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Novel <i>Mycobacterium tuberculosis</i> anti‐σ factor antagonists control σ<sup>F</sup> activity by distinct mechanisms

2002· article· en· W1857957808 on OpenAlexafffund
Jocelyn Beaucher, Sébastien Rodrigue, Pierre‐Étienne Jacques, Issar Smith, Ryszard Brzeziński, Luc Gaudreau

Bibliographic record

VenueMolecular Microbiology · 2002
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthUniversité de Sherbrooke
KeywordsSigma factorBiologyMycobacterium tuberculosisVirulence factorPhosphorylationVirulenceSigmaSuppressorMicrobiologyTuberculosisGeneticsGenePromoterGene expression

Abstract

fetched live from OpenAlex

The aetiological agent of tuberculosis, Mycobacterium tuberculosis, encodes 13 sigma factors, as well as several putative anti-, and anti-anti- sigma factors. Here we show that a sigma factor that has been previously shown to be involved in virulence and persistence processes, sigmaF, can be specifically inhibited by the anti-sigma factor UsfX. Importantly, the inhibitory activity of UsfX, in turn, can be negatively regulated by two novel anti-anti-sigma factors. The first anti-anti-sigma factor seems to be regulated by redox potential, and the second may be regulated by phosphorylation as it is rendered non-functional by the introduction of a mutation that is believed to mimic phosphorylation of the anti-anti-sigma factor. These results suggest that sigmaF activity might be post-translationally modulated by at least two distinct pathways in response to different possible physiological cues, the outcome being consistent with the bacteria's ability to adapt to diverse host environments during disease progression, latency and reactivation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.001

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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.

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

Citations103
Published2002
Admission routes2
Has abstractyes

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