MétaCan
Menu
Back to cohort
Record W4387079007 · doi:10.1002/1873-3468.14740

Siderophore specificities of the <i>Pseudomonas aeruginosa</i> TonB‐dependent transporters ChtA and ActA

2023· article· en· W4387079007 on OpenAlexafffund
Virginie Will, Véronique Gasser, Lauriane Kühn, Sarah Fritsch, David E. Heinrichs, Isabelle J. Schalk

Bibliographic record

VenueFEBS Letters · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCentre National de la Recherche ScientifiqueUniversité de StrasbourgAgence Nationale de la RechercheInternational Business Machines Corporation
KeywordsSiderophorePseudomonas aeruginosaAerobactinMicrobiologyBacteriaBacterial outer membraneVirulenceEnterobactinFerricBiologyCarboxylateEffluxTransporterChemistryBiochemistryEscherichia coliEnterobacteriaceaeGeneGenetics

Abstract

fetched live from OpenAlex

Iron is an essential nutrient for the survival and virulence of Pseudomonas aeruginosa. The pathogen expresses at least 15 different iron-uptake pathways, the majority involving small iron chelators called siderophores. P. aeruginosa produces two siderophores, but can also use many produced by other microorganisms. This implies that the bacterium expresses appropriate TonB-dependent transporters (TBDTs) at the outer membrane to import the ferric form of each of the siderophores used. Here, we show that the two α-carboxylate-type siderophores rhizoferrin-Fe and staphyloferrin A-Fe are transported into P. aeruginosa cells by the TBDT ActA. Among the mixed α-carboxylate/hydroxamate-type siderophores, we found aerobactin-Fe to be transported by ChtA and schizokinen-Fe and arthrobactin-Fe by ChtA and another unidentified TBDT. Our findings enhance the understanding of the adaptability of P. aeruginosa and hold significant implications for developing novel strategies to combat antibiotic resistance.

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 score0.059
Threshold uncertainty score0.435

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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFEBS LettersSame topicBacterial Genetics and BiotechnologyFrench-language works237,207