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Record W2003550923 · doi:10.1073/pnas.1007805107

A small RNA promotes siderophore production through transcriptional and metabolic remodeling

2010· article· en· W2003550923 on OpenAlexafffund
Hubert Salvail, Pascale Lanthier-Bourbonnais, Jason Michael Sobota, Mélissa Caza, Julie-Anna M. Benjamin, Martha Eugènia Sequeira Mendieta, François Lépine, Charles M. Dozois, James A. Imlay, Éric Massé

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Sherbrooke
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsEnterobactinSiderophoreBiologyRepressorSerineNonribosomal peptideBiosynthesisBiochemistryGene expressionGeneEnzyme

Abstract

fetched live from OpenAlex

Siderophores are essential factors for iron (Fe) acquisition in bacteria during colonization and infection of eukaryotic hosts, which restrain iron access through iron-binding protein, such as lactoferrin and transferrin. The synthesis of siderophores by Escherichia coli is considered to be fully regulated at the transcriptional level by the Fe-responsive transcriptional repressor Fur. Here we characterized two different pathways that promote the production of the siderophore enterobactin via the action of the small RNA RyhB. First, RyhB is required for normal expression of an important enterobactin biosynthesis polycistron, entCEBAH. Second, RyhB directly represses the translation of cysE, which encodes a serine acetyltransferase that uses serine as a substrate for cysteine biosynthesis. Reduction of CysE activity by RyhB allows serine to be used as building blocks for enterobactin synthesis through the nonribosomal peptide synthesis pathway. Thus, RyhB plays an essential role in siderophore production and may modulate bacterial virulence through optimization of siderophore production.

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 score0.022
Threshold uncertainty score0.250

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 head0.038
GPT teacher head0.270
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

Citations93
Published2010
Admission routes2
Has abstractyes

Explore more

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