bioRxivNeurosciencePreprint
Alzheimer mutations stabilize synaptotoxic γ-secretase-substrate complexes
Sujan Devkota, Rui ZhouiD, Vaishnavi Nagarajan, Masato MaesakoiD, Hung Nguyen DoiD, Arshad Ali NooraniiD, Caitlin Overmeyer, Sanjay BhattaraiiD, Justin T. DouglasiD, Anita Saraf, Yinglong MiaoiD, Brian D. AckleyiD, Yigong ShiiD, Michael S. WolfeiD
Abstract
Alzheimers disease is characterized pathologically by cerebral deposition of 42-residue amyloid {beta}-peptide (A{beta}42), proteolytically produced from amyloid precursor protein (APP) by {beta}- and {gamma}-secretases.1 Although mutations in APP and presenilin, the catalytic component of {gamma}-secretase, cause familial Alzheimers disease (FAD), a role for A{beta}42 as the primary disease driver has not been clearly established and remains controversial.2,3 Here we show through comprehensive analysis of the multi-step proteolysis of APP substrate C99 by {gamma}-secretase that FAD mutations are consistently deficient in early proteolytic events, not later events that produce secreted A{beta} peptides. Cryo-electron microscopy revealed that a substrate mimetic traps {gamma}-secretase at the transition state for intramembrane proteolysis, and this structure closely aligns with activated enzyme-substrate complex captured by molecular dynamics simulations. In silico simulations and fluorescence lifetime imaging microscopy in cultured cells support stabilization by FAD mutations of enzyme-substrate and/or enzyme-intermediate complexes. Neuronal expression of C99 and/or presenilin-1 in Caenorabditis elegans led to age-dependent synaptic loss only when one of the transgenes carried an FAD mutation. Designed mutations that stabilize the enzyme-substrate complex and block proteolysis likewise led to synaptic loss. Collectively, these findings implicate the stalled process--not the released products--of {gamma}-secretase cleavage of substrates in FAD pathogenesis.
- organism: C. elegans
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- approach: simulation
- Citations
- 1
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