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PIAS1 Regulates Mutant Huntingtin Accumulation and Huntington's Disease-Associated Phenotypes In Vivo.

Neuron | May 4, 2016

The disruption of protein quality control networks is central to pathology in Huntington's disease (HD) and other neurodegenerative disorders. The aberrant accumulation of insoluble high-molecular-weight protein complexes containing the Huntingtin (HTT) protein and SUMOylated protein corresponds to disease manifestation. We previously identified an HTT-selective E3 SUMO ligase, PIAS1, that regulates HTT accumulation and SUMO modification in cells. Here we investigated whether PIAS1 modulation in neurons alters HD-associated phenotypes in vivo. Instrastriatal injection of a PIAS1-directed miRNA significantly improved behavioral phenotypes in rapidly progressing mutant HTT (mHTT) fragment R6/2 mice. PIAS1 reduction prevented the accumulation of mHTT and SUMO- and ubiquitin-modified proteins, increased synaptophysin levels, and normalized key inflammatory markers. In contrast, PIAS1 overexpression exacerbated mHTT-associated phenotypes and aberrant protein accumulation. These results confirm the association between aberrant accumulation of expanded polyglutamine-dependent insoluble protein species and pathogenesis, and they link phenotypic benefit to reduction of these species through PIAS1 modulation.

Pubmed ID: 27146268 RIS Download

Mesh terms: Animals | Brain | Disease Models, Animal | Humans | Huntingtin Protein | Huntington Disease | Mice | Mutation | Nerve Tissue Proteins | Neurons | Nuclear Proteins | Phenotype | Protein Inhibitors of Activated STAT | Small Ubiquitin-Related Modifier Proteins

Data used in this publication

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Associated grants

  • Agency: NINDS NIH HHS, Id: R56 NS090390
  • Agency: NINDS NIH HHS, Id: R01 NS072453
  • Agency: NINDS NIH HHS, Id: R01 NS052789
  • Agency: NINDS NIH HHS, Id: R01 NS076631
  • Agency: NCI NIH HHS, Id: P30 CA062203
  • Agency: NINDS NIH HHS, Id: R01 NS090390

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Exploratory Software for Confidence Intervals

ESCI (pronounced ''esky'') is a set of interactive simulations that run under Microsoft Excel. The set of Excel files lets students explore some key concepts in statistical and quantitative reasoning, including effect sizes, confidence intervals, and meta analysis. These are key concepts in the neuroscience toolkit, but can be particularly difficult to foster in undergraduate and graduate students. With ESCI you can: * explore many Confidence Interval (CI) concepts * calculate and display CIs for your own data, for some simple designs * calculate CIs for Cohen''s standardized effect size d * explore noncentral t distributions and their role in statistical power * use CIs for simple meta-analysis, using original or standardized units * explore all these concepts via vivid interactive graphical simulations. The simulations are free, excellent, and come with detailed supporting materials. Cumming also has a printed textbook companion which is quite good. Cumming, G. (2012). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York: Routledge * Explains estimation, with many examples. * Designed for any discipline that uses statistical significance testing. * For advanced undergraduate and graduate students, and researchers. * Comes with free ESCI software. * May be the first evidence-based statistics textbook. * Assumes only prior completion of any intro statistics course. * See the dance of the confidence intervals, and many other intriguing things. The Excel files truly stand on their own, though, with good feedback from their use in a basic stats class for psych and neuroscience majors. Finally, if you are like me and learned the ''old school'' statistics of null-hypothesis testing, here''s a great article by Gerd Gigerenzer to kick you into the ''new school'' of confidence intervals and effect sizes. Learn the dirty secrets of null hypothesis testing: Gigerenzer G, Kraus S & Vitouch O. (2004). The null ritual: What you always wanted to know about significance testing but were afraid to ask. The Sage Handbook of Quantitative Methodology for the Social Sciences, p. 391-408: http://library.mpib-berlin.mpg.de/ft/gg/GG_Null_2004.pdf

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