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Record W4321452636 · doi:10.1128/aem.01628-22

The Gut Commensal Escherichia coli Aggravates High-Fat-Diet-Induced Obesity and Insulin Resistance in Mice

2023· article· en· W4321452636 on OpenAlexafffund
Tingting Ju, Benjamin C. T. Bourrie, Andrew J. Forgie, Deanna M. Pepin, Stephanie Tollenaar, Consolato Sergi, Benjamin P. Willing

Bibliographic record

VenueApplied and Environmental Microbiology · 2023
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsChildren's Hospital of Eastern OntarioStollery Children's HospitalUniversity of OttawaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaAlberta InnovatesAlberta Innovates - Technology FuturesCanada Research ChairsGovernment of Canada
KeywordsInsulin resistanceObesityEscherichia coliBiologyInsulinMicrobiologyMedicineFood scienceEndocrinologyBiochemistryGene

Abstract

fetched live from OpenAlex

Although identifying specific microbial taxa associated with obesity and type 2 diabetes remains difficult, certain bacteria may play an important role in initiating metabolic inflammation during disease development. Here, we used a mouse model distinguishable by the presence or absence of a commensal Escherichia coli strain in combination with a high-fat diet challenge to investigate the impact of E. coli on host metabolic outcomes. This is the first study to show that the addition of a single bacterial species to an animal already colonized with a complex microbial community can increase severity of metabolic outcomes. This study is of interest to a wide group of researchers because it provides compelling evidence to target the gut microbiota for therapeutic purposes by which personalized medicines can be made for treating metabolic inflammation. The study also provides an explanation for variability in studies investigating host metabolic outcomes and immune response to diet interventions.

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 score0.962
Threshold uncertainty score0.478

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 head0.205
Teacher spread0.197 · 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

Citations41
Published2023
Admission routes2
Has abstractyes

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