Table_2_Meta-Analysis of Differentially Expressed Genes in the Substantia Nigra in Parkinson’s Disease Supports Phenotype-Specific Transcriptome Changes.XLSX
Studies regarding differentially expressed genes (DEGs) in Parkinson’s disease (PD) have focused on common upstream regulators or dysregulated pathways or ontologies; however, the relationships between DEGs and disease-related or cell type-enriched genes have not been systematically studied. Meta-analysis of DEGs (meta-DEGs) are expected to overcome the limitations, such as replication failure and small sample size of previous studies.
PurposeMeta-DEGs were performed to investigate dysregulated genes enriched with neurodegenerative disorder causative or risk genes in a phenotype-specific manner.
MethodsSix microarray datasets from PD patients and controls, for which substantia nigra sample transcriptome data were available, were downloaded from the NINDS data repository. Meta-DEGs were performed using two methods, combining p-values and combing effect size, and common DEGs were used for secondary analyses. Gene sets of cell type-enriched or disease-related genes for PD, Alzheimer’s disease (AD), and hereditary progressive ataxia were constructed by curation of public databases and/or published literatures.
ResultsOur meta-analyses revealed 449 downregulated and 137 upregulated genes. Overrepresentation analyses with cell type-enriched genes were significant in neuron-enriched genes but not in astrocyte- or microglia-enriched genes. Meta-DEGs were significantly enriched in causative genes for hereditary disorders accompanying parkinsonism but not in genes associated with AD or hereditary progressive ataxia. Enrichment of PD-related genes was highly significant in downregulated DEGs but insignificant in upregulated genes.
ConclusionDownregulated meta-DEGs were associated with PD-related genes, but not with other neurodegenerative disorder genes. These results highlight disease phenotype-specific changes in dysregulated genes in PD.
History
References
- https://doi.org//10.1111/ejn.13760
- https://doi.org//10.1007/s004010100395
- https://doi.org//10.1002/ana.24410
- https://doi.org//10.1186/s12920-016-0173-x
- https://doi.org//10.1007/s10048-006-0077-6
- https://doi.org//10.1007/s00401-012-1027-z
- https://doi.org//10.1136/jmedgenet-2018-105703
- https://doi.org//10.1002/mdc3.12700
- https://doi.org//10.3892/mmr.2017.6124
- https://doi.org//10.1016/j.nbd.2014.11.002
- https://doi.org//10.1093/cercor/bht101
- https://doi.org//10.1371/journal.pone.0181349
- https://doi.org//10.1093/bioinformatics/btn647
- https://doi.org//10.1016/j.ygeno.2010.01.003
- https://doi.org//10.1002/mds.27019
- https://doi.org//10.1002/mds.27319
- https://doi.org//10.1371/journal.pone.0161567
- https://doi.org//10.1093/bioinformatics/btp444
- https://doi.org//10.1016/j.jmb.2019.01.045
- https://doi.org//10.1007/s10048-005-0020-2
- https://doi.org//10.1007/s10048-007-0116-y
- https://doi.org//10.3727/000000006783991827
- https://doi.org//10.1093/brain/awn323
- https://doi.org//10.1016/j.nbd.2011.12.021
- https://doi.org//10.1111/imm.12922
- https://doi.org//10.1186/s12920-018-0357-7
- https://doi.org//10.3233/jad-161032
- https://doi.org//10.1038/nprot.2015.052
- https://doi.org//10.1523/jneurosci.1860-14.2014
- https://doi.org//10.1002/ajmg.b.30195
- https://doi.org//10.1016/j.neuron.2015.11.013
Usage metrics
Read the peer-reviewed publication
Categories
- Radiology and Organ Imaging
- Decision Making
- Clinical Nursing: Tertiary (Rehabilitative)
- Image Processing
- Autonomic Nervous System
- Cellular Nervous System
- Biological Engineering
- Sensory Systems
- Central Nervous System
- Neuroscience
- Endocrinology
- Artificial Intelligence and Image Processing
- Signal Processing
- Rehabilitation Engineering
- Biomedical Engineering not elsewhere classified
- Stem Cells
- Neurogenetics
- Developmental Biology