Skip to main content
eScholarship
Open Access Publications from the University of California

S-Nitrosylation of parkin as a novel regulator of p53-mediated neuronal cell death in sporadic Parkinson¿s disease

  • Author(s): Sunico, Carmen R
  • Nakamura, Tomohiro
  • Rockenstein, Edward
  • Mante, Michael
  • Adame, Anthony
  • Chan, Shing
  • Newmeyer, Traci
  • Masliah, Eliezer
  • Nakanishi, Nobuki
  • Lipton, Stuart A
  • et al.
Abstract

Abstract Background Mutations in the gene encoding parkin, a neuroprotective protein with dual functions as an E3 ubiquitin ligase and transcriptional repressor of p53, are linked to familial forms of Parkinson’s disease (PD). We hypothesized that oxidative posttranslational modification of parkin by environmental toxins may contribute to sporadic PD. Results We first demonstrated that S-nitrosylation of parkin decreased its activity as a repressor of p53 gene expression, leading to upregulation of p53. Chromatin immunoprecipitation as well as gel-shift assays showed that parkin bound to the p53 promoter, and this binding was inhibited by S-nitrosylation of parkin. Additionally, nitrosative stress induced apoptosis in cells expressing parkin, and this death was, at least in part, dependent upon p53. In primary mesencephalic cultures, pesticide-induced apoptosis was prevented by inhibition of nitric oxide synthase (NOS). In a mouse model of pesticide-induced PD, both S-nitrosylated (SNO-)parkin and p53 protein levels were increased, while administration of a NOS inhibitor mitigated neuronal death in these mice. Moreover, the levels of SNO-parkin and p53 were simultaneously elevated in postmortem human PD brain compared to controls. Conclusions Taken together, our data indicate that S-nitrosylation of parkin, leading to p53-mediated neuronal cell death, contributes to the pathophysiology of sporadic PD.

Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.

Main Content
Current View