Integrated Liver and Plasma Proteomics in Obese Mice Reveals Complex Metabolic Regulation

Abstract

Obesity leads to the development of nonalcoholic fatty liver disease (NAFLD) and associated alterations to the plasma proteome. To elucidate the underlying changes associated with obesity, we performed liquid chromatography-tandem mass spectrometry in the liver and plasma of obese leptin-deficient ob/ob mice and integrated these data with publicly available transcriptomic and proteomic datasets of obesity and metabolic diseases in preclinical and clinical cohorts. We quantified 7173 and 555 proteins in the liver and plasma proteomes, respectively. The abundance of proteins related to fatty acid metabolism were increased, alongside peroxisomal proliferation in ob/ob liver. Putatively secreted proteins and the secretory machinery were also dysregulated in the liver, which was mirrored by a substantial alteration of the plasma proteome. Greater than 50% of the plasma proteins were differentially regulated, including NAFLD biomarkers, lipoproteins, the 20S proteasome, and the complement and coagulation cascades of the immune system. Integration of the liver and plasma proteomes identified proteins that were concomitantly regulated in the liver and plasma in obesity, suggesting that the systemic abundance of these plasma proteins is regulated by secretion from the liver. Many of these proteins are systemically regulated during type 2 diabetes and/or NAFLD in humans, indicating the clinical importance of liver-plasma cross talk and the relevance of our investigations in ob/ob mice. Together, these analyses yield a comprehensive insight into obesity and provide an extensive resource for obesity research in a prevailing model organism.

Keywords: NAFLD; cross talk; leptin; obesity; proteome.

Graphical abstract

Fig. 1

Lipid metabolic processes are increased in the liver of ob/ob mice.A, ob/ob mice and wild-type (WT) controls were sacrificed for liver (n = 4) and plasma (n = 7) proteomics. Liver samples were prepared using the multienzyme digestion with filter-aided sample preparation (MED-FASP) protocol (28) and measured with a data-dependent acquisition (DDA) method. The resulting spectra were analyzed by MaxQuant software and were quantified using the total protein abundance (TPA) approach (36). Plasma samples were measured with a data-independent acquisition (DIA) method, and the resulting raw data were analyzed using Spectronaut (version 12) and quantified by protein intensities (INT). B, volcano plot of the liver proteome showing 264 significantly upregulated proteins in red and 223 significantly downregulated proteins in blue (FDR < 0.05, S0 = 0.1). C, summed protein abundance of gene ontology biological processes (GOBP) terms related to metabolism. D, ClueGO enrichment analysis of GOBP terms within significantly upregulated proteins. Right-sided hypergeometric test, Benjamini–Hochberg FDR < 0.02, functionally related terms are grouped and color coded based on overlapping proteins. The most significantly regulated terms in each group are labeled. Data are represented as mean ± standard deviation (SD) (n = 4), Student’s t test (summed protein abundance): ∗∗p < 0.01, ∗∗∗ p < 0.001. LC-MS/MS, liquid chromatography–tandem mass spectrometry.

Fig. 2

Peroxisomal proliferation in the liver of ob/ob mice.A, one-dimensional enrichment analysis based on Kyoto encyclopedia of genes and genomes (KEGG) pathways (FDR < 0.02). B, summed protein abundance of the gene ontology cellular compartment (GOCC) term peroxisome. C, partial reproduction of the KEGG Peroxisome pathway, showing proteins involved in peroxisomal biogenesis with proteins denoted as upregulated or downregulated at an FDR < 0.05 or 0.10. D, protein abundance of PEX11 isoforms. Data are represented as mean ± SD (n = 4), permutation-based FDR-corrected Student’s t test (proteome): ^$FDR < 0.05, Student’s t test (summed protein abundance): ∗∗∗p < 0.001.

Fig. 3

The secretory machinery and secreted proteins are dysregulated within the liver of ob/ob mice.A, one-dimensional enrichment analysis based on GOCC terms (FDR < 0.02). B, regulation of proteins annotated to contain a signal peptide (UniProt Keyword). The ten proteins with the largest positive and negative fold-changes, respectively, are listed. C, protein abundance of the ten proteins annotated to the endoplasmic reticulum chaperone complex (GOCC) filtered from the proteome, of which five were significantly downregulated. D, summed protein abundance of the GOCC term Golgi apparatus. E, regulation of proteins annotated to extracellular exosome (GOCC). The ten proteins with the largest positive and negative fold-changes, respectively, are listed. F, protein abundance of the 13 significantly regulated proteins annotated to extracellular matrix filtered from the proteome. Data are represented as mean ± SD (n = 4), permutation-based FDR corrected Student’s t test (proteome): ^$FDR < 0.05, Student’s t test (summed protein abundance): ∗∗∗p < 0.001. GOCC, gene ontology cellular compartment.

Fig. 4

Transcriptional regulation of the liver proteome of ob/ob mice.A, Venn diagram displaying the overlap of proteins and transcripts quantified in the liver proteome and a previously published transcriptome (41) of ob/ob mice. B, scatter plot of the log₂-fold change in the transcriptome and proteome. Proteins/transcripts significantly regulated only in the transcriptome or proteome are displayed in green and purple, respectively; proteins/transcripts significantly regulated in both the transcriptome and proteome are highlighted in orange. C and D, two-dimensional enrichment analysis based on GOBP (C) and GOCC (D) terms for the proteome and transcriptome (FDR < 0.02). GOBP, gene ontology biological processes; GOCC, gene ontology cellular compartment.

Fig. 5

Proteasomal proteins and lipid metabolic processes are increased in the plasma proteome of ob/ob mice.A, volcano plot of the plasma proteome showing 146 significantly upregulated proteins in red and 119 significantly downregulated proteins in blue (FDR < 0.05, S0 = 0.1). B and C, functional enrichment analysis by ClueGO for the significantly downregulated (B) and upregulated (C) proteins. Right-sided hypergeometric test, Benjamini–Hochberg FDR < 0.02, functionally related terms are grouped and color coded based on overlapping proteins. The most significantly regulated terms in each group are labeled in color. D, protein abundance of the ten proteins annotated to the proteasome complex (GOCC). E, hierarchical cluster of the proteins within the purple enrichment term groups (whose most significantly regulated terms were carboxylic acid metabolic process and organic hydroxyl compound metabolic process) in (C). Data are represented as mean ± SD (n = 7), permutation-based FDR-corrected Student’s t test (proteome): ^$FDR < 0.05. GOCC, gene ontology cellular compartment.

Fig. 6

Downregulation of the complement and coagulation cascades in the plasma of ob/ob mice.A, one-dimensional enrichment analysis based on KEGG terms (FDR < 0.02). B, reproduction of the KEGG complement and coagulation cascades pathway with proteins denoted as upregulated or downregulated at an FDR < 0.05 or 0.10. KEGG, Kyoto encyclopedia of genes and genomes.

Fig. 7

Integrated liver and plasma proteomics reveal concomitantly regulated proteins in the liver and plasma of ob/ob mice.A, Venn diagram displaying the overlap of quantified and significantly regulated proteins in the liver and plasma proteomes. B, scatter plot of the log₂-fold-change in the liver and plasma of proteins quantified in both proteomes. Proteins significantly regulated in both proteomes are highlighted in red. C, heat map displaying the systemic (plasma or serum) regulation of proteins found to be concomitantly regulated in liver and plasma of ob/ob mice in human investigations of prediabetes and type diabetes (53), nonalcoholic fatty liver disease (NAFLD) and cirrhosis (12), high-fat diet (HFD) (54), low-calorie diet (LCD) (97), and Roux-en-Y gastric bypass (RYGB) (55).