Evidence map›Paper›PMID 21358288›Full record

ReviewPlant signaling & behavior2011

Alternate approaches to repress endogenous microRNA activity in Arabidopsis thaliana.

Andrew L Eamens, Ming-Bo Wang

Abstract readReview
In one paragraph

Review in Plant signaling & behavior, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. miRNA-Based Technologies in Cancer Therapy.Journal of personalized medicine · 2023
    Review
  4. Article
  5. Long non-coding RNAs and their biological roles in plants.Genomics, proteomics & bioinformatics · 2015
    Review
  6. Review
  7. Article
  8. Article
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

2 authors.

Andrew L EamensSchool of Molecular Bioscience, University of Sydney, Sydney, Australia. andrew.eamens@sydney.edu.au
Ming-Bo Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are an endogenous class of regulatory small RNA (sRNA). In plants, miRNAs are processed from short non-protein-coding messenger RNAs (mRNAs) transcribed from small miRNA genes (MIR genes). Traditionally in the model plant Arabidopsis thaliana (Arabidopsis), the functional analysis of a gene product has relied on the identification of a corresponding T-DNA insertion knockout mutant from a large, randomly-mutagenized population. However, because of the small size of MIR genes and presence of multiple, highly conserved members in most plant miRNA families, it has been extremely laborious and time consuming to obtain a corresponding single, or multiple, null mutant plant line. Our recent study published in Molecular Plant ( 1) outlines an alternate method for the functional characterization of miRNA action in Arabidopsis, termed anti-miRNA technology. Using this approach we demonstrated that the expression of individual miRNAs, or entire miRNA families, can be readily and efficiently knocked-down. Our approach is in addition to two previously reported methodologies that also allow for the targeted suppression of either individual miRNAs, or all members of a MIR gene family; these include miRNA target mimicry and transcriptional gene silencing (TGS) of MIR gene promoters. All three methodologies rely on endogenous gene regulatory machinery and in this article we provide an overview of these technologies and discuss their strengths and weaknesses in inhibiting the activity of their targeted miRNA(s).

Indexed as

ArabidopsisGene SilencingMicroRNAsModels, BiologicalMicroRNAs

Identifiers

PMID21358288
PMCPMC3142414

What OpenQuestion holds

Textmetadata
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.