E-GEOD-42980 - RNA sequencing analysis of abdominal adipose tissue in 7 week old juvenile broiler chickens divergently selected for abdominal fatness or leanness
Released on 16 April 2013, last updated on 4 May 2014
Chickens divergently selected for either high abdominal fat content (fat genotype) or low abdominal fat content (lean genotype) at Station Recherches Avicoles, Institut National de la Recherche Agronomique Nouzilly, France were used to profile abdominal fat gene expression at 7 weeks of age. The fat line (FL) and lean line (LL) chickens differ in various phenotypic and metabolic measurements, including abdominal fatness, plasma glycemia and triiodothyronine (T3). The FL and LL chickens represent unique models for characterizing biomedical and agricultural traits. Massively parallel RNA sequencing (RNA-Seq) was completed on an Illumina HiSeq 2000 System for transcription analysis of FL and LL abdominal fat. Statistical analysis was completed using CLC Genomics Workbench software. A total of 1,703 genes were differentially expressed in the FL versus LL adipose tissue [FDR<0.05 and fold change (FL/LL) > 1.2]. The differentially expressed genes include metabolic enzymes, acute phase proteins, growth factors, coagulation factors, immune factors, vasoregulators and transcription factors involved in various pathways. Several of the functional genes identified are also positional candidate genes within quantitative trait loci (QTL) in an F2 population created from an intercross of the FL and LL lines. Keywords: Divergently selected chickens, fatness, transcriptional profiling, differentially expressed genes Abdominal fat mRNA profiles of fat line (FL) and lean line (LL) chickens at 7 weeks of age were generated by deep sequencing (on an Illumina HiSeq 2000 system) employing several sequencing schemes to determine depth of coverage from 1, 4, and 8 multiplexed libraries per sequencing lane. Transcriptional analysis was completed by averaging short paired-end sequence reads (101 bp) for each bird across three sequencing depths.
RNA-seq of coding RNA
Larry Albert Cogburn <email@example.com>, C Chen, C H Wu, C W Resnyk, E L Bihan-Duval, H Huang, J Simon, L A Cogburn, M J Duclos