Mining Key Regulators of Cell Reprogramming and Prediction Research Based on Deep Learning Neural Networks

Na Ta,Shuai Liu,Yongchun Zuo,Hanshuang Li

doi:10.1109/access.2020.2970442

Abstract

Deciphering the dynamic changes of core factors at different reprogramming stages plays an important role in elucidating the reprogramming mechanism of induced pluripotent stem cells (iPSCs) and improving their induction efficiency. The use of transcription factors (TFs) in combination with histone modification is vital to understand the multiple regulatory of pioneer factor. However, existing studies are not enough to consider the classification of stage-specific gene clusters from the perspective of multi-omic in the process of cell reprogramming. In this study, three stage-specific gene clusters of reprogramming initiation, maturation and stabilization phase were identified by using differential expression analysis. Considering the effects of regional binding preference, we further constructed a quantitative model on different genome regions (promoter, enhancer and enhancer subdivision region) by integrating the DNA binding profiles of Oct4 and three histone modifications (HMs). For promoter and enhancer regions, the receiver operating characteristic curve (Roc curve) of support vector machine (SVM) model was above 0.75 and predictive with the accuracy (Acc) about 66~69%. But on enhancer subdivision region, the convolutional neural network (CNN) model we constructed showed more faithful predictive performance than the model on promoter and enhancer, which Roc curve area can reach 0.87. Taken together, our studies quantitatively reveal the cooperative effects of TFs and HMs on reprogramming stage-specific gene clusters, hoping to provide new sights in mining the key regulators of reprogramming.

Highlights

Cell reprogramming is the biological process of reprogramming the differentiated somatic cell returns to a pluripotent state, or forms an embryonic stem cell line [1], or further develops into a new individual under specific conditions [2]
Differentiated somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs), which is of great significance in cell transplantation, preparation of disease cell models and drug screening [3]
support vector machine (SVM) was based on a machine learning library, and convolutional neural network (CNN) was based on the TensorFlow architecture

Summary

Introduction

Cell reprogramming is the biological process of reprogramming the differentiated somatic cell returns to a pluripotent state, or forms an embryonic stem cell line [1], or further develops into a new individual under specific conditions [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Leyi Wei. The temporal expression patterns of TFs determine whether differentiated somatic cells can return to pluripotent state [4], [5]. Under the comprehensive regulation of TFs, genes show dynamic expression patterns during reprogramming, which shows stage-specific expression of reprogramming initiation, maturation and stabilization phase [6]. Cell reprogramming is often co-regulated by multiple TFs and epigenetic modifications. TFs, as a core factor of the initial programming during

Methods

Results

Conclusion