title: Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis.
abstract: Identifying early warning signals of critical transitions during disease progression is a key to achieving early diagnosis of complex diseases. By exploiting rich information of high-throughput data, a novel model-free method has been developed to detect early warning signals of diseases. Its theoretical foundation is based on dynamical network biomarker (DNB), which is also called as the driver (or leading) network of the disease because components or molecules in DNB actually drive the whole system from one state (e.g. normal state) to another (e.g. disease state). In this article, we first reviewed the concept and main results of DNB theory, and then applied the new method to the analysis of type 2 diabetes mellitus (T2DM). Specifically, based on the temporal-spatial gene expression data of T2DM, we identified tissue-specific DNBs corresponding to the critical transitions occurring in liver, adipose and muscle during T2DM development and progression. Actually, we found that there are two different critical states during T2DM development characterized as responses to insulin resistance and serious inflammation, respectively. Interestingly, a new T2DM-associated function, i.e. steroid hormone biosynthesis, was discovered, and those related genes were significantly dysregulated in liver and adipose at the first critical transition during T2DM deterioration. Moreover, the dysfunction of genes related to responding hormone was also detected in muscle at the similar period. Based on the functional and network analysis on pathogenic molecular mechanism of T2DM, we showed that most of DNB genes, in particular the core ones, tended to be located at the upstream of biological pathways, which implied that DNB genes act as the causal factors rather than the consequence to drive the downstream molecules to change their transcriptional activities. This also validated our theoretical prediction of DNB as the driver network. As shown in this study, DNB can not only signal the emergence of the critical transitions for early diagnosis of diseases, but can also provide the causal network of the transitions for revealing molecular mechanisms of disease initiation and progression at a network level.
authors.affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China. Tel.: +86 21-5492-0100; Fax: +86 21-5497-2551; lnchen@sibs.ac.cn.
authors.forename: Meiyi
authors.initials: M
authors.lastname: Li
authors.forename: Tao
authors.initials: T
authors.lastname: Zeng
authors.forename: Rui
authors.initials: R
authors.lastname: Liu
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D018450:Disease Progression; D042241:Early Diagnosis; D005819:Genetic Markers; D057166:High-Throughput Screening Assays / Q000706:statistics & numerical data; D006801:Humans; D008954:Models, Biological; D051381:Rats; D015398:Signal Transduction; D013256:Steroids / Q000096:biosynthesis; D049490:Systems Biology; D014018:Tissue Distribution
chemical_list: D015415:Biomarkers; D005819:Genetic Markers; D013256:Steroids
keywords: Type-2 diabetes; critical transition; disease progression; dynamical network biomarker (DNB); leading network; multi-tissues