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Big Insights into Small RNAs

Cite this: Biochemistry 2020, 59, 16, 1551–1552
Publication Date (Web):April 14, 2020
https://doi.org/10.1021/acs.biochem.0c00252
Copyright © 2020 American Chemical Society
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Since their discovery, microRNAs (miRNAs) have fascinated scientists. Loaded into an Argonaute (Ago) protein to form an RNA-induced silencing complex (RISC), miRNAs post-transcriptionally repress their targets in animals. They do so predominantly by destabilizing target transcripts and, to some extent, by repressing translation initiation. Each animal miRNA can regulate hundreds of target genes, so they impact nearly every biological process. (1) But understanding the biological functions of a specific miRNA has been an enduring problem because a scientist first must wrestle with the critical issue of predicting targets, a challenge that persists despite a substantial amount of research. However, in exciting new work, David Bartel and colleagues have taken an important step forward in surmounting this obstacle: they have found that a biochemical framework for miRNA–target interactions can be harnessed for predicting targets. (2)

The language of miRNA targeting is base pairing. The main base pairing occurs between the “seed” of the loaded miRNA (nucleotides 2–7) and the corresponding target transcript. Additional interactions around this core region (such as pairing with nucleotide 8 of the miRNA or an A opposite nucleotide 1) can lead to more repression. The different types of sites have a predictable hierarchy in how they respond to miRNAs, which has long guided target prediction algorithms. (1)

More effective targets have higher affinities for the miRNA, which opens up the possibility of biochemical methods for predicting miRNA targets. Indeed, biochemical measurements have quantified the affinities between individual miRNAs and target sequences. (3,4) However, it has not yet been possible to extend these studies to many different sites and miRNAs or to connect these results with the repression mediated in vivo. In addition, the importance of many types of miRNA–target interactions, especially those with bulges and mismatches in the seed interaction (called “noncanonical sites”), has been even more opaque because different miRNAs seem to behave differently.

New results from Bartel and colleagues shed light on many of these issues. (2) Here, they set out to survey the affinity landscape of miRNA–target binding by first performing in vitro bind-and-seq experiments. In this method, a purified RNA-binding protein (in this case, human AGO2 loaded with a specific miRNA) was incubated with a pool of potential target RNA molecules. After equilibrium had been reached, RISC–target complexes were enriched, and the bound target RNAs were identified using next-generation sequencing. This experiment was performed across a wide range of concentrations of RISC (loaded with six different miRNAs) so that relative Kd values with each miRNA could be determined for each site.

By using a randomized library of potential target sequences, Bartel and colleagues could characterize many different types of interactions, even those found very rarely in nature. These results reassuringly recapitulated the known site hierarchy seen for in vivo repression. The importance of seed-matched (“canonical”) targets reflects that base pairing with the seed is the most efficient way to interact effectively with the silencing complex; that is, this strategy requires the fewest nucleotides in the target. The authors also identified noncanonical sites that bind tightly to miRNAs, although these are much more rare because they must be longer (and so are less abundant) to achieve equivalent affinity. Interestingly, these experiments also uncovered the importance of nucleotides immediately surrounding the miRNA site, which can lead to 100-fold differences in affinity (Figure 1).

Figure 1

Figure 1. Biochemical framework for predicting microRNA targets. To predict targets of a microRNA, Bartel and colleagues have a developed a framework based on the affinity between the microRNA and target. Factors that influence the affinity include site hierarchy, nucleotides flanking the target site, the type and nature of noncanonical sites, the type and nature of noncanonical sites, the structural accessibility of the site, and where the site is in the target transcript.

A key take-home from these studies is that Ago shifts the thermodynamic landscapes to minimize the intrinsic differences between different seeds, which would otherwise lead to differences in targeting efficacies for different miRNAs. These results are consistent with previous ones from the Zamore lab (3) and also highlight the importance of thinking about the miRNA–Ago complex in the biochemistry of target recognition.

Importantly, the affinity between the miRNA and target corresponds to repression seen in the cell. This realization allowed Bartel and colleagues to build an improved target prediction algorithm based on a biochemical framework, an approach enabled by the density of data from their bind-and-seq experiments. Finally, the authors turned to a convoluted neural net to extend the framework to other miRNAs not characterized in vitro. Although these predictions for the uncharacterized miRNAs did not perform as well as for the miRNAs directly characterized, the biochemical framework still substantially improved upon previous algorithms, showing the utility of using a biochemical framework.

Similar results were found in a recent complementary paper, this time from the Greenleaf and Zamore laboratories. (5) Here, the goal was improving siRNA prediction. Ago proteins, such as human AGO2, can cleave targets with extended complementary to the small RNA; although RNAi does not typically repress endogenous targets, the cleavage activity forms the basis of siRNA-mediated silencing. Identifying effective siRNAs is important not only for research applications but also for potential therapeutics. In this study, researchers measured association rates, dissociation constants, in vitro cleavage rates, and in vivo knockdown efficiencies for thousands of targets. As with the Bartel lab study, there was synchrony between RISC binding and target cleavage such that they also found that using a biochemical framework enables better prediction of cleavage targets; as with miRNA target prediction, they also learned the binding landscape also depends on the small RNA loaded into RISC. Interestingly, Greenleaf and colleagues suggest that the main differences in affinity are driven by increased dwell times, rather than changes in association rates.

Together, by returning to fundamental biochemical approaches, these studies have dramatically improved our understanding of miRNA–target interactions. Of course, the quest for accurate miRNA target predictions is not done. Indeed, one major task will be to improve target predictions for miRNAs where this type of in vitro experiments has not been performed, and another will be to incorporate features that are farther from the target site. We will also need to extend these results beyond human AGO2 to other small RNA pathways. Nonetheless, the widespread conclusion that a biochemical framework can be used to predict miRNA and siRNA targets gives us a clear direction for how to reach the next horizon of small RNA research.

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  • Corresponding Author
    • Funding

      This work was supported by the RNA Bioscience Initiative, University of Colorado School of Medicine, and National Institutes of Health Grant R35GM128680.

    • Notes
      The author declares no competing financial interest.

    References

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    This article references 5 other publications.

    1. 1
      Bartel, D. P. (2018) Metazoan MicroRNAs. Cell 173, 2051,  DOI: 10.1016/j.cell.2018.03.006
    2. 2
      McGeary, S. E. (2019) The biochemical basis of microRNA targeting efficacy. Science 366, eaav174115,  DOI: 10.1126/science.aav1741
    3. 3
      Salomon, W. E., Jolly, S. M., Moore, M. J., Zamore, P. D., and Serebrov, V. (2015) Single-Molecule Imaging Reveals that Argonaute Reshapes the Binding Properties of Its Nucleic Acid Guides. Cell 162, 8495,  DOI: 10.1016/j.cell.2015.06.029
    4. 4
      Chandradoss, S. D., Schirle, N. T., Szczepaniak, M., MacRae, I. J., and Joo, C. (2015) A Dynamic Search Process Underlies MicroRNA Targeting. Cell 162, 96107,  DOI: 10.1016/j.cell.2015.06.032
    5. 5
      Becker, W. R. (2019) High-Throughput Analysis Reveals Rules for Target RNA Binding and Cleavage by AGO2. Mol. Cell 75, 741755.e11,  DOI: 10.1016/j.molcel.2019.06.012

    Cited By


    This article is cited by 1 publications.

    1. Nancy C. Stellwagen. Using capillary electrophoresis to characterize the hydrodynamic and electrostatic properties of DNA in solutions containing various monovalent cations. ELECTROPHORESIS 2022, 43 (1-2) , 309-326. https://doi.org/10.1002/elps.202100176
    • Abstract

      Figure 1

      Figure 1. Biochemical framework for predicting microRNA targets. To predict targets of a microRNA, Bartel and colleagues have a developed a framework based on the affinity between the microRNA and target. Factors that influence the affinity include site hierarchy, nucleotides flanking the target site, the type and nature of noncanonical sites, the type and nature of noncanonical sites, the structural accessibility of the site, and where the site is in the target transcript.

    • References

      ARTICLE SECTIONS
      Jump To

      This article references 5 other publications.

      1. 1
        Bartel, D. P. (2018) Metazoan MicroRNAs. Cell 173, 2051,  DOI: 10.1016/j.cell.2018.03.006
      2. 2
        McGeary, S. E. (2019) The biochemical basis of microRNA targeting efficacy. Science 366, eaav174115,  DOI: 10.1126/science.aav1741
      3. 3
        Salomon, W. E., Jolly, S. M., Moore, M. J., Zamore, P. D., and Serebrov, V. (2015) Single-Molecule Imaging Reveals that Argonaute Reshapes the Binding Properties of Its Nucleic Acid Guides. Cell 162, 8495,  DOI: 10.1016/j.cell.2015.06.029
      4. 4
        Chandradoss, S. D., Schirle, N. T., Szczepaniak, M., MacRae, I. J., and Joo, C. (2015) A Dynamic Search Process Underlies MicroRNA Targeting. Cell 162, 96107,  DOI: 10.1016/j.cell.2015.06.032
      5. 5
        Becker, W. R. (2019) High-Throughput Analysis Reveals Rules for Target RNA Binding and Cleavage by AGO2. Mol. Cell 75, 741755.e11,  DOI: 10.1016/j.molcel.2019.06.012

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