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Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers

ABSTRACT: The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is to focus on the pre-disease state, a state with high susceptibility before the disease onset, which is considered as the best period for preventive treatment. In order to detect the pre-disease state, we recently proposed mathematical theory called the dynamical network biomarker (DNB) theory based on the critical transition paradigm. Here, we investigated time-course gene expression profiles of a mouse model of metabolic syndrome using 64 whole-genome microarrays based on the DNB theory, and showed the detection of a pre-disease state before metabolic syndrome defined by characteristic behavior of 147 DNB genes. The results of our study demonstrating the existence of a notable pre-disease state before metabolic syndrome may help to design novel and effective therapeutic strategies for preventing metabolic syndrome, enabling just-in-time preemptive interventions.

ORGANISM(S): Mus musculus

PROVIDER: GSE112653 | GEO | 2019/06/24

REPOSITORIES: GEO

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Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.

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Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers

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