Open AccessIdentification of novel immune‐relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis
Author(s) -
Han ZhiJie,
Xue WeiWei,
Tao Lin,
Zhu Feng
Publication year - 2018
Publication title -
cns neuroscience and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 69
eISSN - 1755-5949
pISSN - 1755-5930
DOI - 10.1111/cns.13051
Subject(s) - drugbank , computational biology , gene , drug , drug target , drug repositioning , disease , gene ontology , biology , immune system , inference , bioinformatics , genetics , medicine , computer science , gene expression , pharmacology , artificial intelligence , pathology
Abstract Aims Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune‐relevant AD drug target genes have been discovered up to now, and it is speculated that there are still many potential drug target genes of AD (at least immune‐relevant genes) to be discovered. Thus, this study was designed to identify novel AD drug target genes and explore their biological properties. Methods A combinatorial approach was adopted for the first time to discover AD drug targets by collectively considering ontology inference and network analysis. Moreover, a novel strategy limiting the distance of reasoning and in turn reducing noise interference was further proposed to improve inference performance. Potential AD drug target genes were discovered by integrating information of multiple popular databases (TTD, DrugBank, PharmGKB, AlzGene, and BioGRID). Then, the enrichment analyses of the identified drug targets genes based on nine well‐known pathway‐related databases were conducted to explore the function of the identified potential drug target genes. Results Eighteen potential drug target genes were finally identified, and 13 of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway‐related databases, revealed that the enriched terms were primarily focus on immune‐relevant biological processes. Four of those identified drug target genes are involved in the classical complement pathway and process of antigen presenting. Conclusion The well‐reproducible results showed the good performance of the combinatorial approach, and the remaining five new targets could be a good starting point for our understanding of the pathogenesis and drug discovery of AD. Moreover, this study supported validity of the combinatorial approach integrating ontology inference with network analysis in the discovery of novel drug target for neurological diseases.