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Dynamic Transcriptional Landscape of the Early Chick Embryo

Defining the dynamic transcriptome of the early embryo at high resolution would assist greatly in understanding vertebrate development. Here, we describe the dynamic transcription landscape of early chick embryo development using advanced single-molecule long-read isoform sequencing (Iso-Seq) and RN...

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Detalles Bibliográficos
Autores principales: Ren, Junxiao, Sun, Congjiao, Clinton, Michael, Yang, Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6751280/
https://www.ncbi.nlm.nih.gov/pubmed/31572727
http://dx.doi.org/10.3389/fcell.2019.00196
Descripción
Sumario:Defining the dynamic transcriptome of the early embryo at high resolution would assist greatly in understanding vertebrate development. Here, we describe the dynamic transcription landscape of early chick embryo development using advanced single-molecule long-read isoform sequencing (Iso-Seq) and RNA-Seq technology. Our transcriptomic profiling reflected the time course of chicken embryonic development from day 1 to day 8 of incubation, a period encompassing gastrulation, somitogenesis, and organogenesis. This analysis identified transcriptional isoforms, alternative splicing (AS) events, fusion transcripts, alternative polyadenylation (APA) sites, and novel genes. Our results showed that intron retention (IR) represented the most abundant AS type and displayed distinct features and dynamic modulation during development. Moreover, we constructed a high-resolution expression profile across embryonic development. Our combined expression dataset correlates distinct gene clusters with specific morphological changes, and provides the first framework for the molecular basis of early chicken embryogenesis. Analysis of gene expression in the developing chicken embryo highlighted the dynamic nature and complexity of the chicken transcriptome and demonstrated that dramatically increased IR events are associated with distinct gene sets.