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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

Multiple dimensional analysis of the relationship between modules and pathways.(A) Comparing the nodes similarity between the 8 pathways and the 8 modules structure in Metscape. Based on different contributions of overlapping and non-overlapping nodes between modules and pathways, the corresponding similarity profiles could uncover this trend. (B,C) Pathways convergence in the glycine and glutamate modules, respectively. Different colors indicate different pathways in a module. A node with two or three colors indicates two or three pathways in the same node. (D) Modules divergence in known and unknown pathways. The significant modules are displayed in the middle line, with 8 known pathways (shown in Fig. 4B) and 16 unknown pathways located in the left and right sides, respectively. Modules that can be verified in literature are highlighted in yellow. The PTT biosyn pathway was found to diverge into glycine and tyrosine modules.
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f5: Multiple dimensional analysis of the relationship between modules and pathways.(A) Comparing the nodes similarity between the 8 pathways and the 8 modules structure in Metscape. Based on different contributions of overlapping and non-overlapping nodes between modules and pathways, the corresponding similarity profiles could uncover this trend. (B,C) Pathways convergence in the glycine and glutamate modules, respectively. Different colors indicate different pathways in a module. A node with two or three colors indicates two or three pathways in the same node. (D) Modules divergence in known and unknown pathways. The significant modules are displayed in the middle line, with 8 known pathways (shown in Fig. 4B) and 16 unknown pathways located in the left and right sides, respectively. Modules that can be verified in literature are highlighted in yellow. The PTT biosyn pathway was found to diverge into glycine and tyrosine modules.

Mentions: Based on the pathway structure, the above 8 modules had some overlapping nodes with a certain pathway from the same database. We evaluated the similarity between the pathway and the module by the vectorial angle method23 and sequenced the similarity between these 8 pathways and modules (Fig. 5A). The similarity values for the 8 modules were 87.5% (78% identical ratio and 7 overlapping nodes, Fig. S7H), 86.72% (76% identical ratio and 19 overlapping nodes, Fig. S7C), 84.87% (73% identical ratio and 11 overlapping nodes, Fig. S7E), 78.33% (65% identical ratio and 9 overlapping nodes, Fig. S7G), 67.81% (51% identical ratio and 20 overlapping nodes, Fig. S7B), 63.94% (47% identical ratio and 22 overlapping nodes, Fig. S7A), 39.22% (23% identical ratio and overlapping nodes, Fig. S7F), and 9.13% (4% identical ratio and 2 overlapping nodes, Fig. S7D), respectively (Fig. 5A) (Table S3). It was revealed that these 8 modules not only held higher similarity dependent on increases in the overlap percentage but also preserved diverse metabolic homogeneities despite still having their own unique nodes. These results confirm that the modules in the network have the ability to be overlapped. Information exchange and transmission occur through the overlapping area in the disease network; this will be important for the further study of signal transduction, network cooperation and other behaviors in the metabolic network of cerebral infarction.


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)

Multiple dimensional analysis of the relationship between modules and pathways.(A) Comparing the nodes similarity between the 8 pathways and the 8 modules structure in Metscape. Based on different contributions of overlapping and non-overlapping nodes between modules and pathways, the corresponding similarity profiles could uncover this trend. (B,C) Pathways convergence in the glycine and glutamate modules, respectively. Different colors indicate different pathways in a module. A node with two or three colors indicates two or three pathways in the same node. (D) Modules divergence in known and unknown pathways. The significant modules are displayed in the middle line, with 8 known pathways (shown in Fig. 4B) and 16 unknown pathways located in the left and right sides, respectively. Modules that can be verified in literature are highlighted in yellow. The PTT biosyn pathway was found to diverge into glycine and tyrosine modules.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Multiple dimensional analysis of the relationship between modules and pathways.(A) Comparing the nodes similarity between the 8 pathways and the 8 modules structure in Metscape. Based on different contributions of overlapping and non-overlapping nodes between modules and pathways, the corresponding similarity profiles could uncover this trend. (B,C) Pathways convergence in the glycine and glutamate modules, respectively. Different colors indicate different pathways in a module. A node with two or three colors indicates two or three pathways in the same node. (D) Modules divergence in known and unknown pathways. The significant modules are displayed in the middle line, with 8 known pathways (shown in Fig. 4B) and 16 unknown pathways located in the left and right sides, respectively. Modules that can be verified in literature are highlighted in yellow. The PTT biosyn pathway was found to diverge into glycine and tyrosine modules.
Mentions: Based on the pathway structure, the above 8 modules had some overlapping nodes with a certain pathway from the same database. We evaluated the similarity between the pathway and the module by the vectorial angle method23 and sequenced the similarity between these 8 pathways and modules (Fig. 5A). The similarity values for the 8 modules were 87.5% (78% identical ratio and 7 overlapping nodes, Fig. S7H), 86.72% (76% identical ratio and 19 overlapping nodes, Fig. S7C), 84.87% (73% identical ratio and 11 overlapping nodes, Fig. S7E), 78.33% (65% identical ratio and 9 overlapping nodes, Fig. S7G), 67.81% (51% identical ratio and 20 overlapping nodes, Fig. S7B), 63.94% (47% identical ratio and 22 overlapping nodes, Fig. S7A), 39.22% (23% identical ratio and overlapping nodes, Fig. S7F), and 9.13% (4% identical ratio and 2 overlapping nodes, Fig. S7D), respectively (Fig. 5A) (Table S3). It was revealed that these 8 modules not only held higher similarity dependent on increases in the overlap percentage but also preserved diverse metabolic homogeneities despite still having their own unique nodes. These results confirm that the modules in the network have the ability to be overlapped. Information exchange and transmission occur through the overlapping area in the disease network; this will be important for the further study of signal transduction, network cooperation and other behaviors in the metabolic network of cerebral infarction.

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