SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping

Abstract

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

Figure 1.

Schematic of the SymMap database. (A) Upper panel: the six components contained in SymMap are illustrated in six circles in different colors in the middle. The blue arcs connecting the circles show the six direct associations, with the numbers of associations shown at the left. The gray dotted lines connecting the six components show the nine indirect associations, with the numbers of associations shown in the right. Lower panel: implementation of the functions of SymMap. (B) Illustration of the scheme for extraction, curation and standardization of TCM symptom terms and their relationships to herbs. (C) Illustration of the scheme for expert curation of TCM symptom-MM symptom mapping.

Figure 2.

Characteristics of the SymMap integrative network. (A) Box plots show the distribution of association numbers per item for six direct associations. For each association between component 1–component 2, two boxes are shown. The first box in blue shows the distribution of component 1, whereas the second box in orange shows the distribution of component 2. (B) Bar plots show the total number of associations for seven indirect associations. For each association, three bars are shown. The first bar in blue shows the full set, the second bar in orange show the loosely selected set (P-value <0.05), and the third bar in green shows the stringently selected set (FDR_BH < 0.05). (C) The sources of indirect herb-disease associations are shown. Associations inferred via symptoms are shown in blue, whereas those inferred via ingredients are shown in orange, those inferred via both symptoms and ingredients are shown in green, and those also be curated manually are shown in red. (D) The distribution of node degrees in the heterogeneous network of SymMap, with direct associations shown in blue and indirect associations shown in orange.

Figure 3.

An illustration of the SymMap search. (A) The index page of SymMap shows the database overview. (B) The summary panel in the details page shows descriptive information for the search item. (C) The network panel in the details page shows all related components for the search item, with nodes colored by their source component. Holding the mouse pointer over the node highlights the node and its related edges, while showing its ID and name, as well as a link to its details page. (D) The list panel shown in tables. For each search item, five tables can be selected for the five other related components. For each table, three datasets can be selected by the users: the full set, the loosely selected set with P-values smaller than 0.05, and two stringently selected sets with FDRs (Bonferroni and BH) smaller than 0.05. All related components can be downloaded by pressing the button at the upper right.