Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway
MOTIVATION: With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes/proteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations. RESULTS: Our DPA approach allows for the identification of pathways that display distinct responses across multiple perturbations. To demonstrate the utility and effectiveness, we evaluated DPA under various simulated scenarios and applied it to study insulin action in adipocytes. A major action of insulin in adipocytes is to regulate the movement of proteins from the interior to the cell surface membrane. Quantitative mass spectrometry-based proteomics was used to study this process on a large-scale. The combined dataset comprises four separate treatments. By applying DPA, we identified that several insulin responsive pathways in the plasma membrane trafficking are only partially dependent on the insulin-regulated kinase Akt. We subsequently validated our findings through targeted analysis of key proteins from these pathways using immunoblotting and live cell microscopy. Our results demonstrate that DPA can be applied to dissect pathway networks testing diverse hypotheses and integrating multiple experimental perturbations. AVAILABILITY AND IMPLEMENTATION: The R package 'directPA' is distributed from CRAN under GNU General Public License (GPL)-3 and can be downloaded from: http://cran.r-project.org/web/packages/directPA/index.html CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
|ISBN||1367-4811 (Electronic) 1367-4803 (Linking)|
|Authors||Yang, P.; Patrick, E.; Tan, S. X.; Fazakerley, D. J.; Burchfield, J.; Gribben, C.; Prior, M. J.; James, D. E.; Hwa Yang, Y.;|
|Published Date||2014-01-01 00:00:00|