期刊
PHYSIOLOGICAL GENOMICS
卷 46, 期 6, 页码 195-206出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/physiolgenomics.00151.2013
关键词
RNA-Seq; porcine; hypothalamus; growth; fatness
资金
- Ministerio de Ciencia e Innovacion (MICINN) [AGL2011-29821-C02]
- Spanish Ministerio de Ciencia e Innovacion [BES-2009-025417]
- European Research Council [ERC-2009-AdG:249894]
Previous studies on Iberian x Landrace (IBMAP) pig intercrosses have enabled the identification of several quantitative trait locus (QTL) regions related to growth and fatness traits; however, the genetic variation underlying those QTLs are still unknown. These traits are not only relevant because of their impact on economically important production traits, but also because pig constitutes a widely studied animal model for human obesity and obesity-related diseases. The hypothalamus is the main gland regulating growth, food intake, and fat accumulation. Therefore, the aim of this work was to identify genes and/or gene transcripts involved in the determination of growth and fatness in pig by a comparison of the whole hypothalamic transcriptome (RNA-Seq) in two groups of phenotypically divergent IBMAP pigs. Around 16,000 of the similar to 25.010 annotated genes were expressed in these hypothalamic samples, with most of them showing intermediate expression levels. Functional analyses supported the key role of the hypothalamus in the regulation of growth, fat accumulation, and energy expenditure. Moreover, 58,927 potentially new isoforms were detected. More than 250 differentially expressed genes and novel transcript isoforms were identified between the two groups of pigs. Twenty-one DE genes/transcripts that colocalized in previously identified QTL regions and/or whose biological functions are related to the traits of interest were explored in more detail. Additionally, the transcription factors potentially regulating these genes and the subjacent networks and pathways were also analyzed. This study allows us to propose strong candidate genes for growth and fatness based on expression patterns, genomic location, and network interactions.
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