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Record W2781690565 · doi:10.1016/j.celrep.2017.12.031

Oxygen-Sensitive Remodeling of Central Carbon Metabolism by Archaic eIF5B

2018· article· en· W2781690565 on OpenAlexafffund
J.J.David Ho, Nathan C. Balukoff, Grissel Cervantes, Petrice D. Malcolm, Jonathan R. Krieger, Stephen Lee

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Cancer Institute, Cairo UniversityCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsEukaryotic initiation factorBiologyCell biologyTranslation (biology)BiochemistryMessenger RNAGene

Abstract

fetched live from OpenAlex

The eukaryotic translation initiation factor 5B (eIF5B) is a homolog of IF2, an ancient translation factor that enables initiator methionine-tRNAi Met (met-tRNAi Met ) loading on prokaryotic ribosomes. While it can be traced back to the last universal common ancestor, eIF5B is curiously dispensable in modern aerobic yeast and mammalian cells. Here, we show that eIF5B is an essential element of the cellular hypoxic cap-dependent protein synthesis machinery. System-wide interrogation of dynamic translation machineries by MATRIX (mass spectrometry analysis of active translation factors using ribosome density fractionation and isotopic labeling experiments) demonstrated augmented eIF5B activity in hypoxic translating ribosomes. Global translatome studies revealed central carbon metabolism, cellular hypoxic adaptation, and ATF4-mediated stress response as major eIF5B-dependent pathways. These primordial processes rely on eIF5B even in the presence of oxygen and active eIF2, the canonical recruiter of met-tRNAi Met in eukaryotes. We suggest that aerobic eukarya retained eIF5B/IF2 to remodel anaerobic pathways during episodes of oxygen deficiency.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.538

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 head0.007
GPT teacher head0.214
Teacher spread0.206 · 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

Citations45
Published2018
Admission routes2
Has abstractyes

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