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Record W2046324232 · doi:10.1038/ncomms3914

The pseudogene TUSC2P promotes TUSC2 function by binding multiple microRNAs

2014· article· en· W2046324232 on OpenAlexafffund
Zina Jeyapalan Rutnam, William W. Du, Weining Yang, Xiangling Yang, Burton B. Yang

Bibliographic record

VenueNature Communications · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsPseudogenemicroRNAFunction (biology)Computational biologyBiologyGeneticsGeneGenome

Abstract

fetched live from OpenAlex

Various non-coding regions of the genome, once presumed to be ‘junk’ DNA, have recently been found to be transcriptionally active. In particular, pseudogenes are now known to have important biological roles. Here we report that transcripts of the two tumour suppressor candidate-2 pseudogenes (TUSC2P), found on chromosomes X and Y, are homologous to the 3′-UTR of their corresponding protein coding transcript, TUSC2. TUSC2P and the TUSC2 3′-UTR share many common miRNA-binding sites, including miR-17, miR-93, miR-299-3p, miR-520a, miR-608 and miR-661. We find that ectopic expression of TUSC2P and the TUSC2 3′-UTR inhibits cell proliferation, survival, migration, invasion and colony formation, and increases tumour cell death. By interacting with endogenous miRNAs, TUSC2P and TUSC2 3′-UTR arrest the functions of these miRNAs, resulting in increased translation of TUSC2. The TUSC2P and TUSC2 3′-UTR could thus be used as combinatorial miRNA inhibitors and might have clinical applications. Non-coding RNAs have recently emerged as crucial regulators of gene expression. Here Rutnam et al.identify a pseudogene complementary to the 3′-UTR of the TUSC2 tumour suppressor that regulates TUSC2 levels by acting as a decoy for endogenous microRNAs and thereby inhibits tumorigenesis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.501

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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

Citations105
Published2014
Admission routes2
Has abstractyes

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