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System-wide assembly of pathways and modules hierarchically reveal metabolic mechanism of cerebral ischemia.

Zhu Y, Guo Z, Zhang L, Zhang Y, Chen Y, Nan J, Zhao B, Xiao H, Wang Z, Wang Y - Sci Rep (2015)

Bottom Line: The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions.Our analyses revealed 8 significantly altered pathways by MetPA (Metabolomics Pathway Analysis, impact score >0.10) and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased.We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways.

View Article: PubMed Central - PubMed

Affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.

ABSTRACT
The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions. Based on the fusing maps of comprehensive serum metabolome, fatty acid and amino acid profiling, we identified 35 potential metabolic biomarkers for ischemic stroke. Our analyses revealed 8 significantly altered pathways by MetPA (Metabolomics Pathway Analysis, impact score >0.10) and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased. We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways. We provided a new perspective from the viewpoint of abnormal metabolites for the overall study of ischemic stroke as well as a new method to simplify the network analysis by selecting the more closely connected edges and nodes to build a module map of stroke.

No MeSH data available.


Related in: MedlinePlus

Module topological parameters and interactions.(A) Significant modules were defined by analysis of modularity and entropy. When values of modularity and entropy achieved vertex, 15 modules were defined. (B) Comparing the topological parameters of all modules by MCL method. (C) Visualizing the modular map by Metscape to observe the connections among 8 modules. Each color of the node represents a module, and the edge between them represents the interaction of nodes among the modules. According to different widths, these edges indicate 1, 2, 3 and 5 interactions, respectively. Outside this central map are the specific modules for each colored nodes, and the red nodes are the core nodes for the module.
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f4: Module topological parameters and interactions.(A) Significant modules were defined by analysis of modularity and entropy. When values of modularity and entropy achieved vertex, 15 modules were defined. (B) Comparing the topological parameters of all modules by MCL method. (C) Visualizing the modular map by Metscape to observe the connections among 8 modules. Each color of the node represents a module, and the edge between them represents the interaction of nodes among the modules. According to different widths, these edges indicate 1, 2, 3 and 5 interactions, respectively. Outside this central map are the specific modules for each colored nodes, and the red nodes are the core nodes for the module.

Mentions: The evidence shows that the network of biological systems has the module structure, indicating that some molecules in the network perform some types of biological function. We then divided the constructed metabolic network into 15 different modules by MCL when the maximum values of modularity and entropy were both achieved (Figs 4A, S6). Then, we computed the topological parameter for these modules by MCL to observe the structure differences among the 15 modules (Fig. 4B). The average number of neighbors ranged from 1.33 (threonine and phosphocholine modules) to 2.069 (glutamate module), while the number of nodes ranged from 3 (phosphocholine and threonine modules) to 37 (glycine module). The network density was largely changed, from 0.054 (glycine module) to 0.667 (threonine and phosphocholine modules), and the network heterogeneity spanned from 0.354 (threonine and phosphocholine modules) to 2.917 (glycine module), as well. Most of the modules did not have any multi-edge node pairs, except the tyrosine, glutamate, arachidonate, leucine, glycine ,urate and asparagine modules, which had 6, 5, 4, 3, 2, and 1 node pairs, respectively. The value of network heterogeneity increased with the number of nodes (R = 0.9699), indicating that the topological characteristics of modules became more and more diverse when more nodes were added.


System-wide assembly of pathways and modules hierarchically reveal metabolic mechanism of cerebral ischemia.

Zhu Y, Guo Z, Zhang L, Zhang Y, Chen Y, Nan J, Zhao B, Xiao H, Wang Z, Wang Y - Sci Rep (2015)

Module topological parameters and interactions.(A) Significant modules were defined by analysis of modularity and entropy. When values of modularity and entropy achieved vertex, 15 modules were defined. (B) Comparing the topological parameters of all modules by MCL method. (C) Visualizing the modular map by Metscape to observe the connections among 8 modules. Each color of the node represents a module, and the edge between them represents the interaction of nodes among the modules. According to different widths, these edges indicate 1, 2, 3 and 5 interactions, respectively. Outside this central map are the specific modules for each colored nodes, and the red nodes are the core nodes for the module.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664864&req=5

f4: Module topological parameters and interactions.(A) Significant modules were defined by analysis of modularity and entropy. When values of modularity and entropy achieved vertex, 15 modules were defined. (B) Comparing the topological parameters of all modules by MCL method. (C) Visualizing the modular map by Metscape to observe the connections among 8 modules. Each color of the node represents a module, and the edge between them represents the interaction of nodes among the modules. According to different widths, these edges indicate 1, 2, 3 and 5 interactions, respectively. Outside this central map are the specific modules for each colored nodes, and the red nodes are the core nodes for the module.
Mentions: The evidence shows that the network of biological systems has the module structure, indicating that some molecules in the network perform some types of biological function. We then divided the constructed metabolic network into 15 different modules by MCL when the maximum values of modularity and entropy were both achieved (Figs 4A, S6). Then, we computed the topological parameter for these modules by MCL to observe the structure differences among the 15 modules (Fig. 4B). The average number of neighbors ranged from 1.33 (threonine and phosphocholine modules) to 2.069 (glutamate module), while the number of nodes ranged from 3 (phosphocholine and threonine modules) to 37 (glycine module). The network density was largely changed, from 0.054 (glycine module) to 0.667 (threonine and phosphocholine modules), and the network heterogeneity spanned from 0.354 (threonine and phosphocholine modules) to 2.917 (glycine module), as well. Most of the modules did not have any multi-edge node pairs, except the tyrosine, glutamate, arachidonate, leucine, glycine ,urate and asparagine modules, which had 6, 5, 4, 3, 2, and 1 node pairs, respectively. The value of network heterogeneity increased with the number of nodes (R = 0.9699), indicating that the topological characteristics of modules became more and more diverse when more nodes were added.

Bottom Line: The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions.Our analyses revealed 8 significantly altered pathways by MetPA (Metabolomics Pathway Analysis, impact score >0.10) and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased.We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways.

View Article: PubMed Central - PubMed

Affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.

ABSTRACT
The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions. Based on the fusing maps of comprehensive serum metabolome, fatty acid and amino acid profiling, we identified 35 potential metabolic biomarkers for ischemic stroke. Our analyses revealed 8 significantly altered pathways by MetPA (Metabolomics Pathway Analysis, impact score >0.10) and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased. We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways. We provided a new perspective from the viewpoint of abnormal metabolites for the overall study of ischemic stroke as well as a new method to simplify the network analysis by selecting the more closely connected edges and nodes to build a module map of stroke.

No MeSH data available.


Related in: MedlinePlus