A Trans-omics Approach to Identify Novel Regulators of Hepatic Metabolism — ASN Events
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A Trans-omics Approach to Identify Novel Regulators of Hepatic Metabolism (#168)

Anna C. Calkin 1 , Benjamin L. Parker 2 , Sarah C. Moody 1 , Eser J. Zerenturk 1 , Yingying Liu 1 , Elizabeth J. Tarling 3 , Ross Lazarus 1 , Peter J. Meikle 1 , Thomas Q. Vallim 3 , Aldons J. Lusis 3 , David E. James 2 , Brian G. Drew 1
  1. BakerIDI Heart & Diabetes Institute, Prahran, VIC, Australia
  2. Metabolic Systems Biology Group, Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia
  3. David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

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 ailments 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, metabolomics, 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 diverse inbred mouse strains, which to our knowledge is the largest 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 under the same conditions. We performed deep proteomic analysis on livers (~320) by performing 34 separate TMT-10plex multidimensional LC-MS/MS experiments with SPS-MS3 acquisition on an Orbitrap Fusion. On the same liver samples we performed quantitative lipidomics analysis using LC-MS/MS on an AB Sciex API4000 Q/TRAP and Analyst 1.5 data system.

Results: Proteomic analysis resulted in quantification of >5,000 proteins with excellent reproducibility within strains and significant variance in ~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. Bioinformatics analysis has identified numerous protein-lipid associations. Once combined with pre-exisiting phenomics and genomics data, we will begin dissecting the interacting networks and disease phenotypes.

Conclusions: We have established a high-resolution trans-omics network for the identification of major regulators of hepatic metabolism. We believe this to be the largest of its kind in Australia, if not internationally.

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