Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis

Abstract

Background: Increased dietary cholesterol intake is associated with atherosclerosis. Atherosclerosis development requires a lipid and an inflammatory component. It is unclear where and how the inflammatory component develops. To assess the role of the liver in the evolution of inflammation, we treated ApoE*3Leiden mice with cholesterol-free (Con), low (LC; 0.25%) and high (HC; 1%) cholesterol diets, scored early atherosclerosis and profiled the (patho)physiological state of the liver using novel whole-genome and metabolome technologies.

Results: Whereas the Con diet did not induce early atherosclerosis, the LC diet did so but only mildly, and the HC diet induced it very strongly. With increasing dietary cholesterol intake, the liver switches from a resilient, adaptive state to an inflammatory, pro-atherosclerotic state. The liver absorbs moderate cholesterol stress (LC) mainly by adjusting metabolic and transport processes. This hepatic resilience is predominantly controlled by SREBP-1/-2, SP-1, RXR and PPARalpha. A further increase of dietary cholesterol stress (HC) additionally induces pro-inflammatory gene expression, including pro-atherosclerotic candidate genes. These HC-evoked changes occur via specific pro-inflammatory pathways involving specific transcriptional master regulators, some of which are established, others newly identified. Notably, several of these regulators control both lipid metabolism and inflammation, and thereby link the two processes.

Conclusion: With increasing dietary cholesterol intake the liver switches from a mainly resilient (LC) to a predominantly inflammatory (HC) state, which is associated with early lesion formation. Newly developed, functional systems biology tools allowed the identification of novel regulatory pathways and transcriptional regulators controlling both lipid metabolism and inflammatory responses, thereby providing a rationale for an interrelationship between the two processes.

Figure 1

Analysis of plasma lipids and atherosclerosis. (a) Lipoprotein profiles of the Con, LC and HC groups at ten weeks. (b) Representative photomicrographs of cross-sections of the aortic root area. (c) Total cross-sectional lesion area in the aortic root. (d) Lesion severity of the treatment groups determined according to the lesion classification of the American Heart Association (I-VI). Data are presented as means ± standard deviation. *P < 0.05 versus Con; ^#P < 0.05 versus LC.

Figure 2

Venn diagram of significantly differentially expressed genes in the LC and HC groups compared to the Con group. ANOVA P < 0.01 and FDR (predicted) <0.05 resulted in 2,846 probesets, and subsequent t-tests with P < 0.01 for HC versus Con and/or LC versus Con resulted in the 2,447 probesets shown.

Figure 3

Analysis of the inflammatory pathways activated by the LC and HC diets. A master inflammatory network was generated in MetaCore™ by combining relevant inflammatory pathways. Differentially expressed genes in response to (a) LC and (b) HC treatment were mapped into this master network. The activation of the network by LC treatment was minimal, whereas HC treatment resulted in a profound activation of specific proinflammatory pathways (marked with blue arrows). Red circles indicate transcriptional node points and red rectangles highlight representative downstream target genes that were up-regulated.

Figure 4

Expression of SAA genes. (a) All four isotype genes were dose-dependently increased with increasing dietary cholesterol exposure. *Significant compared to Con, P < 0.01. (b) Plasma SAA levels in response to increasing doses of dietary cholesterol. Female E3L animals (n ≥ 7/condition) were fed the Con diet supplemented with increasing concentrations of cholesterol for 10 weeks. *P < 0.05 versus 0% w/w cholesterol control group. (c) Plasma SAA levels in the HC diet (1% w/w cholesterol) fed female E3L animals (n = 8) over time. *P < 0.05 versus t = 0.

Figure 5

Lipidom analysis of liver homogenates (n = 10 per group). Score plot was derived from PCA. The two component model explained 36.6% (principle component 1; PC# 1) and 24.4% (PC# 2) of the variation in the data.

Figure 6

Representative biological network based on differentially expressed genes of the HC group using MetaCore™ network software and the Analyze Network algorithm. Two representative networks are shown: (a) the C/EBPβ c-jun network and (b) the NF-κB network. A legend for the biological networks is provided in Additional data file 7d. Red dots in the right corner of a gene indicate up-regulation and blue dots down-regulation.