Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data

Abstract

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway

“…Thus, inhibiting normal autocrine signaling in HMECs by blocking EGFR with the monoclonal antibody 225 mAb (42) should show a reciprocal effect to adding exogenous EGF. Because of the relatively low sensitivity of global proteomics measurements in detecting EGF-induced protein changes (34), we used transcriptome assays (microarray and RNA-Seq) as a first-pass surrogate for relative protein abundance. To determine which EGF-induced changes resulted from activation of the MAPK pathway, we identified genes whose expression was modulated by the addition of the MEK inhibitor U0126.…”

A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

“…Bioinformatics is also faced with the challenge of how to best integrate multiple data types. Transcriptome data provides a readout for gene regulation at the mRNA level, however, correlation of mRNA with its associated protein expression can be relatively low [7,8]. Proteome data provides a complementary picture of protein expression levels; but current proteomics technologies provide only limited coverage of the proteome.…”

“…Proteome data provides a complementary picture of protein expression levels; but current proteomics technologies provide only limited coverage of the proteome. Despite these limitations, integration of these discrete data types has merit and can provide significantly improved coverage of signaling networks [7]. Our group and others have developed advanced bioinformatics capabilities to facilitate the integration of diverse data types [7,[9][10][11].…”

Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process

“…A number of transcriptome and proteome data integration studies have already been reported e.g. in cell lines [66][67][68], plants [69], and mammal models [6,[70][71][72][73]. The correlation between mRNA and protein abundances in the cell has been reported to be notoriously poor.…”