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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

Validated metabolites, pathways and modules using independent experiments.(A) Box plot comparing the concentrations of arachidonic acid (sham/ischemia groups) by metabolic analysis and ELISA test. n = 3. (B) Map of the GST Metab on KEGG. (C) Box plot comparing the concentrations of cystathionine, L-cysteine and pyruvate (sham/ischemia groups) by ELISA test. n = 3. (D) Box plot comparing the concentrations of glycine, serine, threonine (sham/ischemia groups) by metabolic analysis. n = 3. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01 as determined by Bonferroni corrected t tests. n = 3.
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f6: Validated metabolites, pathways and modules using independent experiments.(A) Box plot comparing the concentrations of arachidonic acid (sham/ischemia groups) by metabolic analysis and ELISA test. n = 3. (B) Map of the GST Metab on KEGG. (C) Box plot comparing the concentrations of cystathionine, L-cysteine and pyruvate (sham/ischemia groups) by ELISA test. n = 3. (D) Box plot comparing the concentrations of glycine, serine, threonine (sham/ischemia groups) by metabolic analysis. n = 3. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01 as determined by Bonferroni corrected t tests. n = 3.

Mentions: To verify the pathways and modules mentioned above, we selected 4 nodes from 2 pathways for ELISA to identify the variation between the sham and ischemia groups. These nodes contained arachidonate from the arachidonic acid metabolism pathway (Fig. 6A) and cystathionine, L-cysteine and pyruvate, glycine, serine, and threonine from the GST MP(Metscape) (Fig. 6B). The results of arachidonic acid by the two metabolic analysis methods (P = 0.6928) were consistent with those by ELISA (P = 0.6404); the GST MP(Metscape) was activated in the ischemic process, manifesting as an increase in pyruvate (P = 0.0024) by ELISA (Fig. 6C) and reductions in glycine (P = 0.0245), serine (P = 0.0055) and an increase in threonine (P = 0.0316) by metabolic analysis (Fig. 6D); the levels of cystathionine (P = 0.666), and L-cysteine (P = 0.8014) were not significantly altered.


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)

Validated metabolites, pathways and modules using independent experiments.(A) Box plot comparing the concentrations of arachidonic acid (sham/ischemia groups) by metabolic analysis and ELISA test. n = 3. (B) Map of the GST Metab on KEGG. (C) Box plot comparing the concentrations of cystathionine, L-cysteine and pyruvate (sham/ischemia groups) by ELISA test. n = 3. (D) Box plot comparing the concentrations of glycine, serine, threonine (sham/ischemia groups) by metabolic analysis. n = 3. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01 as determined by Bonferroni corrected t tests. n = 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: Validated metabolites, pathways and modules using independent experiments.(A) Box plot comparing the concentrations of arachidonic acid (sham/ischemia groups) by metabolic analysis and ELISA test. n = 3. (B) Map of the GST Metab on KEGG. (C) Box plot comparing the concentrations of cystathionine, L-cysteine and pyruvate (sham/ischemia groups) by ELISA test. n = 3. (D) Box plot comparing the concentrations of glycine, serine, threonine (sham/ischemia groups) by metabolic analysis. n = 3. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01 as determined by Bonferroni corrected t tests. n = 3.
Mentions: To verify the pathways and modules mentioned above, we selected 4 nodes from 2 pathways for ELISA to identify the variation between the sham and ischemia groups. These nodes contained arachidonate from the arachidonic acid metabolism pathway (Fig. 6A) and cystathionine, L-cysteine and pyruvate, glycine, serine, and threonine from the GST MP(Metscape) (Fig. 6B). The results of arachidonic acid by the two metabolic analysis methods (P = 0.6928) were consistent with those by ELISA (P = 0.6404); the GST MP(Metscape) was activated in the ischemic process, manifesting as an increase in pyruvate (P = 0.0024) by ELISA (Fig. 6C) and reductions in glycine (P = 0.0245), serine (P = 0.0055) and an increase in threonine (P = 0.0316) by metabolic analysis (Fig. 6D); the levels of cystathionine (P = 0.666), and L-cysteine (P = 0.8014) were not significantly altered.

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