Evidence map›Paper›PMID 24875477›Full record

ArticleNucleic acids research2014

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow.

Ulf Schmitz, Xin Lai, Felix Winter, Olaf Wolkenhauer, Julio Vera, Shailendra K Gupta

Abstract read
In one paragraph

Article in Nucleic acids research, 2014. 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
–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

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

  1. Guideline
  2. Article
  3. Review
  4. Out-of-Equilibrium ceRNA Crosstalk.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  6. Aptamer-Array-Guided Protein Assembly Enhances Synthetic mRNA Switch Performance.Angewandte Chemie (International ed. in English) · 2022
    Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
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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

6 authors.

Ulf SchmitzDepartment of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany ulf.schmitz@uni-rostock.de.
Xin LaiLaboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Germany.
Felix WinterDepartment of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
Olaf WolkenhauerDepartment of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.
Julio VeraLaboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Germany.
Shailendra K GuptaDepartment of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany Department of Bioinformatics, CSIR-Indian Institute of Toxicology Research, 226001 Lucknow, Uttar Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.

Indexed as

Gene Expression Regulation3' Untranslated RegionsArgonaute ProteinsComputational BiologyDatabases, Nucleic AcidHumansMicroRNAsMolecular Dynamics SimulationNucleic Acid ConformationRNA, MessengerWorkflow3' Untranslated RegionsArgonaute ProteinsMicroRNAsRNA, Messenger

Identifiers

PMID24875477
PMCPMC4081082

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Registered trials

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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.