MétaCan
Menu
Back to cohort
Record W3024257894 · doi:10.1111/imm.13207

Circles of Life: linking metabolic and epigenetic cycles to immunity

2020· review· en· W3024257894 on OpenAlex
Chan‐Wang Jerry Lio, Stanley Ching‐Cheng Huang

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.

Bibliographic record

VenueImmunology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsInstitute of Infection and Immunity
FundersNational Cancer InstituteCase Comprehensive Cancer Center, Case Western Reserve UniversityCancer Research Institute
KeywordsEpigeneticsEpigenomeBiologyMetabolomeDNA methylationEpigenomicsHistoneFunction (biology)Epigenetic regulation of neurogenesisCell biologyGeneticsMetabolomicsDNAGeneHistone methyltransferaseBioinformaticsGene expression

Abstract

fetched live from OpenAlex

Metabolites are the essential substrates for epigenetic modification enzymes to write or erase the epigenetic blueprint in cells. Hence, the availability of nutrients and activity of metabolic pathways strongly influence the enzymatic function. Recent studies have shed light on the choreography between metabolome and epigenome in the control of immune cell differentiation and function, with a major focus on histone modifications. Yet, despite its importance in gene regulation, DNA methylation and its relationship with metabolism is relatively unclear. In this review, we will describe how the metabolic flux can influence epigenetic networks in innate and adaptive immune cells, with a focus on the DNA methylation cycle and the metabolites S-adenosylmethionine and α-ketoglutarate. Future directions will be discussed for this rapidly emerging field.

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 score0.991
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 integrity0.0010.000
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 head0.030
GPT teacher head0.312
Teacher spread0.283 · 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