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MicroRNA signatures of endogenous Huntingtin CAG repeat expansion in mice

Author

Listed:
  • Peter Langfelder
  • Fuying Gao
  • Nan Wang
  • David Howland
  • Seung Kwak
  • Thomas F Vogt
  • Jeffrey S Aaronson
  • Jim Rosinski
  • Giovanni Coppola
  • Steve Horvath
  • X William Yang

Abstract

In Huntington's disease (HD) patients and in model organisms, messenger RNA transcriptome has been extensively studied; in contrast, comparatively little is known about expression and potential role of microRNAs. Using RNA-sequencing, we have quantified microRNA expression in four brain regions and liver, at three different ages, from an allelic series of HD model mice with increasing CAG length in the endogenous Huntingtin gene. Our analyses reveal CAG length-dependent microRNA expression changes in brain, with 159 microRNAs selectively altered in striatum, 102 in cerebellum, 51 in hippocampus, and 45 in cortex. In contrast, a progressive CAG length-dependent microRNA dysregulation was not observed in liver. We further identify microRNAs whose transcriptomic response to CAG length expansion differs significantly among the brain regions and validate our findings in data from a second, independent cohort of mice. Using existing mRNA expression data from the same animals, we assess the possible relationships between microRNA and mRNA expression and highlight candidate microRNAs that are negatively correlated with, and whose predicted targets are enriched in, CAG-length dependent mRNA modules. Several of our top microRNAs (Mir212/Mir132, Mir218, Mir128 and others) have been previously associated with aspects of neuronal development and survival. This study provides an extensive resource for CAG length-dependent changes in microRNA expression in disease-vulnerable and -resistant brain regions in HD mice, and provides new insights for further investigation of microRNAs in HD pathogenesis and therapeutics.

Suggested Citation

  • Peter Langfelder & Fuying Gao & Nan Wang & David Howland & Seung Kwak & Thomas F Vogt & Jeffrey S Aaronson & Jim Rosinski & Giovanni Coppola & Steve Horvath & X William Yang, 2018. "MicroRNA signatures of endogenous Huntingtin CAG repeat expansion in mice," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
  • Handle: RePEc:plo:pone00:0190550
    DOI: 10.1371/journal.pone.0190550
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    References listed on IDEAS

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    1. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
    2. Karen P. Thiebes & Heejin Nam & Xiaolu A. Cambronne & Rongkun Shen & Stacey M. Glasgow & Hyong-Ho Cho & Ji-sun Kwon & Richard H. Goodman & Jae W. Lee & Seunghee Lee & Soo-Kyung Lee, 2015. "miR-218 is essential to establish motor neuron fate as a downstream effector of Isl1–Lhx3," Nature Communications, Nature, vol. 6(1), pages 1-15, November.
    3. Kun Wang & Bo Long & Jian-Qin Jiao & Jian-Xun Wang & Jin-Ping Liu & Qian Li & Pei-Feng Li, 2012. "miR-484 regulates mitochondrial network through targeting Fis1," Nature Communications, Nature, vol. 3(1), pages 1-9, January.
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