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Record W2397748500 · doi:10.1016/j.celrep.2016.04.083

IGF2BP3 Modulates the Interaction of Invasion-Associated Transcripts with RISC

2016· article· en· W2397748500 on OpenAlexafffund
Hanane Ennajdaoui, Jonathan M. Howard, Timothy Sterne-Weiler, Fereshteh Jahanbani, Doyle Coyne, Philip J. Uren, Marija Dargyte, Sol Katzman, Jolene M. Draper, Andrew J. Wallace, Oscar Cazarez, Suzanne Perea Burns, Lindsay Hinck, Andrew D. Smith, Masoud Toloue, Benjamin J. Blencowe, Luiz O. F. Penalva, Jeremy R. Sanford

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesNational Human Genome Research InstituteCanadian Institutes of Health Research
KeywordsGene knockdownGene silencingmicroRNARNA-binding proteinCancer researchRNAMessenger RNABiologyGeneGenetics

Abstract

fetched live from OpenAlex

Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) expression correlates with malignancy, but its role(s) in pathogenesis remains enigmatic. We interrogated the IGF2BP3-RNA interaction network in pancreatic ductal adenocarcinoma (PDAC) cells. Using a combination of genome-wide approaches, we have identified 164 direct mRNA targets of IGF2BP3. These transcripts encode proteins enriched for functions such as cell migration, proliferation, and adhesion. Loss of IGF2BP3 reduced PDAC cell invasiveness and remodeled focal adhesion junctions. Individual nucleotide resolution crosslinking immunoprecipitation (iCLIP) revealed significant overlap of IGF2BP3 and microRNA (miRNA) binding sites. IGF2BP3 promotes association of the RNA-induced silencing complex (RISC) with specific transcripts. Our results show that IGF2BP3 influences a malignancy-associated RNA regulon by modulating miRNA-mRNA interactions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations100
Published2016
Admission routes2
Has abstractyes

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