title: Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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
  
  
  BACKGROUND
  jean.yang@sydney.edu.au
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Systems Biology Group, Biostatistics Branch, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA, School of Mathematics and Statistics, University of Sydney, Diabetes and Obesity Program, Garvan Institute of Medical Research, NSW 2006, Australia and Metabolism in Human Disease Unit, Institute of Molecular and Cellular Biology, A*Star, 61 Biopolis Drive, Proteos 138673, Singapore.
authors.forename: Pengyi
authors.initials: P
authors.lastname: Yang
authors.forename: Ellis
authors.initials: E
authors.lastname: Patrick
authors.forename: Shi-Xiong
authors.initials: SX
authors.lastname: Tan
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Fazakerley
authors.forename: James
authors.initials: J
authors.lastname: Burchfield
authors.forename: Christopher
authors.initials: C
authors.lastname: Gribben
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Prior
authors.forename: David E
authors.initials: DE
authors.lastname: James
authors.forename: Yee
authors.initials: Y
authors.lastname: Hwa Yang
mesh_terms: D017667:Adipocytes / Q000378:metabolism; D001692:Biological Transport; D002462:Cell Membrane / Q000378:metabolism; D007328:Insulin / Q000378:metabolism*; D019869:Phosphatidylinositol 3-Kinases / Q000378:metabolism; D020543:Proteome / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D051057:Proto-Oncogene Proteins c-akt / Q000378:metabolism; D012984:Software
chemical_list: D007328:Insulin; D020543:Proteome; D019869:Phosphatidylinositol 3-Kinases; D051057:Proto-Oncogene Proteins c-akt