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Record W2134513851 · doi:10.1091/mbc.e07-01-0070

Small Interfering RNA-mediated Silencing Induces Target-dependent Assembly of GW/P Bodies

2007· article· en· W2134513851 on OpenAlexafffund
Shangli Lian, Marvin J. Fritzler, Joseph Katz, Takashi Hamazaki, Naohiro Terada, Minoru Satoh, Edward K. L. Chan

Bibliographic record

VenueMolecular Biology of the Cell · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Calgary
FundersNational Institute of Dental and Craniofacial ResearchBreast Cancer Research FoundationNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis Society
KeywordsRNA interferenceGene knockdownBiologySmall interfering RNAGene silencingTrans-acting siRNACell biologyRNA silencingRNATransfectionSmall hairpin RNARNA-induced silencing complexMolecular biologyGeneGenetics

Abstract

fetched live from OpenAlex

Gene silencing using small interfering RNA (siRNA) is a valuable laboratory tool and a promising approach to therapeutics for a variety of human diseases. Recently, RNA interference (RNAi) has been linked to cytoplasmic GW bodies (GWB). However, the correlation between RNAi and the formation of GWB, also known as mammalian processing bodies, remains unclear. In this report, we show that transfection of functional siRNA induced larger and greater numbers of GWB. This siRNA-induced increase of GWB depended on the endogenous expression of the target mRNA. Knockdown of GW182 or Ago2 demonstrated that the siRNA-induced increase of GWB required these two proteins and correlated with RNAi. Furthermore, knockdown of rck/p54 or LSm1 did not prevent the reassembly of GWB that were induced by and correlated with siRNA-mediated RNA silencing. We propose that RNAi is a key regulatory mechanism for the assembly of GWB, and in some cases, GWB may serve as markers for RNAi in mammalian cells.

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 score0.011
Threshold uncertainty score0.589

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 science0.0010.000
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 head0.013
GPT teacher head0.241
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 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

Citations45
Published2007
Admission routes2
Has abstractyes

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