Alteration in Plasma Metabolome in High-Fat Diet-Fed Monocyte Chemotactic Protein-1 Knockout Mice Bearing Pulmonary Metastases of Lewis Lung Carcinoma

Abstract

Both clinical and laboratory studies have shown that monocyte chemotactic protein-1 (MCP-1) is involved in cancer spread. To understand the role of MCP-1 in metabolism in the presence of metastasis, we conducted an untargeted metabolomic analysis of primary metabolism on plasma collected from a study showing that MCP-1 deficiency reduces spontaneous metastasis of Lewis lung carcinoma (LLC) to the lungs in mice fed a high-fat diet (HFD). In a 2 × 2 design, wild-type (WT) or Mcp-1 knockout (Mcp-1 ^-/-) mice maintained on the AIN93G standard diet or HFD were subcutaneously injected with LLC cells to induce lung metastasis. We identified 87 metabolites for metabolomic analysis from this study. Amino acid metabolism was altered considerably in the presence of LLC metastases with the aminoacyl-tRNA biosynthesis pathways as the leading pathway altered. The HFD modified lipid and energy metabolism, evidenced by lower contents of arachidonic acid, cholesterol, and long-chain saturated fatty acids and higher contents of glucose and pyruvic acid in mice fed the HFD. These findings were supported by network analysis showing alterations in fatty acid synthesis and glycolysis/gluconeogenesis pathways between the 2 diets. Furthermore, elevations of the citrate cycle intermediates (citric acid, fumaric acid, isocitric acid, and succinic acid) and glyceric acid in Mcp-1 ^-/- mice, regardless of diet, suggest the involvement of MCP-1 in mitochondrial energy metabolism during LLC metastasis. The present study demonstrates that MCP-1 deficiency and the HFD altered plasma metabolome in mice bearing LLC metastases. These findings can be useful in understanding the impact of obesity on prevention and treatment of cancer metastasis.

Keywords: Lewis lung carcinoma; MCP-1; Metabolome; diet; metastasis; mice; plasma.

Figure 1.

Twenty-five metabolites identified by the hierarchical clustering heatmap analysis in plasma that differed most in wild-type (WT) and Mcp-1^-/- mice fed the AIN93G or high-fat diet (HFD). AIN93G WT (red): WT mice fed the AIN93G diet; AIN93G Mcp-1^-/- (green): Mcp-1^-/- mice fed the AIN93G diet; HFD WT (blue): WT mice fed the HFD; HFD Mcp-1^-/- (cyan): Mcp-1^-/- mice fed the HFD (n = 12 per group for WT mice, n = 10 per group for Mcp-1^-/- mice). Methyl O-D-GP: Methyl O-D-galactopyranoside.

Figure 2.

Components 1 and 2 of the synchronized 3-dimensional plot (A) by spare partial least square-discriminant analysis (sPLS-DA) of plasma metabolites from wild-type (WT) and Mcp-1^-/- mice fed the AIN93G or high-fat diet (HFD) and loading plots of the most influential 10 metabolites in treatment separation among the 4 dietary groups for components 1 (B) and 2 (C). AIN93G WT (red): WT mice fed the AIN93G diet; AIN93G Mcp-1^-/- (green): Mcp-1^-/- mice fed the AIN93G diet; HFD WT (blue): WT mice fed the HFD; HFD Mcp-1^-/- (cyan): Mcp-1^-/- mice fed the HFD (n = 12 per group for WT mice, n = 10 per group for Mcp-1^-/- mice). 1,5-AG: 1,5-anhydroglucitol; Methyl O-D-GP: methyl O-D-galactopyranoside.

Figure 3.

Matched metabolic pathway plot of metabolites identified in plasma from wild-type (WT) and Mcp-1^-/- mice fed the AIN93G or high-fat diet. The x-axis marks the pathway impact and the y-axis marks the pathway enrichment. Each node represents a pathway. The nodes with larger sizes and darker colors (from yellow to red) positioning toward top right region represent higher pathway impact values and higher pathway enrichment. Pathways that are significantly altered are presented with their names next to their nodes. All detected metabolic pathways are presented in Supplemental Table 3.

Figure 4.

Metabolic network of the identified metabolites between wild-type and Mcp-1^-/- mice (A) and between the AIN93G and high-fat diet (B). Colors, from white-yellow to red, indicate levels of impact the metabolites have to the network in an ascending order (the number of connections a node has to other nodes and the number of shortest paths going through the node). Network statistics for analyses A and B are presented in Table 6.