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Record W2005885786 · doi:10.1128/aem.01771-08

Metabolomic Investigation of the Bacterial Response to a Metal Challenge

2008· article· en· W2005885786 on OpenAlexaff
Valentina Tremaroli, Matthew L. Workentine, Aalim M. Weljie, Hans J. Vogel, Howard Ceri, Carlo Viti, Enrico Tatti, Ping Zhang, Alexander P. Hynes, Raymond J. Turner, Davide Zannoni

Bibliographic record

VenueApplied and Environmental Microbiology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Calgary
Fundersnot available
KeywordsMetabolomicsGlutathioneBetaineBiologyBiochemistryOxidative stressMetabolic pathwayMetabolismChemistryMicrobiologyEnzymeBioinformatics

Abstract

fetched live from OpenAlex

Pseudomonas pseudoalcaligenes KF707 is naturally resistant to the toxic metalloid tellurite, but the mechanisms of resistance are not known. In this study we report the isolation of a KF707 mutant (T5) with hyperresistance to tellurite. In order to characterize the bacterial response and the pathways leading to tolerance, we utilized Phenotype MicroArray technology (Biolog) and a metabolomic technique based on nuclear magnetic resonance spectroscopy. The physiological states of KF707 wild-type and T5 cells exposed to tellurite were also compared in terms of viability and reduced thiol content. Our analyses showed an extensive change in metabolism upon the addition of tellurite to KF707 cultures as well as different responses when the wild-type and T5 strains were compared. Even in the absence of tellurite, T5 cells displayed a "poised" physiological status, primed for tellurite exposure and characterized by altered intracellular levels of glutathione, branched-chain amino acids, and betaine, along with increased resistance to other toxic metals and metabolic inhibitors. We conclude that hyperresistance to tellurite in P. pseudoalcaligenes KF707 is correlated with the induction of the oxidative stress response, resistance to membrane perturbation, and reconfiguration of cellular metabolism.

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 score0.617
Threshold uncertainty score0.559

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)0.0010.000

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 head0.007
GPT teacher head0.167
Teacher spread0.160 · 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

Citations112
Published2008
Admission routes1
Has abstractyes

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