TY - JOUR
T1 - (18)F-FDG PET imaging of murine atherosclerosis
T2 - association with gene expression of key molecular markers
AU - Hag, Anne Mette Fisker
AU - Pedersen, Sune Folke
AU - Christoffersen, Christina
AU - Binderup, Tina
AU - Jensen, Mette Munk
AU - Jørgensen, Jesper Tranekjær
AU - Skovgaard, Dorthe
AU - Ripa, Rasmus Sejersten
AU - Kjaer, Andreas
PY - 2012/11/30
Y1 - 2012/11/30
N2 - Aim: To study whether 18F-FDG can be used for in vivo imaging of atherogenesis by examining the correlation between 18F-FDG uptake and gene expression of key molecular markers of atherosclerosis in apoE-/- mice. Methods: Nine groups of apoE-/- mice were given normal chow or high-fat diet. At different time-points, 18F-FDG PET/contrast-enhanced CT scans were performed on dedicated animal scanners. After scans, animals were euthanized, aortas removed, gamma counted, RNA extracted from the tissue, and gene expression of chemo (C-X-C motif) ligand 1 (CXCL-1), monocyte chemoattractant protein (MCP)-1, vascular cell adhesion molecule (VCAM)-1, cluster of differentiation molecule (CD)-68, osteopontin (OPN), lectin-like oxidized LDL-receptor (LOX)-1, hypoxia-inducible factor (HIF)-1α, HIF-2α, vascular endothelial growth factor A (VEGF), and tissue factor (TF) was measured by means of qPCR. Results: The uptake of 18F-FDG increased over time in the groups of mice receiving high-fat diet measured by PET and ex vivo gamma counting. The gene expression of all examined markers of atherosclerosis correlated significantly with 18F-FDG uptake. The strongest correlation was seen with TF and CD68 (p<0.001). A multivariate analysis showed CD68, OPN, TF, and VCAM-1 to be the most important contributors to the uptake of 18F-FDG. Together they could explain 60% of the 18F-FDG uptake. Conclusion: We have demonstrated that 18F-FDG can be used to follow the progression of atherosclerosis in apoE-/- mice. The gene expression of ten molecular markers representing different molecular processes important for atherosclerosis was shown to correlate with the uptake of 18F-FDG. Especially, the gene expressions of CD68, OPN, TF, and VCAM-1 were strong predictors for the uptake.
AB - Aim: To study whether 18F-FDG can be used for in vivo imaging of atherogenesis by examining the correlation between 18F-FDG uptake and gene expression of key molecular markers of atherosclerosis in apoE-/- mice. Methods: Nine groups of apoE-/- mice were given normal chow or high-fat diet. At different time-points, 18F-FDG PET/contrast-enhanced CT scans were performed on dedicated animal scanners. After scans, animals were euthanized, aortas removed, gamma counted, RNA extracted from the tissue, and gene expression of chemo (C-X-C motif) ligand 1 (CXCL-1), monocyte chemoattractant protein (MCP)-1, vascular cell adhesion molecule (VCAM)-1, cluster of differentiation molecule (CD)-68, osteopontin (OPN), lectin-like oxidized LDL-receptor (LOX)-1, hypoxia-inducible factor (HIF)-1α, HIF-2α, vascular endothelial growth factor A (VEGF), and tissue factor (TF) was measured by means of qPCR. Results: The uptake of 18F-FDG increased over time in the groups of mice receiving high-fat diet measured by PET and ex vivo gamma counting. The gene expression of all examined markers of atherosclerosis correlated significantly with 18F-FDG uptake. The strongest correlation was seen with TF and CD68 (p<0.001). A multivariate analysis showed CD68, OPN, TF, and VCAM-1 to be the most important contributors to the uptake of 18F-FDG. Together they could explain 60% of the 18F-FDG uptake. Conclusion: We have demonstrated that 18F-FDG can be used to follow the progression of atherosclerosis in apoE-/- mice. The gene expression of ten molecular markers representing different molecular processes important for atherosclerosis was shown to correlate with the uptake of 18F-FDG. Especially, the gene expressions of CD68, OPN, TF, and VCAM-1 were strong predictors for the uptake.
U2 - 10.1371/journal.pone.0050908
DO - 10.1371/journal.pone.0050908
M3 - Journal article
C2 - 23226424
SN - 1932-6203
VL - 7
SP - e50908
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 11
ER -