Independent channels for miRNA biosynthesis ensure efficient static and dynamic control in the regulation of the early stages of myogenesis

Highlights

• A minimal mathematical model of a complex, recently discovered miRNA-based circuit controlling skeletal muscle cell differentiation is introduced and studied.

• Efficient static control of transcript levels is ensured by competition to bind miRNAs (via the ceRNA effect), which in turn constrains the kinetic parameters controlling the alternative miRNA biosynthesis channels.

• Fast dynamic downregulation of target levels in response to a rapid upshift in miRNA transcription rates is ensured when the miRNA binding kinetics is sufficiently fast.

• The miRNA-decoy system provides a simple and cheap mechanism to achieve fast miRNA-sponge binding kinetics.

• Both miRNA biosynthetic channels are essential to achieve the static control of transcript levels required during differentiation and the rapid dynamical response required to exit the specific differentiation stage.

Abstract

Motivated by recent experimental work, we define and study a deterministic model of the complex miRNA-based regulatory circuit that putatively controls the early stage of myogenesis in human. We aim in particular at a quantitative understanding of (i) the roles played by the separate and independent miRNA biosynthesis channels (one involving a miRNA-decoy system regulated by an exogenous controller, the other given by transcription from a distinct genomic locus) that appear to be crucial for the differentiation program, and of (ii) how competition to bind miRNAs can efficiently control molecular levels in such an interconnected architecture. We show that optimal static control via the miRNA-decoy system constrains kinetic parameters in narrow ranges where the channels are tightly cross-linked. On the other hand, the alternative locus for miRNA transcription can ensure that the fast concentration shifts required by the differentiation program are achieved, specifically via non-linear response of the target to even modest surges in the miRNA transcription rate. While static, competition-mediated regulation can be achieved by the miRNA-decoy system alone, both channels are essential for the circuit’s overall functionality, suggesting that that this type of joint control may represent a minimal optimal architecture in different contexts.

Introduction

Following years of experimental work, microRNAs (miRNAs) –small, endogenous, non-coding RNA molecules ubiquitously found in plant and animal cells– have emerged as key agents in post-transcriptional regulation (Cech and Steitz, 2014). Their primary mode of action is by protein-mediated base-pairing to target RNA molecules. For coding RNAs, this leads to the repression of gene expression through mRNA cleavage or translational inhibition (Bartel, 2004, Bartel, 2009, Hammond, Bernstein, Beach, Hannon, 2000, Kim, Sung, Park, Kim, Kim, Park, Ha, Bae, Kim, Baek, 2016). Their targets however include both coding and non-coding transcripts like long non-coding RNAs (lncRNAs) (Engreitz, Ollikainen, Guttman, 2016, Fatica, Bozzoni, 2014, Ponting, Oliver, Reik, 2009, Rinn, Chang, 2012), which places them at the center of the RNA-based regulatory web. Over time, miRNAs have been found to constitute a remarkably versatile regulatory layer, mediating functions ranging from the buffering of gene expression noise (Siciliano et al., 2013) to the timing of genetic circuits (Cheng et al., 2007). Moreover, they are now known to be profoundly implicated in a variety of developmental and disease processes (Sayed and Abdellatif, 2011). Mathematical models have been employed to address the role of miRNAs in different contexts (Lai et al., 2016), highlighting for instance how miRNA-based regulation may combine with circuit topology (Osella et al., 2011), kinetic heterogeneities (Martirosyan, De Martino, Pagnani, Marinari, 2017a, Martirosyan, Figliuzzi, Marinari, De Martino, 2016) and effects due to competition between miRNA targets (Jens and Rajewsky, 2015) to generate diverse functional outcomes. Competition, in particular, has been hypothesized to affect regulation in a broader, yet more subtle, way through the so-called ‘competing endogenous RNA’ (ceRNA) effect (Salmena et al., 2011), whereby co-regulated targets can establish, under specific conditions (Bosia, Pagnani, Zecchina, 2013, Figliuzzi, De Martino, Marinari, 2014, Figliuzzi, Marinari, De Martino, 2013, Jens, Rajewsky, 2015, Noorbakhsh, Lang, Mehta, 2013), an effective crosstalk with potentially far-reaching implications. While experimental validations are currently putting under scrutiny the question of how effective this mechanism can be in standard conditions (Bosson, Zamudio, Sharp, 2014, Denzler, McGeary, Title, Agarwal, Bartel, Stoffel, 2016, Yuan, Liu, Xie, Zhang, Li, Xie, Wang, 2015), competition has been shown to be central in a number of situations, perhaps most notably in cancer development (Karreth, Reschke, Ruocco, Ng, Chapuy, Léopold, Sjoberg, Keane, Verma, Ala, et al., 2015, Poliseno, Salmena, Zhang, Carver, Haveman, Pandolfi, 2010, Wang, Liu, Wu, Ni, Gu, Qiao, Chen, Sun, Fan, 2010) and muscle cell differentiation (Cesana, Cacchiarelli, Legnini, Santini, Sthandier, Chinappi, Tramontano, Bozzoni, 2011, Neguembor, Jothi, Gabellini, 2014).

Following recent work that has shed new light on its intricate genetic circuitry (Legnini et al., 2014), we focus here on miRNA-based control in early myogenesis. Key actors include two miRNA species (miR-133 and miR-135), two transcription factors (MAML1 and MEF2C), a skeletal muscle-specific lncRNA (linc-MD1) and the RNA-binding human antigen R (HuR) protein (see Fig. 1A). miR-133 can be produced from a precursor RNA (pre-miR-133b) as well as from two independent genomic loci. However, pre-miR-133b also provides the substrate for the synthesis of linc-MD1 through a pathway alternative (and mutually exclusive) to that leading to miR-133. In addition, linc-MD1 possesses two target sites for miR-135 and one for miR-133 and can therefore act as a ‘decoy’ for both miRNAs. The transcription factors MAML1 and MEF2C, both essential in the expression of muscle-specific genes (Shen et al., 2006), are instead targets of miR-133 and miR-135, respectively. As a consequence, linc-MD1 is a ceRNA of MAML1 (resp. MEF2C) and competes with it to bind miR-133 (resp. miR-135). In specific, miRNA sponging activity by linc-MD1 de-represses MAML1 and MEF2C leading to muscle-cell differentiation via the expression of the specific genes controlled by the latter. The HuR mRNA plays a subtle role in controlling the alternative processing of pre-miR-133b into linc-MD1 or miR-133. Most importantly, it competes with linc-MD1 for miR-133, thereby favoring the former’s sponging activity and giving rise to a positive feedforward loop that ultimately affects the levels of both species.

As discussed in Legnini et al. (2014), the trigger that possibly causes the system to exit the feedforward loop, repress muscle-specific gene expression and access later stages of differentiation is an endogenous upregulation of miR-133 transcription from the independent genomic loci. This suggests that the complex regulatory circuitry just described, whereby miRNAs can be synthesized both via a protein controlled switch and from an independent locus, can provide effective control of both timing and molecular levels.

In order to place this issue in a quantitative framework, here we study a schematic version of the above circuitry through a deterministic mathematical model based on mass-action kinetics, focusing specifically on the roles of HuR and of the alternative loci for miRNA transcription. In a nutshell, by characterizing the magnitude of the ceRNA effect and the response to a sudden increase of the transcriptional activity of miR-133, we show, among other things, that, while HuR-controlled regulation of pri-miR-133 processing is crucial to tune molecular levels, miRNA transcription from the alternative loci allows to achieve fast down-regulation of the target transcription factors MAML1 and MEF2C. In particular, fast enough miRNA-ceRNA binding kinetics causes non-linear response of the target level to upshifts in the miRNA biosynthesis rate as moderate as 20%, providing a highly efficient route to amplifying the effect of the differentiation trigger. In order for both mechanisms to be active, though, kinetic parameters need to be coordinated within specific ranges of values, which depend strongly on how sensitive pre-miR-133b processing is to HuR levels. In other words, the space of interaction constants and transcription rates is significantly constrained by crosstalk requirements.

The fact that crosstalk presupposes some degree of parameter tuning is not surprising per se, as strong ceRNA-ceRNA effective interactions at stationarity are known to be mainly achieved through competition when the concentrations of the involved molecular species are nearly equimolar (Bosia, Pagnani, Zecchina, 2013, Figliuzzi, Marinari, De Martino, 2013). Remarkably, though, we find that the system’s dynamic behaviour in such ranges is completely compatible with that observed in time-resolved experiments. This supports the conclusion that the two regulatory elements of the myogenesis clock, namely the HuR-controlled miRNA-decoy system and the alternative locus for miRNA transcription, play different yet coordinated functional roles.