Systems analysis of gene ontology and biological pathways involved in post-myocardial infarction responses

Abstract

Background: Pathway analysis has been widely used to gain insight into essential mechanisms of the response to myocardial infarction (MI). Currently, there exist multiple pathway databases that organize molecular datasets and manually curate pathway maps for biological interpretation at varying forms of organization. However, inconsistencies among different databases in pathway descriptions, frequently due to conflicting results in the literature, can generate incorrect interpretations. Furthermore, although pathway analysis software provides detailed images of interactions among molecules, it does not exhibit how pathways interact with one another or with other biological processes under specific conditions.

Methods: We propose a novel method to standardize descriptions of enriched pathways for a set of genes/proteins using Gene Ontology terms. We used this method to examine the relationships among pathways and biological processes for a set of condition-specific genes/proteins, represented as a functional biological pathway-process network. We applied this algorithm to a set of 613 MI-specific proteins we previously identified.

Results: A total of 96 pathways from Biocarta, KEGG, and Reactome, and 448 Gene Ontology Biological Processes were enriched with these 613 proteins. The pathways were represented as Boolean functions of biological processes, delivering an interactive scheme to organize enriched information with an emphasis on involvement of biological processes in pathways. We extracted a network focusing on MI to demonstrate that tyrosine phosphorylation of Signal Transducer and Activator of Transcription (STAT) protein, positive regulation of collagen metabolic process, coagulation, and positive/negative regulation of blood coagulation have immediate impacts on the MI response.

Conclusions: Our method organized biological processes and pathways in an unbiased approach to provide an intuitive way to identify biological properties of pathways under specific conditions. Pathways from different databases have similar descriptions yet diverse biological processes, indicating variation in their ability to share similar functional characteristics. The coverages of pathways can be expanded with the incorporation of more biological processes, predicting involvement of protein members in pathways. Further, detailed analyses of the functional biological pathway-process network will allow researchers and scientists to explore critical routes in biological systems in the progression of disease.

Figure 1

The graph showing Number of possible edges vs. Cut-off value, and the selected number of edges. Choosing top 1% of the most similar pairs of pathway and biological process considered a reasonable number of pairs of pathways and biological processes with high similarity scores.

Figure 2

Representations of TGF-beta signalling pathway from Biocarta, KEGG and RACTOME in terms of Gene Ontology biological processes in the condition of MI. Box 1: GOBP exclusive to REACTOME REACT_6844: Signaling by TGF beta. Box 2: Common GOBP between REACTOME and KEGG. Box 3: GOBP exclusive to KEGG has04350: TGF-beta Signaling Pathway. Box 4: GOBP exclusive to BioCarta h_tgfbPathway. Box 5: Common GOBP between BioCarta and REACTOME. Box 6: Common GOBP between BioCarta, KEGG and REACTOME.

Figure 3

Sub-network of Cardiomyopathy. Pathways were represented in red while GOBPs were represented in blue. Pathways of Hypertrophic, Dilated and Arrhythmogenic Right Ventricular Cardiomyopathy were shown to be connected to biological processes including leukocyte adhesion, cell-substrated adhesion, and cell-matrix adhesion. Integrin-ECM interactions are required for cell adhesion.

Figure 4

Sub-network of MI. Pathways were represented in while GOBPs were represented in blue. The major underlying processes for MI included coagulation, homeostasis, collagen metabolic/biosynthetic process, calcium ion transport, tissue regeneration, and wound healing.

Figure 5

Logical circuit of h_amiPathway. Logical circuits described the relationships between GO biological processes and the MI pathway. We used multiple input single output logical gates AND and OR, where the GOBP were the inputs and h_amiPathway were the outputs. The extracted network of MI identified five major GOBP terms, including tyrosine phosphorylation of STAT protein (ΔB79), coagulation (ΔB10), negative and positive regulation of blood coagulation (ΔB23 & ΔB30), and positive regulation of collagen metabolic process (ΔB32), required to activate the MI pathway. The labels next to the name of the GOBP terms corresponded to the legend in Figure 6.

Figure 6

The extracted MI network. The acute MI pathway was colored in red while other pathways were colored in light red. Biological processes were represented in blue circles. GOBPs having direct impact on h_amiPathways were represented as blue triangles. A small branch of the network inside the blue rectangle involving coagulation was zoomed out for demonstration. Below are legends for selected pathways and processes (for the complete list of pathways and processes, see Supplemental Table 1). P3: h_amiPathway. P40: h_tgfbPathway. P58: hsa04350:TGF-betaSignalingPathway. P92: REACT_6844:Signaling by TGF beta. B10: coagulation. B30: positive regulation of blood coagulation. B32: positive regulation of collagen metabolic process. B44: positive regulation of protein kinase B signaling cascade. B58: regulation of kinase activity. B49: protein kinase cascade. B79: tyrosine phosphorylation of STAT protein.