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The implications of relationships between human diseases and metabolic subpathways.

Li X, Li C, Shang D, Li J, Han J, Miao Y, Wang Y, Wang Q, Li W, Wu C, Zhang Y, Li X, Yao Q - PLoS ONE (2011)

Bottom Line: We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased.Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged.The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway.

View Article: PubMed Central - PubMed

Affiliation: Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, and College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. lixia@hrbmu.edu.cn

ABSTRACT
One of the challenging problems in the etiology of diseases is to explore the relationships between initiation and progression of diseases and abnormalities in local regions of metabolic pathways. To gain insight into such relationships, we applied the "k-clique" subpathway identification method to all disease-related gene sets. For each disease, the disease risk regions of metabolic pathways were then identified and considered as subpathways associated with the disease. We finally built a disease-metabolic subpathway network (DMSPN). Through analyses based on network biology, we found that a few subpathways, such as that of cytochrome P450, were highly connected with many diseases, and most belonged to fundamental metabolisms, suggesting that abnormalities of fundamental metabolic processes tend to cause more types of diseases. According to the categories of diseases and subpathways, we tested the clustering phenomenon of diseases and metabolic subpathways in the DMSPN. The results showed that both disease nodes and subpathway nodes displayed slight clustering phenomenon. We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased. Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged. Furthermore, the coexpression levels between disease genes and other types of genes were calculated for each subpathway in the DMSPN. The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway.

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Mapping between diseases and subpathways.(A) Distribution of the number of mapped diseases per subpathway. (B) Distribution of the number of mapped subpathways per disease.
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pone-0021131-g003: Mapping between diseases and subpathways.(A) Distribution of the number of mapped diseases per subpathway. (B) Distribution of the number of mapped subpathways per disease.

Mentions: The DMSPN with k = 3 was composed of 545 nodes (302 subpathways and 243 diseases), and 4288 edges (Figure 2). The edges in the DMSPN were denser than expected by chance with P-values<0.001 (see Text S1 and Figure S2C), and most nodes formed a giant connected component, suggesting that the diseases and metabolic subpathways were much closely connected at the system level. The degree of subpathway nodes as well as that of disease nodes (circular and rectangular nodes of different sizes in Figure 2) had fat tails (Figure 3). On average, a subpathway in the DMSPN was associated with 14 diseases, and the initiation and progression of a disease involved 20 subpathways. The degree distribution of subpathway nodes was much broader than that of disease nodes. To further test the differences between the degree distribution of the subpathway and disease nodes, we generated 1000 random networks and compared them with the actual DMSPN (see Text S1). We found that the degree distribution of the subpathway nodes of the actual DMSPN was significantly broader than that of the random networks (P-value<10e–10, see SI Figure S2A), whereas the distribution of disease nodes did not display such a highly significant difference (P-value = 0.02, see SI Figure S2B), suggesting that the different subpathways in the DMSPN possessed the considerable differences with respect to causing diseases. On the one hand, few metabolic subpathways were linked to many diseases. The degrees of only 20 subpathways (nodes with degree>80 in Figure 3A, and ellipse I region at the central top part of Figure 2) among the 302 total subpathways were significantly higher than the degrees of other subpathways. Interestingly, most of these connected subpathways were involved with lipid metabolism (9/20) (red rectangular nodes) and amino acid metabolism (7/20) (orchid rectangular nodes), suggesting that abnormalities in fundamental metabolism may tend to cause more types of disease. For example, for lipid metabolism, five of the nine subpathways were involved with arachidonic acid metabolism. Some studies showed that arachidonic acid could generate many compounds with different cellular or extracellular tissue and organ targets, causing a wide range of clinical effects [26]. Arachidonic acid metabolism has been reported to be associated with different kinds of diseases, including inflammation, hypertension, diabetes, cardiovascular diseases, depression, schizophrenia, Alzheimer's disease and cancer [27]. Of seven amino acid metabolism subpathways, five subpathways belonged to tryptophan metabolism. The dysfunction of tryptophan metabolism has been found to be associated with many diseases including cancer, and psychological, immune and cardiovascular diseases [28], [29], [30], [31]. Of the other 4 of the 20 subpathways with degree>80, the subpathways path:00980_1 and path:00361_1 with degrees of 122 and 105, respectively, belonged to metabolism of xenobiotics by cytochrome P450 and gamma-hexachlorocyclohexane degradation, which were directly related to xenobiotic degradation and metabolism. Cytochrome P450 is involved in the bioactivation and detoxification of a variety of xenobiotics [32]. Abnormal metabolism of xenobiotics by P450 may cause inborn errors in metabolism and contribute to many clinically relevant diseases [33]. The subpathways path:00232_1 and path:00232_2 (light green rectangular nodes) belonged to caffeine metabolism, were associated with 98 diseases in the DMSPN and had been proved to be potentially related to many diseases including Alzheimer's disease, asthma, cancer, diabetes and Parkinson's disease [34]. On the other hand, many metabolic subpathways were connected to only a few diseases in the DMSPN. For example, path:00072_1, path:00512_1 and path:00631_1 were only connected to one disease. The synthesis and degradation pathway of ketone bodies (path:00072_1), which belonged to a subpathway of lipid metabolism, was only associated with lipid-related diseases (ellipse II in Figure 2). Similarly, the O-glycan biosynthesis subpathway (path:00512_1) was only linked to IgA-nephropathy in the DMSPN (ellipse III) [35], and the 1,2-dichloroethane degradation subpathway (path:00631_1), which was found to be associated with the initiation and progression of cancer [36], was only linked to laryngeal cancer (ellipse IV).


The implications of relationships between human diseases and metabolic subpathways.

Li X, Li C, Shang D, Li J, Han J, Miao Y, Wang Y, Wang Q, Li W, Wu C, Zhang Y, Li X, Yao Q - PLoS ONE (2011)

Mapping between diseases and subpathways.(A) Distribution of the number of mapped diseases per subpathway. (B) Distribution of the number of mapped subpathways per disease.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3117879&req=5

pone-0021131-g003: Mapping between diseases and subpathways.(A) Distribution of the number of mapped diseases per subpathway. (B) Distribution of the number of mapped subpathways per disease.
Mentions: The DMSPN with k = 3 was composed of 545 nodes (302 subpathways and 243 diseases), and 4288 edges (Figure 2). The edges in the DMSPN were denser than expected by chance with P-values<0.001 (see Text S1 and Figure S2C), and most nodes formed a giant connected component, suggesting that the diseases and metabolic subpathways were much closely connected at the system level. The degree of subpathway nodes as well as that of disease nodes (circular and rectangular nodes of different sizes in Figure 2) had fat tails (Figure 3). On average, a subpathway in the DMSPN was associated with 14 diseases, and the initiation and progression of a disease involved 20 subpathways. The degree distribution of subpathway nodes was much broader than that of disease nodes. To further test the differences between the degree distribution of the subpathway and disease nodes, we generated 1000 random networks and compared them with the actual DMSPN (see Text S1). We found that the degree distribution of the subpathway nodes of the actual DMSPN was significantly broader than that of the random networks (P-value<10e–10, see SI Figure S2A), whereas the distribution of disease nodes did not display such a highly significant difference (P-value = 0.02, see SI Figure S2B), suggesting that the different subpathways in the DMSPN possessed the considerable differences with respect to causing diseases. On the one hand, few metabolic subpathways were linked to many diseases. The degrees of only 20 subpathways (nodes with degree>80 in Figure 3A, and ellipse I region at the central top part of Figure 2) among the 302 total subpathways were significantly higher than the degrees of other subpathways. Interestingly, most of these connected subpathways were involved with lipid metabolism (9/20) (red rectangular nodes) and amino acid metabolism (7/20) (orchid rectangular nodes), suggesting that abnormalities in fundamental metabolism may tend to cause more types of disease. For example, for lipid metabolism, five of the nine subpathways were involved with arachidonic acid metabolism. Some studies showed that arachidonic acid could generate many compounds with different cellular or extracellular tissue and organ targets, causing a wide range of clinical effects [26]. Arachidonic acid metabolism has been reported to be associated with different kinds of diseases, including inflammation, hypertension, diabetes, cardiovascular diseases, depression, schizophrenia, Alzheimer's disease and cancer [27]. Of seven amino acid metabolism subpathways, five subpathways belonged to tryptophan metabolism. The dysfunction of tryptophan metabolism has been found to be associated with many diseases including cancer, and psychological, immune and cardiovascular diseases [28], [29], [30], [31]. Of the other 4 of the 20 subpathways with degree>80, the subpathways path:00980_1 and path:00361_1 with degrees of 122 and 105, respectively, belonged to metabolism of xenobiotics by cytochrome P450 and gamma-hexachlorocyclohexane degradation, which were directly related to xenobiotic degradation and metabolism. Cytochrome P450 is involved in the bioactivation and detoxification of a variety of xenobiotics [32]. Abnormal metabolism of xenobiotics by P450 may cause inborn errors in metabolism and contribute to many clinically relevant diseases [33]. The subpathways path:00232_1 and path:00232_2 (light green rectangular nodes) belonged to caffeine metabolism, were associated with 98 diseases in the DMSPN and had been proved to be potentially related to many diseases including Alzheimer's disease, asthma, cancer, diabetes and Parkinson's disease [34]. On the other hand, many metabolic subpathways were connected to only a few diseases in the DMSPN. For example, path:00072_1, path:00512_1 and path:00631_1 were only connected to one disease. The synthesis and degradation pathway of ketone bodies (path:00072_1), which belonged to a subpathway of lipid metabolism, was only associated with lipid-related diseases (ellipse II in Figure 2). Similarly, the O-glycan biosynthesis subpathway (path:00512_1) was only linked to IgA-nephropathy in the DMSPN (ellipse III) [35], and the 1,2-dichloroethane degradation subpathway (path:00631_1), which was found to be associated with the initiation and progression of cancer [36], was only linked to laryngeal cancer (ellipse IV).

Bottom Line: We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased.Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged.The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway.

View Article: PubMed Central - PubMed

Affiliation: Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, and College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. lixia@hrbmu.edu.cn

ABSTRACT
One of the challenging problems in the etiology of diseases is to explore the relationships between initiation and progression of diseases and abnormalities in local regions of metabolic pathways. To gain insight into such relationships, we applied the "k-clique" subpathway identification method to all disease-related gene sets. For each disease, the disease risk regions of metabolic pathways were then identified and considered as subpathways associated with the disease. We finally built a disease-metabolic subpathway network (DMSPN). Through analyses based on network biology, we found that a few subpathways, such as that of cytochrome P450, were highly connected with many diseases, and most belonged to fundamental metabolisms, suggesting that abnormalities of fundamental metabolic processes tend to cause more types of diseases. According to the categories of diseases and subpathways, we tested the clustering phenomenon of diseases and metabolic subpathways in the DMSPN. The results showed that both disease nodes and subpathway nodes displayed slight clustering phenomenon. We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased. Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged. Furthermore, the coexpression levels between disease genes and other types of genes were calculated for each subpathway in the DMSPN. The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway.

Show MeSH
Related in: MedlinePlus