Table 1.
Plasma cholesterol, triglyceride and glucose concentrations in the study mice at sacrifice.
Figure 1.
Atherosclerosis progression in Ldlr−/−Apob100/100Mttpflox/flox mice and regression in Ldlr−/−Apob100/100MttpΔ/Δ mice.
(A) Atherosclerosis progression and regression curves. Values are surface lesion area (mean ± SD), assessed by Sudan IV staining, as a percentage of the total area of pinned-out aortas. n = 4–10 per time point. Lesion development in controls without PCL (•) (P<0.001 vs. 30 weeks) and in mice after PCL started at week 30 (▴), 40 (▪), or 50 (). Changes in lesion area between 10 and 20 weeks of low plasma cholesterol were significant only in mice with early lesions (PCL at 30 weeks, P = 0.05). *P = 0.05, ***P<0.001. (B) Representative aortic trees (above) with magnified arches (below) stained with Sudan IV before and 10 and 20 weeks after PCL at 30, 40 and 50 weeks. Graphs indicate degree of regression at that PCL time-point (red).
Figure 2.
Immunohistochemical characteristics of representative frozen sections of aortic roots from Ldlr−/−Apob100/100Mttpflox/flox and Ldlr−/−Apob100/100MttpΔ/Δ mice.
(A–C) Average percent stained area of total aortic root area (right) and representative stained aortic roots (left). Bars indicate SD. Original magnification, 50×. *P<0.05, **P<0.01, and ***P<0.001. (A) Oil-Red-O staining (n = 6–9 per group). (B) CD68 staining (n = 5–8 per group). (C) Sirius Red staining (collagen) (n = 3 per group). (D) Mean plaque stability score (arbitrary units). Bars indicate SD. Average plaque stability scores were divided by total extent of plaque burden to assess stability per mouse (not individual plaques). Inset: magnifications of plaque stability score/mouse at 30, 40, 50, and 60 weeks before regression.
Figure 3.
Transcriptional profiling during regression of aortic atherosclerotic lesions in Ldlr−/−Apob100/100Mttpflox/flox and Ldlr−/−Apob100/100MttpΔ/Δ mice over time.
Differential expression analyses was used to define sets of genes causally and reactively related to atherosclerosis regression in Ldlr−/−Apob100/100MttpΔ/Δ mice. RNA for the transcriptional profiling was isolated from the atherosclerotic aortic arch. Narrow and bold arrows indicate times of PCL and sacrifice, respectively. Colored horizontal lines indicate time frame of transcriptional profiles used for differential expression analysis to define gene sets. Colors indicate when PCL was started: green, 30 weeks; yellow, 40 weeks; red, 50 weeks. (A) To define the PCL-responsive gene sets, we compared transcriptional profiles (4–6 per time point) of PBS-treated, high-cholesterol littermate controls sacrificed at 30, 40 and 50 weeks with those immediately after PCL. (B) To define the regression-reactive gene sets, we compared transcriptional profiles (3–6 per time point) immediately after PCL with those at 10 weeks after PCL (10 per time point).
Figure 4.
PCL-responsive and regression reactive gene sets of atherosclerosis regression.
Venn diagrams showing the percentage/number of differentially expressed genes at 30, 40, and 50 weeks. The colors of the circles indicate when PCL was started: green, 30; yellow, 40 weeks; red, 50 weeks. The percentage in the circles to the left represent the percentage of differentially expressed genes for that section and specific time point. The numbers in circles to the right represent numbers of differentially expressed genes. (A) The PCL-responsive gene sets consist of genes that responded immediately to PCL, initiating regression of early (30 weeks), mature (40 weeks), and advanced (50 weeks) atherosclerosis. (B) The regression-reactive gene sets consist of genes altered in lesions between immediately after PCL and 10 weeks of low plasma cholesterol levels.
Figure 5.
CAD-patient macrophage TF-regulatory coexpression networks of PCL-responsive genes linked to atherosclerosis regression.
To learn more about functional interactions of the PCL-responsive gene sets using human orthologs, we used macrophage mRNA profiles (n = 38) from patients with CAD [29] to infer TF-regulatory gene networks. Red square nodes are TFs. Yellow square nodes are specific master regulatory TFs (Table 4): PPARG for the network in early lesions (30 weeks) and MLL5 for the network in mature lesions (40 weeks) and SRSF10 and XRN2 for the network in advanced lesions (50 weeks). Edges are connections between TFs and their first neighbor. (A) At 30 weeks, 53 genes of 215 human orthologs belonged to the TF-regulatory network (P<0.0051), in which the most connected TFs (master regulators) were PPARA (17 edges) and PPARG (13 edges) (Table 2). The TF-regulatory network of PCL-responsive atherosclerosis regression genes at 30 weeks is magnified in (D) to show all nodes. (B) At 40 weeks, 185 genes of 1087 human orthologs in the causal gene set belonged to the TF-regulatory network (P<0.0013). The most connected TFs were HMGB2, ADORA2A, and TERF1, with 61, 59 and 55 edges, respectively (Table 2). (C) At 50 weeks, 379 genes of 1865 human orthologs in the causal gene set belonged to the TF-regulatory network (P<0.00042), in which the most connected TFs were SRSF10, XRN2, and HMGB1, with 71, 67 and 62 edges, respectively (Table 2). (D) A magnification of the TF regulatory network of PCL-responsive genes at week 30, shown in (A).
Table 2.
Top hubs in the causal TF-regulatory co-expression networks inferred in human macrophages.
Table 3.
Number of affected genes its respective network when the master regulatory TF were silenced with siRNA.
Table 4.
Network specificity of the key master regulators using hypergeometric testing.
Table 5.
Effects of siRNA inhibition of the network top hubs on cholesterol-ester accumulation in a THP-1 foam cell model.