Alzheimer's disease (AD) is the leading cause of dementia and the most common neurodegenerative disorder, affecting over 5.5 million people in United States and 47 million people worldwide. Despite the enormous social economical cost associated with AD, currently the genetic causes for late-onset AD remains poorly characterized and there exist no cures for AD. In this project titled ?Combine computational prediction, network analysis, and genetic screening in C. elegans to uncover neurodegenerative causes in Alzheimer?s disease (AD)?, we propose an integrated systems biology approach that seamlessly combines novel computational genetics prediction, network analysis, and robust genetic screening in C. elegans to uncover neurodegenerative causes in AD. First, we will develop novel data-driven network-based systems approach to identify neurodegenerative candidate genes by leveraging large amounts of phenotypic, genetic and genomic data from humans. Second, we will identify genetic pathways and molecular networks underlying neurodegeneration; Third, we will evaluate the causal effects of identified candidate genes, genetic modifiers and molecular networks in a variety of C. elegans models of neurodegeneration. Our systems biology approach fully utilizes and seamlessly integrate vast amounts of knowledge and data from humans, which then guides our confirmatory genetic screening in C. elegans. The output of our project will be lists of candidate causal genes, genetic modifiers and molecular networks for neurodegeneration, each associated with supporting genetic and phenotypic evidence from both humans and C. elegans. Our study will generate large amounts of data/knowledge/hypotheses that could serve as a starting point for others to conduct hypothesis-driven testing in different animal models of neurodegeneration. We will build a comprehensive knowledge base of Neurodegeneration Genes (NDGenes) and develop interactive web applications to make all the data publicly available. The unique and powerful strength of our project is our ability to seamlessly combine novel computational predictions with genetic screening in C. elegans. Our project will likely lead to discovery of the conserved genes and regulatory networks that contribute to susceptibility or resilience to neurodegeneration, with translational implications for the development of therapeutic interventions for AD and other neurodegenerative disorders.
! Alzheimer's disease (AD) is the leading cause of dementia and the most common neurodegenerative disorder, affecting over 5.5 million people in United States and 47 million people worldwide. Currently, the genetic causes for late-onset AD remains poorly characterized and there exist no cures for AD. In this project titled ?Combine computational prediction, network analysis, and genetic screening in C. elegans to uncover neurodegenerative causes in Alzheimer?s disease (AD)?, we propose an integrated systems biology approach that seamlessly combines novel computational genetics prediction, network analysis, and genetic screening in C. elegans to uncover neurodegenerative causes in AD. Our project will likely lead to discovery of the conserved genes and regulatory networks that contribute to susceptibility or resilience to neurodegeneration, with translational implications for the development of therapeutic interventions for AD and other neurodegenerative disorders.
| Zheng, Chunlei; Xu, Rong (2018) Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data. BMC Bioinformatics 19:500 |
