Transcriptome analysis of Brassica napus pod using RNA-Seq and identification of lipid-related candidate genes.
BACKGROUND
Brassica napus is an important oilseed crop. Dissection of the genetic architecture underlying oil-related biological processes will greatly facilitates the genetic improvement of rapeseed. The differential gene expression during pod development offers a snapshot on the genes responsible for oil accumulation in. To identify candidate genes in the linkage peaks reported previously, we used RNA sequencing (RNA-Seq) technology to analyze the pod transcriptomes of German cultivar Sollux and Chinese inbred line Gaoyou.


METHODS
The RNA samples were collected for RNA-Seq at 5-7, 15-17 and 25-27 days after flowering (DAF). Bioinformatics analysis was performed to investigate differentially expressed genes (DEGs). Gene annotation analysis was integrated with QTL mapping and Brassica napus pod transcriptome profiling to detect potential candidate genes in oilseed.


RESULTS
Four hundred sixty five and two thousand, one hundred fourteen candidate DEGs were identified, respectively, between two varieties at the same stages and across different periods of each variety. Then, 33 DEGs between Sollux and Gaoyou were identified as the candidate genes affecting seed oil content by combining those DEGs with the quantitative trait locus (QTL) mapping results, of which, one was found to be homologous to Arabidopsis thaliana lipid-related genes.


CONCLUSIONS
Intervarietal DEGs of lipid pathways in QTL regions represent important candidate genes for oil-related traits. Integrated analysis of transcriptome profiling, QTL mapping and comparative genomics with other relative species leads to efficient identification of most plausible functional genes underlying oil-content related characters, offering valuable resources for bettering breeding program of Brassica napus.


CONCLUSIONS
This study provided a comprehensive overview on the pod transcriptomes of two varieties with different oil-contents at the three developmental stages.
Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
Hai-Ming
HM
Xu
Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
Xiang-Dong
XD
Kong
State Key Laboratory Breeding Base for Zhejiang Sustainable Pest and Disease Control, Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, 198 Shiqiao Road, Hangzhou, 310021, China.
Fei
F
Chen
State Key Laboratory Breeding Base for Zhejiang Sustainable Pest and Disease Control, Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, 198 Shiqiao Road, Hangzhou, 310021, China.
Ji-Xiang
JX
Huang
Department of Biostatistics and Bioinformatics, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112-2632, USA. xlou@tulane.edu.
Xiang-Yang
XY
Lou
State Key Laboratory Breeding Base for Zhejiang Sustainable Pest and Disease Control, Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, 198 Shiqiao Road, Hangzhou, 310021, China. jyzhao3@yahoo.com.
Jian-Yi
JY
Zhao
D029688:Brassica napus / Q000235:genetics* / Q000378:metabolism; D002874:Chromosome Mapping; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D017343:Genes, Plant; D059014:High-Throughput Nucleotide Sequencing; D050356:Lipid Metabolism / Q000235:genetics*; D053858:Metabolic Networks and Pathways; D058977:Molecular Sequence Annotation; D040641:Quantitative Trait Loci; D059467:Transcriptome