bioRxivNeurosciencePreprint
Multi-omics comparative analyses of synucleinopathy models reveal distinct targets and relevance for drug development
Britney N. LizamaiD, Rick S. Shin, Hilary A. North, Gary C. Look, Aidan Reaver, Kiran Pandey, Duc M. DuongiD, Nicholas T. SeyfriediD, Anthony O. CaggianoiD, Mary E. HambyiD
Abstract
BackgroundThe discovery and development of therapeutics for Parkinsons disease (PD) requires preclinical models and an understanding of the disease mechanisms reflected in each model is crucial to success. ObjectiveTo illuminate disease mechanisms and translational value of two commonly utilized rat models of synucleinopathy - AAV-delivered human mutant hA53T alpha synuclein (-Syn) and -Syn preformed fibril (PFF) injection - using a top-down, unbiased, large-scale approach. MethodsTandem mass tag mass spectrometry (TMT-MS), RNA sequencing, and bioinformatic analyses were used to assess proteins, genes, and pathways disrupted in rat striatum and substantia nigra. Comparative analyses were performed with PD drug candidate targets and an existing human PD and dementia with Lewy body (DLB) proteomics dataset. ResultsUnbiased proteomics identified 388 proteins significantly altered by hA53T--Syn and 1550 by PFF--Syn compared to sham controls. Pathway and correlation analyses of these revealed common and distinct pathophysiological processes altered in each model: dopaminergic signaling/metabolism, mitochondria and energy metabolism, and motor processes were disrupted in AAV-hA53T--Syn, while immune response, intracellular/secretory vesicles, synaptic vesicles, and autophagy were more impacted by PFF--Syn. Synapses, neural growth and remodeling, and protein localization were prominently represented in both models. Analyses revealed potential biomarkers of disease processes and proteins and pathways also altered in patients, elucidating drug targets/ disease mechanisms the models best reflect. ConclusionsAlignment of unbiased multi-omics analyses of AAV-hA53T and PFF--Syn models of synucleinopathy with PD and DLB patient data and PD drug development pipeline candidates identifies optimal models for testing novel therapeutics based on biological mechanisms.
- organism: rat
- assay: proteomics
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: matrix factorization
- Citations
- 0
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