MétaCan
Menu
Back to cohort

Mechanisms of Acid Resistance in<i>Escherichia coli</i>

2013· review· en· W2152434411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Microbiology · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiochemistryPeriplasmic spaceFormate dehydrogenaseEscherichia coliAntiporterDehydrogenaseOxidase testChemistryElectron transport chainInner membraneBiologyEnzymeMembraneCofactor

Abstract

fetched live from OpenAlex

Adaptation to acid stress is an important factor in the transmission of intestinal microbes. The enterobacterium Escherichia coli uses a range of physiological, metabolic, and proton-consuming acid resistance mechanisms in order to survive acid stresses as low as pH 2.0. The physiological adaptations include membrane modifications and outer membrane porins to reduce proton influx and periplasmic and cytoplasmic chaperones to manage the effects of acid damage. The metabolic acid resistance systems couple proton efflux to energy generation via select components of the electron transport chain, including cytochrome bo oxidase, NADH dehydrogenase I, NADH dehydrogenase II, and succinate dehydrogenase. Under anaerobic conditions the formate hydrogen lyase complex catalyzes conversion of cytoplasmic protons to hydrogen gas. Finally, each major proton-consuming acid resistance system has a pyridoxal-5'-phosphate-dependent amino acid decarboxylase that catalyzes proton-dependent decarboxylation of a substrate amino acid to product and CO2, and an inner membrane antiporter that exchanges external substrate for internal product.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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