Transcriptome profiling of the goose (Anser cygnoides) ovaries identify laying and broodiness phenotypes.

• Main Authors: Qi Xu, WenMing Zhao, Yang Chen, YiYu Tong, GuangHui Rong, ZhengYang Huang, Yang Zhang, GuoBing Chang, XinSheng Wu, GuoHong Chen
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2013-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC3566205?pdf=render

BACKGROUND: The geese have strong broodiness and poor egg performance. These characteristics are the key issues that hinder the goose industry development. Yet little is known about the mechanisms responsible for follicle development due to lack of genomic resources. Hence, studies based on high-throughput sequencing technologies are needed to produce a comprehensive and integrated genomic resource and to better understand the biological mechanisms of goose follicle development. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we performed de novo transcriptome assembly and gene expression analysis using short-read sequencing technology (Illumina). We obtained 67,315,996 short reads of 100 bp, which were assembled into 130,514 unique sequences by Trinity strategy (mean size = 753 bp). Based on BLAST results with known proteins, these analyses identified 52,642 sequences with a cut-off E-value above 10(-5). Assembled sequences were annotated with gene descriptions, gene ontology and clusters of orthologous group terms. In addition, we investigated the transcription changes during the goose laying/broodiness period using a tag-based digital gene expression (DGE) system. We obtained a sequencing depth of over 4.2 million tags per sample and identified a large number of genes associated with follicle development and reproductive biology including cholesterol side-chain cleavage enzyme gene and dopamine beta-hydroxylas gene. We confirm the altered expression levels of the two genes using quantitative real-time PCR (qRT-PCR). CONCLUSIONS/SIGNIFICANCE: The obtained goose transcriptome and DGE profiling data provide comprehensive gene expression information at the transcriptional level that could promote better understanding of the molecular mechanisms underlying follicle development and productivity.