November 20, 2019 -- A group of researchers at the Icahn School of Medicine at Mount Sinai have discovered new molecular drivers of Parkinson's disease (PD) and subsequently determined how they impact the function of genes involved in the disease. This was accomplished using a complex statistical technique called multiscale gene network analysis (MGNA). The results were published in Nature Communications on November 20.
PD is classified by loss of dopaminergic neurons in the substantia nigra and presence of Lewy bodies in affected brain regions. Several gene mutations, such as SNCA, LRRK2, and VPS35, have been implicated in cellular pathways that lead to PD. However, these genetic variants account for only around 20% of PD cases. The remaining cases of idiopathic PD are believed to occur to do complex interactions between genes and environmental factors. The fact remains that the etiology and pathophysiology are unclear.
The development of high-throughput molecular profiling has allowed researchers to begin to understand the complex regulatory schemes involved in some diseases. And one tool that has been extensively used is gene network analysis which is an unbiased approached to identify gene co-expression/co-regulation patterns for discovery of novel pathways and gene targets in disease.
Previous work conducted by the researchers utilized large-scale genetic and gene expression data as well as clinical and pathological trains to conduct a multiscale gene network analysis (MGNA) model for Alzheimer's disease. This type of approach has not been utilized in PD research, so with funding from the National Institutes of Health (NIH)/National Institute on Aging (NIA) Accelerating Medicines Partnership - Alzheimer's Disease, the researchers made significant improvements to the technique.
"This multiscale network analysis approach is a powerful way to dissect the molecular mechanisms of complex diseases like Alzheimer's," said Suzana Petanceska, PhD, program director of the AMP-AD Target Discovery program at the NIA, which co-funded the study. "It is exciting to see that AMP-AD can provide new mechanistic insights to Parkinson's disease that could lead to new therapeutic opportunities."
In this study, the researchers developed MGNA models of PD based on existing human brain gene expression data sets and followed with a comprehensive functional validation of a top key regulator.
An integrative MGNA for PD
The team collected gene expression data from eight human PD studies that were merged into one global PD expression data set (n=83) and compared it to control samples (n=70). Co-expressed genes in the network for analyzed for differentially expressed genes (DEGs) and cell-type specific markers. They were then able to infer regulatory relationships between these genes. Overall, they found 946 DEGs between the PD and control groups.
Downregulated genes were associated with impaired neuronal activity particularly neurotransmission, while upregulated genes were associated with spinal cord development and embryonic digit morphogenesis. These results were largely similar to previously published analyses of PD postmortem brains.
By examining cell-type specificity of the modules using the gene signatures of six major brain cell types, including neurons, astrocytes, microglia, endothelial cells, oligodendrocyte precursor cells (OPC), and oligodendrocytes, the researchers were able to determine that cell-type specific functional modules indicate highly coordinated molecular dysregulation often seen in PD. Moreover, they determined that there are shared disease mechanisms across different cell types.
Further models were developed to determine nonlinear relationships which identified 45 key regulators of motor neurons in PD. RALYL, BASP1, ANKRD34C, STMN2, and SYNGR3 as the top five key regulators based on the model.
Validation of key regulators of PD
In order to validate the functional relevance of the model, the team conducted a knockout study of STMN2, which is normally expressed in neurons that produce dopamine (depleted in PD). Using the live-cell imaging assay pHIuorin, they confirmed that STMN2 plays a key role in the regulation of presynaptic activity. Subsequently, they knocked down STMN2 in mice to test its influence on PD. RNA sequencing showed that reduction of STMN2 led to upregulation of nine immune-related genes (GPNMB, SREBF1, STAB1, LHFPL2, PRRG4, CTSB, FNDC3B, PPFIBP1, and COL5A2) that had previously been associated with the disease. The mice in this study induced PD-like behavioral and pathological changes including degeneration of dopaminergic neurons in the substantia nigra and an increase in the concentration of the toxic, modified α-synuclein protein, both hallmarks of the disease.
"This study offers a novel approach to understanding the majority of cases of Parkinson's," said Bin Zhang, PhD, Professor of Genetics and Genomic Sciences at the Icahn Institute for Data Science and Genomic Technology and Director of the Mount Sinai Center for Transformative Disease Modeling at the Icahn School of Medicine at Mount Sinai. "The strategy not only reveals new drivers, but it also elucidates the functional context of the known Parkinson's disease risk factor genes."
The researchers hope that this study will lead to larger-scale studies. Still, "the work opens up a new avenue for studying the disease," said Zhenyu Yue, PhD, Professor of Neurology and Neuroscience at the Icahn School of Medicine and the Director of Basic and Translational Research in Movement Disorders. "The new genes we identified suggest that new pathways should be considered as potential targets for drug development, particularly for idiopathic Parkinson's cases."
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