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Record W2965045048 · doi:10.11159/icbb19.110

Signal Crosstalk between Two Different Agrobacterium Two-Component Systems

2019· article· en· W2965045048 on OpenAlexvenueno aff
Minliang Guo, Yujuan Xu, Dawei Gao, Nan Xu

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsnot available
Fundersnot available
KeywordsCrosstalkComponent (thermodynamics)AgrobacteriumComputer scienceSIGNAL (programming language)Electronic engineeringPhysicsEngineeringBiologyTransformation (genetics)Genetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.238
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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