Interplay of microRNA and epigenetic regulation in the human regulatory network

Abstract

The expression of protein-coding genes is controlled by a complex network of regulatory interactions. It is becoming increasingly appreciated that post-transcriptional repression by microRNAs, a class of small non-coding RNAs, is a key layer of regulation in several biological processes. In this contribution, we discuss the interplay between microRNAs and epigenetic regulators. Among the mixed genetic circuits composed by these two different kinds of regulation, it seems that a central role is played by double-negative feedback loops in which a microRNA inhibits an epigenetic regulator and in turn is controlled at the epigenetic level by the same regulator. We discuss a few relevant properties of this class of network motifs and their potential role in cell differentiation. In particular, using mathematical modeling we show how this particular circuit can exhibit a switch-like behavior between two alternative steady states, while being robust to stochastic transitions between these two states, a feature presumably required for circuits involved in cell fate decision. Finally, we present a list of putative double-negative feedback loops from a literature survey combined with bioinformatic analysis, and discuss in detail a few examples.

Keywords: epigenetic regulation; feedback loops; microRNAs; network motifs.

Figure 1

The bistability region of the toggle switch depends on the degree of repression non-linearity. (A) Cartoon of the interaction scheme that composes the toggle switch: the two genes A and B mutually repress each other. (B) The Hill function H_i(x) describes how the production rate of gene i depends on the repressor concentration x. The exponent n defines the steepness of the curve. In the limit case of n → ∞, the Hill function becomes a step function with a critical repressor concentration h_i at which the target production is switched on. As we argue in the text, this limit case can be considered the suitable description for the transcriptional repression induced by epigenetic regulators such as chromating remodeling factors. (C) The bistability region of the toggle switch is depicted as a function of the amounts of the two gene products A and B. Increasing the steepness of the repressive function (i.e., the parameter n) enlarges the bistability region, thus extending the parameter range in which the circuit is suitable to implement cell fate decisions.

Figure 2

Regulation by miRNAs increases the stability of the toggle switch by controlling the burst size. (A) Stochastic fluctuations in gene expression can induce transitions between the two stable steady states. The figure shows an example of a simulation in which the circuit switches between the situation in which A is actively transcribed while B is switched off to the opposite one. The typical time between these transitions is set by the switching rate, which is a function of the circuit parameters. (B) The switching rate is shown as a function of the effective burst size $\left(b=\frac{k_p}{{\gamma }_m+k s}\right)$, as set by the level of miRNA regulation. The burst size is a major determinant of gene expression noise, and small variations in this parameter can vary the toggle switch stability of several order of magnitude. The different curves correspond to different levels of cataliticity α, i.e., the ability of the miRNA to be recycled and not degraded with the targeted mRNA (see Equation 4). The higher is the recycling ability (α → 0) the less is the circuit stability dependent on the burstiness of the process.

Figure 3

Summary of the results of the bionformatic analysis. The heatmap is a graphical representation of the matrix obtained by annotating all available histone modifications and methylation patterns from the ENCODE epigenetic tracks at the promoters of the pre-miRNAs listed in Table 1. Color code: blue whether overlap exists between the putative pre-miRNA promoter and the corresponding epigenetic track, and white otherwise. Detailed information on the epigenetic markers listed in the rows of the map can be found in the supplementary material.

Figure 4

Workflow of our analysis.