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Regulation of mitophagy by the NSL complex underlies genetic risk for Parkinson's disease at 16q11.2 and MAPT H1 loci.

Marc P M SoutarDaniela MelandriBenjamin O'CallaghanEmily AnnuarioAmy E MonaghanNatalie J WelshKarishma D'SaSebastian GuelfiDavid ZhangAlan PittmanDaniah TrabzuniAnouk H A VerbovenKylie S PanDemis A KiaMagda BictashSonia GandhiHenry HouldenMark R CooksonNael Nadif KasriNicholas W WoodAndrew B SingletonJohn HardyPaul J WhitingCornelis BlauwendraatAlexander J WhitworthClaudia ManzoniMina RytenPatrick A LewisAlexander J Whitworth
Published in: Brain : a journal of neurology (2022)
Parkinson's disease is a common incurable neurodegenerative disease. The identification of genetic variants via genome-wide association studies has considerably advanced our understanding of the Parkinson's disease genetic risk. Understanding the functional significance of the risk loci is now a critical step towards translating these genetic advances into an enhanced biological understanding of the disease. Impaired mitophagy is a key causative pathway in familial Parkinson's disease, but its relevance to idiopathic Parkinson's disease is unclear. We used a mitophagy screening assay to evaluate the functional significance of risk genes identified through genome-wide association studies. We identified two new regulators of PINK1-dependent mitophagy initiation, KAT8 and KANSL1, previously shown to modulate lysine acetylation. These findings suggest PINK1-mitophagy is a contributing factor to idiopathic Parkinson's disease. KANSL1 is located on chromosome 17q21 where the risk associated gene has long been considered to be MAPT. While our data does not exclude a possible association between the MAPT gene and Parkinson's disease, it provides strong evidence that KANSL1 plays a crucial role in the disease. Finally, these results enrich our understanding of physiological events regulating mitophagy and establish a novel pathway for drug targeting in neurodegeneration.
Keyphrases
  • genome wide
  • dna methylation
  • emergency department
  • gene expression
  • transcription factor
  • big data
  • single cell
  • amino acid
  • genome wide analysis