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Functional Multimerization of the Human Telomerase Reverse Transcriptase

2001· article· en· W2164841256 on OpenAlexaff
Tara L. Beattie, Wen Zhou, Murray O. Robinson, Lea Harrington

Bibliographic record

VenueMolecular and Cellular Biology · 2001
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsAmgen (Canada)University of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsTelomeraseTelomerase reverse transcriptaseBiologyProtein subunitReverse transcriptaseTelomerase RNA componentRNAMolecular biologyTelomereSaccharomyces cerevisiaeComplementationBiochemistryGene

Abstract

fetched live from OpenAlex

The telomerase enzyme exists as a large complex (approximately 1,000 kDa) in mammals and at minimum is composed of the telomerase RNA and the catalytic subunit telomerase reverse transcriptase (TERT). In Saccharomyces cerevisiae, telomerase appears to function as an interdependent dimer or multimer in vivo (J. Prescott and E. H. Blackburn, Genes Dev. 11:2790-2800, 1997). However, the requirements for multimerization are not known, and it remained unclear whether telomerase exists as a multimer in other organisms. We show here that human TERT (hTERT) forms a functional multimer in a rabbit reticulocyte lysate reconstitution assay and in human cell extracts. Two separate, catalytically inactive TERT proteins can complement each other in trans to reconstitute catalytic activity. This complementation requires the amino terminus of one hTERT and the reverse transcriptase and C-terminal domains of the second hTERT. The telomerase RNA must associate with only the latter hTERT for reconstitution of telomerase activity to occur. Multimerization of telomerase also facilitates the recognition and elongation of substrates in vitro and in vivo. These data suggest that the catalytic core of human telomerase may exist as a functionally cooperative dimer or multimer 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 score0.028
Threshold uncertainty score0.352

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations143
Published2001
Admission routes1
Has abstractyes

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