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Record W4225117422 · doi:10.1021/acs.jproteome.1c00936

Impact of Carbon Source Supplementations on <i>Pseudomonas aeruginosa</i> Physiology

2022· article· en· W4225117422 on OpenAlexaff
Salomé Sauvage, Charlotte Gaviard, Ali Tahrioui, Laurent Coquet, Hung Lê, Stéphane Alexandre, Ahmed Ben Abdelkrim, Emeline Bouffartigues, Olivier Lesouhaitier, Sylvie Chevalier, Thierry Jouenne, Julie Hardouin

Bibliographic record

VenueJournal of Proteome Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsValacta (Canada)
FundersEuropean Regional Development FundRégion NormandieEuropean Commission
KeywordsPseudomonas aeruginosaBiofilmPyocyaninMicrobiologyBiologyPathogenBacteriaVirulenceMicroorganismAntimicrobialHuman pathogenBiochemistryQuorum sensingGeneGenetics

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa is an opportunistic pathogen highly resistant to a wide range of antimicrobial agents, making its infections very difficult to treat. Since microorganisms need to perpetually adapt to their surrounding environment, understanding the effect of carbon sources on P. aeruginosa physiology is therefore essential to avoid increasing drug-resistance and better fight this pathogen. By a global proteomic approach and phenotypic assays, we investigated the impact of various carbon source supplementations (glucose, glutamate, succinate, and citrate) on the physiology of the P. aeruginosa PA14 strain. A total of 581 proteins were identified as differentially expressed in the 4 conditions. Most of them were more abundant in citrate supplementation and were involved in virulence, motility, biofilm development, and antibiotic resistance. Phenotypic assays were performed to check these hypotheses. By coupling all this data, we highlight the importance of the environment in which the bacterium evolves on its metabolism, and thus the necessity to better understand the metabolic pathways implied in its adaptative response according to the nutrient availability.

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)0.000
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 score0.028
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.364
Teacher spread0.328 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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