Evidence map›Paper›PMID 25653157›Full record

ArticleNucleic acids research2015

Characterization of the mammalian miRNA turnover landscape.

Yanwen Guo, Jun Liu, Sarah J Elfenbein, Yinghong Ma, Mei Zhong, Caihong Qiu, Ye Ding, Jun Lu

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

66 citing papers in PubMed, 1 synthesis or guideline pooled it, 107 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Genes & development · 2026
    Article
  5. Article
  6. Molecular Analysis of miRNA Stability and Decay Dynamics by Northern Blot.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
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  9. Fluorogenic RNA-based biomaterials for imaging and tracking the cargo of extracellular vesicles.Journal of controlled release : official journal of the Controlled Release Society · 2024
    Article
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6 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Yanwen GuoDepartment of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA Yale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA Graduate Program in Biological and Biomedical Sciences, Yale University, New Haven, CT 06510, USA.
Jun LiuDepartment of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA Yale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA.
Sarah J ElfenbeinYale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA Computational Biology and Bioinformatics Program, Yale University School of Medicine, New Haven, CT 06520, USA.
Yinghong MaYale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA.
Mei ZhongYale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA.
Caihong QiuYale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA.
Ye DingWadsworth Center, New York State Department of Health, Albany, NY 12208, USA.
Jun LuDepartment of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA Yale Stem Cell Center, Yale Cancer Center, New Haven, CT 06520, USA Yale Center for RNA Science and Medicine, New Haven, CT 06520, USA jun.lu@yale.edu.
Yale Cancer Center · USYale University · USWadsworth Center · US

Funding

Novel Approaches to Mammalian MicroRNA Target PredictionR01GM099811 · NIGMS · WADSWORTH CENTER · PI DING, YE, LU, JUN · 2012 to 2015
$2.4M
The role of microRNAs in leukemic initiation and maintenanceR01CA149109 · NCI · YALE UNIVERSITY · PI LU, JUN · 2011 to 2015
$1.7M
NCI NIH HHS R01CA149109NIGMS NIH HHS R01 GM099811NIGMS NIH HHS R01GM099811
6 · The paper itself

Abstract

Steady state cellular microRNA (miRNA) levels represent the balance between miRNA biogenesis and turnover. The kinetics and sequence determinants of mammalian miRNA turnover during and after miRNA maturation are not fully understood. Through a large-scale study on mammalian miRNA turnover, we report the co-existence of multiple cellular miRNA pools with distinct turnover kinetics and biogenesis properties and reveal previously unrecognized sequence features for fast turnover miRNAs. We measured miRNA turnover rates in eight mammalian cell types with a combination of expression profiling and deep sequencing. While most miRNAs are stable, a subset of miRNAs, mostly miRNA*s, turnovers quickly, many of which display a two-step turnover kinetics. Moreover, different sequence isoforms of the same miRNA can possess vastly different turnover rates. Fast turnover miRNA isoforms are enriched for 5' nucleotide bias against Argonaute-(AGO)-loading, but also additional 3' and central sequence features. Modeling based on two fast turnover miRNA*s miR-222-5p and miR-125b-1-3p, we unexpectedly found that while both miRNA*s are associated with AGO, they strongly differ in HSP90 association and sensitivity to HSP90 inhibition. Our data characterize the landscape of genome-wide miRNA turnover in cultured mammalian cells and reveal differential HSP90 requirements for different miRNA*s. Our findings also implicate rules for designing stable small RNAs, such as siRNAs.

Indexed as

RNA StabilityAnimalsArgonaute ProteinsCell LineCells, CulturedHSP90 Heat-Shock ProteinsHumansKineticsMiceMicroRNAsNucleic Acid Synthesis InhibitorsRNA IsoformsSequence Analysis, RNATranscription, GeneticArgonaute ProteinsHSP90 Heat-Shock ProteinsMicroRNAsNucleic Acid Synthesis InhibitorsRNA Isoforms

Identifiers

PMID25653157
PMCPMC4344502
OpenAlexW1990612961

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.