An integrative systems genetic analysis of mammalian lipid metabolism

Abstract

Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.

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Fig. 1: Lipidomic analysis of HMDP provides unique insights into lipid regulation and prediction.
Fig. 2: Subcellular co-regulated networks associated with lipid metabolism.
Fig. 3: Systems genetic analysis of proteomic and lipidomic diversity in HMDP mice.
Fig. 4: Proteasomal proteins including PSMD9 are correlated with lipid abundance.
Fig. 5: Modulating PSMD9 regulates hepatic and plasma lipid abundance in mice.

Data availability

All proteomics raw and processed data associated with the paper have been deposited in PRIDE proteomeXchange (http://www.proteomexchange.org/) using the following credentials: HMDP proteomics project accession = PXD010818 (reviewer username/password = reviewer09277@ebi.ac.uk/hyEjKh9x). ACAD11 AP–MS project accession = PXD010788 (reviewer username/password = reviewer14659@ebi.ac.uk/fg65Cfuf). Proteomics of PSMD9 ASO project accession PXD010759 (reviewer username/password = reviewer88344@ebi.ac.uk/7bATnY6j). All HMDP lipidomics raw acquisition files and associated documents are available at https://doi.org/10.6084/m9.figshare.7488854. Any remaining datasets are available in the Supplementary Information files or can be made available upon reasonable request to the corresponding author(s). All biological material will be made available upon reasonable request, with the exception of the ASOs, which are subject to a standing materials transfer agreement between the listed academic institutions and Ionis Therapeutics.

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Acknowledgements

This work was supported in part by the following: ANZ Victorian Medical Research Trust, Baker Heine Trust, Victorian State Government’s OIS Program and National Heart Foundation of Australia (A.C.C., B.G.D. and E.J.Z.); National Health and Medical Research Council of Australia (NHMRC) grants and fellowships (D.E.J. and B.L.P.); NIH grants HL122677, DK112119, DK102559 (T.Q.d.A.V.), HL028481 (A.J.L., T.Q.d.A.V.), HL118161 and HL136543 (E.J.T.); American Heart Association grant SDG18440015 (T.Q.d.A.V.). Baker Bright Sparks Scholarship and Australian Post-graduate Award (M.F.K.). We thank B. Crossett, S. Cordwell and The Sydney University Mass Spectrometry Facility. We are also grateful for the help and guidance of I. Carmichael and S. Bond and our many internal and external collaborators.

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Nature thanks Christa Buechler, Steven Munger and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Contributions

B.G.D., T.Q.d.A.V., A.C.C. and E.J.T. conceived the original concept. B.G.D., T.Q.d.A.V, A.C.C., B.L.P., M.M.S. and D.E.J. conceptualized the study and designed, performed, oversaw, interpreted and generated data and figures. B.L.P. and D.E.J. generated, analysed and interpreted the proteomic datasets. P.J.M. supervised the generation, analysis and interpretation of lipidomics data. A.J.L. advised on study design and systems genetics analysis, and provided access to data, software and reagents. B.G.D., B.L.P., T.Q.d.A.V., M.M.S., A.C.C., M.F.K., S.C.M., Y.L., E.J.Z., N.A.M., E.J.N., M.L.M., B.L.C., P.M., M.J.W., R.C.R.M., K.-Y.P., R. Lazarus and J.M.W. provided reagents, generated data and contributed to figure production. Specifically, B.L.C., P.M. and M.L.M. performed and analysed in vivo ASO experiments and data, and M.J.W., R.C.R.M. and K.-Y.P. performed and provided data for human plasma lipid signature validations. R. Lee provided access to, and expertise pertaining to ASO generation and delivery. M.M.S., B.L.P., B.G.D., K.J., C.P., R. Lazarus and P.Y. performed bioinformatics analyses. B.G.D., B.L.P., T.Q.d.A.V., A.C.C., M.M.S. and D.E.J. wrote the manuscript. All authors read and edited the manuscript.

Corresponding authors

Correspondence to Anna C. Calkin or Thomas Q. de Aguiar Vallim or Brian G. Drew.

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Competing interests

 R. Lee holds shares in Ionis Pharmaceuticals. B.G.D., A.C.C., T.Q.d.A.V. and D.E.J. are inventors on patent PCT/AU2019/050033 pertaining to aspects of the PSMD9 work. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Assessment of proteomic and lipidomic data reproducibility.

a, b, Coefficient of variation (CV) analysis of the proteomics (a) and lipidomics (b) data. Box-and-whisker plots (described as in Fig. 3e). ce, Unsupervised hierarchical clustering of the liver proteomics (c), liver lipidomics (d) and plasma lipidomics (e) data.

Extended Data Fig. 2 Total liver triacylglycerol levels in C57BL/6J mice.

Mice were fed either a normal chow diet (NCD) or a high-fat diet (HFD) for 12 weeks. P value determined by Student’s t-test. Data are mean ± s.e.m., n = 11 chow group; n = 10 HFD group.

Extended Data Fig. 3 Average strain abundance of designated lipid classes in liver and plasma.

ae, Abundance is expressed as area under the curve per mg liver protein or per ml of plasma. Liver scale on left, plasma scale on right a, Triacylglycerol. b, Diacylglycerol. c, Ceramide. d, Cholesterol esters. e, PE(P).

Extended Data Fig. 4 Correlation network analysis of the HMDP liver proteome.

a, Protein:protein (P:P) correlations in the HMDP liver proteome, integrated with CORUM-annotated proteins and protein interactions previously identified by AP–MS. Numbers indicate CORUM accessions, orange lines are HMDP P:P correlations; purple lines are correlations observed in both HMDP and CORUM. be, P:P correlations of selected CORUM complexes including associations not previously identified by AP–MS (green lines). Biweight midcorrelation analyses performed using ranked Benjamin–Hochberg multiple comparison test. Purple lines are known CORUM interactions, orange lines are HMDP P:P and CORUM interactions, green lines are previously unidentified interactions from HMDP P:P data. A thicker line represents a higher bicor value (q < 0.05, n > 50 strains).

Extended Data Fig. 5 Biweight midcorrelation of 108 liver lipid species against 378 liver proteins mapped onto annotated KEGG pathways.

Highlighted are various correlations (orange is positive, aqua is negative) between individual lipid species and proteins in pathways associated with unsaturated fatty acid metabolism, fatty acid degradation and metabolism, lysosomal degradation, and proteolysis. Only proteins containing more than one significant correlation to a lipid and annotated to the KEGG database are shown (biweight midcorrelation using ranked Benjamin–Hochberg multiple comparison test, q < 0.05, n > 50).

Extended Data Fig. 6 Overexpression of PSMD9 in C57BL/6J and DBA/2J mice.

Adenoviral overexpression of PSMD9 in C57BL/6J and DBA/2J mice (n = 9, 7 days after tail-vein injection of 109 plaque-forming units). a, b, Western blot (a) and densitometry (b) of PSMD9 and PDI (loading control) in the livers of mice treated with either control adenovirus (pAdV) or PSMD9 adenovirus. Data are mean ± s.e.m. c, Liver and plasma lipidomics of adenovirus-treated mice. Top panel (above first dotted line) shows relative fold change of total lipid classes. Middle and bottom panels show relative fold changes of individual diacylglycerol and triacylglycerol lipid species, respectively. P values determined by t-test with permutation-based FDR correction. Filled bubbles are significant (q < 0.05) changes, larger bubbles indicate greater significance.

Extended Data Fig. 7 ASO knockdown of PSMD9 in C57BL/6J and DBA/2J mice.

ac, Assessment of hepatotoxicity as measured by plasma levels (U/L) of aspartate transaminase (AST) and alanine transaminase (ALT) (a), percentage of liver weight to body weight (b) and total body weight (c) of mice on a normal chow diet and treated with PBS, control ASO or PSMD9 ASO for 7 days (n = 4 per group, twice-weekly injection at 25 mg kg−1). d, Lipidomic analysis of total diacylglycerols and triacylglycerols in the plasma of mice on a chow diet (n = 4 C57BL/6J, n = 3 DBA/2J mice per group) or a Western diet (n = 6 mice per group, except n = 5 DBA/2J control ASO mice per group) and treated with either control or PSMD9 ASOs (twice-weekly ASO injection at 25 mg kg−1). eg, Assessment of hepatotoxicity as measured by plasma AST and ALT levels (e), percentage change in body weight from baseline (f), and food consumption normalized to body weight (g) from in vivo de novo lipogenesis experimental animals (n = 6 control ASO, n = 8 PSMD9 ASO, 28 days on diet and weekly injection of ASO injection at 25 mg kg−1). *P < 0.05, **P < 0.01 control ASO versus PSMD9 ASO, t-test. Data are mean ± s.e.m.

Extended Data Table 1 Correlations between PSMD9 gene expression and indices of adiposity

Supplementary information

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This file contains a guide for Supplementary Tables 1-16 and the uncropped blots for Figs. 3, 5 and Extended Data Fig. 6.

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Parker, B.L., Calkin, A.C., Seldin, M.M. et al. An integrative systems genetic analysis of mammalian lipid metabolism. Nature 567, 187–193 (2019). https://doi.org/10.1038/s41586-019-0984-y

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