MétaCan
Menu
Back to cohort
Record W4200303709 · doi:10.1016/j.celrep.2021.110087

Emerging connections between gut microbiome bioenergetics and chronic metabolic diseases

2021· review· en· W4200303709 on OpenAlexafffund
Brendan A. Daisley, David W. Koenig, Kathleen Engelbrecht, Liz Doney, Kiel Hards, Kait F. Al, Gregor Reid, Jeremy P. Burton

Bibliographic record

VenueCell Reports · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Urological Oncology Group
KeywordsMetabolomeMicrobiomeBiologyBioenergeticsMetabolomicsFunction (biology)Gut microbiomeComputational biologyMicrobial metabolismBacteriaCell biologyGeneticsBioinformaticsMitochondrion

Abstract

fetched live from OpenAlex

The conventional viewpoint of single-celled microbial metabolism fails to adequately depict energy flow at the systems level in host-adapted microbial communities. Emerging paradigms instead support that distinct microbiomes develop interconnected and interdependent electron transport chains that rely on cooperative production and sharing of bioenergetic machinery (i.e., directly involved in generating ATP) in the extracellular space. These communal resources represent an important subset of the microbial metabolome, designated here as the "pantryome" (i.e., pantry or external storage compartment), that critically supports microbiome function and can exert multifunctional effects on host physiology. We review these interactions as they relate to human health by detailing the genomic-based sharing potential of gut-derived bacterial and archaeal reference strains. Aromatic amino acids, metabolic cofactors (B vitamins), menaquinones (vitamin K2), hemes, and short-chain fatty acids (with specific emphasis on acetate as a central regulator of symbiosis) are discussed in depth regarding their role in microbiome-related metabolic diseases.

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 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 score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.311
Teacher spread0.292 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations84
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicGut microbiota and healthFrench-language works237,207