Evidence map›Paper›PMID 22798498›Full record

ArticleNucleic acids research2012

Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs.

Xin Lai, Ulf Schmitz, Shailendra K Gupta, Animesh Bhattacharya, Manfred Kunz, Olaf Wolkenhauer, Julio Vera

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 7% 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

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 92 citations in OpenAlex.

  1. Guideline
  2. Article
  3. Review
  4. Out-of-Equilibrium ceRNA Crosstalk.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. Advanced computational predictive models of miRNA-mRNA interaction efficiency.Computational and structural biotechnology journal · 2024
    Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Frontiers in immunology · 2020
    Article
  16. Identification of two microRNA nodes as potential cooperative modulators of liver metabolism.Hepatology research : the official journal of the Japan Society of Hepatology · 2019
    Article
  17. Review
  18. Article
  19. Review
  20. Sensory Neuropathy Affects Cardiac miRNA Expression Network TargetingInternational journal of molecular sciences · 2019
    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

7 authors at 3 institutions in 3 countries.

Xin LaiDepartment of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
Ulf Schmitz
Shailendra K Gupta
Animesh Bhattacharya
Manfred Kunz
Olaf Wolkenhauer
Julio Vera
Stellenbosch University · ZAIndian Institute of Toxicology Research · INUniversity of Rostock · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.

Indexed as

Gene Expression RegulationGene Regulatory NetworksCyclin-Dependent Kinase Inhibitor p21KineticsMicroRNAsModels, GeneticCyclin-Dependent Kinase Inhibitor p21MicroRNAs

Identifiers

PMID22798498
PMCPMC3467055
OpenAlexW2104433337

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.