Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

  1. Erich E. Wanker1
  1. 1Max Delbrueck Center for Molecular Medicine, 13125 Berlin, Germany;
  2. 2Institute of Theoretical Biology, Humboldt University of Berlin, 10115 Berlin, Germany;
  3. 3Department of Neuropsychiatry, Charité-Universitaetsmedizin Berlin, 10117 Berlin, Germany;
  4. 4Center for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia V5Z 4H4, Canada;
  5. 5Institute of Biology/Genetics, Free University Berlin, 14195 Berlin, Germany;
  6. 6Department of Medical and Molecular Genetics, King's College London, London SE1 9RT, United Kingdom;
  7. 7Centre for Molecular and Structural Biomedicine, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal
  1. Corresponding author: ewanker{at}mdc-berlin.de
  1. 8 These authors contributed equally to this work.

Abstract

Assemblies of huntingtin (HTT) fragments with expanded polyglutamine (polyQ) tracts are a pathological hallmark of Huntington's disease (HD). The molecular mechanisms by which these structures are formed and cause neuronal dysfunction and toxicity are poorly understood. Here, we utilized available gene expression data sets of selected brain regions of HD patients and controls for systematic interaction network filtering in order to predict disease-relevant, brain region-specific HTT interaction partners. Starting from a large protein–protein interaction (PPI) data set, a step-by-step computational filtering strategy facilitated the generation of a focused PPI network that directly or indirectly connects 13 proteins potentially dysregulated in HD with the disease protein HTT. This network enabled the discovery of the neuron-specific protein CRMP1 that targets aggregation-prone, N-terminal HTT fragments and suppresses their spontaneous self-assembly into proteotoxic structures in various models of HD. Experimental validation indicates that our network filtering procedure provides a simple but powerful strategy to identify disease-relevant proteins that influence misfolding and aggregation of polyQ disease proteins.

Footnotes

  • Received August 1, 2014.
  • Accepted March 11, 2015.

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