bioRxivNeurosciencePreprint
Glycation renders alpha-synuclein oligomeric strain and modulates microglia activation
Manisha KumariiD, Bhoj KumariD, Krishna Singh BishtiD, Tushar Kanti MaitiiD
Abstract
-Synuclein is known to involve in the pathogenesis of Parkinsons diseases (PD) and related disorders. However, it is unclear how its aggregation causes neuronal degeneration and neuroinflammation. Due to intrinsic disorder nature, -synuclein produces a large number of structural ensembles and diverse aggregation intermediates. The post-translational modifications add a new layer of complexity to the aggregation mechanism. Recently, it has been demonstrated that glycation of -synuclein restricts into oligomeric intermediates and causes neuronal toxicity. However, the understanding of aggregation mechanism, dopaminergic neuronal death, and neuroinflammation by the glycated -synuclein is yet to be elucidated. The present study aims to address how glycated synuclein differs in oligomerization and neuroinflammation. The glycation of -synuclein perturbs the aggregation kinetics and prevents the fibrilization through the alteration of surface charges of N-terminal domain residues which prevents membrane binding and seed amplification mechanism. Mass spectrometry-based proteomics analysis of BV2 cells treated with glycated oligomers provides evidence of alteration of endocytic mechanism, mitochondrial dysfunction, and inflammatory cascade. Here, we show that -synuclein oligomers strongly bind to TLR2 and activate the TLR2 mediated signaling. However, glycated -synuclein oligomers impair the TLR2 binding and compromise TLR2 signaling. Interestingly, we also find that the glycated -synuclein oligomers favor NLRP3 inflammasome mediated neuroinflammation compared to non- glycated -synuclein oligomers. In conclusion, our findings suggest that microglia response towards -synuclein is conformation-specific and glycated oligomers can contribute to neurodegeneration differently.
- organism: human
- assay: proteomics
- research focus: cell-cell communication
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 4
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