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Record W2083552531 · doi:10.1371/journal.pone.0098367

The Gluconeogenesis Pathway Is Involved in Maintenance of Enterohaemorrhagic Escherichia coli O157:H7 in Bovine Intestinal Content

2014· article· en· W2083552531 on OpenAlexaff
Yolande Bertin, Christiane Deval, Anne de La Foye, Luke Masson, Victor P. J. Gannon, Josée Harel, Christine Martin, Mickaël Desvaux, Évelyne Forano

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsPublic Health Agency of CanadaUniversité de MontréalBiotechnology Research Institute
Fundersnot available
KeywordsGluconeogenesisBiologyEscherichia coliAmino acidBiochemistryMicrobiologyGlycerolSerineAlanineMetabolismGeneEnzyme

Abstract

fetched live from OpenAlex

Enterohaemorrhagic Escherichia coli (EHEC) are responsible for outbreaks of food- and water-borne illness. The bovine gastrointestinal tract (GIT) is thought to be the principle reservoir of EHEC. Knowledge of the nutrients essential for EHEC growth and survival in the bovine intestine may help in developing strategies to limit their shedding in bovine faeces thus reducing the risk of human illnesses. To identify specific metabolic pathways induced in the animal GIT, the transcriptome profiles of EHEC O157:H7 EDL933 during incubation in bovine small intestine contents (BSIC) and minimal medium supplemented with glucose were compared. The transcriptome analysis revealed that genes responsible for the assimilation of ethanolamine, urea, agmatine and amino acids (Asp, Thr, Gly, Ser and Trp) were strongly up-regulated suggesting that these compounds are the main nitrogen sources for EHEC in BSIC. A central role for the gluconeogenesis pathway and assimilation of gluconeogenic substrates was also pinpointed in EHEC incubated in BSIC. Our results suggested that three amino acids (Asp, Ser and Trp), glycerol, glycerol 3-phosphate, L-lactate and C4-dicarboxylates are important carbon sources for EHEC in BSIC. The ability to use gluconeogenic substrates as nitrogen sources (amino acids) and/or carbon sources (amino acids, glycerol and lactate) may provide a growth advantage to the bacteria in intestinal fluids. Accordingly, aspartate (2.4 mM), serine (1.9 mM), glycerol (5.8 mM) and lactate (3.6 mM) were present in BSIC and may represent the main gluconeogenic substrates potentially used by EHEC. A double mutant of E. coli EDL933 defective for phosphoenolpyruvate synthase (PpsA) and phosphoenolpyruvate carboxykinase (PckA), unable to utilize tricarboxylic acid (TCA) intermediates was constructed. Growth competition experiments between EHEC EDL933 and the isogenic mutant strain in BSIC clearly showed a significant competitive growth advantage of the wild-type strain further illustrating the importance of the gluconeogenesis pathway in maintaining EHEC in the bovine GIT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.055
GPT teacher head0.195
Teacher spread0.139 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2014
Admission routes1
Has abstractyes

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