Pathway databases: making chemical and biological sense of the genomic data flood

Abstract

Pathway databases are a means to systematically associate proteins with their functions and link them into networks that describe the reaction space of an organism. Here, the Reactome Knowledgebase provides a convenient example to illustrate strategies used to assemble such a reaction space based on manually curated experimental data, approaches to semiautomated extension of these manual annotations to infer annotations for a large fraction of a species' proteins, and the use of networks of functional annotations to infer pathway relationships among variant proteins that have been associated with disease risk through genome-wide surveys and resequencing studies of tumors.

Figure 1

Human Hippo signaling pathway. Kinase cascade components STK, LATS, MOB, and SAV1 interact, leading to the phosphorylation and cytosolic sequestration of YAP and WWTR1 transcription factors. Caspase 3 may activate this process by cleaving STK3, increasing its kinase activity. Cytosolic sequestration of YAP and WWTR1 may also be mediated by AMOT and ZO proteins.

Figure 2

Schematic view of Reactome data model. Relationships of event and physical entity classes to one another and to external reference databases and ontologies are shown.

Figure 3

Visualization of curated components of pathways and inferred interacting proteins and small molecules. A. A diagram of the Hippo/STK signaling pathway as annotated in Reactome, using SBGN-like iconography to display proteins and complexes (rectangles) and small molecules (ovals), connected by reaction edges that distinguish inputs (plain end), outputs (arrowhead end) and catalysis (open-circle end) as well as reaction type (open box reaction node, chemical transformation; closed circle, binding). B. A portion of the manually annotated pathway is enlarged and overlaid with proteins found in high-throughput surveys (Wu et al. 2010) to interact with Hippo pathway components STK3 (MST2) or STK4 (MST1) or both. C) The manually annotated pathway is overlaid with small molecules from the ChEMBL database (Gaulton et al. 2012) that bind STK3 (MST2) or STK4 (MST1) or both.

Figure 4

Clustering genes with functional information from curated and inferred pathway database content. Seventy-nine genes whose variants are associated with clinically important red blood cell phenotypes in humans (circles) (van der Harst et al. 2012), together with fifty-five genes identified as their functional interactors (diamonds) are grouped into ten modules with an edge-betweenness strategy (Wu et al. 2010). Modules are distinguished by color; manually annotated interactions between gene products are shown as solid lines; inferred pairwise interactions are shown as dotted lines; arrowheads indicate activation or catalysis; T-bars indicate inhibition. The largest module (34 total nodes, 16 linkers) is enlarged, and the inset shows the quality scores for the inferred pairwise functional interaction between TAL1 (RBC variant gene) and HDAC2 (linker).