacterial histidine kinases
Bibliographic record
Abstract
Two-component signal transduction (TCST) is the most prevalent mechanism employed by microbes to sense and respond to environmental changes. It is characterized by the signal-induced transfer of phosphate from a sensor histidine kinase (HK) to a response regulator (RR), resulting in a cellular response. An emerging theme in the field of TCST signalling is the discovery of auxiliary factors, distinct from the HK and RR, which are capable of influencing phosphotransfer. One group of TCST auxiliary proteins accomplishes this task by acting on HKs. Auxiliary regulators of HKs are widespread and have been identified in all cellular compartments, where they can influence HK activity through interactions with the sensing, transmembrane or enzymatic domains of the HK. The effects of an auxiliary regulator are controlled by its regulated expression, modification and/or through ligand binding. Ultimately, auxiliary regulators can connect a given TCST system to other regulatory networks in the cell or result in regulation of the TCST system in response to an expanded range of stimuli. The studies highlighted in this review draw attention to an emerging view of bacterial TCST systems as core signalling units upon which auxiliary factors act.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".