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

  • Martin Stroedicke
  • , Yacine Bounab
  • , Nadine Strempel
  • , Konrad Klockmeier
  • , Sargon Yigit
  • , Ralf P. Friedrich
  • , Gautam Chaurasia
  • , Shuang Li
  • , Franziska Hesse
  • , Sean Patrick Riechers
  • , Jenny Russ
  • , Cecilia Nicoletti
  • , Annett Boeddrich
  • , Thomas Wiglenda
  • , Christian Haenig
  • , Sigrid Schnoegl
  • , David Fournier
  • , Rona K. Graham
  • , Michael R. Hayden
  • , Stephan Sigrist
  • Gillian P. Bates, Josef Priller, Miguel A. Andrade-Navarro, Matthias E. Futschik, Erich E. Wanker*
*Corresponding author for this work
  • Max Delbrück Center for Molecular Medicine in the Helmholtz Association
  • Humboldt University of Berlin
  • Charité Universitätsmedizin Berlin
  • University of British Columbia
  • Free University of Berlin
  • King's College London
  • University of Algarve

Research output: Contribution to journalArticlepeer-review

Abstract

<jats:p>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.</jats:p>
Original languageEnglish
Pages (from-to)701-713
Number of pages0
JournalGenome Research
Volume25
Issue number5
Early online date23 Apr 2015
DOIs
Publication statusPublished - May 2015

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