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Record W4390113860 · doi:10.1128/mbio.02823-23

Divergent <i>Pseudomonas aeruginosa</i> LpxO enzymes perform site-specific lipid A 2-hydroxylation

2023· article· en· W4390113860 on OpenAlexaff
Casey E. Hofstaedter, Courtney E. Chandler, Charles M. Met, Joseph J. Gillespie, Janette M. Harro, David R. Goodlett, David A. Rasko, Robert K. Ernst

Bibliographic record

VenuemBio · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Victoria
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthCystic Fibrosis Foundation
KeywordsPseudomonas aeruginosaPhenotypeBiologyMicrobiologyPathogenChronic infectionEnzymeHost–pathogen interactionBacteriaImmunologyGeneticsVirulenceImmune systemGeneBiochemistry

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa is an opportunistic pathogen that causes severe infection in hospitalized and chronically ill individuals. During infection, P. aeruginosa undergoes adaptive changes to evade host defenses and therapeutic interventions, increasing mortality and morbidity. Lipid A structural alteration is one such change that P. aeruginosa isolates undergo during chronic lung infection in CF. Investigating genetic drivers of this lipid A structural variation is crucial in understanding P. aeruginosa adaptation during infection. Here, we describe two lipid A dioxygenases with acyl-chain site specificity, each with different evolutionary origins. Further, we show that loss of function in these enzymes occurs in CF clinical isolates, suggesting a potential pathoadaptive phenotype. Studying these bacterial adaptations provides insight into selection pressures of the CF airway on P. aeruginosa phenotypes that persist during chronic infection. Understanding these adaptive changes may ultimately provide clinicians better control over bacterial populations during chronic 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 categoriesInsufficient payload (model declined to judge)
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 score0.422
Threshold uncertainty score1.000

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)0.0000.001

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 head0.233
Teacher spread0.220 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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