Alteration in MicroRNA Expression Governs the Nature and Timing of

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Alteration in microRNA expression governs the nature and timing of cellular fate commitment Dola Sengupta, and Sandip Kar ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.7b00423 • Publication Date (Web): 28 Nov 2017 Downloaded from http://pubs.acs.org on December 4, 2017

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ACS Chemical Neuroscience

Alteration in microRNA expression governs the nature and timing of cellular fate commitment Dola Sengupta1, Sandip Kar1* 1

Department of Chemistry, IIT Bombay, Powai, Mumbai - 400076, India

Abstract In the central nervous system, expression level of transcriptional repressor Hes1 (hairy and enhancer of split-1) tightly controls the alternative cell fate commitment during differentiation as well as the time required for such cellular transitions. A microRNA, miR-9 that interacts with Hes1 in a mutually antagonistic manner, influences both the process of lineage specification and timing of differentiation significantly but the impact of the miR-9 in guiding these events still remains poorly understood. Here we proposed a stochastic mathematical model of miR-9/Hes1 double negative feedback interaction network that at the outset shows how alternative cell fate such as quiescence, progenitor and neuronal states can be accomplished through fine-tuning the Hes1 dynamics by altering the expression level of miR-9. The model simulations further foretell a correlated variation of the period of oscillation of Hes1 and the time delay observed between Hes1 mRNA and protein as the transcription rate of miR-9 increases during the neural progenitor state attainment. Importantly, the model simulations aided by the systematic sensitivity analysis predict that the timing of differentiation to neuronal state crucially depends on the negative regulators (miR-9 and Hes6) of the Hes1. Our results indicate that miR-9/Hes1 interaction network can be effectively exploited for an efficient and well-timed neuronal transformation.

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Keywords: microRNA, Hes1, differentiation dynamics, Mathematical modeling, Stochastic analysis

Introduction MicroRNA, miR-9 decisively regulates the developmental fate choice of neural progenitors in central nervous system (CNS)1–5. In CNS, short-period oscillation of basic helix-loop-helix transcriptional repressor Hes1 is required for the progenitor maintenance whereas, persistent high and low non-oscillatory expression levels of Hes1 seem to associate with the quiescence progenitor and the neuronal state, respectively1,6–9. Experiments in literature1,10,11 had previously revealed that Hes1 auto-repression along with mRNA and protein instability essentially maintains the oscillatory dynamics of Hes1 in the neural progenitor state. Recent experimental findings1 had illustrated the specific role of highly stable miR-9 to influence the Hes1 mRNA half-life over time that leads to the dynamical transition to a non-oscillatory neuronal state. Interestingly, Hes1 protein had also been shown to repress the miR-9 transcription1. These observations suggest that the existence of this double negative feedback loop mechanism operative between miR-9 and Hes1 can play a crucial role in the progenitor maintenance and achieving alternative cell fates. Attempts had been made2,4 to understand the miR-9 regulated Hes1 dynamics even from a mathematical modeling perspective, however, the in depth understanding about the underlying molecular machinery that accomplishes such a well-organized developmental event in and around a fluctuating cellular environment still remains elusive.

In literature, mathematical and computational modeling was used quite extensively to understand the oscillatory dynamics of Hes1 protein12–15. Most of the earlier deterministic models were based on delay differential equations to account for the time delay observed experimentally between Hes1 mRNA and protein peaks that occurs due to the auto-negative feedback regulation present in the Hes1 dynamics2,12– 14,16

. Unfortunately, only a few modeling studies17,18 had incorporated the effect of

molecular fluctuations to elucidate the Hes1 protein and mRNA dynamics. Recent experiments1 had shown that stochastic fluctuations play a crucial role in miR-9 mediated cellular fate determination process by altering the Hes1 mRNA and protein dynamics. Thus a model more precise in terms of mechanistic details of negative


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feedback regulation of Hes1 and at the same time capable of accounting for the stochastic fluctuation will be helpful to decipher the complex dynamical nature of the miR-9-controlled fundamental processes like progenitor maintenance, neuronal differentiation and acquisition of quiescence state.

In this article, we performed a stochastic mathematical modeling study of the doublenegative feedback loop of miR-9 and Hes1 interaction. The proposed model is devoid of any explicit time delay terms in the Hes1 auto-repression part and adequately captures the effect of stochastic fluctuations present in the core network. Deterministic and stochastic analysis of our model demonstrate how one oscillatory state of Hes1 can exist between two non-oscillatory high and low expression levels of Hes1 by just controlling the transcriptional activation of miR-9. Our stochastic simulation

results

reconcile

various

experimental

observations

regarding

developmental fate determination of neural progenitor. It describes further how change in the mir-9 level can alter the oscillatory dynamics of Hes1 and allows the system to keep the track of the approximate time of differentiation. Intriguingly, our model for the first time shows how increasing level of mir-9 can fine tune the experimentally observed time delay present between the Hes1 mRNA and protein dynamics. Furthermore, the model makes unique predictions about the progenitor maintenance and about the onset of neuronal differentiation by varying the effect of Hes6 (Hes family member protein but antagonizes with Hes1) and the co-repressor Groucho(Gro)/transducin-like Enhancer of split (TLE) (Gro/TLE) individually. The model even makes a non-intuitive prediction that the negative regulators of the Hes1 are tightly regulating the timing of differentiation of the neural progenitors to neuron. We believe our study puts forward a more generic and realistic framework to understand progenitor maintenance, neuronal differentiation, and quiescence state attainment by corroborating several experimental observations and making novel testable predictions.

Mathematical model of miR-9/Hes1 interaction network The model, schematized in Fig. 1, describes the double negative feedback loop of Hes1 and miR-9 interaction along with the auto-repression of Hes1 and negative regulation of Hes6 on Hes1. To begin with, we have described the Hes1 auto-



repression feedback to its own transcription by an appropriate molecular mechanism to avoid the time delayed differential equations used in most of the earlier models2,12– 14

.

Figure 1. Schematic representation of Hes1/miR-9 interaction network. (Solid and dotted arrows represent biochemical reactions and catalytic effects respectively and hammer-headed lines represent inhibition process.) The Hes1 protein (H1p) is produced from Hes1 mRNA (H1m) and subsequently gets dimerized (H1p2). This dimerized form of Hes1 interacts with Gro/TLE (GroTLE) protein and forms a hetero-complex (GP2) where the Gro/TLE protein gets hyper-phosphorylated by kinases like CK2. For simplicity we consider four times phosphorylation (P1-4C) of Gro/TLE in GP2. All these hyper-phosphorylated complexes (P1-4C) repress Hes1 transcription as well as pre-miR-9 (Prim) transcription. At the same time the Gro/TLE protein present in the GP2 and in some of the hyperphosphorylated complexes can get phosphorylated at other phosphorylation sites specifically by either MAPK or HIPK2 (represented as X) to produce new complexes represented as iGP2 (where i=1 to 3, represents the one time negatively phosphorylated forms of GP2, P1C and P2C complexes respectively) and can no longer act as transcriptional repressor. Mature miR-9 (miR), transcribed from pre-miR-9 (Prim), binds with Hes1 mRNA and eventually causes degradation of Hes1 mRNA and also regulates Hes1 translation. Hes6 protein (H6p) induces Hes1 protein degradation and represses GP2 formation. Corresponding kinetic equations, description of the variables and values of model-parameters are depicted in Table 1, Tables S1 and S2.


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The dimer form of the transcriptional repressor Hes1 binds with corepressor Groucho(Gro)/transducin-like Enhancer of split (TLE), which leads to Gro/TLE hyper-phosphorylation19–25. These hyper-phosphorylated complexes trigger the autonegative regulation of Hes110,13,26. In our model we assumed that the repression ability of these hyper-phosphorylated Gro/TLE’s rises with the increase in the level of phosphorylation, and it becomes highest for the maximum phosphorylated form (P4C). Moreover, it had been shown in several studies that Gro/TLE either free or in bounded with Hes1 could undergo different types of posttranslational modifications, which eventually inhibit its role as transcriptional repressor20,27,28. For example, it is well known that MAPK and HIPK2 mediated phosphorylation can inhibit Gro/TLE function by producing different inactive complexes20,27,28. In Fig. 1, we have added this negative effect on Gro/TLE by considering a kinase (X), which can play the role of either MAPK or HIPK2 depending on the signaling events responsible for those two effectors.

In addition to this, we consider (i) the putative repression of pre-miR-9 expression by Hes11,2,4 (following the same mechanism as taken for the Hes1 auto-negative regulation), (ii) miR-9 mediated degradation of Hes1 mRNA,1,2,29 (iii) miR-9 controlled inhibition on Hes1 protein translation,1,29 (iv) high stability of mature miR9 compared to Hes1 mRNA due to low turn over rate,1 (v) Negative regulation of Hes6 on Hes119,30. Thus, the model essentially captures the inhibition of Hes1 by miR-9 in two different ways. First, miR-9 forms a complex (Mpi) with Hes1 mRNA and degrades it. Second, the translational efficiency of the Hes1 mRNA within that Mpi complex becomes much less compare to the free Hes1 mRNA. The equations and the description of the variables are provided in Table 1 and Table S1. Modelparameters, their numerical values and sources1,2,10,12,31 are designated in Table S2. We have acquired some of the parameter values such as mRNA and protein degradation rates of Hes1 and miR-9 degradation rate directly from experimental literature1,2,10,12 (as shown in Table S2). It is worthwhile to mention that here we mainly focus to understand the emergent dynamic behavior of the regulatory network described in Fig. 1 to understand how miR-9 controls the Hes1 dynamics. Thus, to explore the effect of different feedback loops present in the network, we have intuitively chosen the remaining parameters in the model by maintaining a reasonable



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expression level of different components in the model. Details of stochastic simulation approach are given in Table S3 and in the method section.

Table 1: Equations governing miR-9/Hes1 interaction network d1 = + − %& ∙ 1 dt + ( . P + . P + . P + . P" )

−( ∙ miR ∙ 1 + , ∙ Mpi

!

1

dH10 = ∙ H10 − % ∙ H10 − ∙ H10 dt ∙ 123456 − GP − P" − P − P − P − 189 − 289 − 389 < + ∙ GP ∙ H60 2

dGP = ∙ H10 ∙ 123456 − GP − P" − P − P − P − 189 − 289 − 389 < − ∙ GP dt ∙ H60 − ∙ GP + > ∙ P" + & ∙ 189 − ∙ GP ∙ X 3 %9@A

%6

= ∙ GP − > ∙ P" − & ∙ P" + ∙ P − &" ∙ P" ∙ X + " ∙ 289 4

dP = & ∙ P" − ∙ P − & ∙ P + ∙ P − &" ∙ P ∙ X + " ∙ 389 dt dP = & ∙ P − ∙ P − & ∙ P + ∙ P dt

5

6

dP = & ∙ P − ∙ P dt

7

d389 = &" ∙ P ∙ X − " ∙ 389 − & ∙ 389 + ∙ 289 dt

8

d289 = &" ∙ P" ∙ X − " ∙ 289 − & ∙ 289 + ∙ 189 + & ∙ 389 − ∙ 289 dt d189 = & ∙ 289 − ∙ 189 − & ∙ 189 + ∙ GP ∙ X dt

9

10

dH106 % ∙ H10 ∙ H60 = 0 ∙ 1 + 0, ∙ Mpi − % " ∙ H10 − dt + H10 dH60 = B − %B ∙ H60 dt

11

12

dPrim ∙ Prim = + − − %0 D D D dt 1 + , ∙ I " + ( . P + . P + .P + . P" ) ∙ Prim

!

13


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dmiR ∙ Prim = − ( ∙ miR ∙ 1 + , ∙ Mpi + 0, ∙ Mpi − % ∙ miR dt 1 + , ∙ I dMpi = ( ∙ miR ∙ 1 − , ∙ Mpi − 0, ∙ Mpi dt

14

15

H10 = 1H106 − 2 ∙ H10 − 2 ∙ GP − 2 ∙ P" − 2 ∙ P − 2 ∙ P − 2 ∙ P − 2 ∙ 189 − 2 ∙ 289 − 2 ∙ 389

Alteration in MicroRNA Expression Governs the Nature and Timing of

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