Application of multi-resolution partitioning of interaction networks to the study of complex disease

Thesis

Large-scale gene expression studies are widely used to identify genes that are differentially expressed between phenotypes relevant to disease. Often thousands of differentially expressed genes (DEGs) are found using this type of analysis, which complicates the interpretation of the data. In this project we treat DEGs as windows into the biological processes that underlie disease. In order to find these processes, we put DEGs into the context in which they perform their functions – through the interactions of their protein products.

Protein-protein interactions can provide biological context to DEGs in the form of functional modules. These modules are groups of proteins that together perform cellular functions. In this thesis we have refined a functional module detection process that consists of two steps. Firstly, community detection methods are applied to protein interaction networks (PINs) to detect groups of interacting proteins, and secondly, the biological coherence of the proteins grouped together is evaluated to select communities that represent potential functional modules.

Two features that are central to this work are the detection of modules at different scales of network organization, and CommWalker, a module evaluation method that we developed which is able to detect signals of poorly-studied functions. By integrating these methods into our functional module detection process, we were able to obtain a good coverage of potential functional modules. Testing for enrichment of DEGs on these functional modules can uncover biological processes that are involved in the contrasted phenotypes and merit further investigation.

We have applied our pipeline to find differentially regulated functions between hypoxic and normoxic breast cancer cell lines, and between M1 and M2 macrophages. Our results generate biological hypotheses of cellular functions that are differentially regulated in the investigated phenotypes, and proteins that are involved in these functions. We were able to validate several proteins in enriched modules which did not correspond to DEGs that were input into the pipeline, which suggests our methodology can reveal new biological insight.

Authors: Luecken, M

• Alternative title: Interpreting gene expression data using functional modules detected in protein-protein interaction networks
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Biological Network Analysis
• Subjects: Bioinformatics