MétaCan
Menu
Back to cohort
Record W91996105

RNA-cleaving properties of human apurinic/apyrimidinic endonuclease 1 (APE1).

2010· article· en· W91996105 on OpenAlexaff
Wan Kim, Dustin T. King, Chow H. Lee

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsEndoribonucleaseAP siteEndonucleaseRNAMessenger RNAMolecular biologyCleaveDNA-(apurinic or apyrimidinic site) lyaseBiologyCell biologyChemistryBiochemistryEnzymeGeneRNase P
DOInot available

Abstract

fetched live from OpenAlex

We have recently identified apurinic/apyrimidinic endonuclease 1 (APE1) as an endoribonuclease that cleaves c-myc mRNA in vitro and regulates c-myc mRNA levels and half-life in cells. This study was undertaken to further unravel the RNA-cleaving properties of APE1. Here, we show that APE1 cleaves RNA in the absence of divalent metal ions and, at 2 mM, Zn(2+), Ni(2+), Cu(2+), or Co(2+) inhibited the endoribonuclease activity of APE1. APE1 is able to cleave CD44 mRNA, microRNAs (miR-21, miR-10b), and three RNA components of SARS-corona virus (orf1b, orf3, spike) suggesting that, when challenged, it can cleave any RNAs in vitro. APE1 does not cleave strong doublestranded regions of RNA and it has a strong preference for 3' of pyrimidine, especially towards UA, CA, and UG sites at single-stranded or weakly paired regions. It also cleaves RNA weakly at UC, CU, AC, and AU sites in single-stranded or weakly paired regions. Finally, we found that APE1 can reduce the ability of the Dicer enzyme to process premiRNAs in vitro. Overall, this study has revealed some previously unknown biochemical properties of APE1 which has implications for its role in vivo.

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 score0.046
Threshold uncertainty score0.320

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 head0.027
GPT teacher head0.229
Teacher spread0.201 · 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

Citations36
Published2010
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicRNA modifications and cancerFrench-language works237,207