Good and bad fat: discovering key distinguishing features with multifactorial proteomics data

Deshpande V1, Humphrey SJ1, Yang P2, Lo K2, Healy ME1, Cooke KC1, Stoeckli J1, Yang JYH2 and James DE1

  1. Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia.
  2. School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia.

Subcutaneous (SC) and visceral (VIS) adipocytes store energy as fat and regulate whole body metabolism. Excessive VIS fat is associated with insulin resistance, a precursor to Type 2 diabetes. In contrast, SC fat may be protective. Despite these important physiological functions, relatively little is known about the molecular features that distinguish these discrete adipose depots. We have used mass spectrometry to construct proteomes of mouse SC and VIS depots, which consist of >7,500 quantified proteins spanning six orders of magnitude. Our study consists of three experimental variables: depot (SC and VIS), diet (normal and high-fat) and sample type (adipocytes and whole tissue). Given this multifactorial experimental design, we devised a computational framework to comprehensively and systematically answer biological questions. This bioinformatic approach involved a series of ANOVA models to stratify the proteome into distinct classes defined by the variable(s) driving the changes in protein expression. We verified that positive control proteins were assigned to expected classes. Our results suggest that adipocytes, rather than the local microenvironment, drive major differences between SC and VIS depots. Of the total proteins, ~2% were upregulated in SC relative to VIS, and ~4% upregulated in VIS relative to SC. These included coenzyme Q and lipolysis proteins in SC, and collagens and cathepsins in VIS. Thus we demonstrate the utility of this proteomic resource and analytic approach in uncovering novel insights into adipocyte biology.