title: An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases.
abstract: Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.
authors.affiliation: National Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.|University of Chinese Academy of Sciences, Beijing, 100049, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Li
authors.affiliation: National Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.|University of Chinese Academy of Sciences, Beijing, 100049, China.
authors.forename: Yaling
authors.initials: Y
authors.lastname: Nie
authors.affiliation: National Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Jingkai
authors.initials: J
authors.lastname: Yu
mesh_terms: D022005:Adherens Junctions / Q000378:metabolism; D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism*; D016000:Cluster Analysis; D020022:Genetic Predisposition to Disease; D006801:Humans; D006816:Huntington Disease / Q000235:genetics / Q000378:metabolism*; D053858:Metabolic Networks and Pathways / Q000235:genetics; D010300:Parkinson Disease / Q000235:genetics / Q000378:metabolism*; D060066:Protein Interaction Maps; D019108:Tight Junctions / Q000378:metabolism; D059467:Transcriptome