Overlap between Central and Peripheral Transcriptomes in Parkinson's Disease but Not Alzheimer's Disease.
Kosar HooshmandGlenda M HallidaySandy S PinedaGreg T SutherlandBoris GuennewigPublished in: International journal of molecular sciences (2022)
Most neurodegenerative disorders take decades to develop, and their early detection is challenged by confounding non-pathological ageing processes. Therefore, the discovery of genes and molecular pathways in both peripheral and brain tissues that are highly predictive of disease evolution is necessary. To find genes that influence Alzheimer's disease (AD) and Parkinson's disease (PD) pathogenesis, human RNA-Seq transcriptomic data from Brodmann Area 9 (BA9) of the dorsolateral prefrontal cortex (DLPFC), whole blood (WB), and peripheral blood mononuclear cells (PBMC) were analysed using a combination of differential gene expression and a random forest-based machine learning algorithm. The results suggest that there is little overlap between PD and AD, and the AD brain signature is unique mainly compared to blood-based samples. Moreover, the AD-BA9 was characterised by changes in 'nervous system development' with Myocyte-specific enhancer factor 2C ( Mef2C ), encoding a transcription factor that induces microglia activation, a prominent feature. The peripheral AD transcriptome was associated with alterations in 'viral process', and FYN , which has been previously shown to link amyloid-beta and tau, was the prominent feature. However, in the absence of any overlap with the central transcriptome, it is unclear whether peripheral FYN levels reflect AD severity or progression. In PD, central and peripheral signatures are characterised by anomalies in 'exocytosis' and specific genes related to the SNARE complex, including Vesicle-associated membrane protein 2 ( VAMP2 ), Syntaxin 1A ( STX1A ), and p21-activated kinase 1 ( PAK1 ). This is consistent with our current understanding of the physiological role of alpha-synuclein and how alpha-synuclein oligomers compromise vesicle docking and neurotransmission. Overall, the results describe distinct disease-specific pathomechanisms, both within the brain and peripherally, for the two most common neurodegenerative disorders.
Keyphrases
- rna seq
- gene expression
- machine learning
- single cell
- genome wide
- transcription factor
- prefrontal cortex
- deep learning
- sars cov
- dna methylation
- climate change
- multiple sclerosis
- inflammatory response
- spinal cord injury
- small molecule
- molecular dynamics
- working memory
- chemotherapy induced
- cerebrospinal fluid
- transcranial direct current stimulation
- high frequency