Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants

Abstract

RNA Polymerase II (Pol II) regulatory cascades involving transcription factors (TFs) and their targets orchestrate the genetic circuitry of every eukaryotic organism. In order to understand how these cascades function, they can be dissected into small genetic networks, each containing just a few Pol II transcribed genes, that generate specific signal-processing outcomes. Small RNA regulatory circuits involve direct regulation of a small RNA by a TF and/or direct regulation of a TF by a small RNA and have been shown to play unique roles in many organisms. Here, we will focus on small RNA regulatory circuits containing Pol II transcribed microRNAs (miRNAs). While the role of miRNA-containing regulatory circuits as modular building blocks for the function of complex networks has long been on the forefront of studies in the animal kingdom, plant studies are poised to take a lead role in this area because of their advantages in probing transcriptional and posttranscriptional control of Pol II genes. The relative simplicity of tissue- and cell-type organization, miRNA targeting, and genomic structure make the Arabidopsis thaliana plant model uniquely amenable for small RNA regulatory circuit studies in a multicellular organism. In this Review, we cover analysis, tools, and validation methods for probing the component interactions in miRNA-containing regulatory circuits. We then review the important roles that plant miRNAs are playing in these circuits and summarize methods for the identification of small genetic circuits that strongly influence plant function. We conclude by noting areas of opportunity where new plant studies are imminently needed.

Figure 1.

Examples of miRNA-Containing Regulatory Circuits. Several examples of small genetic circuits that contain miRNAs, including “miRNA-mediated” circuits. Lower right: Small genetic circuits usually function in context of larger regulatory cascades and can be thought of as signal processing submodules.

Figure 2.

Tag Cluster Shape Categories. Examples of narrow peak (top), broad with peak (middle), and broad/weak peak (bottom) TSS-Seq tag clusters. The horizontal axis of each plot displays a region of genomic sequence, with TAIR10 cDNAs in the region displayed below the axis. The vertical axis displays the number of TSS-Seq reads observed at each nucleotide location in the region. (Reproduced from Morton et al. [2014], Figure 1.)

Figure 3.

TFBS Log-Likelihood Scanning with a PWM. (A) Given a collection of sequences that represent observed binding sites for a TF, a PWM “counts up” the number of As, Cs, Gs, and Ts in each position to describe the chance of finding each nucleotide in this position. The PWM can be visualized as a “logo” that describes how often the TF is expected to bind certain types of sites. (B) An illustration to visualize scanning for binding sites: Only those sites in a promoter sequence that exceed the PWM-specific threshold score are “observed” as putative binding sites. (Adapted from Megraw and Hatzigeorgiou [2010], Figures 1 and 4, for [A] and [B], respectively. Springer Plant MicroRNAs, Methods in Molecular Biology, Chapter 11, Vol. 592, 2010, pp. 149–161, Megraw and Hatzigeorgiou, © Humana Press a part of Springer Science + Business Media, LLC 2009, with permission of Springer.)

Figure 4.

Interpretations of Regulatory Cascades as Interacting Small Genetic Circuits. (A) The literature-supported network controlling adventitious root formation is complex, and the exact outcome depends on timing and concentrations of each component. By visualizing the two small genetic subcircuits on the left and examining the outcome if one component (in this case ARF17) were suddenly up- or downregulated, one can see that the complex circuit on the right can avoid a sudden “shutoff” (or alternatively, “full throttle”) to a downstream process. (B) One can extend this thinking to include other input “components”; here, viewing nitrate response as a miRNA-mediated control circuit for AFB3 regulation suggests one way that a plant can respond to continued nitrate input with a damped “pulse” of AFB3.

Figure 5.

Concept of Network Motif Discovery. How network motif discovery works, illustrated using a small circuit of interest M. The method determines whether M is observed a significantly large number of times in an original network N compared with randomized networks RN. If the P value falls below a small predetermined value, the number of times that M is observed in original network N is considered to be significant and therefore M is called a network motif for N. (Adapted from Megraw et al. [2013].)