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A Module Analysis Approach to Investigate Molecular Mechanism of TCM Formula: A Trial on Shu-feng-jie-du Formula.

Song J, Zhang F, Tang S, Liu X, Gao Y, Lu P, Wang Y, Yang H - Evid Based Complement Alternat Med (2013)

Bottom Line: We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula.This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis.Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis.

View Article: PubMed Central - PubMed

Affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

ABSTRACT
At the molecular level, it is acknowledged that a TCM formula is often a complex system, which challenges researchers to fully understand its underlying pharmacological action. However, module detection technique developed from complex network provides new insight into systematic investigation of the mode of action of a TCM formula from the molecule perspective. We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula. This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis. We employed this approach to study Shu-feng-jie-du (SHU) formula, which aimed at discovering its molecular mechanism in defending against influenza infection. Actually, four primary pharmacological units were identified from the 2-HN for SHU formula and further analysis revealed numbers of biological pathways modulated by the four pharmacological units. 24 out of 40 enriched pathways that were ranked in top 10 corresponding to each of the four pharmacological units were found to be involved in the process of influenza infection. Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis.

No MeSH data available.


Related in: MedlinePlus

(a), (b), (c), and (d) Top 10 enriched pathways and associated herbal compounds corresponding to module 1, 2, 4, and 5, respectively. The herbal compounds are ranked by Promiscuity Index (PI), which is defined as the number of targets connected to a given compound by the preserved CPIs in a detected module. Note that only compounds with PI greater than zero are listed in this figure. The enriched pathways are ranked by the P values calculated in MetaDrug. The circled numbers in brackets after pathway name indicate the major category that pathway belongs to. For example, “ESR1 regulation of G1/S transition” belongs to category 1 and 3, that is, cell cycle and development. The category knowledge is curated from the classification tree of GeneGo pathways in MetaDrug. All pathways in this figure are significant with P values lower than 0.001.
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fig2: (a), (b), (c), and (d) Top 10 enriched pathways and associated herbal compounds corresponding to module 1, 2, 4, and 5, respectively. The herbal compounds are ranked by Promiscuity Index (PI), which is defined as the number of targets connected to a given compound by the preserved CPIs in a detected module. Note that only compounds with PI greater than zero are listed in this figure. The enriched pathways are ranked by the P values calculated in MetaDrug. The circled numbers in brackets after pathway name indicate the major category that pathway belongs to. For example, “ESR1 regulation of G1/S transition” belongs to category 1 and 3, that is, cell cycle and development. The category knowledge is curated from the classification tree of GeneGo pathways in MetaDrug. All pathways in this figure are significant with P values lower than 0.001.

Mentions: We analyzed the underlying biology by performing enrichment analysis with pathways from GeneGo database. For each primary pharmacological unit, we employed the genes within the module as input gene list to search for enriched pathways in GeneGo database. The top 10 enriched pathways corresponding to each module were illustrated in Figure 2. The pathways were sorted according to the P value which measured the significance of a given pathway enriched in the gene list of a pharmacological unit. The bioactive compounds in every pharmacological unit potentially acting on the enriched pathways were also highlighted in Figure 2. The associated herbal compounds were ranked by Promiscuity Index, which was defined as the number of targets connected to a given compound by the preserved CPIs in an identified module (Materials and Methods). From the viewpoint of pathway category, the bioactive compounds in every primary pharmacological unit seemed to particularly interfere with pathways from one or two specific categories. For example, compounds in module 1 generally participate in the processes of cell cycle (4 pathways) and development (4 pathways); the highly enriched pathways of module 2 exhibit high relevance to metabolism (9 pathways), especially the estradiol metabolism (3 pathways); module 4 mostly influence the biological processes related to apoptosis and survival (10 pathways); and module 5 interfere in the activities of cell adhesion (4 pathways) and cytoskeleton remodeling (3 pathways) as well as immune response (3 pathways). Despite of the redundancy of GeneGo pathways, we could see that each of the four pharmacological units tends to regulate relevant pathways from specific categories, which implies that SHU formula carries out pharmacological efficacy by simultaneously intervening pathological activities from distinct aspects at the pathway level. Since the module analysis approach was applied to SHU formula generated explicit results as exhibited in Figure 2, we should verify the reliability of the prediction and evaluate the relevance of SHU formula to influenza infection.


A Module Analysis Approach to Investigate Molecular Mechanism of TCM Formula: A Trial on Shu-feng-jie-du Formula.

Song J, Zhang F, Tang S, Liu X, Gao Y, Lu P, Wang Y, Yang H - Evid Based Complement Alternat Med (2013)

(a), (b), (c), and (d) Top 10 enriched pathways and associated herbal compounds corresponding to module 1, 2, 4, and 5, respectively. The herbal compounds are ranked by Promiscuity Index (PI), which is defined as the number of targets connected to a given compound by the preserved CPIs in a detected module. Note that only compounds with PI greater than zero are listed in this figure. The enriched pathways are ranked by the P values calculated in MetaDrug. The circled numbers in brackets after pathway name indicate the major category that pathway belongs to. For example, “ESR1 regulation of G1/S transition” belongs to category 1 and 3, that is, cell cycle and development. The category knowledge is curated from the classification tree of GeneGo pathways in MetaDrug. All pathways in this figure are significant with P values lower than 0.001.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3860149&req=5

fig2: (a), (b), (c), and (d) Top 10 enriched pathways and associated herbal compounds corresponding to module 1, 2, 4, and 5, respectively. The herbal compounds are ranked by Promiscuity Index (PI), which is defined as the number of targets connected to a given compound by the preserved CPIs in a detected module. Note that only compounds with PI greater than zero are listed in this figure. The enriched pathways are ranked by the P values calculated in MetaDrug. The circled numbers in brackets after pathway name indicate the major category that pathway belongs to. For example, “ESR1 regulation of G1/S transition” belongs to category 1 and 3, that is, cell cycle and development. The category knowledge is curated from the classification tree of GeneGo pathways in MetaDrug. All pathways in this figure are significant with P values lower than 0.001.
Mentions: We analyzed the underlying biology by performing enrichment analysis with pathways from GeneGo database. For each primary pharmacological unit, we employed the genes within the module as input gene list to search for enriched pathways in GeneGo database. The top 10 enriched pathways corresponding to each module were illustrated in Figure 2. The pathways were sorted according to the P value which measured the significance of a given pathway enriched in the gene list of a pharmacological unit. The bioactive compounds in every pharmacological unit potentially acting on the enriched pathways were also highlighted in Figure 2. The associated herbal compounds were ranked by Promiscuity Index, which was defined as the number of targets connected to a given compound by the preserved CPIs in an identified module (Materials and Methods). From the viewpoint of pathway category, the bioactive compounds in every primary pharmacological unit seemed to particularly interfere with pathways from one or two specific categories. For example, compounds in module 1 generally participate in the processes of cell cycle (4 pathways) and development (4 pathways); the highly enriched pathways of module 2 exhibit high relevance to metabolism (9 pathways), especially the estradiol metabolism (3 pathways); module 4 mostly influence the biological processes related to apoptosis and survival (10 pathways); and module 5 interfere in the activities of cell adhesion (4 pathways) and cytoskeleton remodeling (3 pathways) as well as immune response (3 pathways). Despite of the redundancy of GeneGo pathways, we could see that each of the four pharmacological units tends to regulate relevant pathways from specific categories, which implies that SHU formula carries out pharmacological efficacy by simultaneously intervening pathological activities from distinct aspects at the pathway level. Since the module analysis approach was applied to SHU formula generated explicit results as exhibited in Figure 2, we should verify the reliability of the prediction and evaluate the relevance of SHU formula to influenza infection.

Bottom Line: We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula.This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis.Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis.

View Article: PubMed Central - PubMed

Affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

ABSTRACT
At the molecular level, it is acknowledged that a TCM formula is often a complex system, which challenges researchers to fully understand its underlying pharmacological action. However, module detection technique developed from complex network provides new insight into systematic investigation of the mode of action of a TCM formula from the molecule perspective. We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula. This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis. We employed this approach to study Shu-feng-jie-du (SHU) formula, which aimed at discovering its molecular mechanism in defending against influenza infection. Actually, four primary pharmacological units were identified from the 2-HN for SHU formula and further analysis revealed numbers of biological pathways modulated by the four pharmacological units. 24 out of 40 enriched pathways that were ranked in top 10 corresponding to each of the four pharmacological units were found to be involved in the process of influenza infection. Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis.

No MeSH data available.


Related in: MedlinePlus