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Record W2606791689 · doi:10.1111/1462-2920.13755

A global survey of bacterial type III secretion systems and their effectors

2017· article· en· W2606791689 on OpenAlexaff
Yueming Hu, He Huang, Xi Cheng, Xing‐sheng Shu, Aaron P. White, John Stavrinides, Wolfgang Köster, Guoqiang Zhu, Zhendong Zhao, Yejun Wang

Bibliographic record

VenueEnvironmental Microbiology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsBiologyEffectorPhylumGenomeDiversity (politics)Type three secretion systemSecretionEvolutionary biologyComputational biologyGeneticsBacteriaGeneCell biologyVirulencePolitical science

Abstract

fetched live from OpenAlex

The type III secretion system (T3SS) is an important genetic determinant that mediates interactions between Gram-negative bacteria and their eukaryotic hosts. Our understanding of the T3SS continues to expand, yet the availability of new bacterial genomes prompts questions about its diversity, distribution and evolution. Through a comprehensive survey of ∼20 000 bacterial genomes, we identified 174 non-redundant T3SSs from 109 genera and 5 phyla. Many of the bacteria are environmental strains that have not been reported to interact with eukaryotic hosts, while several species groups carry multiple T3SSs. Four ultra-conserved Microsynteny Blocks (MSBs) were defined within the T3SSs, facilitating comprehensive clustering of the T3SSs into 13 major categories, and establishing the largest diversity of T3SSs to date. We subsequently extended our search to identify type III effectors, resulting in 8740 candidate effectors. Lastly, an analysis of the key transcriptional regulators and circuits for the T3SS families revealed that low-level T3SS regulators were more conserved than higher-level regulators. This comprehensive analysis of the T3SSs and their protein effectors provides new insight into the diversity of systems used to facilitate host-bacterial interactions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.242
Teacher spread0.232 · 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 teacher head, 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

Citations81
Published2017
Admission routes1
Has abstractyes

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