MétaCan
Menu
Back to cohort
Record W2130764574 · doi:10.1128/jb.01371-06

Identification of Mycobacterial σ Factor Binding Sites by Chromatin Immunoprecipitation Assays

2006· article· en· W2130764574 on OpenAlexafffund
Sébastien Rodrigue, Joëlle Brodeur, Pierre‐Étienne Jacques, Alain Gervais, Ryszard Brzeziński, Luc Gaudreau

Bibliographic record

VenueJournal of Bacteriology · 2006
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMontreal Clinical Research InstituteUniversité de Sherbrooke
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiologyChromatin immunoprecipitationSigma factorPromoterGeneMycobacterium tuberculosisTranscription factorGenomeIntergenic regionGeneticsMolecular biologyGene expressionTuberculosis

Abstract

fetched live from OpenAlex

Mycobacterium tuberculosis and Mycobacterium bovis are responsible for infections that cause a substantial amount of death, suffering, and loss around the world. Still, relatively little is known about the mechanisms of gene expression in these bacteria. Here, we used genome-wide location assays to identify direct target genes for mycobacterial sigma factors. Chromatin immunoprecipitation assays were performed with M. bovis BCG for Myc-tagged proteins expressed using an anhydrotetracycline-inducible promoter, and enriched DNA fragments were hybridized to a microarray representing intergenic regions from the M. tuberculosis H37Rv genome. Several putative target genes were validated by quantitative PCR. The corresponding transcriptional start sites were identified for sigma(F), sigma(C), and sigma(K), and consensus promoter sequences are proposed. Our conclusions were supported by the results of in vitro transcription assays. We also examined the role of each holoenzyme in the expression of sigma factor genes. Our results revealed that many sigma factors are expressed from autoregulated promoters.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.742
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.019
GPT teacher head0.312
Teacher spread0.292 · 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

Citations89
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of BacteriologySame topicTuberculosis Research and EpidemiologyFrench-language works237,207