Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from...

Area of Science:

• Systems Biology
• Computational Biology
• Genetics

Background:

• Gene inactivation causes diverse phenotypes by affecting biological processes and downstream genes.
• Understanding gene interactions in functional networks is crucial for deciphering phenotype origins.
• Reactome Knowledgebase and Gene Ontology-Causal Activity Models (GO-CAMs) provide computable representations of biological pathways.

Purpose of the Study:

• To develop orthologous mouse GO-CAMs from human Reactome pathways for cross-species knowledge transfer.
• To define sets of causally connected genes using these mouse GO-CAMs.
• To demonstrate that distinct gene network paths lead to distinguishable phenotypes by analyzing glycolysis and gluconeogenesis.

Main Methods:

• Converted human Reactome pathways into mouse GO-CAMs.
• Defined sets of genes functioning in causally connected pathways.
• Cross-queried mouse phenotype annotations in the Mouse Genome Database (MGD) using pathway gene sets.

Main Results:

• Successfully created mouse GO-CAMs to facilitate pathway knowledge transfer between humans and model organisms.
• Identified sets of genes functioning in causally connected ways within the defined pathways.
• Showcased how perturbations in specific causal paths of glycolysis and gluconeogenesis pathways result in distinct phenotypic outcomes.

Conclusions:

• Individual causal pathways within gene networks yield discrete phenotypic outcomes.
• This strategy accurately describes gene interactions and can predict phenotypic outcomes of novel gene variants.
• The approach is applicable to less-studied processes and model systems for identifying potential gene targets.