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Record W2129338431 · doi:10.1128/jb.00817-09

Characterization of Alanine Catabolism in<i>Pseudomonas aeruginosa</i>and Its Importance for Proliferation In Vivo

2009· article· en· W2129338431 on OpenAlexaff
Megan L. Boulette, Patricia J. Baynham, Peter Jorth, Irena Kukavica‐Ibrulj, Aissa Longoria, Karla Barrera, Roger C. Lévesque, Marvin Whiteley

Bibliographic record

VenueJournal of Bacteriology · 2009
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPseudomonas aeruginosaBiologyCatabolismMicrobiologyOperonIn vivoAlanineBiochemistryBacteriaMutantMetabolismAmino acidGeneGenetics

Abstract

fetched live from OpenAlex

The opportunistic pathogen Pseudomonas aeruginosa causes a variety of infections in immunocompromised individuals, including individuals with the heritable disease cystic fibrosis. Like the carbon sources metabolized by many disease-causing bacteria, the carbon sources metabolized by P. aeruginosa at the host infection site are unknown. We recently reported that l-alanine is a preferred carbon source for P. aeruginosa and that two genes potentially involved in alanine catabolism (dadA and dadX) are induced during in vivo growth in the rat peritoneum and during in vitro growth in sputum (mucus) collected from the lungs of individuals with cystic fibrosis. The goals of this study were to characterize factors required for alanine catabolism in P. aeruginosa and to assess the importance of these factors for in vivo growth. Our results reveal that dadA and dadX are arranged in an operon and are required for catabolism of l-alanine. The dad operon is inducible by l-alanine, d-alanine, and l-valine, and induction is dependent on the transcriptional regulator Lrp. Finally, we show that a mutant unable to catabolize dl-alanine displays decreased competitiveness in a rat lung model of infection.

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 score0.834
Threshold uncertainty score0.269

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 studies0.0000.000
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 head0.013
GPT teacher head0.296
Teacher spread0.283 · 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

Citations50
Published2009
Admission routes1
Has abstractyes

Explore more

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