Computational Inference of Gene Regulation from Whole-Transcriptome Analysis of Early Embryos

Computational Inference of Gene Regulation from Whole-Transcriptome Analysis of Early Embryos

Sung-Joon Park (The University of Tokyo, Japan) and Kenta Nakai (The University of Tokyo, Japan)
DOI: 10.4018/978-1-5225-0353-8.ch007
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

To ensure totipotency of new zygotes, immediately after fertilization, two gametes activate genetic and epigenetic processes that highly interplay and lead to sequential waves of gene expression. Since the first report of fertilization a hundred years ago, a long-standing question to what factors govern the dramatic transcriptional changes remains elusive. Recently, significant advances in biological experiment technology and computational methodology have taken place in understanding of the regulatory dynamics during fertilization. This chapter provides an overview of the recent progress in characterizing mammalian fertilization, and focuses on the computational methods applicable to the inference of regulation for understanding early embryo development. In particular, this chapter introduces the linear regression modeling and log-linear graphical modeling to identify potential key regulators in the higher-order conditional distribution where statistical paradox often occurs.
Chapter Preview
Top

Background

Fertilization is the developmental process that is responsible for the maintenance of the species. Fertilization is also to be an error-prone process; in human, 10−30% of fertilized oocytes are aneuploidy causative of birth defects such as Down syndrome (Hassold & Hunt, 2001). Spermatozoa also show abnormalities during cell divisions frequently, which is a reason of infertile male (Egozcue et al., 2005). Understanding why such abnormalities occur and how we discriminate them are needed to prevent diseases and to improve therapeutic applications. Moreover, identifying key molecules of fertilization enhances the efficiency of reprogramming of somatic cells into pluripotent stem cells (Maekawa et al., 2011; Shinagawa et al., 2014; Xu et al., 2015).

Recent technical advances in developmental biology established the global gene expression profile that is often referred to as “programmed waves” (Hamatani, Carter, Sharov, & Ko, 2004; Ko et al., 2000; S. J. Park et al., 2013). Speculations on the gene expression waves using database systems, such as DBTMEE (S. J. Park, Shirahige, Ohsugi, & Nakai, 2015), unveiled the existence of maternal-to-zygotic transition (MZT) that consists of the degradation of maternal factors and the onset of zygotic gene activation (ZGA) (Schultz, 2002). It is thought that maternal proteins and RNAs are necessary for the oocyte maturation and chromatin remodeling, but become harmful to development (M. T. Lee, Bonneau, & Giraldez, 2014; H. Wang & Dey, 2006). Sperm contribution is indispensable to achieve the proper and timely removal and replacement of maternal factors (S. J. Park et al., 2013).

A long-standing question on embryo development is what factors govern the dramatic transcriptional changes immediately after fertilization. Even though available information increased, the regulatory mechanism underlying the sequential waves of gene expression still remains unknown. In this regard, significant recent advances in biological experiment technology and computational methodology have taken place in understanding gene regulatory dynamics. These include high-throughput sequencing for transcriptomes and high-performance reverse engineering algorithms. There is no doubt that these advances help establishing the profile of regulatory dynamics, which gives a clue to the answer.

In the remainder of this chapter, we start by reviewing the latest progress in the contexts of mammalian embryo development. One may be surprised about the current state of huge quantity of knowledge regarding fertilization. We then review the computational methods to infer gene regulatory networks, which mainly focus on the linear regression approach coupled with log-linear graphical model to identify potential key regulators in the higher-order conditional distribution. This chapter will explore the usage of computational inferences that gives shed light on the regulatory dynamics during fertilization.

Complete Chapter List

Search this Book:
Reset