SYM-50-05

Using a multi-omics approach to define the genetic architecture of hepatic lipid metabolism

AC Calkin1, BL Parker2, EJ Tarling3, PJ Meikle1, SC Moody1, EJ Zerenturk1, AJ Lusis3, TQ De Aguiar Vallim3, DE James2 and BG Drew1

  1. Baker IDI Heart & Diabetes Institute
  2. Charles Perkin Centre, University of Sydney
  3. University of California, Los Angeles

Background: The liver controls numerous pathways central to the maintenance of whole body lipid and glucose metabolism. Accordingly, disruption of these pathways promotes diseases including hepatosteatosis, insulin resistance and cardiovascular disease. However, even though these pathologies are amongst the leading causes of death in developed countries, their mechanistic underpinnings are still not well defined.
Aims & Approach: In this study we sought to use a trans-omics approach utilising genetics, phenomics, lipidomics and proteomics to identify novel pathways involved in regulating hepatic metabolism. To do this we took advantage of our exclusive access to a panel of >100 genetically inbred mouse strains, which to our knowledge is the largest and most diverse of its kind in the world, known as the hybrid mouse diversity panel (HMDP) at UCLA.
Methods: We collected livers (n=3) from male mice of 107 HMDP strains that were all housed, fed and sacrificed at the same age and under the same conditions. We performed deep proteomic analysis on livers (~320) by performing 34 separate TMT-10 plex multidimensional LC-MS/MS experiments with SPS-MS3 acquisition on an Orbitrap Fusion. We also performed quantitative lipidomics analysis using LC-MS/MS on an AB Sciex API4000 Q/TRAP system on livers and plasma of the same mice.
Results: Proteomic analysis resulted in quantification of >5,000 proteins with excellent reproducibility within strains and significant variance >2500 proteins between the 107 strains. Lipidomics analysis resulted in quantification of 311 lipid species across 23 lipid classes in which significant variation was observed in >100 lipid species. Subsequent analysis has identified numerous novel proteins that associate with lipid accumulation in the liver, together with many genetic and protein signatures that define hepatic lipid metabolism.
Conclusions: We have established a high-resolution trans-omics network for the identification of major regulators of hepatic lipid metabolism. We believe this to be the largest and highest resolution of its kind in Australia, if not internationally.