IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0198142.html
   My bibliography  Save this article

Human microRNAs preferentially target genes with intermediate levels of expression and its formation by mammalian evolution

Author

Listed:
  • Hisakazu Iwama
  • Kiyohito Kato
  • Hitomi Imachi
  • Koji Murao
  • Tsutomu Masaki

Abstract

MicroRNAs (miRNAs) are short, endogenous RNAs that post-transcriptionally repress mRNAs. Over the course of evolution, many new miRNAs are known to have emerged and added to the existing miRNA repertoires of drosophilids and vertebrates. Despite the large number of miRNAs in existence, the complementary pairing of only ~7 bases between miRNAs and mRNAs is sufficient to induce repression. Thus, miRNA targeting is so widespread that genes coexpressed with a miRNA have evolved to avoid sites that are targeted by the miRNA. Besides this avoidance, little is known about the preferential modes of miRNA targeting. Therefore, to elucidate miRNA targeting preference and avoidance, we evaluated the bias of the number of miRNA targeting occurrences in relation to expression intensities of miRNAs and their coexpressed target mRNAs by surveying transcriptome data from human organs. We found that miRNAs preferentially target genes with intermediate levels of expression, while avoiding highly expressed ones, and that older miRNAs have greater targeting specificity, suggesting that specificity increases during the course of evolution.

Suggested Citation

  • Hisakazu Iwama & Kiyohito Kato & Hitomi Imachi & Koji Murao & Tsutomu Masaki, 2018. "Human microRNAs preferentially target genes with intermediate levels of expression and its formation by mammalian evolution," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    • Handle: RePEc:plo:pone00:0198142
    DOI: 10.1371/journal.pone.0198142
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198142
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0198142&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0198142?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Victor Ambros, 2004. "The functions of animal microRNAs," Nature, Nature, vol. 431(7006), pages 350-355, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. José María Galván-Román & Ángel Lancho-Sánchez & Sergio Luquero-Bueno & Lorena Vega-Piris & Jose Curbelo & Marcos Manzaneque-Pradales & Manuel Gómez & Hortensia de la Fuente & Mara Ortega-Gómez & Javi, 2020. "Usefulness of circulating microRNAs miR-146a and miR-16-5p as prognostic biomarkers in community-acquired pneumonia," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-13, October.
    2. Kshitij Srivastava & Anvesha Srivastava, 2012. "Comprehensive Review of Genetic Association Studies and Meta-Analyses on miRNA Polymorphisms and Cancer Risk," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-1, November.
    3. Xing Chen & Jun Yin & Jia Qu & Li Huang, 2018. "MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-24, August.
    4. Yanyan Wang & Yujie Zhang & Chi Pan & Feixia Ma & Suzhan Zhang, 2015. "Prediction of Poor Prognosis in Breast Cancer Patients Based on MicroRNA-21 Expression: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-13, February.
    5. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2015. "miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer," Biometrics, The International Biometric Society, vol. 71(2), pages 428-438, June.
    6. Xing Chen & Li Huang, 2017. "LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-28, December.
    7. Ang Li & Yingwei Deng & Yan Tan & Min Chen, 2021. "A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-17, June.
    8. Charlotte Glinge & Sebastian Clauss & Kim Boddum & Reza Jabbari & Javad Jabbari & Bjarke Risgaard & Philipp Tomsits & Bianca Hildebrand & Stefan Kääb & Reza Wakili & Thomas Jespersen & Jacob Tfelt-Han, 2017. "Stability of Circulating Blood-Based MicroRNAs – Pre-Analytic Methodological Considerations," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    9. Alexander Link & Verena Becker & Ajay Goel & Thomas Wex & Peter Malfertheiner, 2012. "Feasibility of Fecal MicroRNAs as Novel Biomarkers for Pancreatic Cancer," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-9, August.
    10. Hossain Ahmed & Beyene Joseph, 2013. "Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 743-755, December.
    11. Hai Lian & Lei Wang & Jingmin Zhang, 2012. "Increased Risk of Breast Cancer Associated with CC Genotype of Has-miR-146a Rs2910164 Polymorphism in Europeans," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-7, February.
    12. Fabricio F Costa & Jared M Bischof & Elio F Vanin & Rishi R Lulla & Min Wang & Simone T Sredni & Veena Rajaram & Maria de Fátima Bonaldo & Deli Wang & Stewart Goldman & Tadanori Tomita & Marcelo B Soa, 2011. "Identification of MicroRNAs as Potential Prognostic Markers in Ependymoma," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-10, October.
    13. Le Thi Truc Linh, 2018. "The Microrna 29 family and its regulation," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 8(1), pages 18-27.
    14. Seyedehsadaf Asfa & Halil Ibrahim Toy & Reza Arshinchi Bonab & George P. Chrousos & Athanasia Pavlopoulou & Styliani A. Geronikolou, 2023. "Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach," IJERPH, MDPI, vol. 20(13), pages 1-17, July.
    15. Man-Tang Qiu & Jing-Wen Hu & Xiang-Xiang Ding & Xin Yang & Zhi Zhang & Rong Yin & Lin Xu, 2012. "Hsa-miR-499 rs3746444 Polymorphism Contributes to Cancer Risk: A Meta-Analysis of 12 Studies," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-7, December.
    16. Li Li & Yunjian Sheng & Lin Lv & Jian Gao, 2013. "The Association between Two MicroRNA Variants (miR-499, miR-149) and Gastrointestinal Cancer Risk: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
    17. Julie Massart & Rasmus J. O. Sjögren & Brendan Egan & Christian Garde & Magnus Lindgren & Weifeng Gu & Duarte M. S. Ferreira & Mutsumi Katayama & Jorge L. Ruas & Romain Barrès & Donal J. O’Gorman & Ju, 2021. "Endurance exercise training-responsive miR-19b-3p improves skeletal muscle glucose metabolism," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    18. Blanca Elena Castro-Magdonel & Manuela Orjuela & Diana E Alvarez-Suarez & Javier Camacho & Lourdes Cabrera-Muñoz & Stanislaw Sadowinski-Pine & Aurora Medina-Sanson & Citlali Lara-Molina & Daphne Garcí, 2020. "Circulating miRNome detection analysis reveals 537 miRNAS in plasma, 625 in extracellular vesicles and a discriminant plasma signature of 19 miRNAs in children with retinoblastoma from which 14 are al," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-19, April.
    19. Adam Emmer, 2019. "The careers behind and the impact of solo author articles in Nature and Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 825-840, August.
    20. Ahmed Hossain & Joseph Beyene, 2015. "Application of skew-normal distribution for detecting differential expression to microRNA data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 477-491, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0198142. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.