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Record W2077830374 · doi:10.1093/nar/gki342

Human Ku70/80 interacts directly with hTR, the RNA component of human telomerase

2005· article· en· W2077830374 on OpenAlexaff
Nicholas S. Y. Ting

Bibliographic record

VenueNucleic Acids Research · 2005
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTelomereTelomeraseBiologyKu70Telomerase reverse transcriptaseTelomerase RNA componentRNACell biologySaccharomyces cerevisiaeContext (archaeology)DNAGeneticsDNA repairMolecular biologyGene

Abstract

fetched live from OpenAlex

Maintenance of telomere integrity requires the dynamic interplay between telomerase, telomere-associated proteins and DNA repair proteins. These interactions are vital to suppress DNA damage responses and changes in chromosome dynamics that can result in aneuploidy or other transforming aberrations. The interaction between the DNA repair protein Ku and the RNA component of telomerase (TLC1) in Saccharomyces cerevisiae has been shown to be important for maintaining telomere length. Here, we sought to determine whether this interaction was conserved in higher eukaryotes. Although there is no sequence similarity between TLC1 and the RNA component (hTR) of human telomerase, we show that human Ku70/80 interacts with hTR both in vitro and in a cellular context. Specifically, Ku70/80 interacts with a 47 nt region of the 3' end of hTR, which resembles the stem-loop region of the yeast Ku70/80 binding domain on TLC1. Furthermore, utilizing immunoprecipitation/RT-PCR experiments, we show that Ku interacts with hTR in cell lines deficient in the human telomerase reverse transcriptase protein (hTERT), suggesting that this interaction does not require hTERT. These data suggest that Ku interacts directly with hTR, independent of hTERT, providing evidence for the conservation of the interaction between Ku and telomerase RNA among various species and provide significant insight into how Ku is involved in telomere maintenance in higher eukaryotes.

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)0.001
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 score0.334
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.375
Teacher spread0.312 · 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

Citations113
Published2005
Admission routes1
Has abstractyes

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