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Control of archaellation in <i>Sulfolobus acidocaldarius</i>: unravelling of the regulation of surface structure biosynthesis in Archaea begins

2012· letter· en· W2106588585 on OpenAlexaff
Ken F. Jarrell

Bibliographic record

VenueMolecular Microbiology · 2012
Typeletter
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsQueen's University
Fundersnot available
KeywordsSulfolobus acidocaldariusPilusBiologyOperonSulfolobusHaloarchaeaArchaeaTranscription (linguistics)MutantBiochemistryCell biologyGeneVirulence

Abstract

fetched live from OpenAlex

Archaea have a variety of surface appendages including archaella (archaeal flagella), pili, hami and cannulae. While expected to be energetically expensive to express, studies focused on the regulation of such structures are nevertheless lacking. In the current issue of Molecular Microbiology, Reimann et al. (2012) identified a two-partner system called ArnA and ArnB in Sulfolobus acidocaldarius that interact strongly with each other and are repressors of archaella expression while also having an enhancing effect on the appearance of type IV pili. ArnA is a forkhead-associated domain-containing protein while ArnB is a von Willebrand domain-containing protein. Both proteins can be phosphorylated in vitro by S. acidocaldarius protein kinases. The repression of archaella expression is dependent on dephosphorylation of the Arn proteins. Deletions of arnA or arnB resulted in increased levels of archaella operon proteins and cells that were hypermotile due to increased archaellation. Direct effects of ArnA/ArnB on transcription from fla promoters were demonstrated using arnA and arnB deletion strains but only a modest increase in transcription was demonstrated in each mutant suggesting that the repression effect observed may be due to protein-protein interactions. This paper represents a significant step forward in our understanding of archaeal surface structure biogenesis.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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