Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from...

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Gene inactivation leads to diverse phenotypes by affecting biological pathways.
• Understanding gene interactions within functional networks is crucial for biological insight.
• Existing resources like Reactome and Gene Ontology-Causal Activity Models (GO-CAMs) represent biological pathways computationally.

Approach:

• Developed a computational method to convert Reactome pathways into GO-CAMs.
• Created orthologous mouse GO-CAMs from human Reactome GO-CAMs for cross-species pathway knowledge transfer.
• Cross-queried mouse phenotype annotations in the Mouse Genome Database (MGD) using genes from defined pathway models.

Key Points:

• Mouse GO-CAMs enable the definition of functionally connected gene sets.
• Analysis of gluconeogenesis and glycolysis pathways using GO-CAMs identified discrete phenotypic outcomes from gene perturbations.
• The strategy accurately recovered gene interaction details in well-studied pathways.

Conclusions:

• This computational strategy effectively links gene networks to specific phenotypic outcomes.
• The approach can be applied to less understood biological processes and model systems.
• Predicting novel gene variant phenotypes and identifying therapeutic targets are potential applications.