Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/28401
Title: Transcriptomics of Early Human Development
Authors: NAHENIWELA HERATH MUDIYANSELAGE WISHVA BANDARA HERATH
Keywords: RNA-Seq, Trophoblast, Development, Sequencing, Transcriptomics
Issue Date: 5-May-2011
Source: NAHENIWELA HERATH MUDIYANSELAGE WISHVA BANDARA HERATH (2011-05-05). Transcriptomics of Early Human Development. ScholarBank@NUS Repository.
Abstract: In this thesis I present my attempt to further the knowledge on early human development with emphasis on trophoblast lineage, using RNA-Sequencing (RNA-Seq) technology. RNA-Seq leverages on high throughput next generation sequencing to profile entire transcriptomes with extreme sensitivity and resolution, providing data superior to that of conventional methods available for measuring gene expression. Three major RNA-Seq datasets are presented in this thesis. The first dataset contains information on transcriptomic dynamics of poly A mRNA from a time-course experiment with five time-points (day 0, 2 4 6 and 8), where human embryonic stem cells were differentiated along the trophoblast lineage using an improved differentiation protocol. The second dataset contains transcriptomic data of smallRNA (all RNA transcripts less than 200 nucleotides) during the first three time-points of the above mentioned differentiation protocol. The third dataset is on mouse early development and contains information on the transcriptomes of the 8-cell stage embryo, E3.5 blastocyst, E4.5 blastocyst and E4.5 inner cell mass. This mouse preimplantation dataset is used in a comparative capacity to find molecular mechanisms which are specific to the human system. As an early adapter of the RNA-Seq technology during a time where there were no proper analysis software available, I created a series of programatic workflows in the form of scripts, written using the python programming language, meant to simplify the analysis of RNA-Seq data and to easily identify transcriptomic events such as alternative splicing, novel exon - exon junctions, exon extensions and expression of novel transcripts. These workflows together with the results they provide are also presented in this thesis. Using RNA-Seq datasets and results of programatic workflows mentioned above, this thesis presents a comprehensive view on the transcriptomics of early human trophoblast differentiation. When comparing human and mouse preimplantation data, it was evident that the two systems have considerable differences at the transcriptome level concerning both the expression pattern and expression level of genes. This observation supports the hourglass model of development, where the species of the same animal phylum, for a brief period in their developmental timeline known as the phylotypic stage, show a remarkable similarity with each other, but show considerable differences during the rest of the developmental timeline. Trophoblast development occurs much earlier than the phylotypic stage and therefore shows great divergence in transcriptomics between mouse and human. This is important because it advocates the cautious extrapolation of biological observations made in the mouse system into human - as in the case of most data available for trophoblast differentiation. Looking at novel (i.e. unannotated) transcribed regions of the human genome identified by RNA-Seq, it was evident that trophoblast differentiation induces the expression of a large number of endogenous retroviral sequences. There are instances where these retroviral elements modify transcripts by acting as extra exons or as new promoters resulting in the expression of new transcripts. Therefore this thesis argues that retroviral elements are a major component responsible for the human / primate specific transcriptomic events in early development. Thus they are responsible for the interspecies diversity seen during the pre-phylotypic stages of development in human and mouse.
URI: http://scholarbank.nus.edu.sg/handle/10635/28401
Appears in Collections:Ph.D Theses (Open)

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