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Working Together: Combinatorial Regulation by microRNAs

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MicroRNA Cancer Regulation

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 774))

Abstract

MicroRNAs (miRNAs) negatively regulate gene expression level of mRNA post-transcriptionally. Deep sequencing and large-scale screening methods have yielded about 1,500 miRNA sequences in human. Each miRNA contains a seed sequence that is required, but not sufficient, for the correct matching with its targets. Recent technological advances make it possible to capture the miRNAs with their cognate mRNAs at the RISC complex. These experiments have revealed thousands of validated mRNA-miRNA pairing events. In the context of human stem cells, 90% of the identified transcripts appear to be paired with at least two different miRNAs.

In this chapter, we present a comprehensive outline for a combinatorial regulation mode by miRNAs. Initially, we summarize the computational and experimental evidence that support a combined effect of multiple miRNAs. Then, we describe miRror2.0, a platform specifically convened to consider the likelihood of miRNAs cooperativity in view of the targets, tissues and cell lines. We show that results from miRror2.0 can be further refined by an iterative procedure, calls Psi-miRror that gauges the robustness of the regulation. We illustrate the combinatorial regulation projected onto graphs of human pathways and show that these pathways are amenable to disruption by a small set of miRNAs. Finally, we propose that miRNA combinatorial regulation is an attractive regulatory strategy not only at the level of single target, but also at the level of pathways and cellular homeostasis. The joint operation of miRNAs is a powerful means to overcome the low specificity inherent in each individual miRNA.

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Abbreviations

AGO:

Argonaute

DB:

database

DIS:

disconnecting score

GO:

gene ontology

HITS-CLIP:

high-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation

PAR-CLIP:

photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation

miRNA (miR):

microRNA

ML:

machine learning

MS:

mass spectrometry

ncRNA:

non-coding RNA RISCRNA-induced silencing complex

SILAC:

stable isotope labeling by amino acids in cell culture

UTR:

untranslated region.

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Acknowledgements

We thank Guy Naamati whose contribution to the miRror platform development was seminal. We thank Nati Linial for insightful ideas. We thank Solange Karsenty for maintenance of the miRror website. We thank Manor Askenazi for a critical reading and editing of the manuscript. A student fellowship (O.B) is awarded by the SCCB, the Sudarsky Center for Computational Biology. We apologize to those that we could not cite. In numerous instances we replace the primary citations by review articles due to space constraints. This study is partially supported by the ISF 592/07, the BSF 2007219 and the EU Framework VII of Prospects.

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Authors and Affiliations

  1. Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel

    Yitzhak Friedman & Michal Linial

  2. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel

    Ohad Balaga

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  1. Yitzhak Friedman
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  2. Ohad Balaga
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Correspondence to Michal Linial .

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Editors and Affiliations

  1. Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

    Ulf Schmitz

  2. Dept. of Systems Biology and Bioinformat, Universität Rostock Institut of Computer Science, Rostock, Germany

    Olaf Wolkenhauer

  3. Rostock, Germany

    Julio Vera

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Friedman, Y., Balaga, O., Linial, M. (2013). Working Together: Combinatorial Regulation by microRNAs. In: Schmitz, U., Wolkenhauer, O., Vera, J. (eds) MicroRNA Cancer Regulation. Advances in Experimental Medicine and Biology, vol 774. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5590-1_16

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