4.5 Article

Quantitative genomics of voluntary exercise in mice: transcriptional analysis and mapping of expression QTL in muscle

期刊

PHYSIOLOGICAL GENOMICS
卷 46, 期 16, 页码 593-601

出版社

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/physiolgenomics.00023.2014

关键词

adiposity; body weight; eQTL; experimental evolution; wheel running

资金

  1. National Institutes of Health (NIH) [DK-076050]
  2. NIH [5T32MH-075854-04, DK-056350]

向作者/读者索取更多资源

Motivation and ability both underlie voluntary exercise, each with a potentially unique genetic architecture. Muscle structure and function are one of many morphological and physiological systems acting to simultaneously determine exercise ability. We generated a large (n = 815) advanced intercross line of mice (G(4)) derived from a line selectively bred for increased wheel running (high runner) and the C57BL/6J inbred strain. We previously mapped quantitative trait loci (QTL) contributing to voluntary exercise, body composition, and changes in body composition as a result of exercise. Using brain tissue in a subset of the G(4) (n = 244), we have also previously reported expression QTL (eQTL) colocalizing with the QTL for the higher-level phenotypes. Here, we examined the transcriptional landscape of hind limb muscle tissue via global mRNA expression profiles. Correlations revealed an similar to 1,168% increase in significant relationships between muscle transcript expression levels and the same exercise and body composition phenotypes examined previously in the brain. The exercise trait most often significantly correlated with gene expression in the brain was running duration while in the muscle it was maximum running speed. This difference may indicate that time spent engaging in exercise behavior may be more influenced by central (neurobiological) mechanisms, while intensity of exercise may be largely controlled by peripheral mechanisms. Additionally, we used subsets of cis-acting eQTL, colocalizing with QTL, to identify candidate genes based on both positional and functional evidence. We discuss three plausible candidate genes (Insig2, Prcp, Sparc) and their potential regulatory role.

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