# Network Analysis Identifies Crosstalk Interactions Governing TGF-β Signaling Dynamics during Endoderm Differentiation of Human Embryonic Stem Cells

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## Abstract

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## 1. Introduction

## 2. Methods

#### 2.1. Cell Culture and Treatment

#### 2.1.1. Human ESC Maintenance

_{2}before passaging. Cells were examined under the microscope every day and colonies with observable differentiation were picked and removed before the media changes. The maintenance protocol was adopted from our previous studies [2,9,10].

#### 2.1.2. Experimental Induction of Endoderm from hESCs

#### 2.1.3. Measuring Experimental Dynamics of Signaling Molecules

#### 2.2. Identification of Network Interactions from Experimental Time Series Signaling Data

#### 2.2.1. Details of DBN Algorithm

^{®}(Natick, MA, USA) on Linux 64-bit platform and single core of INTEL

^{®}(Santa Clara, CA, USA) Core™ 2 Quad CPU (Q8400 @ 2.66 GHz).

#### 2.2.2. Constructing the DBNs

## 3. Results and Discussion

#### 3.1. Dynamics of Signaling Molecules during Endoderm Induction

**Figure 1.**Dynamics of key molecules from the TGF-β/SMAD, PI3K/AKT and MAPK/ERK pathways for two endoderm induction conditions. (

**A**–

**F**) p-AKT, t-TGFβRII, p-SMAD2, p-SMAD3, t-SMAD4 and p-ERK dynamics under high and low PI3K conditions. H1 hESCs were treated with 100 ng/mL Activin A in the presence or absence of 1 μM Wortmannin (PI3K inhibitor) for 24 h. The protein levels were quantified using Multiplex MagPix Assay. The mean and standard deviation (number of repeats = 3) in protein levels are represented here as fold change over time 0 levels.

#### 3.2. Predictions of Network Interactions by DBN Analysis on Entire Time Series

#### 3.2.1. DBN Analysis and Consensus Graph

**Figure 2.**Dynamic Bayesian Networks inferred for endoderm induction conditions (

**A**) Consensus graph for high PI3K data. The thickness of the edges reflects the value of edge probabilities (≥0.5); (

**B**) Marginal edge probability table for high PI3K data. The parent node is the node whose value at time step (t − 1) affects the value of child node at time step t; (

**C**) Consensus graph for low PI3K data; (

**D**) Marginal edge probability table for low PI3K data.

#### 3.2.2. High PI3K Condition

#### 3.2.3. Low PI3K Condition

#### 3.2.4. Comparison between Digraphs of High and low PI3K Conditions

#### Influence of Total Receptor Levels

#### Interactions between Intracellular Molecules

#### 3.2.5. Change-points Inferred by cpBGe Model

**Figure 3.**Co-allocation matrices for the high and low PI3K time series. (

**A**) High PI3K condition; (

**B**) Low PI3K condition. The axes represent time step. The actual time values corresponding to the time step are given below the plots. The black/white shading indicates the posterior probability of two time points being assigned to the same mixture component, ranging from 0 (black) to 1 (white). As seen from the figure, there are several time segments inferred from the data, 4 for the high PI3K condition and 3–5 for the low PI3K condition. All nodes show identical change-point behavior (data not shown), although this was not pre-fixed in the algorithm. The crosses indicate the time segments selected for network inference in different time zones.

#### 3.3. Changes in Regulatory Structure across Time Zones

#### 3.3.1. High PI3K Condition

**Figure 4.**Dynamic Bayesian Network inferred for endoderm induction conditions under different time zones and high PI3K. (

**A**) Consensus graph for high PI3K data, early dynamics (t = 0.5, 1, 1.5 h); (

**B**) Marginal edge probability table for high PI3K data, early dynamics; (

**C**) Consensus graph for high PI3K data, late dynamics (t = 6, 12, 18 h); (

**D**) Marginal edge probability table for high PI3K data, late dynamics.

#### 3.3.2. Low PI3K Condition

#### 3.4. Correlation between Molecule Pairs in the Early and Late Time Zones

**Figure 5.**Dynamic Bayesian Network inferred for endoderm induction conditions under different time zones and low PI3K. (

**A**) Consensus graph for low PI3K data, early dynamics (t = 0.5, 1, 1.5 h); (

**B**) Marginal edge probability table for low PI3K data, early dynamics; (

**C**) Consensus graph for low PI3K data, late dynamics (t = 12, 18, 24 h); (

**D**) Marginal edge probability table for low PI3K data, late dynamics.

#### 3.4.1. Influence of Total Receptor Levels

#### 3.4.2. Intracellular Regulation by p-AKT

**Figure 6.**Correlation tables for high and low PI3K condition. (

**A**) Receptor mediated regulation; (

**B**) p-AKT mediated regulation; (

**C**) p-ERK mediated regulation. The Pearson correlation is calculated between the parent nodes at time step (t − 1) and all other nodes at time step t. The early time points 0.5, 1, 1.5 h (both conditions) and the late time points correspond to 6, 12, and 18 for high PI3K and 12, 18, 24 for low PI3K. The average correlation coefficients across 2 repeats (selected in Figure 3) are used for high and low PI3K. However, for the low PI3K late time points, only repeat 3 is used.

#### 3.4.3. Intracellular Regulation by p-ERK

#### 3.5. Network Regulation during Endoderm Differentiation

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Nomenclature

DBN | Dynamic Bayesian Network |

DE | Definitive Endoderm |

hESCs | Human Embryonic Stem Cells |

p-SMAD2 | phosphorylated SMAD2 |

p-SMAD3 | phosphorylated SMAD3 |

p-AKT | phosphorylated AKT |

p-ERK | phosphorylated ERK |

TGFβ | Transforming growth factor-beta |

t-TGFβRII | total TGFβ receptor 2 |

t-SMAD4 | total SMAD4 |

## Conflicts of Interest

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**MDPI and ACS Style**

Mathew, S.; Sundararaj, S.; Banerjee, I.
Network Analysis Identifies Crosstalk Interactions Governing TGF-β Signaling Dynamics during Endoderm Differentiation of Human Embryonic Stem Cells. *Processes* **2015**, *3*, 286-308.
https://doi.org/10.3390/pr3020286

**AMA Style**

Mathew S, Sundararaj S, Banerjee I.
Network Analysis Identifies Crosstalk Interactions Governing TGF-β Signaling Dynamics during Endoderm Differentiation of Human Embryonic Stem Cells. *Processes*. 2015; 3(2):286-308.
https://doi.org/10.3390/pr3020286

**Chicago/Turabian Style**

Mathew, Shibin, Sankaramanivel Sundararaj, and Ipsita Banerjee.
2015. "Network Analysis Identifies Crosstalk Interactions Governing TGF-β Signaling Dynamics during Endoderm Differentiation of Human Embryonic Stem Cells" *Processes* 3, no. 2: 286-308.
https://doi.org/10.3390/pr3020286