Characterizing the network of drugs and their affected metabolic subpathways

Abstract

A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug-metabolic subpathway network (DRSN). This network included 3925 significant drug-metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug-disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.

Figure 1. Schematic of the construction of the drug–metabolic subpathway network.

We generated the drug–metabolic subpathway network (DRSN) based on the drug-affected genes and pathway structure data from KEGG. First, we selected the small molecules which were used as drugs and then found the affected genes for each drug from corresponding expression profiles in CMap. Second, for each drug, we applied the “k-clique” subpathway identification method to identify the statistically significantly enriched subpathways by setting k = 3 and P-value <0.01 to obtain associations between the drugs and metabolic subpathways. Finally, we combined these drug–metabolic subpathway associations to form the DRSN.

Figure 2. The DRSN network.

The circles and rectangles in the network correspond to drugs and metabolic subpathways, respectively. A drug and a metabolic subpathway are connected by an edge if the corresponding drug-affected genes are statistically significantly enriched to the corresponding subpathway. Node size is proportional to the degree of the node. Nodes are colored according to 14 ATC and 11 KEGG pathway categories.

Figure 3. Three examples representing features of DRSN.

All the examples are from the DRSN (A) Some antibiotic drugs were linked to steroid hormone biosynthesis (path:00140_1). (B) Berberine and physostigmine were linked to the same subpathways. (C) Some drugs and subpathways were closely connected closely in the DRSN. The drugs mainly belonged to alimentary tract and metabolism, nervous system, and genitourinary system and sex hormones classes. The subpathways mainly belonged to three pathways: androgen and estrogen metabolism (path:00150), tryptophan metabolism (path:00380) and arachidonic acid metabolism (path:00590).

Figure 4. The relationship between drug dual effects and metabolic subpathways in the DRSN.

(A) Of 1586 connected drug pairs (drugs that were linked to the same subpathways), 67 shared the same indications, compared to 55 drug pairs on average of 1000 randomized 1586 drug pairs. Of 1000 times randomized 1586 drug pairs, there were only 36 times when the number of drug pairs which shared the same indications were more than 67 (P-value = 0.036). (B) The number of side effects shared by connected drug pairs was significantly higher than the number of side effects shared by all drug pairs in the SIDER database (P-value <10). (C) The number of shared side effects significantly increased as the number of the shared subpathways increased between two drugs (P-value = 0.0034). The grey horizontal line is the average number of side-effects all drug pairs shared. The Y axis represents the number of side-effects shared by drug pairs. The X axis represents the number of the same subpathways drug pairs shared. Blue “▪” symbols correspond to the binned average number of side-effects shared by drug pairs. The linear regression model (black line) is used to test the trends in correlations and the significance of the trends is estimated.

Figure 5. Drug–disease associations through affected metabolic subpathways.

(A) There were 743 metabolic subpathways when the setting k = 3. There were 302 disease–related subpathways according to the disease–metabolic subpathways association in the DMSPN and 403 drug-affected subpathways in the DRSN. 230 subpathways were overlapped between the two sets of subpathways. (B) The association scores between each drug class and disease class. The darker color represents the higher association scores.

Figure 6. Tissue-specific differences between therapeutic and non-therapeutic subpathways Therapeutic and non-therapeutic subpathways had tissue-specific differences.

(A) The degree of therapeutic subpathways (THSP) was significantly higher than that of non-therapeutic subpathways (NTHSP) (P-value <10) and all subpathways (ALLSP) (P-value <10). (B) There were no significant differences in the ratios of drug-affected HKGs between the two types of subpathway (P-value = 0.65) as well as that between therapeutic subpathways and all subpathways (P-value = 0.73). (C) The ratios of drug-affected TEGs in drug therapeutic subpathways (average ratio = 0.14) were higher than that of non-therapeutic subpathways (average ratio = 0.065; P-value = 0.0001) and that of all subpathways (average ratio = 0.08; P-value = 0.003) (D) TH coefficients of drug therapeutic subpathway were significant higher than that of non-therapeutics (P = 0.0001) (E) The sum of TH coefficients of drug therapeutic subpathways in different tissues (F) The sum of TH coefficients of drug non-therapeutic subpathways in different tissues.