Abstract
Background: Diseases of the nervous system are widely considered to be caused by genetic mutations, and they have been shown to share pathogenic genes. Discovering the shared mechanisms of these diseases is useful for designing common treatments. Method: In this study, by reviewing 518 articles published after 2007 on 20 diseases of the nervous system, we compiled data on 1607 mutations occurring in 365 genes, totals that are 1.9 and 3.2 times larger than those collected in the Clinvar database, respectively. A combination with the Clinvar data gives 2434 pathogenic mutations and 424 genes. Using this information, we measured the genetic similarities between the diseases according to the number of genes causing two diseases simultaneously. Further detection was carried out on the similarity between diseases in terms of cell types. Disease-related cell types were defined as those with disease-related gene enrichment among the marker genes of cells, as ascertained by analyzing single-cell sequencing data. Enrichment profiles of the disease-related genes over 25 cell types were constructed. The disease similarity in terms of cell types was obtained by calculating the distances between the enrichment profiles of these genes. The same strategy was applied to measure the disease similarity in terms of brain regions by analyzing the gene expression data from 10 brain regions. Results: The disease similarity was first measured in terms of genes. The result indicated that the proportions of overlapped genes between diseases were significantly correlated to the DMN scores (phenotypic similarity), with a Pearson correlation coefficient of 0.40 and P-value = 6.0×10-3. The disease similarity analysis for cell types identified that the distances between enrichment profiles of the disease-related genes were negatively correlated to the DMN scores, with Spearman correlation coefficient = -0.26 (P-value = 1.5 × 10-2). However, the brain region enrichment profile distances of the disease-related genes were not significantly correlated with the DMN score. Besides the similarity of diseases, this study identified novel relationships between diseases and cell types. Conclusion: We manually constructed the most comprehensive dataset to date for genes with mutations related to 20 nervous system diseases. By using this dataset, the similarities between diseases in terms of genes and cell types were found to be significantly correlated to their phenotypic similarity. However, the disease similarities in terms of brain regions were not significantly correlated with the phenotypic similarities. Thus, the phenotypic similarity between the diseases is more likely to be caused by dysfunctions of the same genes or the same types of neurons rather than the same brain regions. The data are collected into the database NeurodisM, which is available at http://biomed-ai.org/neurodism.
Highlights
Diseases of the nervous system are widely considered to be caused by genetic mutations, and they have been shown to share pathogenic genes
The ALPL gene mutations database was designed for hypophosphatasia (Antonarakis, 1998; den Dunnen and Antonarakis, 2003); another database including 614 mutations in 14 genes was designed for Alzheimer’s disease, Frontotemporal dementia, and Parkinson’s disease (Cruts et al, 2012); NeuroDNet is a database collecting 300 genes related to 12 neurodegenerative diseases (Vasaikar et al, 2013); PDmutDB is a database collecting all known mutations and non-pathogenic coding variations in genes related to Parkinson’s disease, which includes 192 pathogenic mutations and 231 mutations with unclear functions (Nuytemans et al, 2010; Cruts et al, 2012); EpilepsyGene is a database containing mutations and genes related to Epilepsy (Ran et al, 2015)
To obtain the genes related to these diseases, we manually reviewed 518 English-language articles published between 2007 to 2017 in PubMed and compiled a list of 1607 mutations in 365 genes identified by sequencing technology
Summary
Diseases of the nervous system are widely considered to be caused by genetic mutations, and they have been shown to share pathogenic genes. Discovering the shared mechanisms of these diseases is useful for designing common treatments. One of the important features of these diseases is that they often occur simultaneously in the same person or with similar symptoms that are considered to be caused by common mechanisms (Kohli et al, 2013; Boison and Aronica, 2015). The shared mutations or genes between diseases can potentially assist the discovery of new pathogenic mutations or genes (Zhao et al, 2017). The most important step toward identifying the shared mutations or genes between diseases is to collect data reflecting the relationship between mutations and the diseases. The ALPL gene mutations database was designed for hypophosphatasia (Antonarakis, 1998; den Dunnen and Antonarakis, 2003); another database including 614 mutations in 14 genes was designed for Alzheimer’s disease, Frontotemporal dementia, and Parkinson’s disease (Cruts et al, 2012); NeuroDNet is a database collecting 300 genes related to 12 neurodegenerative diseases (Vasaikar et al, 2013); PDmutDB is a database collecting all known mutations and non-pathogenic coding variations in genes related to Parkinson’s disease, which includes 192 pathogenic mutations and 231 mutations with unclear functions (Nuytemans et al, 2010; Cruts et al, 2012); EpilepsyGene (http ://61.152.91.49/EpilepsyGene) is a database containing mutations and genes related to Epilepsy (Ran et al, 2015)
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