ASE cell fate simulation


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CSML1.9 version: ZIP | CSML [2007-06-05]
CSML3.0 version: CSML.GZ | CSML [2011-02-09] (work on CIO4.0 and CIO5.0)
Launch on CIOPlayer [2009-12-25]
Launch on CIO [2009-12-25]

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  • Created by Ayumu Saito.


We have applied Hybrid Functional Petri Net with Extention (HFPNe) to model regulatory networks that involve a new key regulator microRNA. As a test case, we selected the cell fate determination model of two gustatory neurons of Caenorhabditis elegans---ASE left (ASEL) and ASE right (ASER). These neurons are morphologically bilaterally symmetric but physically asymmetric in function. Johnston et al. have suggested that their cell fate is determined by the double-negative feedback loop involving the lsy-6 and mir-273 microRNAs. Our simulation model confirms their hypothesis. In addition, other well-known mutants that are related with the double-negative feedback loop are also well-modeled. The new upstream regulator of lsy-6 (lsy-2) that is mentioned in another paper is also integrated into this model for the mechanism of switching between ASEL and ASER without any contradictions.

MicroRNA in gene regulatory networks

MicroRNAs (miRNAs), which were first discovered by Wightman et al. in Caenorhabditis elegans (C. elegans) in 1993, have recently been found to be important factors in the gene regulatory network. miRNA is one of the RNAs that is transcribed from the genome (70mer) in the nucleus, processed by enzymatic cleavage, and finally produced as a small RNA (20--24mer) in the cytoplasm. The miRNA is matured by its incorporation into an RNA-Induced Silencing Complex (RISC). Depending on the type of miRNA incorporated, the RISC binds to specific mRNAs, degrades them, and accordingly inhibits their translation. For example, Wightman et al. reported that the lin-4 miRNA suppresses the lin-14 gene in C. elegans. For this functionality, it has been thought that the miRNA sequences should perfectly match those of the target mRNAs. Recently, it has been found that miRNA with some mismatched sequences can still suppress the translation. Thus, miRNAs will play an important role in studying the gene regulatory network.

DNFL with MicroRNAs

We concentrate attentions on that the new key factor miRNAs can be effectively handled with the HFPNe to other elements architecture, e.g., miRNA itself can be, its regulations to other elements can be modeled, and regulations can be modeled. In particular, we chose to model one of the complicated regulations mediated by miRNAs: a double-negative feedback loop (DNFL) of the lsy-6 and mir-273 miRNAs that will determine the ASE cell fates in C. elegans, i.e., whether the cells will be ASE left (ASEL) or ASE right (ASER). Johnston et al. proposed that the DNFL of these miRNAs determines whether the cells will be ASER or ASEL. The mechanism of differentiation of ASE cells based on the DNFL as follows. For the differentiation of these cells in C. elegans, an NKx-type homeobox gene cog-1 and a zinc-finger transcription factor die-1 are the part of key factors. The cog-1 and die-1 genes are gradually expressed and then promote the differentiation into the ASER and ASEL, respectively. For the differentiation, the cog-1 and mir-273 miRNAs regulate the mRNAs in the following manner. The cog-1 mRNA contains an lsy-6 complementary site in the 3' untranslated region. In contrast, the die-1 miRNA contains two mir-273 complementary sites in the 3' untranslated region. Thus, the actions of cog-1 and die-1 are inhibited under the abundance of lsy-6 and mir-273, respectively, and differentiation into ASER and ASEL cells cannot occur. In addition, die-1 promotes the expression of lsy-6 and cog-1 promotes the expression of mir-273. Thus, the loop formed by cog-1, mir-273, die-1, and lsy-6 is the DNFL (see Figure 1). However, this model was a qualitative one and quantitative aspects of the mechanism were missing. Thus, we created the quantitative ASER-ASEL model with HFPNe and discuss in this paper whether the model of Johnston et al. is in agreement with the in silico model.

Figure 1: Summary of the DNFL. The path involving the steps (1)-(4) forms the double-negative feedback loop. The activation of die-1 (4) leads to the activation of lsy-6 (1) and the suppression of cog-1 (2) and mir-273 (3). On the other hand, the activation of cog-1 (2) leads to the activation of mir-273 (3) and the suppression of die-1 (4) and lsy-6 (1).


The result of the simulation result of the new ASER-ASEL model is presented in Figure 2, where the concentration behaviors of lsy-6(C), cog-1(C), mir-273(C), die-1(C), gcy-7, gcy-6, flp-20, flp-4, gcy-22, and gcy-5 are observed with four initial concentrations of lsy-2(C), i.e., 1.00, 0.40, 0.36, and 0.0. If the initial concentration of lsy-2(C) is zero, the ASER reporter genes---gcy-22 and gcy-5---are expressed and the ASEL reporter genes---gcy-7, gcy-6, flp20, and flp4---are not expressed (Figure 2 (4)). In contrast, if the initial concentration of lsy-2(C) is high (1.0), the ASER reporter genes---gcy-22 and gcy-5---are not expressed and ASEL reporter genes---gcy-7, gcy-6, flp20, and flp-4---are expressed (Figure 2 (1)). With the in silico simulation of the ASER-ASEL model, the qualitative hypothesis of Johnston et al. that the lsy-2 should be the upstream regulator of the ASER-ASEL cell fate determination is confirmed quantitatively. In the in silico model, the initial concentration of lsy-2 that switches from ASER to ASEL is approximately 0.38. As shown in Figure 2 (2), if the initial value of lsy-2 is slightly high (0.40), the expression time of the ASER reporter genes is slower than that if this value is considerably high. However, the final concentrations of the reporter genes are not different between ASER and ASEL. On the other hand, if the initial value of lsy-2 is slightly low (0.36), the expression time of the ASEL reporter genes is slower than that if it is zero. However, the final concentrations of the reporter genes are not different between these two cells. Thus, we could quantitatively conclude that lsy-2 is a key regulator of ASER and ASEL cell fate determination.

Figure 2: Simulation results of the concentration behaviors of the proteins (lsy-2(C), cog-1(C), die-1(C), lim-6(C), gcy-7, gcy-6, gcy-22, gcy-5, flp-20, and flp-4) and miRNAs (lsy-6(C) and mir-273(C)).


  • A. Saito*, M. Nagasaki*, A. Doi, K. Ueno, S. Miyano (*equally contributed), Cell Fate Simulation Model of Gustatory Neurons with MicroRNAs Double-Negative Feedback Loop by Hybrid Functional Petri Net with Extension, Genome Informatics 17(1): 100–111 (2006) View


  • Johnston R J Jr, Hobert O, A novel C. elegans zinc finger transcription factor, lsy-2, required for the cell type-specific expression of the lsy-6 microRNA. Development. 2005;132(24):5451–60. PMID:16291785
  • Johnston R J Jr, Chang S, Etchberger J F, Ortiz C O, Hobert O. MicroRNAs acting in a double-negative feedback loop to control a neuronal cell fate decision. Proc Natl Acad Sci USA. 2005;102(35):12449–54. PMID:16099833
  • Chang S, Johnston R J Jr, Frokjaer-Jensen C, Lockery S, Hobert O. MicroRNAs act sequentially and asymmetrically to control chemosensory laterality in the nematode. Nature. 2004;430:785–9. PMID:15306811