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Record W4416994043 · doi:10.1128/msystems.01507-25

Co-culture metabolomics: a powerful tool for uncovering host-pathogen phenotypes driving <i>Burkholderia</i> infections

2025· article· en· W4416994043 on OpenAlexaff
Stephanie L. Bishop

Bibliographic record

VenuemSystems · 2025
Typearticle
Languageen
FieldMedicine
TopicBurkholderia infections and melioidosis
Canadian institutionsWestern University
Fundersnot available
KeywordsMetabolomicsMetabolic pathwayPhenotypePathogenPeptidoglycanMetabolomeNicotinamide adenine dinucleotideCitric acid cycleIntracellularGlycolysis

Abstract

fetched live from OpenAlex

ABSTRACT Melioidosis, caused by the soil-dwelling pathogen Burkholderia pseudomallei (Bt), is a severe respiratory infection with limited treatment options. To investigate the host-pathogen metabolic interplay occurring during these intracellular infections, Hicks et al. built upon an in vitro co-culture model they developed with airway epithelial cells and Bt as a surrogate pathogen (D. J. Hicks, N. Aiosa, A. Sinha, O. A. Jaiyesimi, et al., mSystems 10:e00611-25, 2025, https://doi.org/10.1128/msystems.00611-25 ). Using an untargeted metabolomics approach tailored to central metabolism, they identified several host pathways that were altered during the Bt infection: polyamine biosynthesis, nicotinamide adenine dinucleotide salvage, and the tricarboxylic acid cycle. In addition, they found that several bacterial metabolites, including methylated nucleotide bases, peptidoglycan precursors, and amino acid derivatives, were altered due to Bt infection. These results show that co-culture metabolomics is an effective strategy for identifying host-pathogen metabolic phenotypes resulting from bacterial infections and can uncover new therapeutic strategies to combat melioidosis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.272
Teacher spread0.263 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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