GATA-4 promotes the differentiation of P19 cells into cardiac myocytes

Hu, Deliang; Chen, Fukun; Liu, Yao-Qiu; Sheng, Yu; Yang, Rong; Kong, Xiangqing; Cao, Kejiang; Gu, Haitao; Qian, Lei

doi:10.3892/ijmm_00000474

Cited by 11 publications

(1 citation statement)

References 23 publications

(27 reference statements)

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“…Somewhat ironically the so-called Michaelis-Menten reaction has not been properly described by Michaelis-Menten. Alternative methods exist to recover the steady state MM equation in addition to those reviewed here, including for example the chemical master equation [18], but the methods listed above provide the main recipes for modeling first order biochemical networks, which are the basics of systems biology.…”

Section: Resultsmentioning

confidence: 99%

Seven competing ways to recover the Michaelis–Menten equation reveal the alternative approaches to steady state modeling

Michel

Ruelle

2013

J Math Chem

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The Michaelis-Menten enzymatic reaction is sufficient to perceive many subtleties of network modeling, including the concentration and time scales separations, the formal equivalence between bulk phase and single-molecule approaches, or the relationships between single-cycle transient probabilities and steady state rates. Seven methods proposed by different authors and yielding the same famous Michaelis-Menten equation, are selected here to illustrate the kinetic and probabilistic use of rate constants and to review basic techniques for handling them. Finally, the general rate of an ordered multistep reaction, of which the Michaelis-Menten reaction is a particular case, is deduced from a Markovian approach. Preliminary tools: the helpful scale separationsThe approximation of concentration and time scales separations is often reasonable in cellular biochemistry, but some discernment is necessary for its proper application, in particular to define pseudo-first order constants and decide which reactions can be considered as in quasiequilibrium compared to others. Concentration scale separationThe wide differences of molecular concentrations in the cell greatly facilitate network modeling. The concentration of the more concentrated reactant, generally called the ligand or the substrate in enzymology, can be associated to second-order constants to give so-called pseudofirst order constants. This approximation strongly simplifies elementary treatments, for example to define hyperbolic saturation functions through equating the concentrations of total and free ligand. To apply the concentration scale separation, it is important to decide which reactant should be fused to the second-order constant. Depending on the cases, the same molecule can behave as either the leading macromolecule or as the ligand. This is the case, for example, for a transcription factor, say the estrogen receptor (ER), activated by the estrogen hormone (E2) and then capable of binding to a given unique gene (G) from the X chromosome. Even if ERs are not very numerous in the cell, they are however much more abundant than the single gene. Hence, ER can be considered as a diffusible ligand whose concentration varies slowly compared to the dynamics of its interaction with the gene. Conversely, when studying the activation of ER by E2, the ligand is now E2, which should be integrated in pseudo-first order rates of ER state changes. If the binding of E2 to ER and the binding of ER to G are to be mixed in the same model, a new approximation intervenes: the time scale separation. Time scale separationIn the example introduced above, the interactions between E2 and ER can be considered as more dynamic than those occuring between ER and G. Time scale separation is particularly important to obtain smooth graded interactions between a ligand and a very unique binding site [1]. This approximation allows kinetic and equilibrium constants to coexist in the same equation, as long proposed [2]. Once built, the first-order network can be treated through different m...

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Section: Resultsmentioning

confidence: 99%

Seven competing ways to recover the Michaelis–Menten equation reveal the alternative approaches to steady state modeling

Michel

Ruelle

2013

J Math Chem

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EGF is required for cardiac differentiation of P19CL6 cells through interaction with GATA-4 in a time- and dose-dependent manner

Song

Xiao

et al. 2014

Cell. Mol. Life Sci.

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The regulation of cardiac differentiation is critical for maintaining normal cardiac development and function. The precise mechanisms whereby cardiac differentiation is regulated remain uncertain. Here, we have identified a GATA-4 target, EGF, which is essential for cardiogenesis and regulates cardiac differentiation in a dose- and time-dependent manner. Moreover, EGF demonstrates functional interaction with GATA-4 in inducing the cardiac differentiation of P19CL6 cells in a time- and dose-dependent manner. Biochemically, GATA-4 forms a complex with STAT3 to bind to the EGF promoter in response to EGF stimulation and cooperatively activate the EGF promoter. Functionally, the cooperation during EGF activation results in the subsequent activation of cyclin D1 expression, which partly accounts for the lack of additional induction of cardiac differentiation by the GATA-4/STAT3 complex. Thus, we propose a model in which the regulatory cascade of cardiac differentiation involves GATA-4, EGF, and cyclin D1.

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Feed-back regulation of disabled-2 (Dab2) p96 isoform for GATA-4 during differentiation of F9 cells

Kim

Bae

Choi

et al. 2012

Biochemical and Biophysical Research Communications

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GATA-4 promotes the differentiation of P19 cells into cardiac myocytes

Cited by 11 publications

References 23 publications

Seven competing ways to recover the Michaelis–Menten equation reveal the alternative approaches to steady state modeling

Seven competing ways to recover the Michaelis–Menten equation reveal the alternative approaches to steady state modeling

EGF is required for cardiac differentiation of P19CL6 cells through interaction with GATA-4 in a time- and dose-dependent manner

Feed-back regulation of disabled-2 (Dab2) p96 isoform for GATA-4 during differentiation of F9 cells

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