Limits...
Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

Li C, Han J, Yao Q, Zou C, Xu Y, Zhang C, Shang D, Zhou L, Zou C, Sun Z, Li J, Zhang Y, Yang H, Gao X, Li X - Nucleic Acids Res. (2013)

Bottom Line: Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases.This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway.Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.

ABSTRACT
Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Show MeSH

Related in: MedlinePlus

Tryptophan metabolism pathway where the differential genes and metabolites of colorectal cancer were annotated. Nodes near asterisk symbol belong to the key subpathway region (path:00380_3) identified by Subpathway-GM. Enzymes (rectangular nodes) mapped by differential genes are shown with red node labels and borders. Metabolites (circle nodes) mapped by differential metabolites were showed with red node borders.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3643575&req=5

gkt161-F3: Tryptophan metabolism pathway where the differential genes and metabolites of colorectal cancer were annotated. Nodes near asterisk symbol belong to the key subpathway region (path:00380_3) identified by Subpathway-GM. Enzymes (rectangular nodes) mapped by differential genes are shown with red node labels and borders. Metabolites (circle nodes) mapped by differential metabolites were showed with red node borders.

Mentions: The most significant of the seven additional subpathways (path:00380_3) belonged to tryptophan metabolism pathway (Figure 3). Subpathway-GM yielded a P-value of 0.00037 (FDR corrected to 0.0029), but the tryptophan metabolism pathway was not considered as significant in Pathway-G, -M (P > 0.05) or IMPaLA (P > 0.01). Abnormality of the key tryptophan–serotonin regions (top region in Figure 3) has been reported to cause tumor cell proliferation in colon and prostate cancers [reviewed in (28)]. Moreover, the key region where tryptophan is converted by indoleamine-2,3-dioxygenase to kynurenime (red arrow region in Figure 3) was closely related to immune activation, cell proliferation and impaired quality of life in colorectal cancer [reviewed in (29)]. Recent studies also showed that the tryptophan-2,3-dioxygenase (TDO)–kynurenime region effectively suppressed antitumor immune responses and promoted tumor cell survival and motility (11). The differential metabolites and enzymes in tryptophan metabolism pathway encoded by differential expressed genes were located in the local cascade region and formed signature nodes in Subpathway-GM. Notably, differential tryptophan was at the center of the pathway. Subpathway-GM used distance similarity information between these signature nodes to identify the pathway, as well as the key subpathway region, effectively.Figure 3.


Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

Li C, Han J, Yao Q, Zou C, Xu Y, Zhang C, Shang D, Zhou L, Zou C, Sun Z, Li J, Zhang Y, Yang H, Gao X, Li X - Nucleic Acids Res. (2013)

Tryptophan metabolism pathway where the differential genes and metabolites of colorectal cancer were annotated. Nodes near asterisk symbol belong to the key subpathway region (path:00380_3) identified by Subpathway-GM. Enzymes (rectangular nodes) mapped by differential genes are shown with red node labels and borders. Metabolites (circle nodes) mapped by differential metabolites were showed with red node borders.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643575&req=5

gkt161-F3: Tryptophan metabolism pathway where the differential genes and metabolites of colorectal cancer were annotated. Nodes near asterisk symbol belong to the key subpathway region (path:00380_3) identified by Subpathway-GM. Enzymes (rectangular nodes) mapped by differential genes are shown with red node labels and borders. Metabolites (circle nodes) mapped by differential metabolites were showed with red node borders.
Mentions: The most significant of the seven additional subpathways (path:00380_3) belonged to tryptophan metabolism pathway (Figure 3). Subpathway-GM yielded a P-value of 0.00037 (FDR corrected to 0.0029), but the tryptophan metabolism pathway was not considered as significant in Pathway-G, -M (P > 0.05) or IMPaLA (P > 0.01). Abnormality of the key tryptophan–serotonin regions (top region in Figure 3) has been reported to cause tumor cell proliferation in colon and prostate cancers [reviewed in (28)]. Moreover, the key region where tryptophan is converted by indoleamine-2,3-dioxygenase to kynurenime (red arrow region in Figure 3) was closely related to immune activation, cell proliferation and impaired quality of life in colorectal cancer [reviewed in (29)]. Recent studies also showed that the tryptophan-2,3-dioxygenase (TDO)–kynurenime region effectively suppressed antitumor immune responses and promoted tumor cell survival and motility (11). The differential metabolites and enzymes in tryptophan metabolism pathway encoded by differential expressed genes were located in the local cascade region and formed signature nodes in Subpathway-GM. Notably, differential tryptophan was at the center of the pathway. Subpathway-GM used distance similarity information between these signature nodes to identify the pathway, as well as the key subpathway region, effectively.Figure 3.

Bottom Line: Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases.This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway.Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.

ABSTRACT
Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Show MeSH
Related in: MedlinePlus