Signal Crosstalk between Two Different Agrobacterium Two-Component Systems
Bibliographic record
Abstract
Bacteria often use the so-called two-component system to transduce signal.A typical bacterial two-component system is comparatively simple and comprises two components, a sensor with histidine kinase activity and its cognate phosphorylatable response regulator [1,2].Most bacteria encode dozens of two-component signaling pathway [2].Although both the histidine kinase sensor and response regulator in each two-component system are part of large, paralogous protein families that are highly similar at both sequence and structural levels, relatively little interaction between histidine kinase sensor and non-cognate response regulator was found, indicating that individual two-component signal transduction systems are highly specific, well insulated and rare cross-talk [3].The high specificity of interaction between sensor and its cognate response regulator is accordant with the requirement for maintaining the faithful flow of signal through two-component system.Agrobacterium uses chemotaxis system to sense a large number of chemicals released by wounded host and VirA/VirG two-component system to induce the virulence gene expression [4,5].Chemotaxis signal transduction system is a special case of two-component system.Its histidine kinase CheA lacks transmembrane sensor domain and has three cognate response regulators, CheY1, CheY2 and CheB.Although the atypical two-component system, chemotaxis system is very different from the typical VirA/VirG two-component system, both of them are showed to be involved in Agrobacterium tumorigenesis [6].Our previous study suggested that chemotaxis signaling and virulence induction signaling may have crosstalk in Agrobacterium [3].Here, three lines of experimental evidences demonstrate the signaling cross-talk between these two two-component systems.1) Chemotaxis signal-driving run pattern of Agrobacterium cheA-deletion mutant could be adjusted by the complementation of VirA.2) Bacterial two hybrid assay showed that VirA interacts with CheY2 and CheA interacts with VirG.3) In vitro pull-down experiment showed that VirA can pull-down CheY2.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".