title: Protein Co-Expression Analysis as a Strategy to Complement a Standard Quantitative Proteomics Approach: Case of a Glioblastoma Multiforme Study.
abstract: Although correlation network studies from co-expression analysis are increasingly popular, they are rarely applied to proteomics datasets. Protein co-expression analysis provides a complementary view of underlying trends, which can be overlooked by conventional data analysis. The core of the present study is based on Weighted Gene Co-expression Network Analysis applied to a glioblastoma multiforme proteomic dataset. Using this method, we have identified three main modules which are associated with three different membrane associated groups; mitochondrial, endoplasmic reticulum, and a vesicle fraction. The three networks based on protein co-expression were assessed against a publicly available database (STRING) and show a statistically significant overlap. Each of the three main modules were de-clustered into smaller networks using different strategies based on the identification of highly connected networks, hierarchical clustering and enrichment of Gene Ontology functional terms. Most of the highly connected proteins found in the endoplasmic reticulum module were associated with redox activity while a core of the unfolded protein response was identified in addition to proteins involved in oxidative stress pathways. The proteins composing the electron transfer chain were found differently affected with proteins from mitochondrial Complex I being more down-regulated than proteins from Complex III. Finally, the two pyruvate kinases isoforms show major differences in their co-expressed protein networks suggesting roles in different cellular locations.
authors: SynthSys and School of Biological Sciences, Waddington building, University of Edinburgh, Edinburgh, United Kingdom, EH9 3BF.
authors: Evangelos I
authors: EI
authors: Kanonidis
authors: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh United Kingdom, EH16 4SB.
authors: Marcia M
authors: MM
authors: Roy
authors: Edinburgh Medical School: Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom, EH8 9AG.
authors: Ruth F
authors: RF
authors: Deighton
authors: SynthSys and School of Biological Sciences, Waddington building, University of Edinburgh, Edinburgh, United Kingdom, EH9 3BF.
authors: Thierry
authors: http://orcid.org/0000-0003-0498-8063
authors: T
authors: Le Bihan
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D053263:Gene Regulatory Networks; D005909:Glioblastoma / Q000378:metabolism*; D008954:Models, Biological; D040901:Proteomics / Q000379:methods*